-
-Bento is a Python toolkit for performing subcellular analysis of spatial transcriptomics data.
-
-# Get started
-Install with Python >=3.8 and <3.11:
-```bash
-pip install bento-tools
-```
-
-Check out the [documentation](https://bento-tools.readthedocs.io/en/latest/) for the installation guide, tutorials, API and more! Read and cite [our preprint](https://doi.org/10.1101/2022.06.10.495510) if you use Bento in your work.
-
-
-# Main Features
-
-
+# Bento
+Bento is a Python toolkit for performing subcellular analysis of spatial transcriptomics data. The package is part of the [Scverse ecosystem](https://scverse.org/packages/#ecosystem). Check out the [documentation](https://bento-tools.readthedocs.io/en/latest/) for installation instructions, tutorials, and API. Cite [our preprint](https://doi.org/10.1101/2022.06.10.495510) if you use Bento in your work. Thanks!
-- Store molecular coordinates and segmentation masks
-- Visualize spatial transcriptomics data at subcellular resolution
-- Compute subcellular spatial features
-- Predict localization patterns and signatures
-- Factor decomposition for high-dimensional spatial feature sets
+
---
[](https://github.com/ckmah/bento-tools/blob/master/LICENSE)
diff --git a/bento/__init__.py b/bento/__init__.py
index 32cbcfc..5f5b8c3 100755
--- a/bento/__init__.py
+++ b/bento/__init__.py
@@ -1,6 +1,8 @@
-from . import datasets
+from . import datasets as ds
from . import io
from . import plotting as pl
-from . import preprocessing as pp
from . import tools as tl
-from ._utils import PATTERN_NAMES, TENSOR_DIM_NAMES
+from . import _utils as ut
+from . import geometry as geo
+from .plotting import _colors as colors
+from ._utils import sync
diff --git a/bento/_constants.py b/bento/_constants.py
new file mode 100644
index 0000000..cdc5186
--- /dev/null
+++ b/bento/_constants.py
@@ -0,0 +1,18 @@
+PATTERN_COLORS = ["#17becf", "#1f77b4", "#7f7f7f", "#ff7f0e", "#d62728"]
+PATTERN_NAMES = ["cell_edge", "cytoplasmic", "none", "nuclear", "nuclear_edge"]
+PATTERN_PROBS = [f"{p}_p" for p in PATTERN_NAMES]
+PATTERN_FEATURES = [
+ "cell_inner_proximity",
+ "nucleus_inner_proximity",
+ "nucleus_outer_proximity",
+ "cell_inner_asymmetry",
+ "nucleus_inner_asymmetry",
+ "nucleus_outer_asymmetry",
+ "l_max",
+ "l_max_gradient",
+ "l_min_gradient",
+ "l_monotony",
+ "l_half_radius",
+ "point_dispersion_norm",
+ "nucleus_dispersion_norm",
+]
diff --git a/bento/_utils.py b/bento/_utils.py
index 1507281..be6ba1f 100644
--- a/bento/_utils.py
+++ b/bento/_utils.py
@@ -1,20 +1,13 @@
import inspect
-from functools import wraps
-
-from anndata import AnnData
-
+import warnings
+import geopandas as gpd
+import pandas as pd
import seaborn as sns
+from anndata import AnnData
+from functools import wraps
+from typing import Iterable
+from shapely import wkt
-PATTERN_NAMES = ["cell_edge", "cytoplasmic", "none", "nuclear", "nuclear_edge"]
-PATTERN_PROBS = [f"{p}_p" for p in PATTERN_NAMES]
-TENSOR_DIM_NAMES = ["layers", "cells", "genes"]
-
-# Colors correspond to order of PATTERN_NAMES: cyan, blue, gray, orange, red
-PATTERN_COLORS = ['#17becf', '#1f77b4', '#7f7f7f', '#ff7f0e', '#d62728']
-
-# Colors to represent each dimension (features, cells, genes); Set2 palette n_colors=3
-DIM_COLORS = ['#66c2a5', '#fc8d62', '#8da0cb']
-# ['#AD6A6C', '#f5b841', '#0cf2c9']
def get_default_args(func):
signature = inspect.signature(func)
@@ -28,7 +21,7 @@ def get_default_args(func):
def track(func):
"""
Track changes in AnnData object after applying function.
-
+
1. First remembers a shallow list of AnnData attributes by listing keys from obs, var, etc.
2. Perform arbitrary task
3. List attributes again, perform simple diff between list of old and new attributes
@@ -70,7 +63,6 @@ def wrapper(*args, **kwds):
modified = False
for attr in old_attr.keys():
-
if attr == "n_obs" or attr == "n_vars":
continue
@@ -146,12 +138,8 @@ def pheno_to_color(pheno, palette):
List of converted colors for each sample, formatted as RGBA tuples.
"""
- import seaborn as sns
-
- if type(palette) is str:
+ if isinstance(palette, str):
palette = sns.color_palette(palette)
- else:
- palette = palette
values = list(set(pheno))
values.sort()
@@ -159,3 +147,176 @@ def pheno_to_color(pheno, palette):
study2color = dict(zip(values, palette))
sample_colors = [study2color[v] for v in pheno]
return study2color, sample_colors
+
+
+def sync(data, copy=False):
+ """
+ Sync existing point sets and associated metadata with data.obs_names and data.var_names
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object
+ copy : bool, optional
+ """
+ adata = data.copy() if copy else data
+
+ if "point_sets" not in adata.uns.keys():
+ adata.uns["point_sets"] = dict(points=[])
+
+ # Iterate over point sets
+ for point_key in adata.uns["point_sets"]:
+ points = adata.uns[point_key]
+
+ # Subset for cells
+ cells = adata.obs_names.tolist()
+ in_cells = points["cell"].isin(cells)
+
+ # Subset for genes
+ in_genes = [True] * points.shape[0]
+ if "gene" in points.columns:
+ genes = adata.var_names.tolist()
+ in_genes = points["gene"].isin(genes)
+
+ # Combine boolean masks
+ valid_mask = (in_cells & in_genes).values
+
+ # Sync points using mask
+ points = points.loc[valid_mask]
+
+ # Remove unused categories for categorical columns
+ for col in points.columns:
+ if points[col].dtype == "category":
+ points[col].cat.remove_unused_categories(inplace=True)
+
+ adata.uns[point_key] = points
+
+ # Sync point metadata using mask
+ for metadata_key in adata.uns["point_sets"][point_key]:
+ if metadata_key not in adata.uns:
+ warnings.warn(
+ f"Skipping: metadata {metadata_key} not found in adata.uns"
+ )
+ continue
+
+ metadata = adata.uns[metadata_key]
+ # Slice DataFrame if not empty
+ if isinstance(metadata, pd.DataFrame) and not metadata.empty:
+ adata.uns[metadata_key] = metadata.loc[valid_mask, :]
+
+ # Slice Iterable if not empty
+ elif isinstance(metadata, list) and any(metadata):
+ adata.uns[metadata_key] = [
+ m for i, m in enumerate(metadata) if valid_mask[i]
+ ]
+ elif isinstance(metadata, Iterable) and metadata.shape[0] > 0:
+ adata.uns[metadata_key] = adata.uns[metadata_key][valid_mask]
+ else:
+ warnings.warn(f"Metadata {metadata_key} is not a DataFrame or Iterable")
+
+ return adata if copy else None
+
+
+def _register_points(data, point_key, metadata_keys):
+ required_cols = ["x", "y", "cell"]
+
+ if point_key not in data.uns.keys():
+ raise ValueError(f"Key {point_key} not found in data.uns")
+
+ points = data.uns[point_key]
+
+ if not all([col in points.columns for col in required_cols]):
+ raise ValueError(
+ f"Point DataFrame must have columns {', '.join(required_cols)}"
+ )
+
+ # Check for valid cells
+ cells = data.obs_names.tolist()
+ if not points["cell"].isin(cells).all():
+ raise ValueError("Invalid cells in point DataFrame")
+
+ # Initialize/add to point registry
+ if "point_sets" not in data.uns.keys():
+ data.uns["point_sets"] = dict()
+
+ if point_key not in data.uns["point_sets"].keys():
+ data.uns["point_sets"][point_key] = []
+
+ if len(metadata_keys) < 0:
+ return
+
+ # Register metadata
+ for key in metadata_keys:
+ # Check for valid metadata
+ if key not in data.uns.keys():
+ raise ValueError(f"Key {key} not found in data.uns")
+
+ n_points = data.uns[point_key].shape[0]
+ metadata_len = data.uns[key].shape[0]
+ if metadata_len != n_points:
+ raise ValueError(
+ f"Metadata {key} must have same length as points {point_key}"
+ )
+
+ # Add metadata key to registry
+ if key not in data.uns["point_sets"][point_key]:
+ data.uns["point_sets"][point_key].append(key)
+
+
+def register_points(point_key: str, metadata_keys: list):
+ """Decorator function to register points to the current `AnnData` object.
+ This keeps track of point sets and keeps them in sync with `AnnData` object.
+
+ Parameters
+ ----------
+ point_key : str
+ Key where points are stored in `data.uns`
+ metadata_keys : list
+ Keys where point metadata are stored in `data.uns`
+ """
+
+ def decorator(func):
+ @wraps(func)
+ def wrapper(*args, **kwds):
+ kwargs = get_default_args(func)
+ kwargs.update(kwds)
+
+ func(*args, **kwds)
+ data = args[0]
+ # Check for required columns
+ return _register_points(data, point_key, metadata_keys)
+
+ return wrapper
+
+ return decorator
+
+
+def sc_format(data, copy=False):
+ """
+ Convert data.obs GeoPandas columns to string for compatibility with scanpy.
+ """
+ adata = data.copy() if copy else data
+
+ shape_names = data.obs.columns.str.endswith("_shape")
+
+ for col in data.obs.columns[shape_names]:
+ adata.obs[col] = adata.obs[col].astype(str)
+
+ return adata if copy else None
+
+
+def geo_format(data, copy=False):
+ """
+ Convert data.obs scanpy columns to GeoPandas compatible types.
+ """
+ adata = data.copy() if copy else data
+
+ shape_names = adata.obs.columns[adata.obs.columns.str.endswith("_shape")]
+
+ adata.obs[shape_names] = adata.obs[shape_names].apply(
+ lambda col: gpd.GeoSeries(
+ col.astype(str).apply(lambda val: wkt.loads(val) if val != "None" else None)
+ )
+ )
+
+ return adata if copy else None
diff --git a/bento/datasets/__init__.py b/bento/datasets/__init__.py
index 8a55f37..55082a3 100755
--- a/bento/datasets/__init__.py
+++ b/bento/datasets/__init__.py
@@ -1 +1,5 @@
-from ._datasets import get_dataset_info, load_dataset, sample_data
+from ._datasets import (
+ get_dataset_info,
+ load_dataset,
+ sample_data,
+)
diff --git a/bento/datasets/_datasets.py b/bento/datasets/_datasets.py
index bd44fbb..b098a60 100755
--- a/bento/datasets/_datasets.py
+++ b/bento/datasets/_datasets.py
@@ -1,6 +1,7 @@
import os
import pandas as pd
+
pkg_resources = None
from ..io import read_h5ad
@@ -18,19 +19,30 @@ def get_dataset_info():
if pkg_resources is None:
import pkg_resources
-
stream = pkg_resources.resource_stream(__name__, "datasets.csv")
return pd.read_csv(stream, index_col=0)
def load_dataset(name, cache=True, data_home="~/bento-data", **kws):
-
+ """Load a builtin dataset.
+
+ Parameters
+ ----------
+ name : str
+ Name of the dataset to load.
+ cache : bool (default: True)
+ If True, try to load from local cache first, download as needed.
+ data_home : str (default: "~/bento-data")
+ Path to directory where datasets are stored.
+ **kws
+ Keyword arguments passed to :func:`bento.io.read_h5ad`.
+ """
datainfo = get_dataset_info()
# Check if dataset name exists
if name not in datainfo.index:
raise KeyError(
- f"No builtin dataset named '{name}'. Use :func:`get_dataset_info` to list info about available datasets."
+ f"No builtin dataset named '{name}'. Use :func:`bento.ds.get_dataset_info` to list info about available datasets."
)
# Sanitize user path
@@ -40,7 +52,6 @@ def load_dataset(name, cache=True, data_home="~/bento-data", **kws):
if not os.path.exists(data_home):
os.makedirs(data_home)
-
# Try to load from local cache first, download as needed
url = datainfo.loc[name, "url"]
cache_path = os.path.join(data_home, os.path.basename(url))
@@ -99,6 +110,6 @@ def sample_data():
global pkg_resources
if pkg_resources is None:
import pkg_resources
-
- stream = pkg_resources.resource_stream(__name__, "seqfish_sample.h5ad")
- return read_h5ad(stream)
\ No newline at end of file
+
+ stream = pkg_resources.resource_stream(__name__, "merfish_sample.h5ad")
+ return read_h5ad(stream)
diff --git a/bento/datasets/datasets.csv b/bento/datasets/datasets.csv
index 49ecb7e..08fb8f5 100644
--- a/bento/datasets/datasets.csv
+++ b/bento/datasets/datasets.csv
@@ -1,5 +1,9 @@
name,n_cells,n_genes,n_points,url,description
-merfish_raw,1153,135,16907948,https://ndownloader.figshare.com/files/29046861,U2-OS cell line profiled with MERFISH
-merfish,1022,135,15315044,https://figshare.com/ndownloader/files/35596979,precomputed version of the U2-OS cell line profiled with MERFISH
-seqfish_raw,211,9506,5306521,https://ndownloader.figshare.com/files/29046873,3T3 cell line profiled with seqFISH+
-seqfish,179,3726,4680210,https://figshare.com/ndownloader/files/35596982,precomputed version of the 3T3 cell line profiled with seqFISH+
\ No newline at end of file
+merfish,1153,135,16907948,https://ndownloader.figshare.com/files/29046861,U2-OS cell line profiled with MERFISH
+merfish_processed,1022,135,15315044,https://figshare.com/ndownloader/files/35596979,precomputed version of the U2-OS cell line profiled with MERFISH
+seqfish,211,9506,5306521,https://ndownloader.figshare.com/files/29046873,3T3 cell line profiled with seqFISH+
+seqfish_processed,179,3726,4680210,https://figshare.com/ndownloader/files/35596982,precomputed version of the 3T3 cell line profiled with seqFISH+
+cardio_control1,,,,https://figshare.com/ndownloader/files/39850386,"vehicle replicate 1, iPSC-derived cardiomyocytes profiled with Molecular Cartography"
+cardio_control2,,,,https://figshare.com/ndownloader/files/39850383,"vehicle replicate 2, iPSC-derived cardiomyocytes profiled with Molecular Cartography"
+cardio_dox1,,,,https://figshare.com/ndownloader/files/39850470,"treatment replicate 1, doxorubicin-treated iPSC-derived cardiomyocytes profiled with Molecular Cartography"
+cardio_dox2,,,,https://figshare.com/ndownloader/files/39850613,"treatment replicate 2, doxorubicin-treated iPSC-derived cardiomyocytes profiled with Molecular Cartography"
\ No newline at end of file
diff --git a/bento/datasets/merfish_sample.h5ad b/bento/datasets/merfish_sample.h5ad
new file mode 100644
index 0000000..b1d1cb6
Binary files /dev/null and b/bento/datasets/merfish_sample.h5ad differ
diff --git a/bento/geometry/__init__.py b/bento/geometry/__init__.py
new file mode 100644
index 0000000..c0bc43a
--- /dev/null
+++ b/bento/geometry/__init__.py
@@ -0,0 +1,9 @@
+from ._geometry import (
+ count_points,
+ crop,
+ get_points,
+ get_points_metadata,
+ get_shape,
+ rename_shapes,
+ sindex_points,
+)
diff --git a/bento/geometry/_geometry.py b/bento/geometry/_geometry.py
new file mode 100644
index 0000000..783d000
--- /dev/null
+++ b/bento/geometry/_geometry.py
@@ -0,0 +1,310 @@
+import re
+from typing import Dict, List, Literal, Optional, Tuple, Union
+
+import geopandas as gpd
+import pandas as pd
+from anndata import AnnData
+from scipy.sparse import coo_matrix
+from shapely import wkt
+from shapely.geometry import Polygon
+from tqdm.auto import tqdm
+
+from .._utils import sync
+
+
+def count_points(
+ data: AnnData, shape_names: List[str], copy: bool = False
+) -> Optional[AnnData]:
+ """Count points in shapes and add as layers to `data`. Expects points to already be indexed to shapes.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object
+ shape_names : str, list
+ List of shape names to index points to
+ copy : bool, optional
+ Whether to return a copy the AnnData object. Default False.
+
+ Returns
+ -------
+ AnnData
+ .layers: Updated layers with count of points in each shape
+ """
+ adata = data.copy() if copy else data
+
+ if isinstance(shape_names, str):
+ shape_names = [shape_names]
+
+ points = get_points(data, asgeo=True)
+
+ if shape_names[0].endswith("_shape"):
+ shape_prefixes = [
+ "_".join(shp_name.split("_shape")[:-1]) for shp_name in shape_names
+ ]
+ else:
+ shape_prefixes = shape_names
+
+ shape_counts = points.groupby(["cell", "gene"], observed=True)[shape_prefixes].sum()
+
+ for shape in shape_counts.columns:
+ pos_counts = shape_counts[shape]
+ pos_counts = pos_counts[pos_counts > 0]
+ values = pos_counts
+
+ row = adata.obs_names.get_indexer(pos_counts.index.get_level_values("cell"))
+ col = adata.var_names.get_indexer(pos_counts.index.get_level_values("gene"))
+ adata.layers[f"{shape}"] = coo_matrix((values, (row, col)))
+
+ return adata if copy else None
+
+
+def sindex_points(
+ data: AnnData, points_key: str, shape_names: List[str], copy: bool = False
+) -> Optional[AnnData]:
+ """Index points to shapes and add as columns to `data.uns[points_key]`.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object
+ points_key : str
+ Key for points DataFrame in `data.uns`
+ shape_names : str, list
+ List of shape names to index points to
+ copy : bool, optional
+ Whether to return a copy the AnnData object. Default False.
+ Returns
+ -------
+ AnnData
+ .uns[points_key]: Updated points DataFrame with boolean column for each shape
+ """
+ adata = data.copy() if copy else data
+
+ if isinstance(shape_names, str):
+ shape_names = [shape_names]
+
+ points = get_points(data, points_key, asgeo=True).sort_values("cell")
+ points = points.drop(
+ columns=shape_names, errors="ignore"
+ ) # Drop columns to overwrite
+ points_grouped = points.groupby("cell", observed=True)
+ cells = list(points_grouped.groups.keys())
+ point_sindex = []
+
+ # Iterate over cells and index points to shapes
+ for cell in tqdm(cells, leave=False):
+ pt_group = points_grouped.get_group(cell)
+
+ # Get shapes to index in current cell
+ cur_shapes = gpd.GeoDataFrame(geometry=data.obs.loc[cell, shape_names].T)
+ cur_sindex = (
+ pt_group.reset_index()
+ .sjoin(cur_shapes, how="left", op="intersects")
+ .drop_duplicates(subset="index", keep="first")
+ .sort_index()
+ .reset_index()["index_right"]
+ .astype(str)
+ )
+ point_sindex.append(cur_sindex)
+
+ # TODO: concat is hella slow
+ point_sindex = (
+ pd.concat(point_sindex, ignore_index=True).str.get_dummies() == 1
+ ).fillna(False)
+ point_sindex.columns = [col.replace("_shape", "") for col in point_sindex.columns]
+
+ # Add new columns to points
+ points[point_sindex.columns] = point_sindex.values
+ adata.uns[points_key] = points
+
+ return adata if copy else None
+
+
+def crop(
+ data: AnnData,
+ xlims: Tuple[int],
+ ylims: Tuple[int],
+ copy: bool = True,
+) -> Optional[AnnData]:
+ """Returns a view of data within specified coordinates.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object
+ xlims : list, optional
+ Upper and lower x limits, by default None
+ ylims : list, optional
+ Upper and lower y limits, by default None
+ copy : bool, optional
+ Whether to return a copy the AnnData object. Default True.
+
+ Returns
+ -------
+ AnnData
+ AnnData object with data cropped to specified coordinates
+ """
+ adata = data.copy() if copy else data
+
+ if len(xlims) < 1 and len(xlims) > 2:
+ return ValueError("Invalid xlims")
+
+ if len(ylims) < 1 and len(ylims) > 2:
+ return ValueError("Invalid ylims")
+
+ xmin, xmax = xlims[0], xlims[1]
+ ymin, ymax = ylims[0], ylims[1]
+ box = Polygon([[xmin, ymin], [xmin, ymax], [xmax, ymax], [xmax, ymin]])
+ in_crop = get_shape(data, "cell_shape").within(box)
+
+ adata = data[in_crop, :]
+ sync(adata, copy=False)
+
+ return adata if copy else None
+
+
+def get_shape(adata: AnnData, shape_name: str) -> gpd.GeoSeries:
+ """Get a GeoSeries of Polygon objects from an AnnData object.
+
+ Parameters
+ ----------
+ adata : AnnData
+ Spatial formatted AnnData object
+ shape_name : str
+ Name of shape column in adata.obs
+
+ Returns
+ -------
+ GeoSeries
+ GeoSeries of Polygon objects
+ """
+ if shape_name not in adata.obs.columns:
+ raise ValueError(f"Shape {shape_name} not found in adata.obs.")
+
+ if adata.obs[shape_name].astype(str).str.startswith("POLYGON").any():
+ return gpd.GeoSeries(
+ adata.obs[shape_name]
+ .astype(str)
+ .apply(lambda val: wkt.loads(val) if val != "None" else None)
+ )
+
+ else:
+ return gpd.GeoSeries(adata.obs[shape_name])
+
+
+def rename_shapes(
+ data: AnnData,
+ mapping: Dict[str, str],
+ points_key: Optional[Union[List[str], None]] = None,
+ points_encoding: Union[List[Literal["label", "onehot"]], None] = None,
+ copy: bool = False,
+) -> Optional[AnnData]:
+ """Rename shape columns in adata.obs and points columns in adata.uns.
+
+ Parameters
+ ----------
+ adata : AnnData
+ Spatial formatted AnnData object
+ mapping : Dict[str, str]
+ Mapping of old shape names to new shape names
+ points_key : list of str, optional
+ List of keys for points DataFrame in `adata.uns`, by default None
+ points_encoding : {"label", "onehot"}, optional
+ Encoding type for each specified points
+ copy : bool, optional
+ Whether to return a copy of the AnnData object. Default False.
+
+ Returns
+ -------
+ AnnData
+ .obs: Updated shape column names
+ .uns[points_key]: Updated points shape(s) columns according to encoding type
+ """
+ adata = data.copy() if copy else data
+ adata.obs.rename(columns=mapping, inplace=True)
+
+ # Map point columns
+ if points_key:
+ # Get mapping for points column names
+ prefix_map = {
+ _get_shape_prefix(shape_name): _get_shape_prefix(new_name)
+ for shape_name, new_name in mapping.items()
+ }
+ # Get mapping for label encoding
+ label_map = {
+ re.sub(r"\D", "", shape_name): re.sub(r"\D", "", new_name)
+ for shape_name, new_name in prefix_map.items()
+ }
+
+ for p_key, p_encoding in zip(points_key, points_encoding):
+ if p_encoding == "label":
+ # Point column name with label encoding
+ col = re.sub(r"\d", "", list(prefix_map.keys())[0])
+ adata.uns[p_key][col] = adata.uns[p_key][col].astype(str).map(label_map)
+
+ elif p_encoding == "onehot":
+ # Remap column names
+ adata.uns[p_key].rename(columns=prefix_map, inplace=True)
+
+ return adata if copy else None
+
+
+def _get_shape_prefix(shape_name):
+ """Get prefix of shape name."""
+ return "_".join(shape_name.split("_")[:-1])
+
+
+def get_points(
+ data: AnnData, key: str = "points", asgeo: bool = False
+) -> Union[pd.DataFrame, gpd.GeoDataFrame]:
+ """Get points DataFrame synced to AnnData object.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object
+ key : str, optional
+ Key for `data.uns` to use, by default "points"
+ asgeo : bool, optional
+ Cast as GeoDataFrame using columns x and y for geometry, by default False
+
+ Returns
+ -------
+ DataFrame or GeoDataFrame
+ Returns `data.uns[key]` as a `[Geo]DataFrame`
+ """
+ points = sync(data, copy=True).uns[key]
+
+ # Cast to GeoDataFrame
+ if asgeo:
+ points = gpd.GeoDataFrame(
+ points, geometry=gpd.points_from_xy(points.x, points.y)
+ )
+
+ return points
+
+
+def get_points_metadata(
+ data: AnnData,
+ metadata_key: str,
+ points_key: str = "points",
+):
+ """Get points metadata synced to AnnData object.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object
+ metadata_key : str
+ Key for `data.uns[key]` to use
+ key : str, optional
+ Key for `data.uns` to use, by default "points"
+
+ Returns
+ -------
+ Series
+ Returns `data.uns[key][metadata_key]` as a `Series`
+ """
+ metadata = sync(data, copy=True).uns[metadata_key]
+ return metadata
diff --git a/bento/io/__init__.py b/bento/io/__init__.py
index f1d52b9..411bba4 100755
--- a/bento/io/__init__.py
+++ b/bento/io/__init__.py
@@ -1 +1 @@
-from ._io import read_h5ad, write_h5ad, concatenate
\ No newline at end of file
+from ._io import read_h5ad, write_h5ad, concatenate, prepare
\ No newline at end of file
diff --git a/bento/io/_io.py b/bento/io/_io.py
index ef24466..bad724a 100755
--- a/bento/io/_io.py
+++ b/bento/io/_io.py
@@ -9,10 +9,12 @@
from shapely.geometry import Polygon
from tqdm.auto import tqdm
import anndata
-# import rasterio
-# import rasterio.features
+import rasterio
+import rasterio.features
import emoji
+from .._utils import sc_format
+
def read_h5ad(filename, backed=None):
"""Load bento processed AnnData object from h5ad.
@@ -32,14 +34,13 @@ def read_h5ad(filename, backed=None):
"""
adata = anndata.read_h5ad(filename, backed=backed)
-
# Load obs columns that are shapely geometries
adata.obs = adata.obs.apply(
lambda col: gpd.GeoSeries(
col.astype(str).apply(lambda val: wkt.loads(val) if val != "None" else None)
)
if col.astype(str).str.startswith("POLYGON").any()
- else gpd.GeoSeries(col)
+ else pd.Series(col)
)
adata.obs.index.name = "cell"
@@ -49,7 +50,7 @@ def read_h5ad(filename, backed=None):
def write_h5ad(data, filename):
- """Write AnnData to h5ad. Casts each GeoDataFrame in adata.uns['masks'] for h5ad compatibility.
+ """Write AnnData to h5ad.
Parameters
----------
@@ -61,13 +62,7 @@ def write_h5ad(data, filename):
# Convert geometry from GeoSeries to list for h5ad serialization compatibility
adata = data.copy()
- adata.obs = adata.obs.apply(
- lambda col: col.apply(lambda val: val.wkt if val is not None else val).astype(
- str
- )
- if col.astype(str).str.startswith("POLYGON").any()
- else col
- )
+ sc_format(adata)
adata.uns["points"] = adata.uns["points"].drop("geometry", axis=1, errors="ignore")
@@ -75,202 +70,203 @@ def write_h5ad(data, filename):
adata.write(filename)
-# def prepare(
-# molecules,
-# cell_seg,
-# x="x",
-# y="y",
-# gene="gene",
-# other_seg=dict(),
-# ):
-# """Prepare AnnData with molecule-level spatial data.
-
-# Parameters
-# ----------
-# molecules : DataFrame
-# Molecule coordinates and annotations.
-# cell_seg : np.array
-# Cell segmentation masks represented as 2D numpy array where 1st and 2nd
-# dimensions correspond to x and y respectively. Connected regions must
-# have same value to be considered a valid shape. Data type must be one
-# of rasterio.int16, rasterio.int32, rasterio.uint8, rasterio.uint16, or
-# rasterio.float32. See rasterio.features.shapes for more details.
-# x : str
-# Column name for x coordinates, by default 'x'.
-# y : str
-# Column name for x coordinates, by default 'y'.
-# gene : str
-# Column name for gene name, by default 'gene'.
-# other_seg
-# Additional keyword arguments are interpreted as additional segmentation
-# masks. The user specified parameter name is used to store these masks as
-# {name}_shape in adata.obs.
-# Returns
-# -------
-# AnnData object
-# """
-# for var in [x, y, gene]:
-# if var not in molecules.columns:
-# return
-
-# pbar = tqdm(total=6)
-# pbar.set_description(emoji.emojize(":test_tube: Loading inputs"))
-# points = molecules[[x, y, gene]]
-# points.columns = ["x", "y", "gene"]
-# points = gpd.GeoDataFrame(
-# points, geometry=gpd.points_from_xy(x=points.x, y=points.y)
-# )
-# points["gene"] = points["gene"].astype("category") # Save memory
-# pbar.update()
-
-# # Load each set of masks as GeoDataFrame
-# # shapes = Series where index = segs.keys() and values = GeoDataFrames
-# segs_dict = {"cell": cell_seg, **other_seg}
-# # Already formatted, select geometry column already
-# if isinstance(cell_seg, gpd.GeoDataFrame):
-# shapes_dict = {
-# shape_name: shape_seg[["geometry"]]
-# for shape_name, shape_seg in segs_dict.items()
-# }
-# # Load shapes from numpy array image
-# elif isinstance(cell_seg, np.array):
-# shapes_dict = {
-# shape_name: _load_shapes_np(shape_seg)
-# for shape_name, shape_seg in segs_dict.items()
-# }
-# else:
-# print("Segmentation mask format not recognized.")
-# pbar.close()
-# return
-# pbar.update()
-
-# # Index shapes to cell
-# pbar.set_description(emoji.emojize(":open_book: Indexing"))
-# obs_shapes = _index_shapes(shapes_dict, "cell")
-# pbar.update()
-
-# # Index points for all shapes
-# point_index = dict()
-# for col in obs_shapes.columns:
-# shp_gdf = gpd.GeoDataFrame(geometry=obs_shapes[col])
-# shp_name = '_'.join(str(col).split('_')[:-1])
-# point_index[shp_name] = _index_points(points, shp_gdf)
-# point_index = pd.DataFrame.from_dict(point_index)
-# pbar.update()
-
-# # Main long dataframe for reformatting
-# pbar.set_description(emoji.emojize(":computer_disk: Formatting"))
-# uns_points = pd.concat(
-# [
-# points[["x", "y", "gene"]].reset_index(drop=True),
-# point_index.reset_index(drop=True),
-# ],
-# axis=1,
-# )
-
-# # Remove extracellular points
-# uns_points = uns_points.loc[uns_points["cell"] != "-1"]
-# if len(uns_points) == 0:
-# print("No molecules found within cells. Data not processed.")
-# pbar.close()
-# return
-# uns_points[["cell", "gene"]] = uns_points[["cell", "gene"]].astype('category')
-
-# # Aggregate points to counts
-# expression = (
-# uns_points[["cell", "gene"]]
-# .groupby(["cell", "gene"])
-# .apply(lambda x: x.shape[0])
-# .reset_index()
-# )
-
-# # Create cell x gene matrix
-# cellxgene = expression.pivot_table(
-# index="cell", columns="gene", aggfunc="sum"
-# ).fillna(0)
-# cellxgene.columns = cellxgene.columns.get_level_values("gene")
-# pbar.update()
-
-# # Create scanpy anndata object
-# pbar.set_description(emoji.emojize(":package: Create AnnData"))
-# adata = anndata.AnnData(X=cellxgene)
-# obs_shapes = obs_shapes.reindex(index=adata.obs.index)
-# adata.obs = pd.concat([adata.obs, obs_shapes], axis=1)
-# adata.obs.index = adata.obs.index.astype(str)
-
-# # Save cell, gene, batch, and other shapes as categorical type to save memory
-# uns_points["cell"] = uns_points["cell"].astype("category")
-# uns_points["gene"] = uns_points["gene"].astype("category")
-# for shape_name in list(other_seg.keys()):
-# uns_points[shape_name] = uns_points[shape_name].astype('category')
-
-# adata.uns = {"points": uns_points}
-
-# pbar.set_description(emoji.emojize(":bento_box: Finished!"))
-# pbar.update()
-# pbar.close()
-# return adata
-
-
-# def _load_shapes_np(seg_img):
-# """Extract shapes from segmentation image.
-
-# Parameters
-# ----------
-# seg_img : np.array
-# Segmentation masks represented as 2D numpy array where 1st and 2nd dimensions correspond to x and y respectively.
-
-# Returns
-# -------
-# GeoDataFrame
-# Single column GeoDataFrame where each row is a single Polygon.
-# """
-# seg_img = seg_img.astype("uint16")
-# contours = rasterio.features.shapes(seg_img) # rasterio to generate contours
-# # Convert to shapely Polygons
-# polygons = [Polygon(p["coordinates"][0]) for p, v in contours]
-# shapes = gpd.GeoDataFrame(geometry=gpd.GeoSeries(polygons)) # Cast to GeoDataFrame
-# shapes.drop(
-# shapes.area.sort_values().tail(1).index, inplace=True
-# ) # Remove extraneous shape
-# shapes = shapes[shapes.geom_type != "MultiPolygon"]
-
-# shapes.index = shapes.index.astype(str)
-
-# # Cleanup polygons
-# # mask.geometry = mask.geometry.buffer(2).buffer(-2)
-# # mask.geometry = mask.geometry.apply(unary_union)
-
-# return shapes
-
-
-# def _load_shapes_json(seg_json):
-# """Extract shapes from python object loaded with json.
-
-# Parameters
-# ----------
-# seg_json : list
-# list loaded by json.load(file)
-
-# Returns
-# -------
-# GeoDataFrame
-# Each row represents a single shape,
-# """
-# polys = []
-# for i in range(len(seg_json)):
-# polys.append(Polygon(seg_json[i]["coordinates"][0]))
-
-# shapes = gpd.GeoDataFrame(geometry=gpd.GeoSeries(polys))
-# shapes = shapes[shapes.geom_type != "MultiPolygon"]
-
-# shapes.index = shapes.index.astype(str)
-
-# # Cleanup polygons
-# # mask.geometry = mask.geometry.buffer(2).buffer(-2)
-# # mask.geometry = mask.geometry.apply(unary_union)
-
-# return shapes
+def prepare(
+ molecules,
+ cell_seg,
+ x="x",
+ y="y",
+ gene="gene",
+ other_seg=dict(),
+):
+ """Prepare AnnData with molecule-level spatial data.
+
+ Parameters
+ ----------
+ molecules : DataFrame
+ Molecule coordinates and annotations.
+ cell_seg : np.array
+ Cell segmentation masks represented as 2D numpy array where 1st and 2nd
+ dimensions correspond to x and y respectively. Connected regions must
+ have same value to be considered a valid shape. Data type must be one
+ of rasterio.int16, rasterio.int32, rasterio.uint8, rasterio.uint16, or
+ rasterio.float32. See rasterio.features.shapes for more details.
+ x : str
+ Column name for x coordinates, by default 'x'.
+ y : str
+ Column name for x coordinates, by default 'y'.
+ gene : str
+ Column name for gene name, by default 'gene'.
+ other_seg
+ Additional keyword arguments are interpreted as additional segmentation
+ masks. The user specified parameter name is used to store these masks as
+ {name}_shape in adata.obs.
+ Returns
+ -------
+ AnnData object
+ """
+ for var in [x, y, gene]:
+ if var not in molecules.columns:
+ return
+
+ pbar = tqdm(total=6)
+ pbar.set_description(emoji.emojize(":test_tube: Loading inputs"))
+ points = molecules[[x, y, gene]]
+ points.columns = ["x", "y", "gene"]
+ points = gpd.GeoDataFrame(
+ points, geometry=gpd.points_from_xy(x=points.x, y=points.y)
+ )
+ points["gene"] = points["gene"].astype("category") # Save memory
+ pbar.update()
+
+ # Load each set of masks as GeoDataFrame
+ # shapes = Series where index = segs.keys() and values = GeoDataFrames
+ segs_dict = {"cell": cell_seg, **other_seg}
+ # Already formatted, select geometry column already
+ if isinstance(cell_seg, gpd.GeoDataFrame):
+ shapes_dict = {
+ shape_name: shape_seg[["geometry"]]
+ for shape_name, shape_seg in segs_dict.items()
+ }
+ # Load shapes from numpy array image
+ elif isinstance(cell_seg, np.ndarray):
+ shapes_dict = {
+ shape_name: _load_shapes_np(shape_seg)
+ for shape_name, shape_seg in segs_dict.items()
+ }
+ else:
+ print("Segmentation mask format not recognized.")
+ pbar.close()
+ return
+ pbar.update()
+
+ # Index shapes to cell
+ pbar.set_description(emoji.emojize(":open_book: Indexing"))
+ obs_shapes = _index_shapes(shapes_dict, "cell")
+ pbar.update()
+
+ # Index points for all shapes
+ # TODO: refactor to use geometry.sindex_points
+ point_index = dict()
+ for col in obs_shapes.columns:
+ shp_gdf = gpd.GeoDataFrame(geometry=obs_shapes[col])
+ shp_name = "_".join(str(col).split("_")[:-1])
+ point_index[shp_name] = _index_points(points, shp_gdf)
+ point_index = pd.DataFrame.from_dict(point_index)
+ pbar.update()
+
+ # Main long dataframe for reformatting
+ pbar.set_description(emoji.emojize(":computer_disk: Formatting"))
+ uns_points = pd.concat(
+ [
+ points[["x", "y", "gene"]].reset_index(drop=True),
+ point_index.reset_index(drop=True),
+ ],
+ axis=1,
+ )
+
+ # Remove extracellular points
+ uns_points = uns_points.loc[uns_points["cell"] != "-1"]
+ if len(uns_points) == 0:
+ print("No molecules found within cells. Data not processed.")
+ pbar.close()
+ return
+ uns_points[["cell", "gene"]] = uns_points[["cell", "gene"]].astype("category")
+
+ # Aggregate points to counts
+ expression = (
+ uns_points[["cell", "gene"]]
+ .groupby(["cell", "gene"])
+ .apply(lambda x: x.shape[0])
+ .reset_index()
+ )
+
+ # Create cell x gene matrix
+ cellxgene = expression.pivot_table(
+ index="cell", columns="gene", aggfunc="sum"
+ ).fillna(0)
+ cellxgene.columns = cellxgene.columns.get_level_values("gene")
+ pbar.update()
+
+ # Create scanpy anndata object
+ pbar.set_description(emoji.emojize(":package: Create AnnData"))
+ adata = anndata.AnnData(X=cellxgene)
+ obs_shapes = obs_shapes.reindex(index=adata.obs.index)
+ adata.obs = pd.concat([adata.obs, obs_shapes], axis=1)
+ adata.obs.index = adata.obs.index.astype(str)
+
+ # Save cell, gene, batch, and other shapes as categorical type to save memory
+ uns_points["cell"] = uns_points["cell"].astype("category")
+ uns_points["gene"] = uns_points["gene"].astype("category")
+ for shape_name in list(other_seg.keys()):
+ uns_points[shape_name] = uns_points[shape_name].astype("category")
+
+ adata.uns = {"points": uns_points}
+
+ pbar.set_description(emoji.emojize(":bento_box: Finished!"))
+ pbar.update()
+ pbar.close()
+ return adata
+
+
+def _load_shapes_np(seg_img):
+ """Extract shapes from segmentation image.
+
+ Parameters
+ ----------
+ seg_img : np.array
+ Segmentation masks represented as 2D numpy array where 1st and 2nd dimensions correspond to x and y respectively.
+
+ Returns
+ -------
+ GeoDataFrame
+ Single column GeoDataFrame where each row is a single Polygon.
+ """
+ seg_img = seg_img.astype("uint16")
+ contours = rasterio.features.shapes(seg_img) # rasterio to generate contours
+ # Convert to shapely Polygons
+ polygons = [Polygon(p["coordinates"][0]) for p, v in contours]
+ shapes = gpd.GeoDataFrame(geometry=gpd.GeoSeries(polygons)) # Cast to GeoDataFrame
+ shapes.drop(
+ shapes.area.sort_values().tail(1).index, inplace=True
+ ) # Remove extraneous shape
+ shapes = shapes[shapes.geom_type != "MultiPolygon"]
+
+ shapes.index = shapes.index.astype(str)
+
+ # Cleanup polygons
+ # mask.geometry = mask.geometry.buffer(2).buffer(-2)
+ # mask.geometry = mask.geometry.apply(unary_union)
+
+ return shapes
+
+
+def _load_shapes_json(seg_json):
+ """Extract shapes from python object loaded with json.
+
+ Parameters
+ ----------
+ seg_json : list
+ list loaded by json.load(file)
+
+ Returns
+ -------
+ GeoDataFrame
+ Each row represents a single shape,
+ """
+ polys = []
+ for i in range(len(seg_json)):
+ polys.append(Polygon(seg_json[i]["coordinates"][0]))
+
+ shapes = gpd.GeoDataFrame(geometry=gpd.GeoSeries(polys))
+ shapes = shapes[shapes.geom_type != "MultiPolygon"]
+
+ shapes.index = shapes.index.astype(str)
+
+ # Cleanup polygons
+ # mask.geometry = mask.geometry.buffer(2).buffer(-2)
+ # mask.geometry = mask.geometry.apply(unary_union)
+
+ return shapes
def _index_shapes(shapes, cell_key):
@@ -320,7 +316,6 @@ def _index_shapes(shapes, cell_key):
# Add indexed shapes as new column in GeoDataFrame
indexed_shapes[f"{shape_name}_shape"] = geometry
-
# Cells are rows, intersecting shape sets are columns
indexed_shapes = indexed_shapes.rename(columns={"geometry": "cell_shape"})
indexed_shapes.index = indexed_shapes.index.astype(str)
@@ -356,6 +351,9 @@ def _index_points(points, shapes):
def concatenate(adatas):
+ # Read point registry to identify point sets to concatenate
+ # TODO
+
uns_points = []
for i, adata in enumerate(adatas):
points = adata.uns["points"].copy()
@@ -363,7 +361,7 @@ def concatenate(adatas):
if "batch" not in points.columns:
points["batch"] = i
- points["cell"] = points["cell"].astype(str) + "-" + points["batch"].astype(str)
+ points["cell"] = points["cell"].astype(str) + "-" + str(i)
uns_points.append(points)
diff --git a/bento/plotting/__init__.py b/bento/plotting/__init__.py
index 805cc9a..235a4a8 100755
--- a/bento/plotting/__init__.py
+++ b/bento/plotting/__init__.py
@@ -1,3 +1,5 @@
-from ._plotting import (lp_dist, lp_gene_dist, lp_genes, lp_diff, # umap,
- cellplot, qc_metrics, sig_samples)
-from ._tensor_tools import lp_signatures
+from ._multidimensional import flux_summary, obs_stats
+
+from ._lp import lp_diff, lp_dist, lp_gene_dist, lp_genes
+from ._plotting import points, density, shapes, flux, fluxmap, fe
+from ._signatures import colocation, factor, signatures, signatures_error
diff --git a/bento/plotting/_colors.py b/bento/plotting/_colors.py
new file mode 100644
index 0000000..8597fd0
--- /dev/null
+++ b/bento/plotting/_colors.py
@@ -0,0 +1,21 @@
+import matplotlib as mpl
+from matplotlib.colors import LinearSegmentedColormap
+import numpy as np
+import seaborn as sns
+
+blue_rgb = np.array([30, 136, 229]) / 255
+red_rgb = np.array([255, 13, 87]) / 255
+red2blue = LinearSegmentedColormap.from_list("red2blue", [blue_rgb, "white", red_rgb])
+red2blue_dark = LinearSegmentedColormap.from_list(
+ "red2blue_dark", [blue_rgb, "black", red_rgb]
+)
+
+mpl.cm.register_cmap("red2blue", red2blue)
+mpl.cm.register_cmap("red2blue_dark", red2blue_dark)
+
+red_light = sns.light_palette(red_rgb, as_cmap=True)
+blue_light = sns.light_palette(blue_rgb, as_cmap=True)
+
+red_dark = sns.dark_palette(red_rgb, as_cmap=True)
+red_dark = LinearSegmentedColormap.from_list("red_dark", [[0, 0, 0], red_rgb])
+blue_dark = LinearSegmentedColormap.from_list("blue_dark", [[0, 0, 0], blue_rgb])
diff --git a/bento/plotting/_layers.py b/bento/plotting/_layers.py
new file mode 100644
index 0000000..f25d1d7
--- /dev/null
+++ b/bento/plotting/_layers.py
@@ -0,0 +1,154 @@
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import geopandas as gpd
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from scipy.interpolate import griddata
+from shapely.geometry import Polygon
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+
+from ..geometry import get_points, get_shape, get_points_metadata
+
+
+def _scatter(points, ax, hue=None, size=None, style=None, **kwargs):
+ semantic_vars = list(set([hue, size, style]))
+ semantic_vars = (
+ None if semantic_vars == [] else [v for v in semantic_vars if v is not None]
+ )
+
+ if ax is None:
+ ax = plt.gca()
+
+ scatter_kws = dict(s=2, c="grey", linewidth=0)
+ scatter_kws.update(kwargs)
+
+ # Let matplotlib scatter handle color if it's in hex format
+ if hue and str(points[hue].iloc[0]).startswith("#"):
+ scatter_kws["c"] = points[hue]
+ hue = None
+
+ sns.scatterplot(
+ data=points, x="x", y="y", hue=hue, size=size, style=style, ax=ax, **scatter_kws
+ )
+
+
+def _hist(points, ax, hue=None, **kwargs):
+ if ax is None:
+ ax = plt.gca()
+
+ hist_kws = dict()
+ hist_kws.update(kwargs)
+
+ sns.histplot(data=points, x="x", y="y", hue=hue, ax=ax, **hist_kws)
+
+
+def _kde(points, ax, hue=None, **kwargs):
+ if ax is None:
+ ax = plt.gca()
+
+ kde_kws = dict(zorder=1, fill=True)
+ kde_kws.update(kwargs)
+
+ sns.kdeplot(data=points, x="x", y="y", hue=hue, ax=ax, **kde_kws)
+
+
+def _polygons(adata, shape, ax, hue=None, hide_outside=False, **kwargs):
+ """Plot shapes with GeoSeries plot function."""
+
+ shapes = gpd.GeoDataFrame(geometry=get_shape(adata, shape))
+
+ edge_color = "none"
+ face_color = "none"
+
+ # If hue is specified, use it to color faces
+ if hue:
+ shapes[hue] = adata.obs.reset_index()[hue].values
+ edge_color = sns.axes_style()["axes.edgecolor"]
+ face_color = "none" # let GeoDataFrame plot function handle facecolor
+
+ style_kwds = dict(
+ linewidth=0.5, edgecolor=edge_color, facecolor=face_color, zorder=2
+ )
+ style_kwds.update(kwargs)
+ shapes.plot(ax=ax, column=hue, **style_kwds)
+
+ if hide_outside:
+ # get axes limits
+ xmin, xmax = ax.get_xlim()
+ ymin, ymax = ax.get_ylim()
+
+ # get min range
+ # min_range = min(xmax - xmin, ymax - ymin)
+ # buffer_size = 0.0 * (min_range)
+
+ # Create shapely polygon from axes limits
+ axes_poly = gpd.GeoDataFrame(
+ geometry=gpd.GeoSeries(
+ [Polygon([(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)])]
+ )
+ # .buffer(0)
+ )
+ axes_poly.overlay(shapes, how="difference").plot(
+ ax=ax,
+ linewidth=0,
+ facecolor=sns.axes_style()["axes.facecolor"],
+ zorder=1.99,
+ )
+
+
+def _raster(adata, res, color, points_key="cell_raster", cbar=False, ax=None, **kwargs):
+ """Plot gradient."""
+
+ if ax is None:
+ ax = plt.gca()
+
+ points = get_points(adata, key=points_key)
+ step = 1 / res
+ color_values = get_points_metadata(adata, metadata_key=color, points_key=points_key)
+ # Infer value format and convert values to rgb
+ # Handle color names and (r, g, b) tuples with matplotlib
+ v1 = color_values[0]
+ if isinstance(v1, str) or (
+ isinstance(v1, tuple) and v1.min() >= 0 and v1.max() <= 1
+ ):
+ rgb = np.array([mpl.colors.to_rgb(c) for c in color_values])
+ else:
+ rgb = color_values.reshape(-1, 1)
+
+ # Get subplot xy grid bounds
+ minx, maxx = points["x"].min(), points["x"].max()
+ miny, maxy = points["y"].min(), points["y"].max()
+
+ # Define grid coordinates
+ grid_x, grid_y = np.mgrid[
+ minx : maxx + step : step,
+ miny : maxy + step : step,
+ ]
+
+ values = []
+ for channel in range(rgb.shape[1]):
+ values.append(
+ griddata(
+ points[["x", "y"]].values,
+ rgb[:, channel],
+ (grid_x, grid_y),
+ method="nearest",
+ fill_value=0,
+ ).T
+ )
+ img = np.stack(values, axis=-1)
+
+ img_kws = dict(interpolation="none")
+ img_kws.update(kwargs)
+
+ im = ax.imshow(img, extent=(minx, maxx, miny, maxy), origin="lower", **img_kws)
+ ax.autoscale(False)
+
+ if cbar:
+ cax = inset_axes(ax, width="20%", height="4%", loc="upper right", borderpad=1.5)
+ cbar = plt.colorbar(im, orientation="horizontal", cax=cax)
+ # cbar.ax.tick_params(axis="x", direction="in", pad=-12)
diff --git a/bento/plotting/_lp.py b/bento/plotting/_lp.py
new file mode 100755
index 0000000..ac5689f
--- /dev/null
+++ b/bento/plotting/_lp.py
@@ -0,0 +1,190 @@
+from typing import List, Tuple, Union
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from anndata import AnnData
+from upsetplot import UpSet, from_indicators
+
+from .._constants import PATTERN_COLORS, PATTERN_NAMES
+from ..tools import lp_stats
+from ._utils import savefig
+from ._multidimensional import _radviz
+
+
+@savefig
+def lp_dist(data, percentage=False, scale=1, fname=None):
+ """Plot pattern combination frequencies as an UpSet plot.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ percentage : bool, optional
+ If True, label each bar as a percentage else label as a count, by default False
+ scale : int, optional
+ scale > 1 scales the plot larger, scale < 1 scales. the plot smaller, by default 1
+ fname : str, optional
+ Save the figure to specified filename, by default None
+ """
+ sample_labels = data.uns["lp"]
+ sample_labels = sample_labels == 1
+
+ # Sort by degree, then pattern name
+ sample_labels["degree"] = -sample_labels[PATTERN_NAMES].sum(axis=1)
+ sample_labels = (
+ sample_labels.reset_index()
+ .sort_values(["degree"] + PATTERN_NAMES, ascending=False)
+ .drop("degree", axis=1)
+ )
+
+ upset = UpSet(
+ from_indicators(PATTERN_NAMES, data=sample_labels),
+ element_size=scale * 40,
+ min_subset_size=sample_labels.shape[0] * 0.001,
+ facecolor="lightgray",
+ sort_by=None,
+ show_counts=(not percentage),
+ show_percentages=percentage,
+ )
+
+ for p, color in zip(PATTERN_NAMES, PATTERN_COLORS):
+ if sample_labels[p].sum() > 0:
+ upset.style_subsets(present=p, max_degree=1, facecolor=color)
+
+ upset.plot()
+ plt.suptitle(f"Localization Patterns\n{sample_labels.shape[0]} samples")
+
+
+@savefig
+def lp_gene_dist(data, fname=None):
+ """Plot the cell fraction distribution of each pattern as a density plot.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ fname : str, optional
+ Save the figure to specified filename, by default None
+ """
+ lp_stats(data)
+
+ col_names = [f"{p}_fraction" for p in PATTERN_NAMES]
+ gene_frac = data.var[col_names]
+ gene_frac.columns = PATTERN_NAMES
+ # Plot frequency distributions
+ sns.displot(
+ data=gene_frac,
+ kind="kde",
+ multiple="layer",
+ height=3,
+ palette=PATTERN_COLORS,
+ )
+ plt.xlim(0, 1)
+ sns.despine()
+
+
+@savefig
+def lp_genes(
+ data: AnnData,
+ groupby: str = "gene",
+ annotate: Union[int, List[str], None] = None,
+ sizes: Tuple[int] = (2, 100),
+ size_norm: Tuple[int] = (0, 100),
+ ax: plt.Axes = None,
+ fname: str = None,
+ **kwargs,
+):
+ """
+ Plot the pattern distribution of each group in a RadViz plot. RadViz projects
+ an N-dimensional data set into a 2D space where the influence of each dimension
+ can be interpreted as a balance between the influence of all dimensions.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ groupby : str
+ Grouping variable, default "gene"
+ annotate : int, list of str, optional
+ Annotate the top n genes or a list of genes, by default None
+ sizes : tuple
+ Minimum and maximum point size to scale points, default (2, 100)
+ size_norm : tuple
+ Minimum and maximum data values to scale point size, default (0, 100)
+ ax : matplotlib.Axes, optional
+ Axis to plot on, by default None
+ fname : str, optional
+ Save the figure to specified filename, by default None
+ **kwargs
+ Options to pass to matplotlib plotting method.
+ """
+ lp_stats(data, groupby)
+
+ palette = dict(zip(PATTERN_NAMES, PATTERN_COLORS))
+
+ n_cells = data.n_obs
+ gene_frac = data.uns["lp_stats"][PATTERN_NAMES] / n_cells
+
+ gene_logcount = data.X.mean(axis=0, where=data.X > 0)
+ gene_logcount = np.log2(gene_logcount + 1)
+ gene_frac["logcounts"] = gene_logcount
+
+ cell_fraction = (
+ 100
+ * data.uns["points"].groupby("gene", observed=True)["cell"].nunique()
+ / n_cells
+ )
+ gene_frac["cell_fraction"] = cell_fraction
+
+ scatter_kws = dict(sizes=sizes, size_norm=size_norm)
+ scatter_kws.update(kwargs)
+ _radviz(gene_frac, annotate=annotate, ax=ax, **scatter_kws)
+
+
+@savefig
+def lp_diff(data: AnnData, phenotype: str, fname: str = None):
+ """Visualize gene pattern frequencies between groups of cells by plotting
+ log2 fold change and -log10p, similar to volcano plot. Run after :func:`bento.tl.lp_diff()`
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ phenotype : str
+ Variable used to group cells when calling :func:`bento.tl.lp_diff()`.
+ fname : str, optional
+ Save the figure to specified filename, by default None
+ """
+ diff_stats = data.uns[f"diff_{phenotype}"]
+
+ palette = dict(zip(PATTERN_NAMES, PATTERN_COLORS))
+ g = sns.relplot(
+ data=diff_stats,
+ x="log2fc",
+ y="-log10padj",
+ size=4,
+ hue="pattern",
+ col="phenotype",
+ col_wrap=3,
+ height=2.5,
+ palette=palette,
+ s=20,
+ linewidth=0,
+ )
+
+ g.set_titles(col_template="{col_name}")
+
+ for ax in g.axes:
+ ax.axvline(0, lw=0.5, c="grey") # -log2fc = 0
+ ax.axvline(-2, lw=1, c="pink", ls="dotted") # log2fc = -2
+ ax.axvline(2, lw=1, c="pink", ls="dotted") # log2fc = 2
+ ax.axhline(
+ -np.log10(0.05), c="pink", ls="dotted", zorder=0
+ ) # line where FDR = 0.05
+ sns.despine()
+
+ return g
diff --git a/bento/plotting/_multidimensional.py b/bento/plotting/_multidimensional.py
new file mode 100644
index 0000000..8878902
--- /dev/null
+++ b/bento/plotting/_multidimensional.py
@@ -0,0 +1,433 @@
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import matplotlib as mpl
+import matplotlib.patheffects as pe
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from adjustText import adjust_text
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import quantile_transform
+
+from ._colors import red_light
+from ._utils import savefig
+
+
+def _quantiles(data: pd.DataFrame, x: str, **kwargs):
+ """Plot quantiles on top of a stripplot.
+
+ Parameters
+ ----------
+ data : pd.DataFrame
+ Dataframe with x column
+ x : str
+ Column to plot quantiles for
+ """
+ ax = plt.gca()
+
+ ylims = ax.get_ylim()
+ ymargin = 0.3 * (ylims[1] - ylims[0])
+ quants = np.nanpercentile(data[x], [0, 25, 50, 75, 100])
+ palette = sns.color_palette("red2blue", n_colors=len(quants) - 1)
+ linecolor = sns.axes_style()["axes.edgecolor"]
+
+ xys = [(q, ylims[0]) for q in quants[:-1]]
+ widths = [quants[i + 1] - quants[i] for i in range(len(quants) - 1)]
+ height = ymargin
+ rects = [
+ mpl.patches.Rectangle(
+ xy,
+ width=w,
+ height=height,
+ facecolor=c,
+ alpha=0.8,
+ linewidth=1,
+ edgecolor=linecolor,
+ clip_on=False,
+ )
+ for xy, w, c in zip(xys, widths, palette)
+ ]
+
+ for rect in rects:
+ ax.add_patch(rect)
+
+
+@savefig
+def obs_stats(
+ data,
+ obs_cols=[
+ "cell_area",
+ "cell_aspect_ratio",
+ "cell_density",
+ "nucleus_area",
+ "nucleus_aspect_ratio",
+ "nucleus_density",
+ ],
+ s=3,
+ alpha=0.3,
+ rug=False,
+ fname=None,
+):
+ """Plot shape statistic distributions for each cell.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ cols : list
+ List of obs columns to plot
+ groupby : str, optional
+ Column in obs to groupby, by default None
+ """
+ stats_long = data.obs.melt(value_vars=obs_cols)
+ stats_long["quantile"] = stats_long.groupby("variable")["value"].transform(
+ lambda x: quantile_transform(x.values.reshape(-1, 1), n_quantiles=100).flatten()
+ )
+
+ stats_long["shape"] = stats_long["variable"].apply(lambda x: x.split("_")[0])
+ stats_long["var"] = stats_long["variable"].apply(
+ lambda x: "_".join(x.split("_")[1:])
+ )
+
+ linecolor = sns.axes_style()["axes.edgecolor"]
+
+ g = sns.FacetGrid(
+ data=stats_long,
+ row="var",
+ col="shape",
+ height=1.2,
+ aspect=2,
+ sharex=False,
+ sharey=False,
+ margin_titles=True,
+ )
+ g.map_dataframe(
+ sns.stripplot,
+ x="value",
+ color=linecolor,
+ s=s,
+ alpha=alpha,
+ rasterized=True,
+ )
+ g.map_dataframe(_quantiles, x="value")
+ g.add_legend()
+ sns.move_legend(g, "upper left", bbox_to_anchor=(1, 1))
+
+ for ax, var in zip(g.axes.flat, stats_long["variable"].unique()):
+ ax.set_xlabel("")
+ ax.set_yticks([])
+ ax.ticklabel_format(axis="x", style="sci", scilimits=(-2, 4))
+ sns.despine(ax=ax, left=True)
+ g.set_titles(row_template="{row_name}", col_template="{col_name}")
+
+ def plot_median(data, **kwargs):
+ plt.axvline(data.median(), **kwargs)
+
+ g.map(plot_median, "value", c=linecolor, lw=1.5, zorder=3)
+
+
+@savefig
+def flux_summary(
+ data,
+ groupby=None,
+ group_order=None,
+ annotate=None,
+ adjust=True,
+ palette=red_light,
+ annot_color=None,
+ sizes=(5, 30),
+ size_norm=(10, 100),
+ dim_order=None,
+ legend=True,
+ height=5,
+ fname=None,
+):
+ """
+ Plot RNAflux summary with a radviz plot describing gene embedding across flux clusters.
+ """
+
+ comp_key = f"{groupby}_comp_stats"
+ if groupby and comp_key in data.uns.keys():
+ comp_stats = data.uns[comp_key]
+ if group_order is None:
+ groups = list(comp_stats.keys())
+ else:
+ groups = group_order
+ ngroups = len(groups)
+ fig, axes = plt.subplots(1, ngroups, figsize=(ngroups * height * 1.1, height))
+ if axes is not np.ndarray:
+ axes = np.array([axes])
+
+ # Plot each group separately
+ for group, ax in zip(groups, axes.flat):
+ group_comp = comp_stats[group]
+
+ show_legend = False
+ if legend and ax == axes.flat[-1]:
+ show_legend = True
+
+ _radviz(
+ group_comp,
+ annotate=annotate,
+ adjust=adjust,
+ palette=palette,
+ annot_color=annot_color,
+ sizes=sizes,
+ size_norm=size_norm,
+ dim_order=dim_order,
+ legend=show_legend,
+ ax=ax,
+ )
+ ax.set_title(group, fontsize=12)
+ else:
+ return _radviz(
+ comp_stats,
+ annotate=annotate,
+ adjust=adjust,
+ palette=palette,
+ annot_color=annot_color,
+ sizes=sizes,
+ size_norm=size_norm,
+ dim_order=dim_order,
+ legend=legend,
+ )
+
+
+def _radviz(
+ comp_stats,
+ annotate=None,
+ adjust=True,
+ palette=red_light,
+ annot_color=None,
+ sizes=None,
+ size_norm=None,
+ dim_order="auto",
+ legend=True,
+ ax=None,
+):
+ """Plot a radviz plot of gene values across fields.
+
+ Parameters
+ ----------
+ comp_stats : DataFrame
+ Gene composition stats
+ palette : str, optional
+ Color palette, by default None
+ sizes : tuple, optional
+ Size range for scatter plot, by default None
+ size_norm : tuple, optional
+ Size range for scatter plot, by default None
+ dim_order : "auto", None, or list, optional
+ Sort dimensions for more intuitive visualization, by default "auto".
+ If "auto", sort dimensions by maximizing cosine similarity of adjacent
+ dimensions. If None, do not sort dimensions. If list, use provided order.
+ gridsize : int, optional
+ Gridsize for hexbin plot, by default 20
+ ax : matplotlib.Axes, optional
+ Axes to plot on, by default None
+ """
+ with plt.rc_context({"font.size": 14}):
+ # RADVIZ plot
+ if not ax:
+ figsize = (5, 5)
+ plt.figure(figsize=figsize)
+ ax = plt.gca()
+
+ edgecolor = sns.axes_style()["axes.edgecolor"]
+
+ # Infer annot_color from theme
+ if annot_color is None:
+ annot_color = edgecolor
+
+ # Remove unexpressed genes
+ ndims = comp_stats.columns.get_loc("logcounts") - 1
+ dims = comp_stats.columns[: ndims + 1]
+ stat_cols = comp_stats.columns[ndims + 1 :]
+ comp_stats = comp_stats[comp_stats[dims].sum(axis=1) > 0]
+
+ # Determine best dimension ordering by maximizing cosine similarity of adjacent dimensions
+ if not dim_order:
+ dim_order = dims
+ elif dim_order == "auto":
+ dim_order = _sort_dimensions(comp_stats[dims])
+ elif isinstance(dim_order, list):
+ dim_order = dim_order
+ else:
+ raise ValueError(f"Invalid dim_order: {dim_order}")
+
+ comp_stats = comp_stats.reindex([*dim_order, *stat_cols], axis=1)
+
+ # Plot the "circular" axis, labels and point positions
+ comp_stats["_"] = ""
+ pd.plotting.radviz(comp_stats[[*dim_order, "_"]], "_", s=0, ax=ax)
+ ax.get_legend().remove()
+
+ # Get points
+ pts = []
+ for c in ax.collections:
+ pts.extend(c.get_offsets().data)
+
+ pts = np.array(pts).reshape(-1, 2)
+ xy = pd.DataFrame(pts, index=comp_stats.index)
+
+ # Get vertices and origin
+ center = ax.patches[0]
+ vertices = ax.patches[1:]
+
+ # Add polygon as background
+ poly = plt.Polygon(
+ [v.center for v in vertices],
+ facecolor="none",
+ edgecolor=edgecolor,
+ zorder=1,
+ )
+ ax.add_patch(poly)
+
+ # Add lines from origin to vertices
+ for v in vertices:
+ line_xy = np.array([center.center, v.center])
+ ax.add_line(
+ plt.Line2D(
+ line_xy[:, 0],
+ line_xy[:, 1],
+ linestyle=":",
+ linewidth=1,
+ color=edgecolor,
+ zorder=1,
+ alpha=0.4,
+ )
+ )
+ v.remove()
+
+ # Hide 2D axes
+ ax.axis(False)
+
+ # Point size ~ percent of cells in group
+ cell_fraction = comp_stats["cell_fraction"]
+ cell_fraction = cell_fraction.apply(lambda x: round(x, 1))
+ size_key = "Fraction of cells\n in group (%)"
+ xy[size_key] = cell_fraction
+
+ # Hue ~ mean log2(count + 1)
+ log_count = comp_stats["logcounts"]
+ hue_key = "Mean log2(cnt + 1)\n in group"
+ xy[hue_key] = log_count
+
+ # Remove phantom points
+ # ax.collections = ax.collections[1:]
+
+ sns.kdeplot(
+ data=xy,
+ x=0,
+ y=1,
+ shade=True,
+ cmap="binary",
+ zorder=0.9,
+ ax=ax,
+ )
+
+ # Plot points
+ sns.scatterplot(
+ data=xy,
+ x=0,
+ y=1,
+ hue=hue_key,
+ palette=palette,
+ size=size_key,
+ sizes=sizes,
+ size_norm=size_norm,
+ linewidth=0.5,
+ # alpha=0.6,
+ edgecolor="white",
+ legend=legend,
+ ax=ax,
+ )
+ scatter = ax.collections[0]
+
+ if legend:
+ plt.legend(bbox_to_anchor=[1.1, 1], fontsize=10, frameon=False)
+
+ # Annotate top points
+ if annotate:
+ if isinstance(annotate, int):
+ # Get top ranked genes by entropy
+ from scipy.stats import entropy
+
+ top_genes = (
+ comp_stats.loc[:, dims]
+ .apply(lambda gene_comp: entropy(gene_comp), axis=1)
+ .sort_values(ascending=True)
+ .index[:annotate]
+ )
+ top_xy = xy.loc[top_genes]
+ else:
+ top_xy = xy.loc[[g for g in annotate if g in xy.index]]
+
+ # Plot top points
+ sns.scatterplot(
+ data=top_xy,
+ x=0,
+ y=1,
+ hue=hue_key,
+ palette=palette,
+ size=size_key,
+ sizes=sizes,
+ size_norm=size_norm,
+ linewidth=1,
+ facecolor=None,
+ edgecolor=annot_color,
+ legend=False,
+ ax=ax,
+ )
+
+ # Add text labels
+ if annot_color == "black":
+ stroke_color = "white"
+ elif annot_color == "white":
+ stroke_color = "black"
+ else:
+ stroke_color = "black"
+ texts = [
+ ax.text(
+ row[0],
+ row[1],
+ i,
+ fontsize=8,
+ weight="medium",
+ path_effects=[pe.withStroke(linewidth=2, foreground=stroke_color)],
+ )
+ for i, row in top_xy.iterrows()
+ ]
+
+ # Adjust text positions
+ if adjust:
+ print("Adjusting text positions...")
+ adjust_text(
+ texts,
+ expand_points=(2, 2),
+ add_objects=[scatter],
+ arrowprops=dict(arrowstyle="-", color=edgecolor, lw=1),
+ ax=ax,
+ )
+
+
+def _sort_dimensions(composition):
+ sim = cosine_similarity(composition.T, composition.T)
+ sim = pd.DataFrame(sim, index=composition.columns, columns=composition.columns)
+ dim_order = sim.sample(3, random_state=11).index.tolist()
+
+ # Insert dimensions greedily
+ for dim in sim.columns:
+ if dim in dim_order:
+ continue
+
+ insert_score = []
+ for dim_i, dim_j in zip([dim_order[-1]] + dim_order[:-1], dim_order):
+ insert_score.append(np.mean(sim.loc[dim, [dim_i, dim_j]]))
+
+ insert_pos = np.argmax(insert_score)
+ dim_order.insert(insert_pos, dim)
+ return dim_order
diff --git a/bento/plotting/_plotting.py b/bento/plotting/_plotting.py
index 5c625b6..0cf580f 100755
--- a/bento/plotting/_plotting.py
+++ b/bento/plotting/_plotting.py
@@ -2,533 +2,431 @@
warnings.filterwarnings("ignore")
-import geopandas
-import matplotlib as mpl
import matplotlib.pyplot as plt
-from matplotlib_scalebar.scalebar import ScaleBar
-import numpy as np
-import pandas as pd
-from pandas.plotting import radviz
import seaborn as sns
-from upsetplot import UpSet, from_indicators
-
-from tqdm.auto import tqdm
+from matplotlib_scalebar.scalebar import ScaleBar
+from ..geometry import get_points
+from ._layers import _raster, _scatter, _hist, _kde, _polygons
from ._utils import savefig
-from .._utils import PATTERN_NAMES, PATTERN_COLORS, TENSOR_DIM_NAMES
-from ..preprocessing import get_points
-from ..tools._lp import lp_stats
+from .._utils import sync
+from ._colors import red2blue, red2blue_dark
-@savefig
-def qc_metrics(adata, fname=None):
+def _prepare_points_df(adata, semantic_vars=None):
"""
- Plot quality control metric distributions.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- fname : str, optional
- Save the figure to specified filename, by default None
+ Prepare points DataFrame for plotting. This function will concatenate the appropriate semantic variables as columns to points data.
"""
- color = "lightseagreen"
-
- fig, axs = plt.subplots(2, 3, figsize=(8, 5))
+ points = get_points(adata, key="points")
+ cols = list(set(["x", "y", "cell"]))
- kde_params = dict(color=color, shade=True, legend=False)
-
- with sns.axes_style("ticks"):
- sns.kdeplot(adata.obs["total_counts"], ax=axs[0][0], **kde_params)
+ if semantic_vars is None or len(semantic_vars) == 0:
+ return points[cols]
+ else:
+ vars = [v for v in semantic_vars if v is not None]
+ cols.extend(vars)
+
+ # Add semantic variables to points; priority: points, obs, points metadata
+ for var in vars:
+ if var in points.columns:
+ continue
+ elif var in adata.obs.columns:
+ points[var] = adata.obs.reindex(points["cell"].values)[var].values
+ elif var in adata.uns["point_sets"]["points"]:
+ if len(adata.uns[var].shape) > 1:
+ raise ValueError(f"Variable {var} is not 1-dimensional")
+ points[var] = adata.uns[var]
+ else:
+ raise ValueError(f"Variable {var} not found in points or obs")
- sns.distplot(
- adata.X.flatten() + 1,
- color=color,
- kde=False,
- hist_kws=dict(log=True),
- ax=axs[0][1],
- )
+ return points[cols]
- sns.kdeplot(adata.obs["n_genes_by_counts"], ax=axs[0][2], **kde_params)
-
- sns.kdeplot(adata.obs["cell_area"], ax=axs[1][0], **kde_params)
-
- sns.kdeplot(adata.obs["cell_density"], ax=axs[1][1], **kde_params)
-
- dual_colors = sns.light_palette(color, n_colors=2, reverse=True)
- no_nucleus_count = (adata.obs["nucleus_shape"] == None).sum()
- pie_values = [adata.n_obs - no_nucleus_count, no_nucleus_count]
- pie_percents = np.array(pie_values) / adata.n_obs * 100
- pie_labels = [
- f"Yes\n{pie_values[0]} ({pie_percents[0]:.1f}%)",
- f"No\n{pie_values[1]} ({pie_percents[1]:.1f}%)",
- ]
- axs[1][2].pie(pie_values, labels=pie_labels, colors=dual_colors)
- pie_inner = plt.Circle((0, 0), 0.6, color="white")
- axs[1][2].add_artist(pie_inner)
-
- sns.despine()
-
- titles = [
- "Transcripts per Cell",
- "Transcripts per Gene",
- "Genes per Cell",
- "Cell Area",
- "Transcript Density",
- "Cell has Nucleus",
- ]
- xlabels = [
- "mRNA count",
- "Gene count",
- "Gene count",
- "Pixels",
- "Transcripts/pixel",
- "",
- ]
-
- for i, ax in enumerate(np.array(axs).reshape(-1)):
- # ax.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))
- ax.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter("%1.e"))
- ax.set_xlabel(xlabels[i], fontsize=12)
- ax.set_title(titles[i], fontsize=14)
- ax.grid(False)
-
- plt.tight_layout()
+def _setup_ax(
+ ax=None,
+ dx=0.1,
+ units="um",
+ square=False,
+ axis_visible=False,
+ frame_visible=True,
+ **kwargs,
+):
+ """Setup axis for plotting. TODO make decorator?"""
+ if ax is None:
+ ax = plt.gca()
-@savefig
-def lp_dist(data, percentage=False, scale=1, fname=None):
- """Plot pattern combination frequencies as an UpSet plot.
+ # Infer font color from theme
+ edgecolor = sns.axes_style()["axes.edgecolor"]
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- percentage : bool, optional
- If True, label each bar as a percentage else label as a count, by default False
- scale : int, optional
- scale > 1 scales the plot larger, scale < 1 scales. the plot smaller, by default 1
- fname : str, optional
- Save the figure to specified filename, by default None
- """
- sample_labels = []
- for p in PATTERN_NAMES:
- p_df = data.to_df(p).reset_index().melt(id_vars="cell")
- p_df = p_df[~p_df["value"].isna()]
- p_df = p_df.set_index(["cell", "gene"])
- sample_labels.append(p_df)
-
- sample_labels = pd.concat(sample_labels, axis=1) == 1
- sample_labels = sample_labels == 1
- sample_labels.columns = PATTERN_NAMES
-
- # Drop unlabeled samples
- # sample_labels = sample_labels[sample_labels.sum(axis=1) > 0]
-
- # Sort by degree, then pattern name
- sample_labels["degree"] = -sample_labels[PATTERN_NAMES].sum(axis=1)
- sample_labels = (
- sample_labels.reset_index()
- .sort_values(["degree"] + PATTERN_NAMES, ascending=False)
- .drop("degree", axis=1)
+ scalebar = ScaleBar(
+ dx,
+ units,
+ location="lower right",
+ box_alpha=0,
+ color=edgecolor,
+ frameon=False,
+ scale_loc="top",
)
+ ax.add_artist(scalebar)
- upset = UpSet(
- from_indicators(PATTERN_NAMES, data=sample_labels),
- element_size=scale * 40,
- min_subset_size=sample_labels.shape[0] * 0.001,
- facecolor="lightgray",
- sort_by=None,
- show_counts=(not percentage),
- show_percentages=percentage,
- )
+ ax_kws = dict(aspect=1, box_aspect=None)
+
+ if not axis_visible:
+ ax_kws.update(
+ dict(
+ xticks=[],
+ yticks=[],
+ xticklabels=[],
+ yticklabels=[],
+ ylabel=None,
+ xlabel=None,
+ xmargin=0.01,
+ ymargin=0.01,
+ )
+ )
- for p, color in zip(PATTERN_NAMES, PATTERN_COLORS):
- if sample_labels[p].sum() > 0:
- upset.style_subsets(present=p, max_degree=1, facecolor=color)
+ if square:
+ ax_kws["box_aspect"] = 1
- upset.plot()
- plt.suptitle(f"Localization Patterns\n{data.n_obs} cells, {data.n_vars} genes")
+ ax_kws.update(kwargs)
+ plt.setp(ax, **ax_kws)
+ ax.spines[["top", "right", "bottom", "left"]].set_visible(frame_visible)
+ return ax
-@savefig
-def lp_gene_dist(data, fname=None):
- """Plot the cell fraction distribution of each pattern as a density plot.
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- fname : str, optional
- Save the figure to specified filename, by default None
- """
- lp_stats(data)
-
- col_names = [f"{p}_fraction" for p in PATTERN_NAMES]
- gene_frac = data.var[col_names]
- gene_frac.columns = PATTERN_NAMES
- # Plot frequency distributions
- sns.displot(
- data=gene_frac,
- kind="kde",
- multiple="layer",
- height=3,
- palette=PATTERN_COLORS,
+@savefig
+def points(
+ data,
+ batch=None,
+ hue=None,
+ size=None,
+ style=None,
+ shapes=None,
+ hide_outside=True,
+ title=None,
+ dx=0.1,
+ units="um",
+ square=False,
+ axis_visible=False,
+ frame_visible=True,
+ ax=None,
+ shapes_kws=dict(),
+ fname=None,
+ **kwargs,
+):
+ # Default use first obs batch
+ if batch is None:
+ batch = data.obs["batch"].iloc[0]
+ adata = data[data.obs["batch"] == batch]
+ title = f"batch {batch}" if not title else title
+
+ ax = _setup_ax(
+ ax=ax,
+ dx=dx,
+ units=units,
+ square=square,
+ axis_visible=axis_visible,
+ frame_visible=frame_visible,
+ title=title,
)
- plt.xlim(0, 1)
- sns.despine()
+
+ points = _prepare_points_df(adata, semantic_vars=[hue, size, style])
+ _scatter(points, hue=hue, size=size, style=style, ax=ax, **kwargs)
+ _shapes(adata, shapes=shapes, hide_outside=hide_outside, ax=ax, **shapes_kws)
@savefig
-def lp_genes(
+def density(
data,
- kind="scatter",
- hue="Pattern",
- sizes=(2, 100),
- gridsize=20,
- random_state=4,
+ batch=None,
+ kind="hist",
+ hue=None,
+ shapes=None,
+ hide_outside=True,
+ axis_visible=False,
+ frame_visible=True,
+ title=None,
+ dx=0.1,
+ units="um",
+ square=False,
ax=None,
+ shape_kws=dict(),
fname=None,
**kwargs,
):
- """
- Plot the pattern distribution of each gene in a RadViz plot. RadViz projects
- an N-dimensional data set into a 2D space where the influence of each dimension
- can be interpreted as a balance between the influence of all dimensions.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- kind : str
- 'Scatter' for scatter plot, 'hex' for hex plot, default "scatter"
- hue : str
- Name of columns in data.obs to color points, default "Pattern"
- sizes : tuple
- Minimum and maximum point size to scale points, default (2, 100)
- gridsize : int
- Number of hex bins along each axis, default 20
- fname : str, optional
- Save the figure to specified filename, by default None
- **kwargs
- Options to pass to matplotlib plotting method.
- """
- lp_stats(data)
-
- palette = dict(zip(PATTERN_NAMES, PATTERN_COLORS))
+ # Default use first obs batch
+ if batch is None:
+ batch = data.obs["batch"].iloc[0]
+ adata = data[data.obs["batch"] == batch]
+ title = f"batch {batch}" if title is None else title
+
+ ax = _setup_ax(
+ ax=ax,
+ dx=dx,
+ units=units,
+ square=square,
+ axis_visible=axis_visible,
+ frame_visible=frame_visible,
+ title=title,
+ )
- # RADVIZ plot
- if not ax:
- figsize = (6, 6)
- fig = plt.figure(figsize=figsize)
+ points = _prepare_points_df(adata, semantic_vars=[hue])
+ if kind == "hist":
+ _hist(points, hue=hue, ax=ax, **kwargs)
+ elif kind == "kde":
+ _kde(points, hue=hue, ax=ax, **kwargs)
- # Use Plot the "circular" axis and labels, hide points
- # TODO move "pattern" computation to lp_stats
- col_names = [f"{p}_fraction" for p in PATTERN_NAMES]
- gene_frac = data.var[col_names]
- gene_frac.columns = PATTERN_NAMES
- gene_frac["Pattern"] = gene_frac.idxmax(axis=1)
- gene_frac_copy = gene_frac.copy()
- gene_frac_copy["Pattern"] = ""
-
- if hue and hue != "Pattern":
- gene_frac = gene_frac.join(data.var[hue])
-
- if not ax:
- ax = radviz(gene_frac_copy, "Pattern", s=0)
- else:
- radviz(gene_frac_copy, "Pattern", s=0, ax=ax)
- del gene_frac_copy
- ax.get_legend().remove()
- circle = plt.Circle((0, 0), radius=1, color="black", fill=False)
- ax.add_patch(circle)
-
- # Hide 2D axes
- ax.axis(False)
-
- # Get points
- pts = []
- for c in ax.collections:
- pts.extend(c.get_offsets().data)
-
- pts = np.array(pts).reshape(-1, 2)
- xy = pd.DataFrame(pts, index=gene_frac.index)
- xy["Pattern"] = gene_frac["Pattern"]
-
- # Plot points as scatter or hex
- if kind == "scatter":
-
- del ax.collections[0]
-
- # Scale point size by max
- xy["Fraction of cells"] = gene_frac.iloc[:, :5].max(axis=1)
-
- # Plot points
- sns.scatterplot(
- data=xy.sample(frac=1, random_state=random_state),
- x=0,
- y=1,
- size="Fraction of cells",
- hue=hue,
- sizes=sizes,
- linewidth=0,
- palette=palette,
- ax=ax,
- **kwargs,
- )
- plt.legend(bbox_to_anchor=(1.05, 0.5), loc="center left", frameon=False)
-
- elif kind == "hex":
- # Hexbin
- xy.plot.hexbin(
- x=0,
- y=1,
- gridsize=gridsize,
- extent=(-1, 1, -1, 1),
- cmap=sns.light_palette("lightseagreen", as_cmap=True),
- mincnt=1,
- colorbar=False,
- ax=ax,
- **kwargs,
- )
- # [left, bottom, width, height]
- plt.colorbar(
- ax.collections[-1], cax=fig.add_axes([1, 0.4, 0.05, 0.3]), label="genes"
- )
+ _shapes(adata, shapes=shapes, hide_outside=hide_outside, ax=ax, **shape_kws)
@savefig
-def lp_diff(data, phenotype, fname=None):
- """Visualize gene pattern frequencies between groups of cells by plotting
- log2 fold change and -log10p, similar to volcano plot. Run after `bento.tl.lp_diff()`
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- phenotype : str
- Variable used to group cells when calling `bento.tl.lp_diff()`.
- fname : str, optional
- Save the figure to specified filename, by default None
- """
- diff_stats = data.uns[f"diff_{phenotype}"]
-
- g = sns.relplot(
- data=diff_stats,
- x=f"log2fc",
- y="-log10padj",
- size=4,
- hue="pattern",
- col="phenotype",
- col_wrap=3,
- height=2.5,
- palette="tab10",
- s=20,
- linewidth=0,
+def flux(
+ data,
+ batch=None,
+ res=0.05,
+ shapes=None,
+ hide_outside=True,
+ axis_visible=False,
+ frame_visible=True,
+ title=None,
+ dx=0.1,
+ units="um",
+ square=False,
+ ax=None,
+ shape_kws=dict(),
+ fname=None,
+ **kwargs,
+):
+ # Default use first obs batch
+ if batch is None:
+ batch = data.obs["batch"].iloc[0]
+ adata = data[data.obs["batch"] == batch]
+ title = f"batch {batch}" if not title else title
+
+ ax = _setup_ax(
+ ax=ax,
+ dx=dx,
+ units=units,
+ square=square,
+ axis_visible=axis_visible,
+ frame_visible=frame_visible,
+ title=title,
)
- for ax in g.axes:
- ax.axvline(0, lw=0.5, c="grey") # -log2fc = 0
- ax.axvline(-2, lw=1, c="pink", ls="dotted") # log2fc = -2
- ax.axvline(2, lw=1, c="pink", ls="dotted") # log2fc = 2
- ax.axhline(
- -np.log10(0.05), c="pink", ls="dotted", zorder=0
- ) # line where FDR = 0.05
- sns.despine()
-
- return g
+ _raster(adata, res=res, color="flux_color", ax=ax, **kwargs)
+ _shapes(adata, shapes=shapes, hide_outside=hide_outside, ax=ax, **shape_kws)
@savefig
-def cellplot(
- adata,
- hue=None,
- kind="hist",
- col="batch",
- legend=True,
- palette=None,
- hue_order=None,
- hue_norm=None,
- col_wrap=None,
- col_order=None,
- shape_names=["cell_shape", "nucleus_shape"],
+def fe(
+ data,
+ gs,
+ batch=None,
+ res=0.05,
+ shapes=None,
+ cmap=None,
+ cbar=True,
+ hide_outside=True,
+ axis_visible=False,
+ frame_visible=True,
+ title=None,
dx=0.1,
units="um",
- height=6,
- facet_kws=None,
+ square=False,
+ ax=None,
+ shape_kws=dict(),
fname=None,
**kwargs,
):
- # Get points
- points = get_points(adata, asgeo=False)
-
- # Add all shape_names if None
- if shape_names == None:
- shape_names = adata.obs.columns[adata.obs.columns.str.endswith("_shape")]
+ # Default use first obs batch
+ if batch is None:
+ batch = data.obs["batch"].iloc[0]
+ adata = data[data.obs["batch"] == batch]
+ sync(adata)
+ title = f"batch {batch}" if not title else title
+
+ ax = _setup_ax(
+ ax=ax,
+ dx=dx,
+ units=units,
+ square=square,
+ axis_visible=axis_visible,
+ frame_visible=frame_visible,
+ title=title,
+ )
- # Convert shape_names to list
- shape_names = [shape_names] if isinstance(shape_names, str) else shape_names
+ if cmap is None:
+ if sns.axes_style()["axes.facecolor"] == "white":
+ cmap = red2blue
+ elif sns.axes_style()["axes.facecolor"] == "black":
+ cmap = red2blue_dark
- # Get obs attributes starting with shapes
- obs_attrs = list(shape_names)
+ _raster(adata, res=res, color=gs, cmap=cmap, cbar=cbar, ax=ax, **kwargs)
+ _shapes(adata, shapes=shapes, hide_outside=hide_outside, ax=ax, **shape_kws)
- # Include col if exists
- if col and (col == "cell" or (col in adata.obs.columns and col in points.columns)):
- obs_attrs.append(col)
- else:
- col = None
-
- obs_attrs = list(set(obs_attrs))
-
- # Get shapes
- shapes = adata.obs.reset_index()[obs_attrs]
-
- if col:
- # Make sure col is same type across points and shapes
- if points[col].dtype != shapes[col].dtype:
- points[col] = points[col].astype(str)
- shapes[col] = shapes[col].astype(str)
-
- # Subset to specified col values only; less filtering = faster plotting
- if col_order:
- points = points[points[col].isin(col_order)]
- shapes = shapes[shapes[col].isin(col_order)]
-
- # Remove unused categories in points
- if col or hue:
- for cat in points.columns:
- points[cat] = (
- points[cat].cat.remove_unused_categories()
- if points[cat].dtype == "category"
- else points[cat]
- )
- # Convert shapes to GeoDataFrames AFTER filtering
- shapes = geopandas.GeoDataFrame(shapes, geometry="cell_shape")
-
- # Get updated
- kws = dict(sharex=False, sharey=False)
- if isinstance(facet_kws, dict):
- kws.update(facet_kws)
-
- # https://stackoverflow.com/questions/32633322/changing-aspect-ratio-of-subplots-in-matplotlib
- g = sns.FacetGrid(
- points,
- col=col,
- hue=hue,
- legend_out=legend,
- palette=palette,
- hue_order=hue_order,
- col_wrap=col_wrap,
- col_order=col_order,
- height=height,
- aspect=1,
- margin_titles=False,
- **kws,
+@savefig
+def shapes(
+ data,
+ batch=None,
+ shapes=None,
+ color=None,
+ color_style="outline",
+ hide_outside=True,
+ dx=0.1,
+ units="um",
+ axis_visible=False,
+ frame_visible=True,
+ title=None,
+ square=False,
+ ax=None,
+ fname=None,
+ **kwargs,
+):
+ # Default use first obs batch
+ if batch is None:
+ batch = data.obs["batch"].iloc[0]
+ adata = data[data.obs["batch"] == batch]
+ title = f"batch {batch}" if not title else title
+
+ ax = _setup_ax(
+ ax=ax,
+ dx=dx,
+ units=units,
+ square=square,
+ axis_visible=axis_visible,
+ frame_visible=frame_visible,
+ title=title,
)
- if kind == "scatter":
- scatter_kws = dict(linewidth=0, s=5)
- scatter_kws.update(**kwargs)
- g.map_dataframe(sns.scatterplot, x="x", y="y", **scatter_kws)
- elif kind == "hist":
- hist_kws = dict(cmap="viridis", binwidth=15)
- hist_kws.update(**kwargs)
- g.map_dataframe(sns.histplot, x="x", y="y", **hist_kws)
- elif kind == "hex":
- hex_kws = dict(cmap="viridis", mincnt=1, linewidth=0, gridsize=100)
- hex_kws.update(**kwargs)
- g.map_dataframe(plt.hexbin, x="x", y="y", **hex_kws)
-
- if shapes.shape[0] > 0:
- if col:
- shapes = shapes.groupby(col)
-
- # Get max ax radius across groups
- ax_radii = []
- for k, ax in g.axes_dict.items():
- s = shapes.get_group(k)
- # Determine fixed radius of each subplot
- cell_bounds = s.bounds
- cell_maxw = cell_bounds["maxx"].max() - cell_bounds["minx"].min()
- cell_maxh = cell_bounds["maxy"].max() - cell_bounds["miny"].min()
- ax_radius = 1.1 * (max(cell_maxw, cell_maxh) / 2)
- ax_radii.append(ax_radius)
-
- ax_radius = max(ax_radii)
-
- for k, ax in tqdm(g.axes_dict.items()):
- s = shapes.get_group(k)
- shape_subplot(s, shape_names, dx, units, ax_radius=ax_radius, ax=ax)
-
- else:
- shape_subplot(shapes, shape_names, dx, units, ax=g.ax)
-
- if legend:
- g.add_legend()
-
- g.set_titles(template="")
-
- # box_aspect for Axes, aspect for data
- g.set(
- xticks=[],
- yticks=[],
- xlabel=None,
- ylabel=None,
- xmargin=0,
- ymargin=0,
- facecolor="black",
- box_aspect=1,
- aspect=1,
+ if shapes and not isinstance(shapes, list):
+ shapes = [shapes]
+
+ _shapes(
+ adata,
+ shapes=shapes,
+ color=color,
+ color_style=color_style,
+ hide_outside=hide_outside,
+ ax=ax,
+ **kwargs,
)
- g.tight_layout()
-def shape_subplot(data, shape_names, dx, units, ax, ax_radius=None):
- # Gather all shapes and plot
- all_shapes = geopandas.GeoSeries(data[shape_names].values.flatten())
- all_shapes.plot(
- color=(0, 0, 0, 0), edgecolor=(1, 1, 1, 0.8), lw=1, aspect=None, ax=ax
- )
+def _shapes(
+ data,
+ shapes=None,
+ color=None,
+ color_style="outline",
+ hide_outside=True,
+ ax=None,
+ **kwargs,
+):
+ """Plot layer(s) of shapes.
- # Set axes boundaries to be square; make sure size of cells are relative to one another
- if ax_radius:
- s_bound = data.bounds
- centerx = np.mean([s_bound["minx"].min(), s_bound["maxx"].max()])
- centery = np.mean([s_bound["miny"].min(), s_bound["maxy"].max()])
- ax.set_xlim(centerx - ax_radius, centerx + ax_radius)
- ax.set_ylim(centery - ax_radius, centery + ax_radius)
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ shapes : list, optional
+ List of shapes to plot, by default None. If None, will plot cell and nucleus shapes by default.
+ color : str, optional
+ Color name, by default None. If None, will use default theme color.
+ color_style : "outline" or "fill"
+ Whether to color the outline or fill of the shape, by default "outline".
+ hide_outside : bool, optional
+ Whether to hide molecules outside of cells, by default True.
+ ax : matplotlib.axes.Axes, optional
+ Axis to plot on, by default None. If None, will use current axis.
+ """
+ if shapes is None:
+ shapes = ["cell", "nucleus"]
- for spine in ax.spines.values():
- spine.set(edgecolor="white", linewidth=1)
+ shape_names = []
+ for s in shapes:
+ if str(s).endswith("_shape"):
+ shape_names.append(s)
+ else:
+ shape_names.append(f"{s}_shape")
+
+ # Save list of names to remove if not in data.obs
+ shape_names = [name for name in shape_names if name in data.obs.columns]
+ missing_names = [name for name in shape_names if name not in data.obs.columns]
+
+ if len(missing_names) > 0:
+ warnings.warn("Shapes not found in data: " + ", ".join(missing_names))
+
+ geo_kws = dict(edgecolor="none", facecolor="none")
+ if color_style == "outline":
+ geo_kws["edgecolor"] = color
+ geo_kws["facecolor"] = "none"
+ elif color_style == "fill":
+ geo_kws["facecolor"] = color
+ geo_kws["edgecolor"] = "black"
+ geo_kws.update(**kwargs)
+
+ for name in shape_names:
+ hide = False
+ if name == "cell_shape" and hide_outside:
+ hide = True
+
+ _polygons(
+ data,
+ name,
+ hide_outside=hide,
+ ax=ax,
+ **geo_kws,
+ )
- # Create scale bar
- scalebar = ScaleBar(
- dx, units, location="lower right", color="white", box_alpha=0, scale_loc="top"
- )
- ax.add_artist(scalebar)
+def fluxmap(
+ data,
+ batch=None,
+ palette="tab10",
+ hide_outside=True,
+ ax=None,
+ fname=None,
+ **kwargs,
+):
+ """Plot fluxmap shapes in different colors. Wrapper for :func:`bt.pl.shapes()`.
-def sig_samples(data, n=5, col_wrap=2):
- for f in data.uns["tensor_loadings"][TENSOR_DIM_NAMES[0]]:
- top_genes = (
- data.uns["tensor_loadings"]["genes"]
- .sort_values(f, ascending=False)
- .index.tolist()[:n]
- )
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ batch : str, optional
+ Batch to plot, by default None. If None, will use first batch.
+ palette : str or dict, optional
+ Color palette, by default "tab10". If dict, will use dict to map shape names to colors.
+ ax : matplotlib.axes.Axes, optional
+ Axis to plot on, by default None. If None, will use current axis.
+ fname : str, optional
+ Filename to save figure to, by default None. If None, will not save figure.
+ """
- top_cells = (
- data.uns["tensor_loadings"]["cells"]
- .sort_values(f, ascending=False)
- .index.tolist()[:n]
+ # Plot fluxmap shapes
+ if isinstance(palette, dict):
+ colormap = palette
+ else:
+ fluxmap_shapes = [s for s in data.obs.columns if s.startswith("fluxmap")]
+ fluxmap_shapes.sort()
+ colors = sns.color_palette(palette, n_colors=len(fluxmap_shapes))
+ colormap = dict(zip(fluxmap_shapes, colors))
+
+ shape_kws = dict(color_style="fill")
+ shape_kws.update(kwargs)
+
+ for s, c in colormap.items():
+ shapes(
+ data,
+ batch=batch,
+ shapes=s,
+ color=c,
+ hide_outside=hide_outside,
+ ax=ax,
+ **shape_kws,
)
- cellplot(
- data[top_cells, top_genes],
- kind="scatter",
- hue="gene",
- col="cell",
- col_wrap=col_wrap,
- height=2,
- )
- # plt.suptitle(f)
+ # Plot base cell and nucleus shapes
+ shapes(data, batch=batch, ax=ax, fname=fname)
diff --git a/bento/plotting/_signatures.py b/bento/plotting/_signatures.py
new file mode 100644
index 0000000..c96296a
--- /dev/null
+++ b/bento/plotting/_signatures.py
@@ -0,0 +1,329 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from scipy.stats import zscore
+
+from .._constants import PATTERN_COLORS, PATTERN_PROBS
+from ._colors import red2blue, red_light
+from ._utils import savefig
+
+
+@savefig
+def signatures(adata, rank, fname=None):
+ """Plot signatures for specified rank across each dimension.
+
+ bento.tl.signatures() must be run first.
+
+ Parameters
+ ----------
+ adata : anndata.AnnData
+ Spatial formatted AnnData
+ rank : int
+ Rank of signatures to plot
+ fname : str, optional
+ Path to save figure, by default None
+ """
+ sig_key = f"r{rank}_signatures"
+ layer_g = sns.clustermap(
+ np.log2(adata.uns[sig_key] + 1).T,
+ col_cluster=False,
+ row_cluster=False,
+ col_colors=pd.Series(PATTERN_COLORS, index=PATTERN_PROBS),
+ standard_scale=0,
+ cmap=red_light,
+ linewidth=1,
+ linecolor="black",
+ figsize=(adata.uns[sig_key].shape[0], adata.uns[sig_key].shape[1] + 1),
+ )
+ sns.despine(ax=layer_g.ax_heatmap, top=False, right=False)
+ plt.suptitle("Layers")
+
+ gs_shape = adata.varm[sig_key].shape
+ gene_g = sns.clustermap(
+ np.log2(adata.varm[sig_key] + 1).T,
+ row_cluster=False,
+ cmap=red_light,
+ standard_scale=0,
+ figsize=(gs_shape[0], gs_shape[1] + 1),
+ )
+ sns.despine(ax=gene_g.ax_heatmap, top=False, right=False)
+ plt.suptitle("Genes")
+
+ os_shape = adata.obsm[sig_key].shape
+ cell_g = sns.clustermap(
+ np.log2(adata.obsm[sig_key] + 1).T,
+ row_cluster=False,
+ col_cluster=True,
+ standard_scale=0,
+ xticklabels=False,
+ # col_colors=pheno_to_color(adata.obs["leiden"], palette="tab20")[1],
+ cmap=red_light,
+ figsize=(os_shape[0], os_shape[1] + 1),
+ )
+ sns.despine(ax=cell_g.ax_heatmap, top=False, right=False)
+ plt.suptitle("Cells")
+
+
+@savefig
+def signatures_error(adata, fname=None):
+ """Plot error for each rank.
+
+ bento.tl.signatures() must be run first.
+
+ Parameters
+ ----------
+ adata : anndata.AnnData
+ Spatial formatted AnnData
+ fname : str, optional
+ Path to save figure, by default None
+ """
+ errors = adata.uns["signatures_error"]
+ sns.lineplot(data=errors, x="rank", y="rmse", ci=95, marker="o")
+ sns.despine()
+
+ return errors
+
+
+def colocation(
+ adata,
+ rank,
+ n_top=[None, None, 5],
+ z_score=[False, True, True],
+ cut=None,
+ show_labels=[True, False, True],
+ cluster=[False, True, False],
+ self_pairs=False,
+ figsize=(8, 6),
+ fname=None,
+):
+ """Plot colocation of signatures for specified rank across each dimension.
+
+ Parameters
+ ----------
+ adata : anndata.AnnData
+ Spatial formatted AnnData
+ rank : int
+ Rank of signatures to plot
+ n_top : int, optional
+ Number of top features to plot, by default 10
+ z_score : bool, optional
+ Whether to z-score each column of the matrix, by default False
+ cut : float, optional
+ Max cut-off for z-score color mapping, by default None
+ show_labels : list, optional
+ Whether to show labels for each dimension, by default [True, False, False]
+ cluster : list, optional
+ Whether to cluster rows, by default [False, True, True]
+ self_pairs : [True, False, "only"], optional
+ Whether to include self-pairs, value "only" shows only self-pairs, by default True
+ fname : str, optional
+ Path to save figure, by default None
+ """
+ factors = adata.uns["factors"][rank].copy()
+ labels = adata.uns["tensor_labels"].copy()
+ names = adata.uns["tensor_names"].copy()
+
+ # Perform z-scaling upfront
+ for i in range(len(factors)):
+ if isinstance(z_score, list):
+ z = z_score[i]
+ else:
+ z = z_score
+
+ if z:
+ factors[i] = zscore(factors[i], axis=0)
+
+ pairs = []
+ for p in labels["pair"]:
+ pair = p.split("_")
+ pairs.append(pair)
+
+ # Filter out self-pairs appropriately
+ valid_pairs = [True] * len(pairs)
+ if self_pairs == "only":
+ valid_pairs = [True if p[0] == p[1] else False for p in pairs]
+ elif not self_pairs:
+ valid_pairs = [True if p[0] != p[1] else False for p in pairs]
+
+ valid_pairs = np.array(valid_pairs)
+
+ factors[2] = factors[2][valid_pairs]
+ labels["pair"] = labels["pair"][valid_pairs]
+
+ if self_pairs == "only":
+ labels["pair"] = [p.split("_")[0] for p in labels["pair"]]
+
+ factor(
+ factors,
+ labels,
+ names,
+ n_top=n_top,
+ cut=cut,
+ show_labels=show_labels,
+ cluster=cluster,
+ figsize=figsize,
+ fname=fname,
+ )
+
+
+@savefig
+def factor(
+ factors,
+ labels,
+ names,
+ n_top=False,
+ cut=None,
+ show_labels=False,
+ cluster=True,
+ figsize=None,
+ fname=None,
+):
+ """
+ Plot a heatmap representation of a loadings matrix, optionally z-scored and subsetted to the n_top rows of each factor.
+
+ Parameters
+ ----------
+ factors : list of np.ndarray
+ List of factors to plot, in the order [layers, cells, *]
+ labels : dict
+ Dict of {name: labels} for each factor
+ names : list of str
+ List of names for each factor, in the order [layers, cells, *]
+ n_top : int or list of int, optional
+ Number of top features to plot, by default None. If None, all features are plotted.
+ show_labels : bool or list of bool, optional
+ Whether to show labels, by default None. If None, labels are shown.
+ cluster : bool or list of bool, optional
+ Whether to cluster rows, by default False. If False, rows are not clustered.
+ """
+ n_factors = len(factors)
+ fig, axes = plt.subplots(
+ 1,
+ n_factors,
+ figsize=figsize,
+ gridspec_kw=dict(
+ width_ratios=[1] + [4] * (n_factors - 1),
+ wspace=0.05,
+ ),
+ layout="constrained",
+ )
+
+ for i, name in enumerate(names):
+ factor = factors[i]
+ feature_labels = labels[name]
+ factor = pd.DataFrame(factor, index=feature_labels)
+ factor.columns.name = "Factors"
+
+ name = names[i]
+
+ if isinstance(n_top, list):
+ n = n_top[i]
+ else:
+ n = n_top
+
+ if isinstance(cut, list):
+ cu = cut[i]
+ else:
+ cu = cut
+
+ if isinstance(show_labels, list):
+ show_l = show_labels[i]
+ else:
+ show_l = show_labels
+
+ if isinstance(cluster, list):
+ c = cluster[i]
+ else:
+ c = cluster
+
+ if i == 0:
+ factor = factor.T
+ square = True
+ else:
+ square = False
+
+ _plot_loading(
+ factor,
+ name=name,
+ n_top=n,
+ cut=cu,
+ show_labels=show_l,
+ cluster=c,
+ ax=axes[i],
+ square=square,
+ )
+
+
+def _plot_loading(df, name, n_top, cut, show_labels, cluster, ax, **kwargs):
+ """
+ Plot a heatmap representation of a loadings matrix, optionally z-scored and subsetted to the n_top rows of each factor.
+
+ Parameters
+ ----------
+ df : np.ndarray
+ Matrix to plot
+ name : str
+ Name of factor
+ n_top : int
+ Number of top features to plot
+ cut : float
+ Cut-off for z-score color mapping
+ show_labels : bool
+ Whether to show row labels
+ cluster : bool
+ Whether to cluster rows
+ ax : matplotlib.axes.Axes
+ Axes to plot heatmap on
+ cbar_ax : matplotlib.axes.Axes
+ Axes to plot colorbar on
+ kwargs : dict
+ Additional keyword arguments to pass to sns.heatmap
+ """
+
+ # Optionally z-score each column
+ cmap = red_light
+ center = None
+ vmin = None
+ vmax = None
+ if df.min().min() < 0:
+ cmap = red2blue
+ center = 0
+
+ # Optionally set cut-off for z-score color mapping
+ if cut:
+ vmin = max(-abs(cut), df.min().min())
+ vmax = min(abs(cut), df.max().max())
+
+ # Subset to factor
+ if n_top:
+ top_indices = []
+ for col in df.columns:
+ top_indices.extend(
+ df.sort_values(col, ascending=False).head(n_top).index.tolist()
+ )
+ df = df.loc[top_indices]
+
+ # Get hierarchical clustering row order
+ if cluster:
+ row_order = sns.clustermap(df, col_cluster=False).dendrogram_row.reordered_ind
+ plt.close()
+ df = df.iloc[row_order]
+
+ # Plot heatmap
+ sns.heatmap(
+ df,
+ center=center,
+ cmap=cmap,
+ cbar_kws=dict(shrink=0.5, aspect=10),
+ yticklabels=show_labels,
+ vmin=vmin,
+ vmax=vmax,
+ ax=ax,
+ rasterized=True,
+ **kwargs,
+ )
+
+ ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
+ ax.set_title(f"{name}: [{df.shape[0]} x {df.shape[1]}]")
+ sns.despine(ax=ax, right=False, top=False)
diff --git a/bento/plotting/_tensor_tools.py b/bento/plotting/_tensor_tools.py
deleted file mode 100644
index 31ac491..0000000
--- a/bento/plotting/_tensor_tools.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from sklearn.preprocessing import LabelEncoder, MinMaxScaler
-
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pandas as pd
-import numpy as np
-
-from .._utils import TENSOR_DIM_NAMES, PATTERN_COLORS
-from ._utils import savefig
-
-
-def _get_loading(data, load, dim, scale):
-
- if dim == TENSOR_DIM_NAMES[0]:
- cluster_labels = np.zeros(len(load))
- unit_order = load.index
-
- elif dim == TENSOR_DIM_NAMES[1]:
- cluster_labels = data.obs["td_cluster"].sort_values().dropna()
- unit_order = cluster_labels.index.tolist()
- elif dim == TENSOR_DIM_NAMES[2]:
- cluster_labels = data.var["td_cluster"].sort_values().dropna()
- unit_order = cluster_labels.index.tolist()
-
- load_df = pd.DataFrame(
- [
- range(load.shape[0]),
- load.loc[unit_order],
- cluster_labels,
- load.index.tolist(),
- ],
- index=["index", "load", "group", "sample"],
- ).T
- load_df = load_df.astype({"index": int, "load": float, "group": str, "sample": str})
-
- if scale:
- load_df["load"] = MinMaxScaler().fit_transform(load_df[["load"]])
-
- load_df["group"] = (
- load_df["group"].str.replace("Factor", "Group").astype("category")
- )
- return load_df
-
-
-@savefig
-def lp_signatures(data, scale=True, fname=None):
- factors = list(data.uns["tensor_loadings"][TENSOR_DIM_NAMES[0]].columns)
- n_factors = len(factors)
- n_dims = len(TENSOR_DIM_NAMES)
-
- fig = plt.figure(figsize=(3 * n_dims, n_factors))
-
- # Create grid such that there is space between each row (factor), an extra row above and below everything, and an extra column for legends
- gs = fig.add_gridspec(
- n_factors,
- n_dims + 1,
- width_ratios=[0.5, 1, 1, 0.1],
- hspace=0.2,
- wspace=0.2
- )
-
- PATTERN_COL = 0
- LEGEND_COL = n_dims
- FACTOR_ROWS = [i for i in range(n_factors)]
-
- # Plot pattern loadings as barplot
- for factor, row in zip(factors, FACTOR_ROWS):
- if row == FACTOR_ROWS[0]:
- ax = fig.add_subplot(gs[row, PATTERN_COL])
- else:
- ax = fig.add_subplot(gs[row, PATTERN_COL], sharex=fig.axes[0])
-
- pattern_dim = TENSOR_DIM_NAMES[0]
- load = data.uns["tensor_loadings"][pattern_dim][factor]
- load_df = _get_loading(data, load, pattern_dim, scale=False)
-
- # Feature barplots
- ax.bar(
- x=load.index.fillna("na").tolist(),
- height=load_df["load"],
- color=PATTERN_COLORS,
- # alpha=0.5,
- # ax=ax,
- )
-
- if row == FACTOR_ROWS[0]:
- ax.set_title(str(pattern_dim).capitalize(), weight="600")
-
- # Set row labels
- ax.set_ylabel(factor, labelpad=3.5*len(factor), rotation=0, weight="600")
-
- # Turn off xlabels except bottom row
- if factor != factors[-1]:
- ax.set(xlabel=None)
- else:
- ax.set(xlabel="loading")
-
- # Turn off yticks
- ax.set(xticklabels=[])
- ax.tick_params(axis="x", which="both", length=3)
-
- # Format spines
- sns.despine(ax=ax, left=True)
- ax.spines["bottom"].set_color("#aaaaaa")
-
- # Plot cell and gene loadings as heatmaps
- for factor, row in zip(factors, FACTOR_ROWS):
- # Populate index 1 and 2 columns
- for col, dim in zip(range(1, n_dims), TENSOR_DIM_NAMES[1:]):
-
- ax = fig.add_subplot(gs[row, col])
- load = data.uns["tensor_loadings"][dim][factor]
- load_df = _get_loading(data, load, dim, scale)
-
- # Plot column title if first row
- if row == FACTOR_ROWS[0]:
- ax0 = fig.add_subplot(gs[0, col])
- ax0.set_title(f"{str(dim).capitalize()} ({len(load)})", weight="600")
- ax0.axis("off")
-
- # Plot colorbar for cell column
- if row == FACTOR_ROWS[0] and col == 1:
- cbar = True
- cbar_ax = fig.add_subplot(gs[FACTOR_ROWS[0], n_dims])
- cmap = 'Purples'
- # Plot colorbar for gene column
- elif row == FACTOR_ROWS[0] and col == 2 and not scale:
- cbar = True
- cbar_ax = fig.add_subplot(gs[FACTOR_ROWS[1], n_dims])
- else:
- cbar = False
-
- # Set colormap for cell loadings
- if col == 1:
- cmap = 'Purples'
- # Set colormap for gene loadings
- elif col == 2:
- cmap = 'Reds'
-
- # Colormap limits for zscoring
- if scale:
- vmin = 0
- vmax = 1
-
- # Plot (z-scored) loadings as heatmap
- sns.heatmap(
- load_df[["load"]].T,
- ax=ax,
- xticklabels=False,
- yticklabels=False,
- cmap="RdBu_r",
- center=0,
- vmin=vmin,
- vmax=vmax,
- cbar=cbar,
- cbar_ax=cbar_ax,
- )
- else:
- vmin = data.uns['tensor_loadings'][dim].min().min()
- vmax = data.uns['tensor_loadings'][dim].max().max()
-
- # Plot (z-scored) loadings as heatmap
- sns.heatmap(
- load_df[["load"]].T,
- ax=ax,
- xticklabels=False,
- yticklabels=False,
- cmap=cmap,
- vmin=vmin,
- vmax=vmax,
- cbar=cbar,
- cbar_ax=cbar_ax,
- )
-
- # Format heatmap
- ax.tick_params(axis="y", which="both", length=0)
- ax.set(ylabel=None)
- sns.despine(ax=ax, top=False, bottom=False, left=False, right=False)
-
-
-
\ No newline at end of file
diff --git a/bento/plotting/_utils.py b/bento/plotting/_utils.py
index 21ee0ea..ce996c4 100644
--- a/bento/plotting/_utils.py
+++ b/bento/plotting/_utils.py
@@ -1,7 +1,7 @@
-import matplotlib.pyplot as plt
import inspect
from functools import wraps
+import matplotlib.pyplot as plt
def get_default_args(func):
@@ -24,14 +24,18 @@ def wrapper(*args, **kwds):
kwargs.update(kwds)
plot_fn(*args, **kwds)
-
- fname = kwargs['fname']
- rc = {'svg.fonttype': 'none', 'font.family':'Arial'}
+
+ fname = kwargs["fname"]
+ rc = {
+ "svg.fonttype": "none",
+ "font.family": "sans-serif",
+ "font.sans-serif": "Arial",
+ "pdf.fonttype": 42,
+ }
if fname:
with plt.rc_context(rc):
- plt.savefig(fname, dpi=400)
+ plt.savefig(fname, dpi=400, pad_inches=0, bbox_inches="tight")
+
+ print(f"Saved to {fname}")
-
- print(f'Saved to {fname}')
-
- return wrapper
\ No newline at end of file
+ return wrapper
diff --git a/bento/preprocessing/__init__.py b/bento/preprocessing/__init__.py
deleted file mode 100755
index 49d4726..0000000
--- a/bento/preprocessing/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from ._preprocessing import get_layers, get_points, set_points
diff --git a/bento/preprocessing/_preprocessing.py b/bento/preprocessing/_preprocessing.py
deleted file mode 100755
index 094a1de..0000000
--- a/bento/preprocessing/_preprocessing.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import geopandas as gpd
-
-from .._utils import track
-
-
-def get_points(data, asgeo=False):
- """Get points DataFrame.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData object
- asgeo : bool, optional
- Cast as GeoDataFrame using columns x and y for geometry, by default False
-
- Returns
- -------
- DataFrame or GeoDataFrame
- Returns `data.uns['points']` as a `[Geo]DataFrame`
- """
- points = data.uns["points"]
-
- cells = data.obs_names.tolist()
- genes = data.var_names.tolist()
-
- # Subset for cells
- in_cells = points["cell"].isin(cells)
- in_genes = points["gene"].isin(genes)
-
- # Subset for genes
- points = points.loc[in_cells & in_genes]
-
- # Remove unused categories for categorical columns
- for col in points.columns:
- if points[col].dtype == "category":
- points[col].cat.remove_unused_categories(inplace=True)
-
- # Cast to GeoDataFrame
- if asgeo:
- points = gpd.GeoDataFrame(
- points, geometry=gpd.points_from_xy(points.x, points.y)
- )
-
- return points
-
-
-@track
-def set_points(data, copy=False):
- """Set points for the given `AnnData` object, data. Call this setter
- to keep the points DataFrame in sync.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData object
- copy : bool
- Return a copy of `data` instead of writing to data, by default False.
- Returns
- -------
- _type_
- _description_
- """
- adata = data.copy() if copy else data
- points = get_points(adata)
- adata.uns["points"] = points
- return adata if copy else None
-
-
-def get_layers(data, layers, min_count=None):
- """Get values of layers reformatted as a long-form dataframe.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData object
- layers : list of str
- all values must to be keys in data.layers
- min_count : int, default None
- minimum number of molecules (count) required to include in output
- Returns
- -------
- DataFrame
- rows are samples indexed as (cell, gene) and columns are features
- """
- sample_index = (
- data.to_df()
- .reset_index()
- .melt(id_vars="cell")
- .dropna()
- .set_index(["cell", "gene"])
- )
-
- if min_count:
- sample_index = sample_index[sample_index["value"] >= min_count].drop(
- "value", axis=1
- )
-
- for layer in layers:
- values = (
- data.to_df(layer)
- .reset_index()
- .melt(id_vars="cell")
- .set_index(["cell", "gene"])
- )
- values.columns = [layer]
- sample_index = sample_index.join(values)
-
- return sample_index[layers]
diff --git a/bento/tools/__init__.py b/bento/tools/__init__.py
index 9fabd91..49495e3 100755
--- a/bento/tools/__init__.py
+++ b/bento/tools/__init__.py
@@ -1,38 +1,13 @@
-from ._cell_features import (
- analyze_cells,
- cell_features,
- cell_area,
- cell_aspect_ratio,
- cell_bounds,
- cell_density,
- cell_moments,
- cell_morph_open,
- cell_perimeter,
- cell_radius,
- cell_span,
- is_nuclear,
- nucleus_area_ratio,
- nucleus_area,
- nucleus_aspect_ratio,
- nucleus_offset,
- raster_cell,
-)
-from ._colocation import coloc_quotient
-from ._lp import PATTERN_MODEL_FEATURE_NAMES, lp, lp_diff, lp_stats
-from ._sample_features import (
- analyze_samples,
- sample_features,
- PointDispersion,
- RipleyStats,
- ShapeAsymmetry,
- ShapeDispersion,
- ShapeEnrichment,
- ShapeProximity,
-)
-from ._shapes import inner_edge, outer_edge
-from ._tensor_tools import (
- TENSOR_DIM_NAMES,
- decompose_tensor,
- lp_signatures,
- select_tensor_rank,
+from ._colocation import coloc_quotient, colocation
+from ._composition import comp_diff
+from ._flux import flux, fluxmap
+from ._flux_enrichment import fe, fe_fazal2019, gene_sets, load_gene_sets
+from ._lp import lp, lp_diff, lp_stats
+from ._point_features import analyze_points, list_point_features, register_point_feature
+from ._shape_features import (
+ analyze_shapes,
+ obs_stats,
+ register_shape_feature,
+ list_shape_features,
)
+from ._decomposition import decompose, to_tensor
diff --git a/bento/tools/_cell_features.py b/bento/tools/_cell_features.py
deleted file mode 100755
index 56a21bd..0000000
--- a/bento/tools/_cell_features.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import geopandas as gpd
-import matplotlib.path as mplPath
-import numpy as np
-from scipy.spatial import distance, distance_matrix
-from shapely.geometry import Point
-from tqdm.auto import tqdm
-
-from .._utils import track
-
-
-@track
-def analyze_cells(data, feature_names, progress=True, copy=False):
- """Compute multiple cell features at once. Convenience function instead of making
- separate calls to compute each feature.
-
- A list of available cell features and their names is stored in the dict `bento.tl.cell_features`.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- feature_names : list of str
- list of feature names
- copy : bool, optional
- _description_, by default False
-
- Returns
- -------
- _type_
- _description_
- """
- adata = data.copy() if copy else data
-
- if not isinstance(feature_names, list):
- feature_names = [feature_names]
-
- feature_names = list(set(feature_names))
-
- if progress:
- for f in tqdm(feature_names):
- cell_features[f].__wrapped__(adata)
- else:
- for f in feature_names:
- cell_features[f].__wrapped__(adata)
-
- return adata if copy else None
-
-
-@track
-def cell_span(data, copy=False):
- """Compute the length of the longest diagonal of each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_span']` : float
- Length of longest diagonal for each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- def get_span(poly):
- shape_coo = np.array(poly.coords.xy).T
- return int(distance_matrix(shape_coo, shape_coo).max())
-
- span = gpd.GeoSeries(data=adata.obs["cell_shape"]).exterior.apply(get_span)
-
- adata.obs["cell_span"] = span
-
- return adata if copy else None
-
-
-@track
-def cell_bounds(data, copy=False):
- """Compute the minimum and maximum coordinate values that bound each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_minx']` : float
- x-axis lower bound of each polygon in `obs['cell_shape']`
- `obs['cell_miny']` : float
- y-axis lower bound of each polygon in `obs['cell_shape']`
- `obs['cell_maxx']` : float
- x-axis upper bound of each polygon in `obs['cell_shape']`
- `obs['cell_maxy']` : float
- y-axis upper bound of each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- bounds = gpd.GeoSeries(data=adata.obs["cell_shape"]).bounds
- adata.obs["cell_minx"] = bounds["minx"]
- adata.obs["cell_miny"] = bounds["miny"]
- adata.obs["cell_maxx"] = bounds["maxx"]
- adata.obs["cell_maxy"] = bounds["maxy"]
-
- return adata if copy else None
-
-
-@track
-def cell_moments(data, copy=False):
- """Compute the second moment of each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_moment']` : float
- The second moment for each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- if "cell_raster" not in adata.obs:
- raster_cell(adata)
-
- cell_rasters = adata.obs["cell_raster"]
- shape_centroids = gpd.GeoSeries(adata.obs["cell_shape"]).centroid
- moments = [
- _second_moment(np.array(centroid.xy).reshape(1, 2), cell_raster)
- for centroid, cell_raster in zip(shape_centroids, cell_rasters)
- ]
-
- adata.obs["cell_moment"] = moments
-
- return adata if copy else None
-
-
-def _second_moment(centroid, pts):
- """
- Calculate second moment of points with centroid as reference.
- Parameters
- ----------
- centroid : [1 x 2] float
- pts : [n x 2] float
- """
- centroid = np.array(centroid).reshape(1, 2)
- radii = distance.cdist(centroid, pts)
- second_moment = np.sum(radii * radii / len(pts))
- return second_moment
-
-
-def _raster_polygon(poly):
- """
- Generate a grid of points contained within the poly. The points lie on
- a 2D grid, with vertices spaced 1 unit apart.
- """
- minx, miny, maxx, maxy = poly.bounds
- x, y = np.meshgrid(
- np.arange(minx, maxx, step=float(1)),
- np.arange(miny, maxy, step=float(1)),
- )
- x = x.flatten()
- y = y.flatten()
- xy = np.array([x, y]).T
- poly_path = mplPath.Path(np.array(poly.exterior.xy).T)
- poly_cell_mask = poly_path.contains_points(xy)
- xy = xy[poly_cell_mask]
- return xy
-
-
-@track
-def raster_cell(data, copy=False):
- """Generate a grid of points contained within each cell. The points lie on
- a 2D grid, with vertices spaced 1 unit apart.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['raster']` : np.array
- 2D array of grid points for each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- raster = adata.obs["cell_shape"].apply(_raster_polygon)
- adata.obs["cell_raster"] = raster
-
- return adata if copy else None
-
-
-def _aspect_ratio(poly):
- # get coordinates of min bounding box vertices around polygon
- x, y = poly.minimum_rotated_rectangle.exterior.coords.xy
-
- # get length of bound box sides
- edge_length = (
- Point(x[0], y[0]).distance(Point(x[1], y[1])),
- Point(x[1], y[1]).distance(Point(x[2], y[2])),
- )
-
- # length = longest side, width = shortest side
- length, width = max(edge_length), min(edge_length)
-
- # return long / short ratio
- return length / width
-
-
-@track
-def cell_aspect_ratio(data, copy=False):
- """Compute the aspect ratio of the minimum rotated rectangle that contains each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_aspect_ratio']` : np.array
- Ratio of long / short axis for each polygon in `obs['cell_shape']`
- """
-
- adata = data.copy() if copy else data
-
- ar = adata.obs["cell_shape"].apply(lambda poly: _aspect_ratio(poly))
- adata.obs["cell_aspect_ratio"] = ar
-
- return adata if copy else None
-
-
-@track
-def cell_density(data, copy=False):
- """Compute the RNA density of each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_density']` : np.array
- Density (total cell counts / cell area) of each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- cell_area.__wrapped__(adata)
-
- count = adata.X.sum(axis=1)
- adata.obs["cell_density"] = count / adata.obs["cell_area"]
-
- return adata if copy else None
-
-
-@track
-def cell_area(data, copy=False):
- """Compute the area of each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_area']` : np.array
- Area of each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- # Calculate pixel-wise area
- # TODO: unit scale?
- area = gpd.GeoSeries(adata.obs["cell_shape"]).area
- adata.obs["cell_area"] = area
-
- return adata if copy else None
-
-
-@track
-def cell_perimeter(data, copy=False):
- """Compute the perimeter of each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_perimeter']` : np.array
- Perimeter of each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- adata.obs["cell_perimeter"] = gpd.GeoSeries(adata.obs["cell_shape"]).length
-
- return adata if copy else None
-
-
-@track
-def cell_radius(data, overwrite=False, copy=False):
- """Compute the radius of each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_radius']` : np.array
- Radius of each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- if not overwrite and "cell_radius" in adata.obs.columns:
- return adata if copy else None
-
- cells = gpd.GeoSeries(adata.obs["cell_shape"])
-
- # Get average distance of cell boundary to centroid
- cell_radius = cells.apply(
- lambda c: distance.cdist(
- np.array(c.centroid).reshape(1, 2), np.array(c.exterior.xy).T
- ).mean()
- )
- adata.obs["cell_radius"] = cell_radius
-
- return adata if copy else None
-
-
-@track
-def cell_morph_open(data, proportion, overwrite=False, copy=False):
- """Compute the opening (morphological) of distance d for each cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- copy : bool, optional
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `obs['cell_open_{d}_shape']` : Polygons
- Ratio of long / short axis for each polygon in `obs['cell_shape']`
- """
- adata = data.copy() if copy else data
-
- shape_name = f"cell_open_{proportion}_shape"
-
- if not overwrite and shape_name in adata.obs.columns:
- return adata if copy else None
-
- # Compute cell radius as needed
- cell_radius.__wrapped__(adata)
-
- cells = gpd.GeoSeries(adata.obs["cell_shape"])
- d = proportion * data.obs["cell_radius"]
-
- # Opening
- adata.obs[shape_name] = cells.buffer(-d).buffer(d)
-
- return adata if copy else None
-
-
-@track
-def nucleus_area_ratio(data, copy=False):
- adata = data.copy() if copy else data
-
- cell_area.__wrapped__(adata)
- nucleus_area.__wrapped__(adata)
- adata.obs["nucleus_area_ratio"] = adata.obs["nucleus_area"] / adata.obs["cell_area"]
-
- return adata if copy else None
-
-
-@track
-def nucleus_offset(data, copy=False):
- adata = data.copy() if copy else data
-
- cell_centroid = gpd.GeoSeries(adata.obs["cell_shape"]).centroid
- nucleus_centroid = gpd.GeoSeries(adata.obs["nucleus_shape"]).centroid
-
- cell_radius.__wrapped__(adata)
- offset = cell_centroid.distance(nucleus_centroid, align=False)
- offset = offset.apply(abs)
-
- adata.obs["nucleus_offset"] = offset
-
- return adata if copy else None
-
-
-@track
-def nucleus_area(data, copy=False):
- adata = data.copy() if copy else data
-
- adata.obs["nucleus_area"] = gpd.GeoSeries(adata.obs["nucleus_shape"]).area
-
- return adata if copy else None
-
-
-@track
-def nucleus_aspect_ratio(data, copy=False):
- adata = data.copy() if copy else data
-
- ar = adata.obs["nucleus_shape"].apply(lambda poly: _aspect_ratio(poly))
- adata.obs["nucleus_aspect_ratio"] = ar
-
- return adata if copy else None
-
-
-@track
-def is_nuclear(data, shape_name, overwrite=False, copy=False):
- """
- Check if shape_name is contained within the nucleus.
- TODO speed up with sjoin
- """
- adata = data.copy() if copy else data
-
- shape_prefix = shape_name.split("_shape")[0]
- if not overwrite and f"{shape_prefix}_in_nucleus" in adata.obs.columns:
- return adata if copy else None
-
- if shape_name == "nucleus_shape":
- adata.obs["nucleus_in_nucleus"] = True
- else:
- shapes = gpd.GeoSeries(data.obs[shape_name])
- nuclei = gpd.GeoSeries(data.obs["nucleus_shape"])
-
- shape_in_nucleus = shapes.within(nuclei)
- adata.obs[f"{shape_prefix}_in_nucleus"] = shape_in_nucleus
-
- return adata if copy else None
-
-
-cell_features = dict(
- cell_span=cell_span,
- cell_bounds=cell_bounds,
- cell_moments=cell_moments,
- raster_cell=raster_cell,
- cell_aspect_ratio=cell_aspect_ratio,
- cell_density=cell_density,
- cell_area=cell_area,
- cell_perimeter=cell_perimeter,
- cell_radius=cell_radius,
- cell_morph_open=cell_morph_open,
- nucleus_area=nucleus_area,
- nucleus_area_ratio=nucleus_area_ratio,
- nucleus_aspect_ratio=nucleus_aspect_ratio,
- nucleus_offset=nucleus_offset,
-)
-"""Dict of cell feature names : function. Pass a list of feature name(s) to
-`bento.tl.analyze_cells()` to compute them.
-"""
diff --git a/bento/tools/_colocation.py b/bento/tools/_colocation.py
index 6a3815f..142a952 100644
--- a/bento/tools/_colocation.py
+++ b/bento/tools/_colocation.py
@@ -1,232 +1,247 @@
+from typing import List
+
+import emoji
import numpy as np
import pandas as pd
-from numpy.random import default_rng
-from dask import dataframe as dd
-from dask.diagnostics import ProgressBar
-from sklearn.neighbors import NearestNeighbors
+import seaborn as sns
+import sparse
+from anndata import AnnData
+from kneed import KneeLocator
+from tqdm.auto import tqdm
+
+from .._utils import track
+from ..geometry import get_points
+from ._neighborhoods import _count_neighbors
+from ._decomposition import decompose
+
+
+@track
+def colocation(
+ data: AnnData,
+ ranks: List[int],
+ iterations: int = 3,
+ plot_error: bool = True,
+ copy: bool = False,
+):
+ """Decompose a tensor of pairwise colocalization quotients into signatures.
+
+ Parameters
+ ----------
+ adata : AnnData
+ Spatial formatted AnnData object.
+ ranks : list
+ List of ranks to decompose the tensor.
+ iterations : int
+ Number of iterations to run the decomposition.
+ plot_error : bool
+ Whether to plot the error of the decomposition.
+ copy : bool
+ Whether to return a copy of the AnnData object. Default False.
+ Returns
+ -------
+ adata : AnnData
+ .uns['factors']: Decomposed tensor factors.
+ .uns['factors_error']: Decomposition error.
+ """
+ adata = data.copy() if copy else data
+
+ print("Preparing tensor...")
+ _colocation_tensor(adata, copy=copy)
+
+ tensor = adata.uns["tensor"]
+
+ print(emoji.emojize(":running: Decomposing tensor..."))
+ factors, errors = decompose(tensor, ranks, iterations=iterations)
+
+ if plot_error and errors.shape[0] > 1:
+ kl = KneeLocator(
+ errors["rank"], errors["rmse"], direction="decreasing", curve="convex"
+ )
+ kl.plot_knee()
+ sns.lineplot(data=errors, x="rank", y="rmse", ci=95, marker="o")
+
+ adata.uns["factors"] = factors
+ adata.uns["factors_error"] = errors
+
+ print(emoji.emojize(":heavy_check_mark: Done."))
+ return adata if copy else None
+
+
+def _colocation_tensor(data: AnnData, copy: bool = False):
+ """
+ Convert a dictionary of colocation quotient values in long format to a dense tensor.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object.
+ copy : bool
+ Whether to return a copy of the AnnData object. Default False.
+ """
+ adata = data.copy() if copy else data
+
+ clqs = adata.uns["clq"]
+
+ clq_long = []
+ for shape, clq in clqs.items():
+ clq["compartment"] = shape
+ clq_long.append(clq)
+
+ clq_long = pd.concat(clq_long, axis=0)
+ clq_long["pair"] = (
+ clq_long["gene"].astype(str) + "_" + clq_long["neighbor"].astype(str)
+ )
+
+ label_names = ["compartment", "cell", "pair"]
+ labels = dict()
+ label_orders = []
+ for name in label_names:
+ label, order = np.unique(clq_long[name], return_inverse=True)
+ labels[name] = label
+ label_orders.append(order)
+
+ label_orders = np.array(label_orders)
+
+ s = sparse.COO(label_orders, data=clq_long["log_clq"].values)
+ tensor = s.todense()
+ print(tensor.shape)
+ adata.uns["tensor"] = tensor
+ adata.uns["tensor_labels"] = labels
+ adata.uns["tensor_names"] = label_names
+ return adata
+
+
+@track
def coloc_quotient(
- data, n_neighbors=25, radius=None, min_count=5, permutations=10, copy=False
+ data: AnnData,
+ shapes: List[str] = ["cell_shape"],
+ radius: int = 20,
+ min_points: int = 10,
+ min_cells: int = 0,
+ copy: bool = False,
):
- """Calculate pairwise gene colocalization quotient in each cell. Specify either n_neighbors or radius, for knn neighbors or radius neighbors.
+ """Calculate pairwise gene colocalization quotient in each cell.
Parameters
----------
adata : AnnData
- Anndata formatted spatial data.
- n_neighbors : int
- Number of nearest neighbors to consider, default 25
+ Spatial formatted AnnData object.
+ shapes : list
+ Specify which shapes to compute colocalization separately.
radius : int
- Max radius to search for neighboring points, default None
- min_count : int
- Minimum points needed to be eligible for analysis. default 5
- permutations : int
- Number of permutations to compute significance TODO account for multiple testing
+ Unit distance to count neighbors, default 20
+ min_points : int
+ Minimum number of points for sample to be considered for colocalization, default 10
+ min_cells : int
+ Minimum number of cells for gene to be considered for colocalization, default 0
+ copy : bool
+ Whether to return a copy of the AnnData object. Default False.
Returns
-------
adata : AnnData
- .uns['coloc_quotient']: Pairwise gene colocalization similarity within each cell formatted as a long dataframe.
+ .uns['clq']: Pairwise gene colocalization similarity within each cell formatted as a long dataframe.
"""
adata = data.copy() if copy else data
- points = data.uns["points"][["x", "y", "gene", "cell"]]
- points = points[
- points["cell"].isin(data.obs_names) & points["gene"].isin(data.var_names)
- ]
-
- ngroups = points.groupby(["cell"]).ngroups
- if ngroups > 0:
- npartitions = min(100, ngroups)
-
- with ProgressBar():
- cell_metrics = (
- dd.from_pandas(points.set_index("cell"), npartitions=npartitions)
- .groupby(["cell"])
- .apply(
- lambda df: _cell_clq(
- df, n_neighbors, radius, min_count, permutations
- ),
- meta=object,
- )
- .compute()
- )
-
- colnames = {
- "neighbor": str,
- "neighbor_count": int,
- "neighbor_fraction": float,
- "quotient": float,
- "pvalue": float,
- "gene": str,
- }
- # cell_metrics = cell_metrics.drop('level_1', axis=1).astype(meta)
- cell_labels = [
- [cell] * len(v) for cell, v in zip(cell_metrics.index, cell_metrics.values)
- ]
- cell_labels = np.concatenate(cell_labels)
-
- cell_metrics = pd.DataFrame(np.concatenate(cell_metrics.values), columns=list(colnames.keys()))
- cell_metrics = cell_metrics.astype(colnames)
- cell_metrics["cell"] = cell_labels
-
-
- adata.uns["coloc_quotient"] = cell_metrics
+ all_clq = dict()
+ for shape in shapes:
+ shape_col = "_".join(str(shape).split("_")[:-1])
+ points = get_points(adata, asgeo=False)
+ points[shape_col] = points[shape_col].astype(str)
+ points = (
+ points.query(f"{shape_col} != '-1'")
+ .sort_values("cell")[["cell", "gene", "x", "y"]]
+ .reset_index(drop=True)
+ )
+
+ # Keep genes expressed in at least min_cells cells
+ gene_counts = points.groupby("gene").size()
+ valid_genes = gene_counts[gene_counts >= min_cells].index
+ points = points[points["gene"].isin(valid_genes)]
+
+ # Partition so {chunksize} cells per partition
+ cells, group_loc = np.unique(
+ points["cell"].astype(str),
+ return_index=True,
+ )
+
+ end_loc = np.append(group_loc[1:], points.shape[0])
+
+ cell_clqs = []
+ for cell, start, end in tqdm(
+ zip(cells, group_loc, end_loc), desc=shape, total=len(cells)
+ ):
+ cell_points = points.iloc[start:end]
+ cell_clq = _cell_clq(cell_points, adata.n_vars, radius, min_points)
+ cell_clq["cell"] = cell
+
+ cell_clqs.append(cell_clq)
+
+ cell_clqs = pd.concat(cell_clqs)
+ cell_clqs[["cell", "gene", "neighbor"]] = (
+ cell_clqs[["cell", "gene", "neighbor"]].astype(str).astype("category")
+ )
+ cell_clqs["log_clq"] = cell_clqs["clq"].replace(0, np.nan).apply(np.log2)
+
+ # Save to uns['clq'] as adjacency list
+ all_clq[shape] = cell_clqs
+
+ adata.uns["clq"] = all_clq
return adata if copy else None
-def _cell_clq(cell_points, n_neighbors, radius, min_count, permutations):
+def _cell_clq(cell_points, n_genes, radius, min_points):
# Count number of points for each gene
- counts = cell_points["gene"].value_counts()
+ gene_counts = cell_points["gene"].value_counts()
- # Only keep genes >= min_count
- counts = counts[counts >= min_count]
- valid_genes = counts.sort_index().index.tolist()
- counts = counts[valid_genes]
+ # Keep genes with at least min_count
+ gene_counts = gene_counts[gene_counts >= min_points]
+
+ if len(gene_counts) < 2:
+ return pd.DataFrame()
# Get points
- valid_points = cell_points[cell_points["gene"].isin(valid_genes)]
- n_points = valid_points.shape[0]
+ valid_points = cell_points[cell_points["gene"].isin(gene_counts.index)]
# Cleanup gene categories
- valid_points["gene"] = valid_points["gene"].cat.remove_unused_categories()
-
- # Get neighbors within fixed outer_radius for every point
- if n_neighbors:
- nn = NearestNeighbors(n_neighbors=n_neighbors).fit(valid_points[["x", "y"]])
- point_index = nn.kneighbors(valid_points[["x", "y"]], return_distance=False)
- elif radius:
- nn = NearestNeighbors(radius=radius).fit(valid_points[["x", "y"]])
- point_index = nn.radius_neighbors(
- valid_points[["x", "y"]], return_distance=False
- )
+ # valid_points["gene"] = valid_points["gene"].cat.remove_unused_categories()
+
+ # Count number of source points that have neighbor gene
+ point_neighbors = _count_neighbors(
+ valid_points, n_genes, radius=radius, agg="binary"
+ ).toarray()
+ neighbor_counts = (
+ pd.DataFrame(point_neighbors, columns=valid_points["gene"].cat.categories)
+ .groupby(valid_points["gene"].values)
+ .sum()
+ .reset_index()
+ .melt(id_vars="index")
+ .query("value > 0")
+ )
+ neighbor_counts.columns = ["gene", "neighbor", "count"]
+ clq_df = _clq_statistic(neighbor_counts, gene_counts)
+
+ return clq_df
+
+
+def _clq_statistic(neighbor_counts, counts):
+ """
+ Compute the colocation quotient for each gene pair.
- # Flatten adjacency list to pairs
- source_index = []
- neighbor_index = []
- for source, neighbors in zip(range(valid_points.shape[0]), point_index):
- source_index.extend([source] * len(neighbors))
- neighbor_index.extend(neighbors)
-
- source_index = np.array(source_index)
- neighbor_index = np.array(neighbor_index)
-
- # Remove self neighbors
- is_self = source_index == neighbor_index
- source_index = source_index[~is_self]
- neighbor_index = neighbor_index[~is_self]
-
- # Remove duplicate neighbors
- _, is_uniq = np.unique(neighbor_index, return_index=True)
- source_index = source_index[is_uniq]
- neighbor_index = neighbor_index[is_uniq]
-
- # Index to gene mapping; dict for fast lookup
- index2gene = valid_points["gene"].reset_index(drop=True).to_dict()
-
- # Map to genes
- source_genes = np.array([index2gene[i] for i in source_index])
- neighbor_genes = np.array([index2gene[i] for i in neighbor_index])
-
- # Preshuffle neighbors for permutations
- perm_neighbors = []
- if permutations > 0:
- # Permute neighbors
- rng = default_rng()
- for i in range(permutations):
- perm_neighbors.append(rng.permutation(neighbor_genes))
-
- neighbor_space = {g: 0 for g in valid_genes}
-
- # Iterate across genes
- stats_list = []
-
- for cur_gene, cur_total in zip(valid_genes, counts[valid_genes]):
-
- # Select pairs where source = gene of interest
- cur_neighbor_genes = neighbor_genes[source_genes == cur_gene]
-
- # Count neighbors
- obs_genes, obs_count = np.unique(cur_neighbor_genes, return_counts=True)
-
- # Save counts and order with dict
- obs_space = neighbor_space.copy()
- obs_space.update(zip(obs_genes, obs_count))
- obs_count = np.array(list(obs_space.values()))
-
- # Calculate colocation quotient for all neighboring genes
- # print(obs_count, counts)
- obs_quotient = (obs_count / cur_total) / ((counts - 1) / (n_points - 1))
- obs_quotient = np.expand_dims(obs_quotient, 0)
-
- obs_fraction = obs_count / counts
-
- # Perform permutations for significance
- if permutations > 0:
- perm_counts = []
- for i in range(permutations):
- # Count neighbors
- perm_genes, perm_count = np.unique(
- perm_neighbors[i], return_counts=True
- )
-
- # Save counts
- perm_space = neighbor_space.copy()
- perm_space.update(dict(zip(perm_genes, perm_count)))
- perm_counts.append(np.array(list(perm_space.values())))
-
- # (permutations, len(valid_genes)) array
- perm_counts = np.array(perm_counts)
-
- # Calculate colocation quotient
- perm_quotients = (perm_counts / cur_total) / (
- (counts.values - 1) / (n_points - 1)
- )
-
- # Fraction of times statistic is greater than permutations
- pvalue = (
- 2
- * np.array(
- [
- np.greater_equal(obs_quotient, perm_quotients).sum(axis=0),
- np.less_equal(obs_quotient, perm_quotients).sum(axis=0),
- ]
- ).min(axis=0)
- / permutations
- )
-
- stats_list.append(
- np.array(
- [
- obs_fraction.index,
- obs_count,
- obs_fraction.values,
- obs_quotient[0],
- pvalue,
- [cur_gene] * len(obs_count),
- ]
- )
- )
-
- else:
- stats_list.append(
- np.array(
- [
- obs_fraction.index,
- obs_count,
- obs_fraction.values,
- obs_quotient[0],
- [1] * len(obs_count),
- [cur_gene] * len(obs_count),
- ]
- )
- )
-
- # stats_df = pd.DataFrame(
- # np.concatenate(stats_list, axis=1).T,
- # index=["neighbor", "neighbor_count", "neighbor_fraction", "quotient", "pvalue", "gene"],
- # ).T
- return np.concatenate(stats_list, axis=1).T
+ Parameters
+ ----------
+ neighbor_counts : pd.DataFrame
+ Dataframe with columns "gene", "neighbor", and "count".
+ counts : pd.Series
+ Series of raw gene counts.
+ """
+ clq_df = neighbor_counts.copy()
+ clq_df["clq"] = (clq_df["count"] / counts.loc[clq_df["gene"]].values) / (
+ counts.loc[clq_df["neighbor"]].values / counts.sum()
+ )
+ return clq_df.drop("count", axis=1)
diff --git a/bento/tools/_composition.py b/bento/tools/_composition.py
new file mode 100644
index 0000000..df9a364
--- /dev/null
+++ b/bento/tools/_composition.py
@@ -0,0 +1,112 @@
+from scipy.stats import wasserstein_distance
+from sklearn.metrics.pairwise import paired_distances
+
+import pandas as pd
+import numpy as np
+
+from ..geometry import get_points
+from .._utils import track
+
+from anndata import AnnData
+
+
+def _get_compositions(points: pd.DataFrame, shape_names: list) -> pd.DataFrame:
+ """Compute the mean composition of each gene across shapes.
+
+ Parameters
+ ----------
+ points : pandas.DataFrame
+ Points indexed to shape_names denoted by boolean columns.
+ shape_names : list of str
+ Names of shapes to calculate compositions for.
+
+ Returns
+ -------
+ comp_data : DataFrame
+ For every gene return the composition of each shape, mean log(counts) and cell fraction.
+
+ """
+
+ dims = ["_".join(s.split("_")[:-1]) for s in shape_names]
+
+ n_cells = points["cell"].nunique()
+ points_grouped = points.groupby(["cell", "gene"], observed=True)
+ counts = points_grouped[dims].sum()
+ total_counts = points_grouped.size()
+ comps = counts.divide(total_counts, axis=0).fillna(0) # Normalize rows
+
+ genes = points["gene"].unique()
+ gene_comps = (
+ comps.groupby("gene", observed=True).mean().reindex(genes, fill_value=0)
+ )
+
+ gene_logcount = (
+ points.groupby("gene", observed=True).size().reindex(genes, fill_value=0)
+ )
+ gene_logcount = np.log2(gene_logcount + 1)
+ cell_fraction = (
+ 100 * points.groupby("gene", observed=True)["cell"].nunique() / n_cells
+ )
+
+ stats = pd.DataFrame(
+ [gene_logcount, cell_fraction], index=["logcounts", "cell_fraction"]
+ ).T
+
+ comp_stats = pd.concat([gene_comps, stats], axis=1)
+
+ return comp_stats
+
+
+@track
+def comp_diff(
+ data: AnnData, shape_names: list, groupby: str, ref_group: str, copy: bool = False
+):
+ """Calculate the average difference in gene composition for shapes across batches of cells. Uses the Wasserstein distance.
+
+ Parameters
+ ----------
+ data : anndata.AnnData
+ Spatial formatted AnnData object.
+ shape_names : list of str
+ Names of shapes to calculate compositions for.
+ groupby : str
+ Key in `adata.obs` to group cells by.
+ ref_group : str
+ Reference group to compare other groups to.
+ copy : bool
+ Return a copy of `data` instead of writing to data, by default False.
+
+ Returns
+ -------
+ adata : anndata.AnnData
+ Returns `adata` if `copy=True`, otherwise adds fields to `data`:
+
+ """
+
+ adata = data.copy() if copy else data
+
+ points = get_points(data)
+
+ # Get average gene compositions for each batch
+ comp_stats = dict()
+ for group, pt_group in points.groupby(groupby):
+ comp_stats[group] = _get_compositions(pt_group, shape_names)
+
+ ref_comp = comp_stats[ref_group]
+
+ dims = [s.replace("_shape", "") for s in shape_names]
+ for group in comp_stats.keys():
+ if group == ref_group:
+ continue
+
+ diff_key = f"{group}_diff"
+ comp_stats[group][diff_key] = pd.Series(
+ paired_distances(
+ comp_stats[group][dims].reindex(ref_comp.index, fill_value=1e-10),
+ ref_comp[dims],
+ metric=wasserstein_distance,
+ ),
+ index=ref_comp.index,
+ )
+
+ adata.uns[f"{groupby}_comp_stats"] = comp_stats
diff --git a/bento/tools/_decomposition.py b/bento/tools/_decomposition.py
new file mode 100644
index 0000000..1909fa1
--- /dev/null
+++ b/bento/tools/_decomposition.py
@@ -0,0 +1,157 @@
+from typing import List, Literal
+
+import numpy as np
+import pandas as pd
+import tensorly as tl
+from anndata import AnnData
+from scipy.stats import zscore
+from tensorly.decomposition import non_negative_parafac
+from tqdm.auto import tqdm
+
+from .._utils import track
+
+
+def decompose(
+ tensor: np.ndarray,
+ ranks: List[int],
+ iterations: int = 3,
+ device: Literal["auto", "cpu", "cuda"] = "auto",
+ random_state: int = 11,
+):
+ """
+ Perform tensor decomposition on an input tensor, optionally automatically selecting the best rank across a list of ranks.
+
+ Parameters
+ ----------
+ tensor : np.ndarray
+ numpy array
+ ranks : int or list of int
+ Rank(s) to perform decomposition.
+ iterations : int, 3 by default
+ Number of times to run decomposition to compute confidence interval at each rank. Only the best iteration for each rank is saved.
+ device : str, optional
+ Device to use for decomposition. If "auto", will use GPU if available. By default "auto".
+ random_state : int, optional
+ Random state for decomposition. By default 11.
+
+ Returns
+ -------
+ factors_per_rank : dict
+ Dictionary of factors for each rank.
+ errors : pd.DataFrame
+ Dataframe of errors for each rank.
+ """
+ # Replace nans with 0 for decomposition
+ tensor_mask = ~np.isnan(tensor)
+ tensor[~tensor_mask] = 0
+
+ if isinstance(ranks, int):
+ ranks = [ranks]
+
+ # Use gpu if available
+ tensor = tl.tensor(tensor)
+ try:
+ import torch
+
+ tl.set_backend("pytorch")
+ tensor = tl.tensor(tensor)
+
+ if ((device == "auto") or (device == "cuda")) and torch.cuda.is_available():
+ device = "cuda"
+ tensor = tensor.to("cuda")
+ else:
+ device = "cpu"
+
+ except ImportError:
+ torch = None
+ device = "cpu"
+
+ factors_per_rank = dict()
+ errors = []
+ for rank in tqdm(ranks, desc=f"Device {device}"):
+ best_factor = None
+ best_error = np.inf
+ for i in range(iterations):
+ # non-negative parafac decomposition
+
+ # TODO update to hals when random_state is supported
+ weights, factors = non_negative_parafac(
+ tensor, rank, init="random", random_state=random_state
+ )
+
+ if device == "cuda":
+ weights = weights.cpu()
+ factors = [f.cpu() for f in factors]
+ tensor = tensor.cpu()
+
+ # calculate error ignoring missing values
+ tensor_mu = tl.cp_to_tensor((weights, factors))
+
+ if device == "cuda":
+ error = rmse(tensor, tensor_mu).numpy()
+ else:
+ error = rmse(tensor, tensor_mu)
+
+ if error < best_error:
+ best_error = error
+
+ if torch:
+ best_factor = [f.numpy() for f in factors]
+ else:
+ best_factor = factors
+
+ factors_per_rank[rank] = best_factor
+ errors.append([best_error, rank])
+
+ errors = pd.DataFrame(errors, columns=["rmse", "rank"])
+ errors["rmse"] = errors["rmse"].astype(float)
+
+ return factors_per_rank, errors
+
+
+def rmse(tensor, tensor_mu):
+ return np.sqrt((tensor[tensor != 0] - tensor_mu[tensor != 0]) ** 2).mean()
+
+
+@track
+def to_tensor(
+ data: AnnData, layers: List[str], scale: bool = False, copy: bool = False
+):
+ """
+ Generate tensor from data where dimensions are (layers, cells, genes).
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ layers : list of str
+ Keys in data.layers to build tensor.
+ scale : bool
+ Z scale across cells for each layer, by default False.
+ copy : bool
+ Return a copy of `data` instead of writing to data, by default False.
+
+ Returns
+ -------
+ adata : anndata.AnnData
+ `uns['tensor']` : np.ndarray
+ 3D numpy array of shape (len(layers), adata.n_obs, adata.n_vars)
+ """
+ adata = data.copy() if copy else data
+
+ # Build tensor from specified layers
+ tensor = []
+ for l in layers:
+ tensor.append(adata.to_df(l).values)
+
+ # Save tensor values
+ tensor = np.array(tensor)
+
+ # Z scale across cells for each layer
+ if scale:
+ for i, layer in enumerate(tensor):
+ tensor[i] = zscore(layer, axis=1, nan_policy="omit")
+
+ adata.uns["tensor"] = np.array(tensor)
+
+ return adata
diff --git a/bento/tools/_features.py b/bento/tools/_features.py
deleted file mode 100755
index b7412c8..0000000
--- a/bento/tools/_features.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import warnings
-
-import numpy as np
-import pandas as pd
-from joblib import Parallel, delayed
-from sklearn.neighbors import NearestNeighbors
-from tqdm.auto import tqdm
-
-from ..preprocessing import get_points
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-
-def coloc_sim(data, radius=3, min_count=5, n_cores=1, copy=False):
- """Calculate pairwise gene colocalization similarity with the cross L function.
-
- Parameters
- ----------
- adata : AnnData
- Anndata formatted spatial data.
- radius : int
- Max radius to search for neighboring points, by default 3
- min_count : int
- Minimum points needed to be eligible for analysis.
- Returns
- -------
- adata : AnnData
- .uns['coloc_sim']: Pairwise gene colocalization similarity within each cell formatted as a long dataframe.
- """
- adata = data.copy() if copy else data
-
- # Filter points and counts by min_count
- counts = adata.to_df()
-
- # Helper function to apply per cell
- def cell_coloc_sim(p, g_density, name):
-
- # Get xy coordinates
- xy = p[["x", "y"]].values
-
- # Get neighbors within fixed outer_radius for every point
- nn = NearestNeighbors(radius=radius).fit(xy)
- distances, point_index = nn.radius_neighbors(xy, return_distance=True)
-
- # Enumerate point-wise gene labels
- gene_index = p["gene"].reset_index(drop=True).cat.remove_unused_categories()
-
- # Convert to adjacency list of points, no double counting
- neighbor_pairs = []
- for g1, neighbors, n_dists in zip(gene_index.values, point_index, distances):
- for g2, d in zip(neighbors, n_dists):
- neighbor_pairs.append([g1, g2, d])
-
- # Calculate pair-wise gene similarity
- neighbor_pairs = pd.DataFrame(neighbor_pairs, columns=["g1", "g2", "p_dist"])
-
- # Keep minimum distance to g2 point
- neighbor_pairs = neighbor_pairs.groupby(["g1", "g2"]).agg("min").reset_index()
- neighbor_pairs.columns = ["g1", "g2", "point_dist"]
-
- # Map to gene index
- neighbor_pairs["g2"] = neighbor_pairs["g2"].map(gene_index)
-
- # Count number of points within distance of increasing radius
- r_step = 0.5
- expected_counts = [
- lambda dists: (dists <= r).sum()
- for r in np.arange(r_step, radius + r_step, r_step)
- ]
- metrics = (
- neighbor_pairs.groupby(["g1", "g2"])
- .agg({"point_dist": expected_counts})
- .reset_index()
- )
-
- # Colocalization metric: max of L_ij(r) for r <= radius
- g2_density = g_density.loc[metrics["g2"].tolist()].values
- metrics["sim"] = (
- (metrics["point_dist"].divide(g2_density * np.pi, axis=0))
- .pow(0.5)
- .max(axis=1)
- )
- metrics["cell"] = name
-
- # Ignore self colocalization
- # metrics = metrics.loc[metrics["g1"] != metrics["g2"]]
-
- return metrics[["cell", "g1", "g2", "sim"]]
-
- # Only keep genes >= min_count in each cell
- gene_densities = []
- counts.apply(lambda row: gene_densities.append(row[row >= min_count]), axis=1)
- # Calculate point density per gene per cell
- gene_densities /= adata.obs["cell_area"]
- gene_densities = gene_densities.values
-
- # TODO dask
- cell_metrics = Parallel(n_jobs=n_cores)(
- delayed(cell_coloc_sim)(
- get_points(adata, cells=g_density.name, genes=g_density.index.tolist(), asgeo=True),
- g_density,
- g_density.name,
- )
- for g_density in tqdm(gene_densities)
- )
-
- cell_metrics = pd.concat(cell_metrics)
- cell_metrics.columns = cell_metrics.columns.get_level_values(0)
-
- # Make symmetric (Lij = Lji)
- cell_metrics["pair"] = cell_metrics.apply(
- lambda row: "-".join(sorted([row["g1"], row["g2"]])), axis=1
- )
- cell_symmetric = cell_metrics.groupby(["cell", "pair"]).mean()
-
- # Retain gene pair names
- cell_symmetric = (
- cell_metrics.set_index(["cell", "pair"])
- .drop("sim", axis=1)
- .join(cell_symmetric)
- .reset_index()
- )
-
- # Aggregate across cells
- coloc_agg = cell_symmetric.groupby(["pair"])["sim"].mean().to_frame()
- coloc_agg = (
- coloc_agg.join(cell_symmetric.set_index("pair").drop(["sim", "cell"], axis=1))
- .reset_index()
- .drop_duplicates()
- )
-
- # Save coloc similarity
- cell_metrics[["cell", "g1", "g2", "pair"]].astype("category", copy=False)
- coloc_agg[["g1", "g2", "pair"]].astype("category", copy=False)
- adata.uns["coloc_sim"] = cell_metrics
- adata.uns["coloc_sim_agg"] = coloc_agg
-
- return adata if copy else None
-
-
-def get_gene_set_coloc_agg(data, genes):
- """
- For a list of genes, return their pairwise colocalization with each other.
-
- Parameters
- ----------
- data : AnnData
- AnnData formatted spatial data.
- gene : list of str
- The names of genes, must be present in data.var.
-
- Returns
- -------
- pd.DataFrame
- """
- sim = data.uns["coloc_sim_agg"]
- return sim.loc[sim["g1"].isin(genes) & sim["g2"].isin(genes)]
-
-
-def get_cell_coloc(data, cell):
- """Get pair-wise gene colocalization for a given cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- cell : str
- Name of cell present in data.obs_names.
-
- Returns
- -------
- DataFrame
- """
- cell_coloc_sim = data.uns["coloc_sim"][data.uns["coloc_sim"]["cell"] == cell]
- cell_coloc_sim = cell_coloc_sim.sort_values(by=["sim"], ascending=False)
-
- return cell_coloc_sim
-
-
-def get_gene_coloc(data, cell, gene):
- """Get colocalization of a single gene with all other genes, for a particular cell.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- cell : str
- Name of cell present in data.obs_names.
- gene : str
- Name of a gene present in data.var_names.
-
- Returns
- -------
- DataFrame
- """
-
- cell_coloc_sim = data.uns["coloc_sim"][data.uns["coloc_sim"]["cell"] == cell]
- gene_coloc_sim = cell_coloc_sim[cell_coloc_sim["g1"] == gene]
- gene_coloc_sim = gene_coloc_sim.sort_values(by=["sim"], ascending=False)
-
- return gene_coloc_sim
-
-
-def get_cell_coloc_agg(data):
- """Get aggregated pairwise colocalization similarity across all cells.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
-
- Returns
- -------
- DataFrame
- """
- coloc_sim_agg = data.uns["coloc_sim_agg"]
- coloc_sim_agg = coloc_sim_agg.sort_values(by=["sim"], ascending=False)
-
- return coloc_sim_agg
-
-
-def get_gene_coloc_agg(data, gene):
- """
- For a given gene, return its colocalization with all other genes sorted by highest similarity first.
-
- Assumes colocalization is precomputed with bento.tl.coloc_sim.
-
- Parameters
- ----------
- data : AnnData
- AnnData formatted spatial data.
- gene : str
- The name of a gene, must be present in data.var.
-
- Returns
- -------
- pd.DataFrame
- Sorted subset of data.uns['coloc_sim_agg'].
- """
- coloc_sim_agg = data.uns["coloc_sim_agg"]
- gene_coloc_agg = coloc_sim_agg[coloc_sim_agg["g1"] == gene]
- gene_coloc_agg = gene_coloc_agg.sort_values(by=["sim"], ascending=False)
-
- return gene_coloc_agg
diff --git a/bento/tools/_flux.py b/bento/tools/_flux.py
new file mode 100644
index 0000000..907ad1f
--- /dev/null
+++ b/bento/tools/_flux.py
@@ -0,0 +1,390 @@
+from typing import Iterable, Literal, Optional, Union
+
+import decoupler as dc
+import emoji
+import geopandas as gpd
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+import numpy as np
+import pandas as pd
+import pkg_resources
+import rasterio
+import shapely
+from anndata import AnnData
+from kneed import KneeLocator
+from minisom import MiniSom
+from scipy.sparse import csr_matrix, vstack
+from sklearn.decomposition import TruncatedSVD
+from sklearn.preprocessing import StandardScaler, minmax_scale, quantile_transform
+from sklearn.utils import resample
+from tqdm.auto import tqdm
+
+from bento._utils import register_points, track
+from bento.geometry import get_points, sindex_points
+from bento.tools._neighborhoods import _count_neighbors
+from bento.tools._shape_features import analyze_shapes
+
+
+@track
+@register_points("cell_raster", ["flux", "flux_embed", "flux_color"])
+def flux(
+ data: AnnData,
+ method: Literal["knn", "radius"] = "radius",
+ n_neighbors: Optional[int] = None,
+ radius: Optional[int] = 50,
+ res: int = 0.1,
+ random_state: int = 11,
+ copy: bool = False,
+):
+ """
+ RNAflux: Embedding each pixel as normalized local composition normalized by cell composition.
+ For k-nearest neighborhoods or "knn", method, specify n_neighbors. For radius neighborhoods, specify radius.
+ The default method is "radius" with radius=50. RNAflux requires a minimum of 4 genes per cell to compute all embeddings properly.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object.
+ method: str
+ Method to use for local neighborhood. Either 'knn' or 'radius'.
+ n_neighbors : int
+ Number of neighbors to use for local neighborhood.
+ radius : float
+ Radius to use for local neighborhood.
+ res : float
+ Resolution to use for rendering embedding. Default 0.05 samples at 5% original resolution (5 units between pixels)
+ copy : bool
+ Whether to return a copy the AnnData object. Default False.
+
+ Returns
+ -------
+ adata : AnnData
+ .uns["flux"] : scipy.csr_matrix
+ [pixels x genes] sparse matrix of normalized local composition.
+ .uns["flux_embed"] : np.ndarray
+ [pixels x components] array of embedded flux values.
+ .uns["flux_color"] : np.ndarray
+ [pixels x 3] array of RGB values for visualization.
+ .uns["flux_genes"] : list
+ List of genes used for embedding.
+ .uns["flux_variance_ratio"] : np.ndarray
+ [components] array of explained variance ratio for each component.
+ """
+ if n_neighbors is None and radius is None:
+ radius = 50
+
+ adata = data.copy() if copy else data
+
+ adata.uns["points"] = get_points(adata).sort_values("cell")
+
+ points = get_points(adata)[["cell", "gene", "x", "y"]]
+
+ # embeds points on a uniform grid
+ pbar = tqdm(total=3)
+ pbar.set_description(emoji.emojize("Embedding"))
+ step = 1 / res
+ # Get grid rasters
+ analyze_shapes(
+ adata,
+ "cell_shape",
+ "raster",
+ progress=False,
+ feature_kws=dict(raster={"step": step}),
+ )
+ # Long dataframe of raster points
+ adata.uns["cell_raster"] = adata.uns["cell_raster"].sort_values("cell")
+ raster_points = adata.uns["cell_raster"]
+
+ # Extract gene names and codes
+ gene_names = points["gene"].cat.categories.tolist()
+ n_genes = len(gene_names)
+
+ points_grouped = points.groupby("cell")
+ rpoints_grouped = raster_points.groupby("cell")
+ cells = list(points_grouped.groups.keys())
+
+ cell_composition = adata[cells, gene_names].X.toarray()
+
+ # Compute cell composition
+ cell_composition = cell_composition / (cell_composition.sum(axis=1).reshape(-1, 1))
+ cell_composition = np.nan_to_num(cell_composition)
+
+ # Embed each cell neighborhood independently
+ cell_fluxs = []
+ for i, cell in enumerate(tqdm(cells, leave=False)):
+ cell_points = points_grouped.get_group(cell)
+ rpoints = rpoints_grouped.get_group(cell)
+ if method == "knn":
+ gene_count = _count_neighbors(
+ cell_points,
+ n_genes,
+ rpoints,
+ n_neighbors=n_neighbors,
+ agg=None,
+ )
+ elif method == "radius":
+ gene_count = _count_neighbors(
+ cell_points,
+ n_genes,
+ rpoints,
+ radius=radius,
+ agg=None,
+ )
+ gene_count = gene_count.toarray()
+ # embedding: distance neighborhood composition and cell composition
+ # Compute composition of neighborhood
+ flux_composition = gene_count / (gene_count.sum(axis=1).reshape(-1, 1))
+ cflux = flux_composition - cell_composition[i]
+ cflux = StandardScaler(with_mean=False).fit_transform(cflux)
+
+ # Convert back to sparse matrix
+ cflux = csr_matrix(cflux)
+
+ cell_fluxs.append(cflux)
+
+ # Stack all cells
+ cell_fluxs = vstack(cell_fluxs) if len(cell_fluxs) > 1 else cell_fluxs[0]
+ cell_fluxs.data = np.nan_to_num(cell_fluxs.data)
+ pbar.update()
+
+ # todo: Slow step, try algorithm="randomized" may be faster
+ pbar.set_description(emoji.emojize("Reducing"))
+ n_components = min(n_genes - 1, 10)
+ pca_model = TruncatedSVD(
+ n_components=n_components, algorithm="randomized", random_state=random_state
+ ).fit(cell_fluxs)
+ flux_embed = pca_model.transform(cell_fluxs)
+ variance_ratio = pca_model.explained_variance_ratio_
+
+ # For color visualization of flux embeddings
+ flux_color = vec2color(flux_embed, fmt="hex", vmin=0.1, vmax=0.9)
+ pbar.update()
+
+ pbar.set_description(emoji.emojize("Saving"))
+ adata.uns["flux"] = cell_fluxs # sparse gene embedding
+ adata.uns["flux_genes"] = gene_names # gene names
+ adata.uns["flux_embed"] = flux_embed
+ adata.uns["flux_variance_ratio"] = variance_ratio
+ adata.uns["flux_color"] = flux_color
+
+ pbar.set_description(emoji.emojize("Done. :bento_box:"))
+ pbar.update()
+ pbar.close()
+
+ return adata if copy else None
+
+
+def vec2color(
+ vec: np.ndarray,
+ fmt: Literal[
+ "rgb",
+ "hex",
+ ] = "hex",
+ vmin: float = 0,
+ vmax: float = 1,
+):
+ """Convert vector to color."""
+ color = quantile_transform(vec[:, :3])
+ color = minmax_scale(color, feature_range=(vmin, vmax))
+
+ if fmt == "rgb":
+ pass
+ elif fmt == "hex":
+ color = np.apply_along_axis(mpl.colors.to_hex, 1, color, keep_alpha=True)
+ return color
+
+
+@track
+def fluxmap(
+ data: AnnData,
+ n_clusters: Union[Iterable[int], int] = range(2, 9),
+ num_iterations: int = 1000,
+ train_size: float = 0.2,
+ res: float = 0.1,
+ random_state: int = 11,
+ plot_error: bool = True,
+ copy: bool = False,
+):
+ """Cluster flux embeddings using self-organizing maps (SOMs) and vectorize clusters as Polygon shapes.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object.
+ n_clusters : int or list
+ Number of clusters to use. If list, will pick best number of clusters
+ using the elbow heuristic evaluated on the quantization error.
+ num_iterations : int
+ Number of iterations to use for SOM training.
+ train_size : float
+ Fraction of cells to use for SOM training. Default 0.2.
+ res : float
+ Resolution used for rendering embedding. Default 0.05.
+ random_state : int
+ Random state to use for SOM training. Default 11.
+ plot_error : bool
+ Whether to plot quantization error. Default True.
+ copy : bool
+ Whether to return a copy the AnnData object. Default False.
+
+ Returns
+ -------
+ adata : AnnData
+ .uns["cell_raster"] : DataFrame
+ Adds "fluxmap" column denoting cluster membership.
+ .uns["points"] : DataFrame
+ Adds "fluxmap#" columns for each cluster.
+ .obs : GeoSeries
+ Adds "fluxmap#_shape" columns for each cluster rendered as (Multi)Polygon shapes.
+ """
+ adata = data.copy() if copy else data
+
+ # Check if flux embedding has been computed
+ if "flux_embed" not in adata.uns:
+ raise ValueError(
+ "Flux embedding has not been computed. Run `bento.tl.flux()` first."
+ )
+
+ flux_embed = adata.uns["flux_embed"]
+ raster_points = adata.uns["cell_raster"]
+
+ if isinstance(n_clusters, int):
+ n_clusters = [n_clusters]
+
+ if isinstance(n_clusters, range):
+ n_clusters = list(n_clusters)
+
+ # Subsample flux embeddings for faster training
+ if train_size > 1:
+ raise ValueError("train_size must be less than 1.")
+ if train_size == 1:
+ flux_train = flux_embed
+ if train_size < 1:
+ flux_train = resample(
+ flux_embed,
+ n_samples=int(train_size * flux_embed.shape[0]),
+ random_state=random_state,
+ )
+
+ # Perform SOM clustering over n_clusters range and pick best number of clusters using elbow heuristic
+ pbar = tqdm(total=4)
+ pbar.set_description(emoji.emojize(f"Optimizing # of clusters"))
+ som_models = {}
+ quantization_errors = []
+ for k in tqdm(n_clusters, leave=False):
+ som = MiniSom(1, k, flux_train.shape[1], random_seed=random_state)
+ som.random_weights_init(flux_train)
+ som.train(flux_train, num_iterations, random_order=False, verbose=False)
+ som_models[k] = som
+ quantization_errors.append(som.quantization_error(flux_embed))
+
+ # Use kneed to find elbow
+ if len(n_clusters) > 1:
+ kl = KneeLocator(
+ n_clusters, quantization_errors, curve="convex", direction="decreasing"
+ )
+ best_k = kl.elbow
+
+ if plot_error:
+ kl.plot_knee()
+ plt.show()
+
+ if best_k is None:
+ print("No elbow found. Rerun with a fixed k or a different range.")
+ return
+
+ else:
+ best_k = n_clusters[0]
+ pbar.update()
+
+ # Use best k to assign each sample to a cluster
+ pbar.set_description(f"Assigning to {best_k} clusters")
+ som = som_models[best_k]
+ winner_coordinates = np.array([som.winner(x) for x in flux_embed]).T
+
+ # Indices start at 0, so add 1
+ qnt_index = np.ravel_multi_index(winner_coordinates, (1, best_k)) + 1
+ raster_points["fluxmap"] = qnt_index
+ adata.uns["cell_raster"] = raster_points.copy()
+
+ pbar.update()
+
+ # Vectorize polygons in each cell
+ pbar.set_description(emoji.emojize("Vectorizing domains"))
+ cells = raster_points["cell"].unique().tolist()
+ # Scale down to render resolution
+ # raster_points[["x", "y"]] = raster_points[["x", "y"]] * res
+
+ # Cast to int
+ raster_points[["x", "y", "fluxmap"]] = raster_points[["x", "y", "fluxmap"]].astype(
+ int
+ )
+
+ rpoints_grouped = raster_points.groupby("cell")
+ fluxmap_df = dict()
+ for cell in tqdm(cells, leave=False):
+ rpoints = rpoints_grouped.get_group(cell)
+
+ # Fill in image at each point xy with fluxmap value by casting to dense matrix
+ image = (
+ csr_matrix(
+ (
+ rpoints["fluxmap"],
+ (
+ (rpoints["y"] * res).astype(int),
+ (rpoints["x"] * res).astype(int),
+ ),
+ )
+ )
+ .todense()
+ .astype("int16")
+ )
+
+ # Find all the contours
+ contours = rasterio.features.shapes(image)
+ polygons = np.array([(shapely.geometry.shape(p), v) for p, v in contours])
+ shapes = gpd.GeoDataFrame(
+ polygons[:, 1],
+ geometry=gpd.GeoSeries(polygons[:, 0]).T,
+ columns=["fluxmap"],
+ )
+
+ # Remove background shape
+ shapes["fluxmap"] = shapes["fluxmap"].astype(int)
+ shapes = shapes[shapes["fluxmap"] != 0]
+
+ # Group same fields as MultiPolygons
+ shapes = shapes.dissolve("fluxmap")["geometry"]
+
+ fluxmap_df[cell] = shapes
+
+ fluxmap_df = pd.DataFrame.from_dict(fluxmap_df).T
+ fluxmap_df.columns = "fluxmap" + fluxmap_df.columns.astype(str) + "_shape"
+
+ # Upscale to match original resolution
+ fluxmap_df = fluxmap_df.apply(
+ lambda col: gpd.GeoSeries(col).scale(
+ xfact=1 / res, yfact=1 / res, origin=(0, 0)
+ )
+ )
+ pbar.update()
+
+ pbar.set_description("Saving")
+ old_cols = adata.obs.columns[adata.obs.columns.str.startswith("fluxmap")]
+ adata.obs = adata.obs.drop(old_cols, axis=1, errors="ignore")
+
+ adata.obs[fluxmap_df.columns] = fluxmap_df.reindex(adata.obs_names)
+
+ old_cols = adata.uns["points"].columns[
+ adata.uns["points"].columns.str.startswith("fluxmap")
+ ]
+ adata.uns["points"] = adata.uns["points"].drop(old_cols, axis=1)
+
+ # TODO SLOW
+ sindex_points(adata, "points", fluxmap_df.columns.tolist())
+ pbar.update()
+ pbar.set_description("Done")
+ pbar.close()
+
+ return adata if copy else None
diff --git a/bento/tools/_flux_enrichment.py b/bento/tools/_flux_enrichment.py
new file mode 100644
index 0000000..ddb63a7
--- /dev/null
+++ b/bento/tools/_flux_enrichment.py
@@ -0,0 +1,199 @@
+from typing import Optional
+
+import decoupler as dc
+
+import pandas as pd
+import pkg_resources
+from anndata import AnnData
+
+from bento._utils import track, _register_points
+
+
+def fe_fazal2019(data: AnnData, copy: bool = False, **kwargs) -> Optional[AnnData]:
+ """Compute enrichment scores from subcellular compartment gene sets from Fazal et al. 2019 (APEX-seq).
+ See `bento.tl.fe` docs for parameter details.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object.
+ copy : bool
+ Return a copy instead of writing to `adata`. Default False.
+ Returns
+ -------
+ DataFrame
+ Enrichment scores for each gene set.
+ """
+ adata = data.copy() if copy else data
+
+ gene_sets = load_gene_sets("fazal2019")
+ fe(adata, net=gene_sets, **kwargs)
+
+ return adata if copy else None
+
+
+def fe_xia2019(data: AnnData, copy: bool = False, **kwargs) -> Optional[AnnData]:
+ """Compute enrichment scores from subcellular compartment gene sets from Xia et al. 2019 (MERFISH 10k U2-OS).
+ See `bento.tl.fe` docs for parameters details.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object.
+ copy : bool
+ Return a copy instead of writing to `adata`. Default False.
+ Returns
+ -------
+ DataFrame
+ Enrichment scores for each gene set.
+ """
+ adata = data.copy() if copy else data
+
+ gene_sets = load_gene_sets("xia2019")
+ fe(adata, gene_sets, **kwargs)
+
+ return adata if copy else None
+
+
+@track
+def fe(
+ data: AnnData,
+ net: pd.DataFrame,
+ source: Optional[str] = "source",
+ target: Optional[str] = "target",
+ weight: Optional[str] = "weight",
+ batch_size: int = 10000,
+ min_n: int = 0,
+ copy: bool = False,
+) -> Optional[AnnData]:
+ """
+ Perform functional enrichment on point embeddings. Wrapper for decoupler wsum function.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData object.
+ net : DataFrame
+ DataFrame with columns "source", "target", and "weight". See decoupler API for more details.
+ source : str, optional
+ Column name for source nodes in `net`. Default "source".
+ target : str, optional
+ Column name for target nodes in `net`. Default "target".
+ weight : str, optional
+ Column name for weights in `net`. Default "weight".
+ batch_size : int
+ Number of points to process in each batch. Default 10000.
+ min_n : int
+ Minimum number of targets per source. If less, sources are removed.
+ copy : bool
+ Return a copy instead of writing to `adata`. Default False.
+
+ Returns
+ -------
+ adata : AnnData
+ uns["flux_fe"] : DataFrame
+ Enrichment scores for each gene set.
+ """
+
+ adata = data.copy() if copy else data
+
+ # Make sure embedding is run first
+ if "flux" not in data.uns:
+ print("Run bento.tl.flux first.")
+ return
+
+ mat = adata.uns["flux"] # sparse matrix in csr format
+ zero_rows = mat.getnnz(1) == 0
+
+ samples = adata.uns["cell_raster"].index.astype(str)
+ features = adata.uns["flux_genes"]
+
+ enrichment = dc.run_wsum(
+ mat=[mat, samples, features],
+ net=net,
+ source=source,
+ target=target,
+ weight=weight,
+ batch_size=batch_size,
+ min_n=min_n,
+ verbose=True,
+ )
+
+ scores = enrichment[1].reindex(index=samples)
+
+ for col in scores.columns:
+ score_key = f"flux_{col}"
+ adata.uns[score_key] = scores[col].values
+
+ # Manually call register_points since it is dynamic
+ _register_points(adata, "cell_raster", [score_key])
+
+ _fe_stats(adata, net, source=source, target=target, copy=copy)
+
+ return adata if copy else None
+
+
+def _fe_stats(
+ data: AnnData,
+ net: pd.DataFrame,
+ source: str = "source",
+ target: str = "target",
+ copy: bool = False,
+):
+ adata = data.copy() if copy else data
+
+ # rows = cells, columns = pathways, values = count of genes in pathway
+ expr_binary = adata.to_df() >= 5
+ # {cell : present gene list}
+ expr_genes = expr_binary.apply(lambda row: adata.var_names[row], axis=1)
+
+ # Count number of genes present in each pathway
+ net_ngenes = net.groupby(source).size().to_frame().T.rename(index={0: "n_genes"})
+
+ sources = []
+ # common_genes = {} # list of [cells: gene set overlaps]
+ common_ngenes = [] # list of [cells: overlap sizes]
+ for source, group in net.groupby(source):
+ sources.append(source)
+ common = expr_genes.apply(lambda genes: set(genes).intersection(group[target]))
+ # common_genes[source] = np.array(common)
+ common_ngenes.append(common.apply(len))
+
+ fe_stats = pd.concat(common_ngenes, axis=1)
+ fe_stats.columns = sources
+
+ adata.uns["fe_stats"] = fe_stats
+ # adata.uns["fe_genes"] = common_genes
+ adata.uns["fe_ngenes"] = net_ngenes
+
+ return adata if copy else None
+
+
+gene_sets = dict(
+ fazal2019="fazal2019.csv",
+ xia2019="xia2019.csv",
+)
+
+
+def load_gene_sets(name):
+ """Load a gene set from bento.
+
+ Parameters
+ ----------
+ name : str
+ Name of gene set to load.
+
+ Returns
+ -------
+ DataFrame
+ Gene set.
+ """
+ global pkg_resources
+ if pkg_resources is None:
+ import pkg_resources
+
+ fname = gene_sets[name]
+ stream = pkg_resources.resource_stream(__name__, f"gene_sets/{fname}")
+ gs = pd.read_csv(stream)
+
+ return gs
diff --git a/bento/tools/_lp.py b/bento/tools/_lp.py
index dd700f9..5df62c6 100755
--- a/bento/tools/_lp.py
+++ b/bento/tools/_lp.py
@@ -4,20 +4,18 @@
import numpy as np
import pandas as pd
import statsmodels.formula.api as sfm
-from dask import dataframe as dd
-from dask.diagnostics import ProgressBar
from patsy import PatsyError
from statsmodels.tools.sm_exceptions import PerfectSeparationError
from tqdm.auto import tqdm
-
-from .._utils import PATTERN_NAMES, PATTERN_PROBS, track
-from ..preprocessing import get_layers
+from anndata import AnnData
+from .._utils import track
+from .._constants import PATTERN_NAMES, PATTERN_FEATURES
tqdm.pandas()
@track
-def lp(data, min_count=5, copy=False):
+def lp(data: AnnData, groupby: str = "gene", copy: bool = False):
"""Predict transcript subcellular localization patterns.
Patterns include: cell edge, cytoplasmic, nuclear edge, nuclear, none
@@ -25,116 +23,96 @@ def lp(data, min_count=5, copy=False):
----------
data : AnnData
Spatial formatted AnnData object
- min_count : int
- Minimum expression count per sample; otherwise ignore sample
+ groupby : str or list of str, optional (default: None)
+ Key in `data.uns['points'] to groupby, by default None. Always treats each cell separately
copy : bool
Return a copy of `data` instead of writing to data, by default False.
Returns
-------
- Depending on `copy`, returns or updates `adata.layers` with the
- `'cell_edge'`, `'cytoplasm'`, `'none'`, `'nuclear'`, and `'nuclear_edge'`
- fields for their respective localization pattern labels.
+ adata : AnnData
+ .uns['lp']: DataFrame
+ Localization pattern indicator matrix.
+ .uns['lpp']: DataFrame
+ Localization pattern probabilities.
"""
adata = data.copy() if copy else data
- # Compute features if missing TODO currently recomputes everything
- if not all(f in data.layers.keys() for f in PATTERN_MODEL_FEATURE_NAMES):
- features = [
- "cell_proximity",
- "nucleus_proximity",
- "cell_asymmetry",
- "nucleus_asymmetry",
- "ripley_stats",
- "point_dispersion",
- "nucleus_dispersion",
- ]
- bento.tl.analyze_samples(data, features)
-
- X_df = get_layers(adata, PATTERN_MODEL_FEATURE_NAMES, min_count)
+ if isinstance(groupby, str):
+ groupby = [groupby]
+
+ # Compute features
+ feature_key = f"cell_{'_'.join(groupby)}_features"
+ if feature_key not in adata.uns.keys() or not all(
+ f in adata.uns[feature_key].columns for f in PATTERN_FEATURES
+ ):
+ bento.tl.analyze_points(
+ adata,
+ "cell_shape",
+ ["proximity", "asymmetry", "ripley", "point_dispersion_norm"],
+ groupby=groupby,
+ recompute=True,
+ )
+ bento.tl.analyze_points(
+ adata,
+ "nucleus_shape",
+ ["proximity", "asymmetry", "shape_dispersion_norm"],
+ groupby=groupby,
+ recompute=True,
+ )
+ X_df = adata.uns[feature_key][PATTERN_FEATURES]
+ # Load trained model
model_dir = "/".join(bento.__file__.split("/")[:-1]) + "/models"
model = pickle.load(open(f"{model_dir}/rf_calib_20220514.pkl", "rb"))
+ # Compatibility with newer versions of scikit-learn
+ for cls in model.calibrated_classifiers_:
+ cls.estimator = cls.base_estimator
+
+ # Predict patterns
pattern_prob = pd.DataFrame(
- model.predict_proba(X_df.values), index=X_df.index, columns=PATTERN_NAMES
+ model.predict_proba(X_df.values),
+ columns=PATTERN_NAMES,
)
- thresholds = [0.45300, 0.43400, 0.37900, 0.43700, 0.50500]
- # Save each pattern to adata
- for p, pp, thresh in zip(PATTERN_NAMES, PATTERN_PROBS, thresholds):
- indicator_df = (
- (pattern_prob >= thresh)
- .reset_index()
- .pivot(index="cell", columns="gene", values=p)
- .replace({True: 1, False: 0})
- .reindex(index=adata.obs_names, columns=adata.var_names)
- .astype(float)
- )
- indicator_df.columns.name = "gene"
-
- prob_df = (
- pattern_prob.reset_index()
- .pivot(index="cell", columns="gene", values=p)
- .reindex(index=adata.obs_names, columns=adata.var_names)
- .astype(float)
- )
- # Save to adata.layers
- adata.layers[p] = indicator_df
- adata.layers[pp] = prob_df
+ # Add cell and groupby identifiers
+ pattern_prob.index = adata.uns[feature_key].set_index(["cell", *groupby]).index
- # Run without decorator
- lp_stats.__wrapped__(adata)
+ # Threshold probabilities to get indicator matrix
+ thresholds = [0.45300, 0.43400, 0.37900, 0.43700, 0.50500]
+ indicator_df = (pattern_prob >= thresholds).replace({True: 1, False: 0})
+ adata.uns["lp"] = indicator_df.reset_index()
+ adata.uns["lpp"] = pattern_prob.reset_index()
return adata if copy else None
@track
-def lp_stats(data, copy=False):
+def lp_stats(data: AnnData, copy: bool = False):
"""Computes frequencies of localization patterns across cells and genes.
Parameters
----------
data : AnnData
- Spatial formatted AnnData object
+ Spatial formatted AnnData object.
copy : bool
- Return a copy of `data` instead of writing to data, by default False.
+ Whether to return a copy of the AnnData object. Default False.
Returns
-------
- [type]
- [description]
+ adata : AnnData
+ .uns['lp_stats']: DataFrame of localization pattern frequencies.
"""
adata = data.copy() if copy else data
- detected = np.ones(adata.shape)
-
- for c in PATTERN_NAMES:
- detected = detected & ~data.to_df(c).isna()
-
- adata.var["n_detected"] = detected.sum().astype(int)
- adata.var[f"fraction_detected"] = (adata.var["n_detected"] / adata.n_obs).astype(
- float
- )
-
- adata.obs["n_detected"] = detected.sum(axis=1).astype(int)
- adata.obs[f"fraction_detected"] = (adata.obs["n_detected"] / adata.n_vars).astype(
- float
- )
+ lp = adata.uns["lp"]
- for c in PATTERN_NAMES:
- counts = adata.to_df(c)
+ cols = lp.columns
+ groupby = list(cols[~cols.isin(PATTERN_NAMES)])
+ groupby.remove("cell")
- # Save frequencies across cells
- adata.var[f"{c}_count"] = counts.fillna(0).sum(axis=0).astype(int)
- adata.var[f"{c}_fraction"] = (adata.var[f"{c}_count"] / adata.n_obs).astype(
- float
- )
-
- # Save frequencies across genes
- adata.obs[f"{c}_count"] = counts.fillna(0).sum(axis=1).astype(int)
- adata.obs[f"{c}_fraction"] = (adata.obs[f"{c}_count"] / adata.n_vars).astype(
- float
- )
+ g_pattern_counts = lp.groupby(groupby).apply(lambda df: df[PATTERN_NAMES].sum())
+ adata.uns["lp_stats"] = g_pattern_counts
return adata if copy else None
@@ -145,22 +123,33 @@ def _lp_logfc(data, phenotype=None):
Parameters
----------
data : AnnData
- Anndata formatted spatial data.
+ Spatial formatted AnnData object.
phenotype : str
Variable grouping cells for differential analysis. Must be in data.obs.columns.
+
+ Returns
+ -------
+ gene_fc_stats : DataFrame
+ log2 fold change of patterns between groups in phenotype.
"""
+ stats = data.uns["lp_stats"]
if phenotype not in data.obs.columns:
raise ValueError("Phenotype is invalid.")
phenotype_vector = data.obs[phenotype]
+ pattern_df = data.uns["lp"].copy()
+ groups_name = stats.index.name
+ pattern_df[["cell", groups_name]] = data.uns[f"cell_{groups_name}_features"][
+ ["cell", groups_name]
+ ]
+
gene_fc_stats = []
for c in PATTERN_NAMES:
-
# save pattern frequency to new column, one for each group
group_freq = (
- data.to_df(c)
+ pattern_df.pivot(index="cell", columns=groups_name, values=c)
.replace("none", np.nan)
.astype(float)
.groupby(phenotype_vector)
@@ -184,7 +173,7 @@ def log2fc(group_col):
# log2fc(group frequency / mean other frequency)
log2fc = np.log2((group_col + 1) / (rest_mean + 1))
results = log2fc.to_frame("log2fc")
- results["phenotype"] = f"{phenotype}_{group_name}"
+ results["phenotype"] = group_name
return results
# Compute log2fc of group / mean(rest) for each group
@@ -204,11 +193,16 @@ def log2fc(group_col):
return gene_fc_stats
-def _lp_diff_gene(cell_by_pattern, phenotype, phenotype_vector):
+def _lp_diff_gene(cell_by_pattern, phenotype_vector):
"""Perform pairwise comparison between groupby and every class.
Parameters
----------
+ cell_by_pattern : DataFrame
+ Cell by pattern matrix.
+ phenotype_vector : Series
+ Series of cell groupings.
+
Returns
-------
DataFrame
@@ -218,7 +212,7 @@ def _lp_diff_gene(cell_by_pattern, phenotype, phenotype_vector):
# One hot encode categories
group_dummies = pd.get_dummies(pd.Series(phenotype_vector))
- group_dummies.columns = [f"{phenotype}_{g}" for g in group_dummies.columns]
+ # group_dummies.columns = [f"{phenotype}_{g}" for g in group_dummies.columns]
group_names = group_dummies.columns.tolist()
group_data = pd.concat([cell_by_pattern, group_dummies], axis=1)
group_data.columns = group_data.columns.astype(str)
@@ -247,7 +241,6 @@ def _lp_diff_gene(cell_by_pattern, phenotype, phenotype_vector):
r = r.reset_index().rename({"index": "pattern"}, axis=1)
results.append(r)
- #
except (
np.linalg.LinAlgError,
ValueError,
@@ -263,89 +256,67 @@ def _lp_diff_gene(cell_by_pattern, phenotype, phenotype_vector):
@track
-def lp_diff(data, phenotype=None, continuous=False, min_cells=10, copy=False):
+def lp_diff(
+ data: AnnData, phenotype: str = None, continuous: bool = False, copy: bool = False
+):
"""Gene-wise test for differential localization across phenotype of interest.
Parameters
----------
data : AnnData
- Anndata formatted spatial data.
+ Spatial formatted AnnData object.
phenotype : str
- Variable grouping cells for differential analysis. Must be in data.obs_names.
+ Variable grouping cells for differential analysis. Must be in data.obs.columns.
continuous : bool
Whether the phenotype is continuous or categorical. By default False.
- n_cores : int, optional
- cores used for multiprocessing, by default 1
copy : bool
Return a copy of `data` instead of writing to data, by default False.
+
+ Returns
+ -------
+ adata : AnnData
+ Spatial formatted AnnData object.
+ .uns['diff_{phenotype}'] : DataFrame
+ Long DataFrame with differential localization test results across phenotype groups.
"""
adata = data.copy() if copy else data
- # Note which samples were detected and classified
- detected = np.ones(data.shape)
- for c in PATTERN_NAMES:
- detected = detected & ~data.to_df(c).isna()
-
- # Only look at genes detected in >= min_cells
- valid_genes = detected.sum(axis=0) >= min_cells
- print(f"{sum(valid_genes)} genes detected in at least {min_cells} cells.")
+ stats = adata.uns["lp_stats"]
# Retrieve cell phenotype
phenotype_vector = adata.obs[phenotype].tolist()
+ # TODO untested/incomplete
if continuous:
pattern_dfs = {}
+
+ # Compute correlation for each point group along cells
for p in PATTERN_NAMES:
- p_df = adata.to_df(p).loc[:, valid_genes]
- p_corr = p_df.corrwith(phenotype_vector, drop=True)
- pattern_dfs[p] = p_df
+ p_labels = adata.uns["lp"][p]
+ groups_name = stats.index.name
+ p_labels[["cell", groups_name]] = adata.uns[f"cell_{groups_name}_features"][
+ ["cell", groups_name]
+ ]
+ p_labels = p_labels.pivot(index="cell", columns="gene", values=p)
+ p_corr = p_labels.corrwith(phenotype_vector, drop=True)
+ pattern_dfs[p] = p_labels
else:
- # Load and flatten pattern layers
- pattern_df = []
- for p in PATTERN_NAMES:
- p_df = adata.to_df(p).loc[:, valid_genes].reset_index().melt(id_vars="cell")
- p_df["pattern"] = p
- pattern_df.append(p_df)
-
- # [Sample by patterns] where sample id = [cell, gene] pair
- pattern_df = pd.concat(pattern_df)
- pattern_df = pattern_df.pivot(
- index=["cell", "gene"], columns="pattern", values="value"
- ).reset_index()
-
- # Fit logit for each gene
- meta = {
- "pattern": str,
- "dy/dx": float,
- "std_err": float,
- "z": float,
- "pvalue": float,
- "ci_low": float,
- "ci_high": float,
- "phenotype": str,
- }
-
- # diff_output = pattern_df.groupby("gene").progress_apply(
- # lambda gp: _lp_diff_gene(gp, phenotype, phenotype_vector)
- # )
-
- with ProgressBar():
- diff_output = (
- dd.from_pandas(pattern_df, chunksize=100)
- .groupby("gene")
- .apply(
- lambda gp: _lp_diff_gene(gp, phenotype, phenotype_vector), meta=meta
- )
- .reset_index()
- .compute()
- )
-
- # Format pattern column
- # diff_output = pd.concat(diff_output)
+ # [Sample by patterns] where sample id = [cell, group] pair
+ pattern_df = adata.uns["lp"].copy()
+ groups_name = stats.index.name
+ pattern_df[["cell", groups_name]] = adata.uns[f"cell_{groups_name}_features"][
+ ["cell", groups_name]
+ ]
+
+ diff_output = (
+ pattern_df.groupby(groups_name)
+ .progress_apply(lambda gp: _lp_diff_gene(gp, phenotype_vector))
+ .reset_index()
+ )
# FDR correction
- diff_output["padj"] = diff_output["pvalue"] * diff_output["gene"].nunique()
+ diff_output["padj"] = diff_output["pvalue"] * diff_output[groups_name].nunique()
results = diff_output.dropna()
@@ -362,8 +333,8 @@ def lp_diff(data, phenotype=None, continuous=False, min_cells=10, copy=False):
# Join log2fc results to p value df
results = (
- results.set_index(["gene", "pattern", "phenotype"])
- .join(log2fc_stats.set_index(["gene", "pattern", "phenotype"]))
+ results.set_index([groups_name, "pattern", "phenotype"])
+ .join(log2fc_stats.set_index([groups_name, "pattern", "phenotype"]))
.reset_index()
)
@@ -374,20 +345,3 @@ def lp_diff(data, phenotype=None, continuous=False, min_cells=10, copy=False):
adata.uns[f"diff_{phenotype}"] = results
return adata if copy else None
-
-
-PATTERN_MODEL_FEATURE_NAMES = [
- "cell_inner_proximity",
- "nucleus_inner_proximity",
- "nucleus_outer_proximity",
- "cell_inner_asymmetry",
- "nucleus_inner_asymmetry",
- "nucleus_outer_asymmetry",
- "l_max",
- "l_max_gradient",
- "l_min_gradient",
- "l_monotony",
- "l_half_radius",
- "point_dispersion",
- "nucleus_dispersion",
-]
diff --git a/bento/tools/_neighborhoods.py b/bento/tools/_neighborhoods.py
new file mode 100644
index 0000000..40a9ed6
--- /dev/null
+++ b/bento/tools/_neighborhoods.py
@@ -0,0 +1,114 @@
+import numpy as np
+import pandas as pd
+from scipy.sparse import csr_matrix
+from sklearn.neighbors import NearestNeighbors
+
+
+def _count_neighbors(
+ points, n_genes, query_points=None, n_neighbors=None, radius=None, agg="gene"
+):
+ """Build nearest neighbor index for points.
+
+ Parameters
+ ----------
+ points : pd.DataFrame
+ Points dataframe. Must have columns "x", "y", and "gene".
+ n_genes : int
+ Number of genes in overall dataset. Used to initialize unique gene counts.
+ query_points : pd.DataFrame, optional
+ Points to query. If None, use points_df. Default None.
+ n_neighbors : int
+ Number of nearest neighbors to consider per gene.
+ agg : "gene", "binary", None
+ Whether to aggregate nearest neighbors counts. "Gene" aggregates counts by gene, whereas "binary" counts neighbors only once per point. If None, return neighbor counts for each point.
+ Default "gene".
+ Returns
+ -------
+ DataFrame or dict of dicts
+ If agg is True, returns a DataFrame with columns "gene", "neighbor", and "count".
+ If agg is False, returns a list of dicts, one for each point. Dict keys are gene names, values are counts.
+
+ """
+ if n_neighbors and radius:
+ raise ValueError("Only specify one of n_neighbors or radius, not both.")
+ if not n_neighbors and not radius:
+ raise ValueError("Neither n_neighbors or radius is specified, one required.")
+
+ if query_points is None:
+ query_points = points
+
+ # Build knn index
+ if n_neighbors:
+ # Can't find more neighbors than total points
+ try:
+ n_neighbors = min(n_neighbors, points.shape[0])
+ neighbor_index = (
+ NearestNeighbors(n_neighbors=n_neighbors, n_jobs=-1)
+ .fit(points[["x", "y"]])
+ .kneighbors(query_points[["x", "y"]], return_distance=False)
+ )
+ except ValueError as e:
+ raise ValueError(e)
+ elif radius:
+ try:
+ neighbor_index = (
+ NearestNeighbors(radius=radius, n_jobs=-1)
+ .fit(points[["x", "y"]])
+ .radius_neighbors(query_points[["x", "y"]], return_distance=False)
+ )
+ except ValueError:
+ print(points.shape, query_points.shape)
+
+ # Get gene-level neighbor counts for each gene
+ if agg == "gene":
+ gene_code = points["gene"].values
+ source_genes, source_indices = np.unique(gene_code, return_index=True)
+
+ gene_index = []
+
+ for g, gi in zip(source_genes, source_indices):
+ # First get all points for this gene
+ g_neighbors = np.unique(neighbor_index[gi].flatten())
+ # get unique neighbor points
+ g_neighbors = gene_code[g_neighbors] # Get point gene names
+ neighbor_names, neighbor_counts = np.unique(
+ g_neighbors, return_counts=True
+ ) # aggregate neighbor gene counts
+
+ for neighbor, count in zip(neighbor_names, neighbor_counts):
+ gene_index.append([g, neighbor, count])
+
+ gene_index = pd.DataFrame(gene_index, columns=["gene", "neighbor", "count"])
+
+ return gene_index
+
+ else:
+ # Get gene-level neighbor counts for each point
+ gene_codes = points["gene"].cat.codes.values
+ neighborhood_sizes = np.array([len(n) for n in neighbor_index])
+ flat_nindex = np.concatenate(neighbor_index)
+ # Get gene name for each neighbor
+ flat_ncodes = gene_codes[flat_nindex]
+
+ point_ncounts = []
+ cur_pos = 0
+ # np.bincount only works on ints but much faster than np.unique
+ # https://stackoverflow.com/questions/66037744/2d-vectorization-of-unique-values-per-row-with-condition
+ for s in neighborhood_sizes:
+ cur_codes = flat_ncodes[cur_pos : cur_pos + s]
+ point_neighbor_counts = np.bincount(cur_codes, minlength=n_genes)
+ # Count number of times each gene is a neighbor of a given point
+ if agg == "binary":
+ n_indicator = (point_neighbor_counts > 0).astype(int)
+ point_ncounts.append(n_indicator)
+
+ # Quantify abundance of each gene as a neighbor of a given point
+ elif agg is None:
+ point_ncounts.append(point_neighbor_counts)
+
+ cur_pos += s
+
+ point_ncounts = np.array(point_ncounts)
+ point_ncounts = csr_matrix(point_ncounts)
+
+ return point_ncounts
diff --git a/bento/tools/_point_features.py b/bento/tools/_point_features.py
new file mode 100644
index 0000000..80d427d
--- /dev/null
+++ b/bento/tools/_point_features.py
@@ -0,0 +1,842 @@
+import warnings
+from shapely.errors import ShapelyDeprecationWarning
+
+warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
+
+
+from abc import ABCMeta, abstractmethod
+from typing import List, Optional, Union
+
+import numpy as np
+import pandas as pd
+from anndata import AnnData
+from astropy.stats.spatial import RipleysKEstimator
+from scipy.spatial import distance
+from scipy.stats.stats import spearmanr
+from tqdm.auto import tqdm
+import re
+
+from .. import tools as tl
+from .._utils import track
+from ..geometry import get_points
+
+
+@track
+def analyze_points(
+ data: AnnData,
+ shape_names: List[str],
+ feature_names: List[str],
+ groupby: Optional[Union[str, List[str]]] = None,
+ recompute=False,
+ progress: bool = False,
+ copy: bool = False,
+):
+ """Calculate the set of specified `features` for each point group. Groups are within each cell.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatially formatted AnnData
+ shape_names : str or list of str
+ Names of the shapes to analyze.
+ feature_names : str or list of str
+ Names of the features to analyze.
+ groupby : str or list of str, optional
+ Key(s) in `data.uns['points'] to groupby, by default None. Always treats each cell separately
+ copy : bool
+ Return a copy of `data` instead of writing to data, by default False.
+
+ Returns
+ -------
+ adata : anndata.AnnData
+ .uns["point_featu]
+ See the output of each :class:`PointFeature` in `features` for keys added.
+ `.obsm[`cell_`]`
+ DataFrame with rows aligned to `adata.obs_names` and `features` as columns.
+
+ """
+ adata = data.copy() if copy else data
+
+ # Cast to list if not already
+ if isinstance(shape_names, str):
+ shape_names = [shape_names]
+
+ # Cast to list if not already
+ if isinstance(feature_names, str):
+ feature_names = [feature_names]
+
+ # Make sure groupby is a list
+ if isinstance(groupby, str):
+ groupby = ["cell", groupby]
+ elif isinstance(groupby, list):
+ groupby = ["cell"] + groupby
+ else:
+ groupby = ["cell"]
+
+ # Make sure all groupby keys are in point columns
+ for g in groupby:
+ if g not in get_points(adata).columns:
+ raise ValueError(f"Groupby key {g} not found in point columns.")
+
+ # Generate feature x shape combinations
+ feature_combos = [point_features[f](s) for f in feature_names for s in shape_names]
+
+ # Compile dependency set of features and attributes
+ cell_features = set()
+ obs_attrs = set()
+ for f in feature_combos:
+ cell_features.update(f.cell_features)
+ obs_attrs.update(f.attributes)
+
+ cell_features = list(cell_features)
+ obs_attrs = list(obs_attrs)
+
+ print("Crunching shape features...")
+ tl.analyze_shapes(
+ adata, "cell_shape", cell_features, progress=progress, recompute=recompute
+ )
+
+ # Make sure attributes are present
+ attrs_found = set(obs_attrs).intersection(set(adata.obs.columns.tolist()))
+ if len(attrs_found) != len(obs_attrs):
+ raise KeyError(f"df does not have all columns: {obs_attrs}.")
+
+ # extract cell attributes
+ points_df = (
+ get_points(adata, asgeo=True)
+ .set_index("cell")
+ .join(data.obs[obs_attrs])
+ .reset_index()
+ )
+
+ for g in groupby:
+ points_df[g] = points_df[g].astype("category")
+ # Handle categories as strings to avoid ambiguous cat types
+ # for col in points_df.loc[:, (points_df.dtypes == "category").values]:
+ # points_df[col] = points_df[col].astype(str)
+
+ # Handle shape indexes as strings to avoid ambiguous types
+ for shape_name in adata.obs.columns[adata.obs.columns.str.endswith("_shape")]:
+ shape_prefix = "_".join(shape_name.split("_")[:-1])
+ if shape_prefix in points_df.columns:
+ points_df[shape_prefix] = points_df[shape_prefix].astype(str)
+
+ # Calculate features for a sample
+ def process_sample(df):
+ sample_output = {}
+ for f in feature_combos:
+ sample_output.update(f.extract(df))
+ return sample_output
+
+ # Process all samples in a partition
+ def process_partition(partition_df):
+ # Groupby by cell and groupby keys and process each sample
+ out = partition_df.groupby(groupby, observed=True).apply(process_sample)
+ return pd.DataFrame.from_records(out.values, index=out.index)
+
+ # Process points of each cell separately
+ cells, group_loc = np.unique(
+ points_df["cell"],
+ return_index=True,
+ )
+
+ end_loc = np.append(group_loc[1:], points_df.shape[0])
+
+ output = []
+ print("Crunching point features...")
+ if progress:
+ group_locs = tqdm(zip(group_loc, end_loc), total=len(cells))
+ else:
+ group_locs = zip(group_loc, end_loc)
+ for start, end in group_locs:
+ cell_points = points_df.iloc[start:end]
+ output.append(process_partition(cell_points))
+ output = pd.concat(output)
+
+ # Save and overwrite existing
+ print("Saving results...")
+ output_key = "_".join([*groupby, "features"])
+ if output_key in adata.uns:
+ adata.uns[output_key][output.columns] = output.reset_index(drop=True)
+ else:
+ adata.uns[output_key] = output.reset_index()
+
+ print("Done.")
+ return adata if copy else None
+
+
+class PointFeature(metaclass=ABCMeta):
+ """Abstract class for calculating sample features. A sample is defined as the set of
+ molecules corresponding to a single cell-gene pair.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features.
+ attributes : int
+ Names (keys) used to store computed cell-level features.
+ """
+
+ def __init__(self, shape_name):
+ self.cell_features = set()
+ self.attributes = set()
+
+ if shape_name:
+ self.attributes.add(shape_name)
+ self.shape_name = shape_name
+ self.shape_prefix = "_".join(shape_name.split("_")[:-1])
+
+ @abstractmethod
+ def extract(self, df):
+ """Calculates this feature for a given sample.
+
+ Parameters
+ ----------
+ df : DataFrame
+ Assumes each row is a molecule and that columns `x`, `y`, `cell`, and `gene` are present.
+ """
+ return df
+
+
+class ShapeProximity(PointFeature):
+ """For a set of points, computes the proximity of points within `shape_name`
+ as well as the proximity of points outside `shape_name`. Proximity is defined as
+ the average absolute distance to the specified `shape_name` normalized by cell
+ radius. Values closer to 0 denote farther from the `shape_name`, values closer
+ to 1 denote closer to the `shape_name`.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_inner_proximity"`: proximity of points inside `shape_name`
+ `"{shape_prefix}_outer_proximity"`: proximity of points outside `shape_name`
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+ self.cell_features.add("radius")
+ self.attributes.add("cell_radius")
+ self.shape_name = shape_name
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get shape polygon
+ shape = df[self.shape_name].values[0]
+
+ # Skip if no shape
+ if not shape:
+ return {
+ f"{self.shape_prefix}_inner_proximity": 0,
+ f"{self.shape_prefix}_outer_proximity": 0,
+ }
+
+ # Get points
+ points_geo = df["geometry"]
+
+ # Check for points within shape, assume all are intracellular
+ if self.shape_prefix == "cell":
+ inner = np.array([True] * len(df))
+ else:
+ inner = df[self.shape_prefix] != "-1"
+ outer = ~inner
+
+ inner_dist = np.nan
+ outer_dist = np.nan
+
+ if inner.sum() > 0:
+ inner_dist = points_geo[inner].distance(shape.boundary).mean()
+
+ if outer.sum() > 0:
+ outer_dist = points_geo[outer].distance(shape.boundary).mean()
+
+ # Scale from [0, 1], where 1 is close and 0 is far.
+ cell_radius = df["cell_radius"].values[0]
+ inner_proximity = (cell_radius - inner_dist) / cell_radius
+ outer_proximity = (cell_radius - outer_dist) / cell_radius
+
+ if np.isnan(inner_proximity):
+ inner_proximity = 0
+
+ if np.isnan(outer_proximity):
+ outer_proximity = 0
+
+ return {
+ f"{self.shape_prefix}_inner_proximity": inner_proximity,
+ f"{self.shape_prefix}_outer_proximity": outer_proximity,
+ }
+
+
+class ShapeAsymmetry(PointFeature):
+ """For a set of points, computes the asymmetry of points within `shape_name`
+ as well as the asymmetry of points outside `shape_name`. Asymmetry is defined as
+ the offset between the centroid of points to the centroid of the specified
+ `shape_name`, normalized by cell radius. Values closer to 0 denote symmetry,
+ values closer to 1 denote asymmetry.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ cell_attributes : int
+ Names (keys) used to store computed cell-level features
+ shape_name : str
+ Name of shape to use, must be column name in input DataFrame
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_inner_asymmetry"`: asymmetry of points inside `shape_name`
+ `"{shape_prefix}_outer_asymmetry"`: asymmetry of points outside `shape_name`
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+ self.cell_features.add("radius")
+ self.attributes.add("cell_radius")
+ self.shape_name = shape_name
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get shape polygon
+ shape = df[self.shape_name].values[0]
+
+ # Skip if no shape
+ if shape is None:
+ return {
+ f"{self.shape_prefix}_inner_asymmetry": 0,
+ f"{self.shape_prefix}_outer_asymmetry": 0,
+ }
+
+ # Get points
+ points_geo = df["geometry"]
+
+ # Check for points within shape, assume all are intracellular
+ if self.shape_prefix == "cell":
+ inner = np.array([True] * len(df))
+ else:
+ inner = df[self.shape_prefix] != "-1"
+ outer = ~inner
+
+ inner_to_centroid = np.nan
+ outer_to_centroid = np.nan
+
+ if inner.sum() > 0:
+ inner_to_centroid = points_geo[inner].distance(shape.centroid).mean()
+
+ if outer.sum() > 0:
+ outer_to_centroid = points_geo[outer].distance(shape.centroid).mean()
+
+ # Values [0, 1], where 1 is asymmetrical and 0 is symmetrical.
+ cell_radius = df["cell_radius"].values[0]
+ inner_asymmetry = inner_to_centroid / cell_radius
+ outer_asymmetry = outer_to_centroid / cell_radius
+
+ if np.isnan(inner_asymmetry):
+ inner_asymmetry = 0
+
+ if np.isnan(outer_asymmetry):
+ outer_asymmetry = 0
+
+ return {
+ f"{self.shape_prefix}_inner_asymmetry": inner_asymmetry,
+ f"{self.shape_prefix}_outer_asymmetry": outer_asymmetry,
+ }
+
+
+class PointDispersionNorm(PointFeature):
+ """For a set of points, calculates the second moment of all points in a cell
+ relative to the centroid of the total RNA signal. This value is normalized by
+ the second moment of a uniform distribution within the cell boundary.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ cell_attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"point_dispersion"`: measure of point dispersion
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+ self.cell_features.add("raster")
+
+ attrs = ["cell_raster"]
+ self.attributes.update(attrs)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get precomputed cell centroid and raster
+ pt_centroid = df[["x", "y"]].values.mean(axis=0).reshape(1, 2)
+ cell_raster = df["cell_raster"].values[0]
+
+ # Skip if no raster
+ if not np.array(cell_raster).flatten().any():
+ return {"point_dispersion_norm": np.nan}
+
+ # calculate points moment
+ point_moment = _second_moment(pt_centroid, df[["x", "y"]].values)
+ cell_moment = _second_moment(pt_centroid, cell_raster)
+
+ # Normalize by cell moment
+ norm_moment = point_moment / cell_moment
+
+ return {"point_dispersion_norm": norm_moment}
+
+
+class ShapeDispersionNorm(PointFeature):
+ """For a set of points, calculates the second moment of all points in a cell relative to the
+ centroid of `shape_name`. This value is normalized by the second moment of a uniform
+ distribution within the cell boundary.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ cell_attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_dispersion"`: measure of point dispersion relative to `shape_name`
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+
+ self.cell_features.add("raster")
+ attrs = ["cell_raster"]
+ self.attributes.update(attrs)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get shape polygon
+ shape = df[self.shape_name].values[0]
+
+ # Skip if no shape
+ if not shape:
+ return {f"{self.shape_prefix}_dispersion_norm": np.nan}
+
+ # Get precomputed shape centroid and raster
+ cell_raster = df["cell_raster"].values[0]
+
+ # calculate points moment
+ point_moment = _second_moment(shape.centroid, df[["x", "y"]].values)
+ cell_moment = _second_moment(shape.centroid, cell_raster)
+
+ # Normalize by cell moment
+ norm_moment = point_moment / cell_moment
+
+ return {f"{self.shape_prefix}_dispersion_norm": norm_moment}
+
+
+class ShapeDistance(PointFeature):
+ """For a set of points, computes the distance of points within `shape_name`
+ as well as the distance of points outside `shape_name`.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_inner_distance"`: distance of points inside `shape_name`
+ `"{shape_prefix}_outer_distance"`: distance of points outside `shape_name`
+ """
+
+ # Cell-level features needed for computing sample-level features
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get shape polygon
+ shape = df[self.shape_name].values[0]
+
+ # Skip if no shape
+ if not shape:
+ return {
+ f"{self.shape_prefix}_inner_distance": np.nan,
+ f"{self.shape_prefix}_outer_distance": np.nan,
+ }
+
+ # Get points
+ points_geo = df["geometry"].values
+
+ # Check for points within shape, assume all are intracellular
+ if self.shape_prefix == "cell":
+ inner = np.array([True] * len(df))
+ else:
+ inner = df[self.shape_prefix] != "-1"
+ outer = ~inner
+
+ if inner.sum() > 0:
+ inner_dist = points_geo[inner].distance(shape.boundary).mean()
+ else:
+ inner_dist = np.nan
+
+ if outer.sum() > 0:
+ outer_dist = points_geo[outer].distance(shape.boundary).mean()
+ else:
+ outer_dist = np.nan
+
+ return {
+ f"{self.shape_prefix}_inner_distance": inner_dist,
+ f"{self.shape_prefix}_outer_distance": outer_dist,
+ }
+
+
+class ShapeOffset(PointFeature):
+ """For a set of points, computes the offset of points within `shape_name`
+ as well as the offset of points outside `shape_name`. Offset is defined as
+ the offset between the centroid of points to the centroid of the specified
+ `shape_name`.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+ shape_name : str
+ Name of shape to use, must be column name in input DataFrame
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_inner_offset"`: offset of points inside `shape_name`
+ `"{shape_prefix}_outer_offset"`: offset of points outside `shape_name`
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get shape polygon
+ shape = df[self.shape_name].values[0]
+
+ # Skip if no shape
+ if not shape:
+ return {
+ f"{self.shape_prefix}_inner_offset": np.nan,
+ f"{self.shape_prefix}_outer_offset": np.nan,
+ }
+
+ # Get points
+ points_geo = df["geometry"].values
+
+ # Check for points within shape, assume all are intracellular
+ if self.shape_prefix == "cell":
+ inner = np.array([True] * len(df))
+ else:
+ inner = df[self.shape_prefix] != "-1"
+ outer = ~inner
+
+ if inner.sum() > 0:
+ inner_to_centroid = points_geo[inner].distance(shape.centroid).mean()
+ else:
+ inner_to_centroid = np.nan
+
+ if outer.sum() > 0:
+ outer_to_centroid = points_geo[outer].distance(shape.centroid).mean()
+ else:
+ outer_to_centroid = np.nan
+
+ return {
+ f"{self.shape_prefix}_inner_offset": inner_to_centroid,
+ f"{self.shape_prefix}_outer_offset": outer_to_centroid,
+ }
+
+
+class PointDispersion(PointFeature):
+ """For a set of points, calculates the second moment of all points in a cell
+ relative to the centroid of the total RNA signal.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"point_dispersion"`: measure of point dispersion
+ """
+
+ # shape_name set to None to follow the same convention as other shape features
+ def __init__(self, shape_name=None):
+ super().__init__(shape_name)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get precomputed cell centroid and raster
+ pt_centroid = df[["x", "y"]].values.mean(axis=0).reshape(1, 2)
+
+ # calculate points moment
+ point_moment = _second_moment(pt_centroid, df[["x", "y"]].values)
+
+ return {"point_dispersion": point_moment}
+
+
+class ShapeDispersion(PointFeature):
+ """For a set of points, calculates the second moment of all points in a cell relative to the
+ centroid of `shape_name`.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_dispersion"`: measure of point dispersion relative to `shape_name`
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get shape polygon
+ shape = df[self.shape_name].values[0]
+
+ # Skip if no shape
+ if not shape:
+ return {f"{self.shape_prefix}_dispersion": np.nan}
+
+ # calculate points moment
+ point_moment = _second_moment(shape.centroid, df[["x", "y"]].values)
+
+ return {f"{self.shape_prefix}_dispersion": point_moment}
+
+
+class RipleyStats(PointFeature):
+ """For a set of points, calculates properties of the L-function. The L-function
+ measures spatial clustering of a point pattern over the area of the cell.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+
+ Returns
+ -------
+ dict
+ `"l_max": The max value of the L-function evaluated at r=[1,d], where d is half the cell’s maximum diameter.
+ `"l_max_gradient"`: The max value of the gradient of the above L-function.
+ `"l_min_gradient"`: The min value of the gradient of the above L-function.
+ `"l_monotony"`: The correlation of the L-function and r=[1,d].
+ `"l_half_radius"`: The value of the L-function evaluated at 1/4 of the maximum cell diameter.
+
+ """
+
+ def __init__(self, shape_name=None):
+ super().__init__(shape_name)
+ self.cell_features.update(["span", "bounds", "area"])
+
+ self.attributes.update(
+ [
+ "cell_span",
+ "cell_minx",
+ "cell_miny",
+ "cell_maxx",
+ "cell_maxy",
+ "cell_area",
+ ]
+ )
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get precomputed centroid and cell moment
+ cell_span = df["cell_span"].values[0]
+ cell_minx = df["cell_minx"].values[0]
+ cell_miny = df["cell_miny"].values[0]
+ cell_maxx = df["cell_maxx"].values[0]
+ cell_maxy = df["cell_maxy"].values[0]
+ cell_area = df["cell_area"].values[0]
+
+ estimator = RipleysKEstimator(
+ area=cell_area,
+ x_min=cell_minx,
+ y_min=cell_miny,
+ x_max=cell_maxx,
+ y_max=cell_maxy,
+ )
+
+ quarter_span = cell_span / 4
+ radii = np.linspace(1, quarter_span * 2, num=int(quarter_span * 2))
+
+ # Get points
+ points_geo = df["geometry"].values
+ points_geo = np.array([points_geo.x, points_geo.y]).T
+
+ # Compute ripley function stats
+ stats = estimator.Hfunction(data=points_geo, radii=radii, mode="none")
+
+ # Max value of the L-function
+ l_max = max(stats)
+
+ # Max and min value of the gradient of L
+ ripley_smooth = pd.Series(stats).rolling(5).mean()
+ ripley_smooth.dropna(inplace=True)
+
+ # Can't take gradient of single number
+ if len(ripley_smooth) < 2:
+ ripley_smooth = np.array([0, 0])
+
+ ripley_gradient = np.gradient(ripley_smooth)
+ l_max_gradient = ripley_gradient.max()
+ l_min_gradient = ripley_gradient.min()
+
+ # Monotony of L-function in the interval
+ l_monotony = spearmanr(radii, stats)[0]
+
+ # L-function at L/4 where length of the cell L is max dist between 2 points on polygon defining cell border
+ l_half_radius = estimator.Hfunction(
+ data=points_geo, radii=[quarter_span], mode="none"
+ )[0]
+
+ result = {
+ "l_max": l_max,
+ "l_max_gradient": l_max_gradient,
+ "l_min_gradient": l_min_gradient,
+ "l_monotony": l_monotony,
+ "l_half_radius": l_half_radius,
+ }
+
+ return result
+
+
+class ShapeEnrichment(PointFeature):
+ """For a set of points, calculates the fraction of points within `shape_name`
+ out of all points in the cell.
+
+ Attributes
+ ----------
+ cell_features : int
+ Set of cell-level features needed for computing sample-level features
+ attributes : int
+ Names (keys) used to store computed cell-level features
+ shape_name : str
+ Name of shape to use, must be column name in input DataFrame
+
+ Returns
+ -------
+ dict
+ `"{shape_prefix}_enrichment"`: enrichment fraction of points in `shape_name`
+ """
+
+ def __init__(self, shape_name):
+ super().__init__(shape_name)
+
+ def extract(self, df):
+ df = super().extract(df)
+
+ # Get points outside shape
+ points_geo = df["geometry"]
+
+ # Check for points within shape, assume all are intracellular
+ if self.shape_prefix == "cell":
+ enrichment = 1.0
+ else:
+ inner_count = (df[self.shape_prefix] != "-1").sum()
+ enrichment = inner_count / float(len(points_geo))
+
+ return {f"{self.shape_prefix}_enrichment": enrichment}
+
+
+def _second_moment(centroid, pts):
+ """
+ Calculate second moment of points with centroid as reference.
+
+ Parameters
+ ----------
+ centroid : [1 x 2] float
+ pts : [n x 2] float
+ """
+ centroid = np.array(centroid).reshape(1, 2)
+ radii = distance.cdist(centroid, pts)
+ second_moment = np.sum(radii * radii / len(pts))
+ return second_moment
+
+
+def list_point_features():
+ """Return a DataFrame of available point features. Pulls descriptions from function docstrings.
+
+ Returns
+ -------
+ list
+ List of available point features.
+ """
+
+ # Get point feature descriptions from docstrings
+ df = dict()
+ for k, v in point_features.items():
+ description = v.__doc__.split("Attributes")[0].strip()
+ description = re.sub("\s +", " ", description)
+ df[k] = description
+
+ return df
+
+
+point_features = dict(
+ proximity=ShapeProximity,
+ asymmetry=ShapeAsymmetry,
+ point_dispersion_norm=PointDispersionNorm,
+ shape_dispersion_norm=ShapeDispersionNorm,
+ distance=ShapeDistance,
+ offset=ShapeOffset,
+ point_dispersion=PointDispersion,
+ shape_dispersion=ShapeDispersion,
+ ripley=RipleyStats,
+ shape_enrichment=ShapeEnrichment,
+)
+
+
+def register_point_feature(name: str, FeatureClass: PointFeature):
+ """Register a new point feature function.
+
+ Parameters
+ ----------
+ name : str
+ Name of feature function
+ func : class
+ Class that extends PointFeature. Needs to override abstract functions.
+ """
+
+ point_features[name] = FeatureClass
+
+ print(f"Registered point feature '{name}' to `bento.tl.shape_features`.")
diff --git a/bento/tools/_sample_features.py b/bento/tools/_sample_features.py
deleted file mode 100644
index 148294e..0000000
--- a/bento/tools/_sample_features.py
+++ /dev/null
@@ -1,589 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-from astropy.stats.spatial import RipleysKEstimator
-from scipy.stats.stats import spearmanr
-from scipy.spatial import distance
-
-import dask_geopandas
-import geopandas as gpd
-import numpy as np
-import pandas as pd
-from dask.diagnostics import ProgressBar
-from tqdm.auto import tqdm
-
-from .. import tools as tl
-from .._utils import track
-from ..preprocessing import get_points
-
-
-def analyze_samples(data, features, copy=False):
- """Calculate the set of specified `features` for every sample, defined as the set of
- molecules corresponding to every cell-gene pair.
-
- Parameters
- ----------
- data : AnnData
- Spatially formatted AnnData
- features : list of :class:`SampleFeature`
- List of :class:`SampleFeature` to compute.
- chunks : int, optional
- Number of partitions to use, passed to `dask`, by default None.
- chunksize : int, optional
- Size of partitions, passed to `dask`, by default None.
- copy : bool
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
- `.layers[`keys`]`
- See the output of each :class:`SampleFeature` in `features` for keys added.
- """
- adata = data.copy() if copy else data
-
- pbar = tqdm(desc="Cell features", total=3)
- # Cast features to type list
- if not isinstance(features, list):
- features = [features]
- features = [sample_features[f] for f in features]
-
- cell_features = set() # Cell-level fns to run
- cell_attributes = set() # Cell-level attributes needed to compute sample features
- for f in features:
- cell_features.update(f.cell_features)
- cell_attributes.update(f.cell_attributes)
-
- cell_features = list(cell_features)
- cell_attributes = list(cell_attributes)
-
- tl.analyze_cells(adata, cell_features, progress=False)
-
- # Make sure attributes are present
- attrs_found = set(cell_attributes).intersection(set(adata.obs.columns.tolist()))
- if len(attrs_found) != len(cell_attributes):
- raise KeyError(f"df does not have all columns: {cell_attributes}.")
-
- pbar.update()
-
- pbar.set_description("Sample features")
- # extract cell attributes
- points_df = (
- get_points(adata, asgeo=True)
- .set_index("cell")
- .join(data.obs[cell_attributes])
- .reset_index()
- .sort_values(["cell", "gene"])
- .reset_index(drop=True)
- )
-
- # Handle categories as strings to avoid ambiguous cat types
- for col in points_df.loc[:,(points_df.dtypes == 'category').values]:
- points_df[col] = points_df[col].astype(str)
-
- # Handle shape indexes as strings to avoid ambiguous types
- for shape_name in adata.obs.columns[adata.obs.columns.str.endswith('_shape')]:
- shape_prefix = '_'.join(shape_name.split('_')[:-1])
- if shape_prefix in points_df.columns:
- points_df[shape_prefix] = points_df[shape_prefix].astype(str)
-
- # Calculate features for a sample
- def process_sample(df):
- sample_output = {}
- for f in features:
- sample_output.update(f.extract(df))
- return sample_output
-
- # Process all samples in a partition
- def process_partition(partition_df):
- return partition_df.groupby(["cell", "gene"], observed=True).apply(
- process_sample
- )
-
- # Cast to dask dataframe
- ddf = dask_geopandas.from_geopandas(points_df, npartitions=1)
-
- # Partition so only 1000 groups per groupby
- _, group_loc = np.unique(
- points_df["cell"].astype(str) + "-" + points_df["gene"].astype(str),
- return_index=True,
- )
- divisions = [group_loc[loc] for loc in range(0, len(group_loc), 1000)]
- divisions.append(len(points_df) - 1)
- ddf = ddf.repartition(divisions=divisions)
-
- # Parallel process each partition
- with ProgressBar():
- # Run on a single sample to get output metadata
- meta_output = process_partition(points_df.head())
- meta = pd.DataFrame(meta_output.tolist(), index=meta_output.index)
- output = ddf.map_partitions(process_partition, meta=meta.dtypes).compute()
-
- pbar.update()
- pbar.set_description("Saving to AnnData")
-
- # Format from Series of dicts to DataFrame
- output = pd.DataFrame(output.tolist(), index=output.index).reset_index()
-
- # Save results to data layers
- feature_names = output.columns[~output.columns.isin(["cell", "gene"])]
- for feature_name in feature_names:
- adata.layers[feature_name] = (
- output.pivot(index="cell", columns="gene", values=feature_name)
- .reindex(index=adata.obs_names, columns=adata.var_names)
- .astype(float)
- )
-
- pbar.update()
- pbar.set_description('Done!')
- pbar.close()
-
-
-class SampleFeature(metaclass=ABCMeta):
- """Abstract class for calculating sample features. A sample is defined as the set of
- molecules corresponding to a single cell-gene pair.
-
- Parameters
- ----------
- metaclass : _type_, optional
- _description_, by default ABCMeta
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features.
- cell_attributes : int
- Names (keys) used to store computed cell-level features.
- """
-
- @abstractmethod
- def __init__(self):
- self.cell_features = set()
- self.cell_attributes = set()
-
- @abstractmethod
- def extract(self, df):
- """Calculates this feature for a given sample.
-
- Parameters
- ----------
- df : DataFrame
- Assumes each row is a molecule and that columns `x`, `y`, `cell`, and `gene` are present.
- """
-
- return df
-
-
-class ShapeProximity(SampleFeature):
- """For a set of points, computes the proximity of points within `shape_name`
- as well as the proximity of points outside `shape_name`. Proximity is defined as
- the average absolute distance to the specified `shape_name` normalized by cell
- radius. Values closer to 0 denote farther from the `shape_name`, values closer
- to 1 denote closer to the `shape_name`.
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features
- cell_attributes : int
- Names (keys) used to store computed cell-level features
-
- Returns
- -------
- dict
- `"{shape_prefix}_inner_proximity"`: proximity of points inside `shape_name`
- `"{shape_prefix}_outer_proximity"`: proximity of points outside `shape_name`
- """
-
- def __init__(self, shape_name):
- super().__init__()
- self.cell_features.add("cell_radius")
-
- attrs = [shape_name, "cell_radius"]
- self.cell_attributes.update(attrs)
-
- self.shape_name = shape_name
-
- def extract(self, df):
- df = super().extract(df)
-
- shape_prefix = self.shape_name.split("_shape")[0]
-
- # Get shape polygon
- shape = df[self.shape_name].values[0]
-
- # Get points
- points_geo = df["geometry"].values
-
- # Check for points within shape, assume all are intracellular
- if shape_prefix == "cell":
- inner = np.array([True] * len(df))
- else:
- inner = df[shape_prefix] != "-1"
- outer = ~inner
-
- inner_dist = np.nan
- outer_dist = np.nan
-
- if inner.sum() > 0:
- inner_dist = points_geo[inner].distance(shape.boundary).mean()
-
- if outer.sum() > 0:
- outer_dist = points_geo[outer].distance(shape.boundary).mean()
-
- # Scale from [0, 1], where 1 is close and 0 is far.
- cell_radius = df["cell_radius"].values[0]
- inner_proximity = (cell_radius - inner_dist) / cell_radius
- outer_proximity = (cell_radius - outer_dist) / cell_radius
-
- if np.isnan(inner_proximity):
- inner_proximity = 0
-
- if np.isnan(outer_proximity):
- outer_proximity = 0
-
- return {
- f"{shape_prefix}_inner_proximity": inner_proximity,
- f"{shape_prefix}_outer_proximity": outer_proximity,
- }
-
-
-class ShapeAsymmetry(SampleFeature):
- """For a set of points, computes the asymmetry of points within `shape_name`
- as well as the asymmetry of points outside `shape_name`. Asymmetry is defined as
- the offset between the centroid of points to the centroid of the specified
- `shape_name`, normalized by cell radius. Values closer to 0 denote symmetry,
- values closer to 1 denote asymmetry.
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features
- cell_attributes : int
- Names (keys) used to store computed cell-level features
- shape_name : str
- Name of shape to use, must be column name in input DataFrame
-
- Returns
- -------
- dict
- `"{shape_prefix}_inner_asymmetry"`: asymmetry of points inside `shape_name`
- `"{shape_prefix}_outer_asymmetry"`: asymmetry of points outside `shape_name`
- """
-
- def __init__(self, shape_name):
- super().__init__()
- self.cell_features.add("cell_radius")
-
- attrs = [shape_name, "cell_radius"]
- self.cell_attributes.update(attrs)
-
- self.shape_name = shape_name
-
- def extract(self, df):
- df = super().extract(df)
-
- shape_prefix = self.shape_name.split("_shape")[0]
-
- # Get shape polygon
- shape = df[self.shape_name].values[0]
-
- # Get points
- points_geo = df["geometry"].values
-
- # Check for points within shape, assume all are intracellular
- if shape_prefix == "cell":
- inner = np.array([True] * len(df))
- else:
- inner = df[shape_prefix] != "-1"
- outer = ~inner
-
- inner_to_centroid = np.nan
- outer_to_centroid = np.nan
-
- if inner.sum() > 0:
- inner_to_centroid = points_geo[inner].distance(shape.centroid).mean()
-
- if outer.sum() > 0:
- outer_to_centroid = points_geo[outer].distance(shape.centroid).mean()
-
- # Values [0, 1], where 1 is asymmetrical and 0 is symmetrical.
- cell_radius = df["cell_radius"].values[0]
- inner_asymmetry = inner_to_centroid / cell_radius
- outer_asymmetry = outer_to_centroid / cell_radius
-
- if np.isnan(inner_asymmetry):
- inner_asymmetry = 0
-
- if np.isnan(outer_asymmetry):
- outer_asymmetry = 0
-
- return {
- f"{shape_prefix}_inner_asymmetry": inner_asymmetry,
- f"{shape_prefix}_outer_asymmetry": outer_asymmetry,
- }
-
-
-class PointDispersion(SampleFeature):
- """For a set of points, calculates the second moment of all points in a cell
- relative to the centroid of the total RNA signal. This value is normalized by
- the second moment of a uniform distribution within the cell boundary.
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features
- cell_attributes : int
- Names (keys) used to store computed cell-level features
-
- Returns
- -------
- dict
- `"point_dispersion"`: measure of point dispersion
- """
-
- def __init__(self):
- super().__init__()
- self.cell_features.add("raster_cell")
-
- attrs = ["cell_raster"]
- self.cell_attributes.update(attrs)
-
- def extract(self, df):
- df = super().extract(df)
-
- # Get precomputed cell centroid and raster
- pt_centroid = df[["x", "y"]].values.mean(axis=0).reshape(1, 2)
- cell_raster = df["cell_raster"].values[0]
-
- # calculate points moment
- point_moment = _second_moment(pt_centroid, df[["x", "y"]].values)
- cell_moment = _second_moment(pt_centroid, cell_raster)
-
- # Normalize by cell moment
- norm_moment = point_moment / cell_moment
-
- return {"point_dispersion": norm_moment}
-
-
-class ShapeDispersion(SampleFeature):
- """For a set of points, calculates the second moment of all points in a cell relative to the
- centroid of `shape_name`. This value is normalized by the second moment of a uniform
- distribution within the cell boundary.
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features
- cell_attributes : int
- Names (keys) used to store computed cell-level features
-
- Returns
- -------
- dict
- `"{shape_prefix}_dispersion"`: measure of point dispersion relative to `shape_name`
- """
-
- def __init__(self, shape_name):
- super().__init__()
-
- self.cell_features.add("raster_cell")
- attrs = [shape_name, "cell_raster"]
- self.cell_attributes.update(attrs)
-
- self.shape_name = shape_name
-
- def extract(self, df):
- df = super().extract(df)
-
- # Get shape polygon
- shape = df[self.shape_name].values[0]
-
- # Get precomputed shape centroid and raster
- cell_raster = df["cell_raster"].values[0]
-
- # calculate points moment
- point_moment = _second_moment(shape.centroid, df[["x", "y"]].values)
- cell_moment = _second_moment(shape.centroid, cell_raster)
-
- # Normalize by cell moment
- norm_moment = point_moment / cell_moment
-
- shape_prefix = self.shape_name.split("_shape")[0]
-
- return {f"{shape_prefix}_dispersion": norm_moment}
-
-
-class RipleyStats(SampleFeature):
- """For a set of points, calculates properties of the L-function. The L-function
- measures spatial clustering of a point pattern over the area of the cell.
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features
- cell_attributes : int
- Names (keys) used to store computed cell-level features
-
- Returns
- -------
- dict
- `"l_max": The max value of the L-function evaluated at r=[1,d], where d is half the cell’s maximum diameter.
- `"l_max_gradient"`: The max value of the gradient of the above L-function.
- `"l_min_gradient"`: The min value of the gradient of the above L-function.
- `"l_monotony"`: The correlation of the L-function and r=[1,d].
- `"l_half_radius"`: The value of the L-function evaluated at 1/4 of the maximum cell diameter.
-
- """
-
- def __init__(self):
- super().__init__()
- self.cell_features.update(["cell_span", "cell_bounds", "cell_area"])
-
- self.cell_attributes.update(
- [
- "cell_span",
- "cell_minx",
- "cell_miny",
- "cell_maxx",
- "cell_maxy",
- "cell_area",
- ]
- )
-
- def extract(self, df):
- df = super().extract(df)
-
- # Get precomputed centroid and cell moment
- cell_span = df["cell_span"].values[0]
- cell_minx = df["cell_minx"].values[0]
- cell_miny = df["cell_miny"].values[0]
- cell_maxx = df["cell_maxx"].values[0]
- cell_maxy = df["cell_maxy"].values[0]
- cell_area = df["cell_area"].values[0]
-
- estimator = RipleysKEstimator(
- area=cell_area,
- x_min=cell_minx,
- y_min=cell_miny,
- x_max=cell_maxx,
- y_max=cell_maxy,
- )
-
- quarter_span = cell_span / 4
- radii = np.linspace(1, quarter_span * 2, num=int(quarter_span * 2))
-
- # Get points
- points_geo = df["geometry"].values
- points_geo = np.array([points_geo.x, points_geo.y]).T
-
- # Compute ripley function stats
- stats = estimator.Hfunction(data=points_geo, radii=radii, mode="none")
-
- # Max value of the L-function
- l_max = max(stats)
-
- # Max and min value of the gradient of L
- ripley_smooth = pd.Series(stats).rolling(5).mean()
- ripley_smooth.dropna(inplace=True)
-
- # Can't take gradient of single number
- if len(ripley_smooth) < 2:
- ripley_smooth = np.array([0, 0])
-
- ripley_gradient = np.gradient(ripley_smooth)
- l_max_gradient = ripley_gradient.max()
- l_min_gradient = ripley_gradient.min()
-
- # Monotony of L-function in the interval
- l_monotony = spearmanr(radii, stats)[0]
-
- # L-function at L/4 where length of the cell L is max dist between 2 points on polygon defining cell border
- l_half_radius = estimator.Hfunction(
- data=points_geo, radii=[quarter_span], mode="none"
- )[0]
-
- result = {
- "l_max": l_max,
- "l_max_gradient": l_max_gradient,
- "l_min_gradient": l_min_gradient,
- "l_monotony": l_monotony,
- "l_half_radius": l_half_radius,
- }
-
- return result
-
-
-class ShapeEnrichment(SampleFeature):
- """For a set of points, calculates the fraction of points within `shape_name`
- out of all points in the cell.
-
- Attributes
- ----------
- cell_features : int
- Set of cell-level features needed for computing sample-level features
- cell_attributes : int
- Names (keys) used to store computed cell-level features
- shape_name : str
- Name of shape to use, must be column name in input DataFrame
-
- Returns
- -------
- dict
- `"{shape_prefix}_enrichment"`: enrichment fraction of points in `shape_name`
- """
-
- def __init__(self, shape_name):
- super().__init__()
-
- attrs = [shape_name]
- self.cell_attributes.update(attrs)
-
- self.shape_name = shape_name
-
- def extract(self, df):
- df = super().extract(df)
-
- shape_prefix = self.shape_name.split("_shape")[0]
-
- # Get points outside shape
- points_geo = df["geometry"]
-
- # Check for points within shape, assume all are intracellular
- if shape_prefix == "cell":
- enrichment = 1.0
- else:
- inner_count = (df[shape_prefix] != "-1").sum()
- enrichment = inner_count / float(len(points_geo))
-
- return {f"{shape_prefix}_enrichment": enrichment}
-
-
-def _second_moment(centroid, pts):
- """
- Calculate second moment of points with centroid as reference.
-
- Parameters
- ----------
- centroid : [1 x 2] float
- pts : [n x 2] float
- """
- centroid = np.array(centroid).reshape(1, 2)
- radii = distance.cdist(centroid, pts)
- second_moment = np.sum(radii * radii / len(pts))
- return second_moment
-
-
-sample_features = dict(
- cell_proximity=ShapeProximity("cell_shape"),
- nucleus_proximity=ShapeProximity("nucleus_shape"),
- cell_asymmetry=ShapeAsymmetry("cell_shape"),
- nucleus_asymmetry=ShapeAsymmetry("nucleus_shape"),
- point_dispersion=PointDispersion(),
- nucleus_dispersion=ShapeDispersion("nucleus_shape"),
- ripley_stats=RipleyStats(),
- nucleus_enrichment=ShapeEnrichment("nucleus_shape"),
-)
-"""Dict of sample feature names : function. Pass a list of feature name(s) to
-`bento.tl.analyze_samples()` to compute them.
-"""
diff --git a/bento/tools/_shape_features.py b/bento/tools/_shape_features.py
new file mode 100755
index 0000000..a06f005
--- /dev/null
+++ b/bento/tools/_shape_features.py
@@ -0,0 +1,560 @@
+import warnings
+from shapely.errors import ShapelyDeprecationWarning
+
+warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
+
+
+from typing import Callable, Dict, List, Union
+
+import matplotlib.path as mplPath
+import numpy as np
+import pandas as pd
+from anndata import AnnData
+from scipy.spatial import distance, distance_matrix
+from shapely.geometry import MultiPolygon, Point
+from tqdm.auto import tqdm
+
+from .._utils import sync, track
+from ..geometry import get_points, get_shape
+
+
+def _area(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the area of each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ shape_name : str
+ Key in `data.obs` that contains the shape information.
+
+ Fields
+ ------
+ .obs['{shape}_area'] : float
+ Area of each polygon
+ """
+
+ feature_key = f"{shape_name.split('_')[0]}_area"
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ # Calculate pixel-wise area
+ area = get_shape(data, shape_name).area
+
+ data.obs[feature_key] = area
+
+
+def _poly_aspect_ratio(poly):
+ """Compute the aspect ratio of the minimum rotated rectangle that contains a polygon."""
+
+ if not poly:
+ return np.nan
+
+ # get coordinates of min bounding box vertices around polygon
+ x, y = poly.minimum_rotated_rectangle.exterior.coords.xy
+
+ # get length of bound box sides
+ edge_length = (
+ Point(x[0], y[0]).distance(Point(x[1], y[1])),
+ Point(x[1], y[1]).distance(Point(x[2], y[2])),
+ )
+
+ # length = longest side, width = shortest side
+ length, width = max(edge_length), min(edge_length)
+
+ # return long / short ratio
+ return length / width
+
+
+def _aspect_ratio(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the aspect ratio of the minimum rotated rectangle that contains each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ shape_name : str
+ Key in `data.obs` that contains the shape information.
+
+ Fields
+ ------
+ .obs['{shape}_aspect_ratio'] : float
+ Ratio of major to minor axis for each polygon
+ """
+
+ feature_key = f"{shape_name.split('_')[0]}_aspect_ratio"
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ ar = get_shape(data, shape_name).apply(_poly_aspect_ratio)
+ data.obs[feature_key] = ar
+
+
+def _bounds(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the minimum and maximum coordinate values that bound each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ shape_name : str
+ Key in `data.obs` that contains the shape information.
+
+ Fields
+ ------
+ .obs['{shape}_minx'] : float
+ x-axis lower bound of each polygon
+ .obs['{shape}_miny'] : float
+ y-axis lower bound of each polygon
+ .obs['{shape}_maxx'] : float
+ x-axis upper bound of each polygon
+ .obs['{shape}_maxy'] : float
+ y-axis upper bound of each polygon
+ """
+
+ feature_keys = [
+ f"{shape_name.split('_')[0]}_{k}" for k in ["minx", "miny", "maxx", "maxy"]
+ ]
+ if all([k in data.obs.keys() for k in feature_keys]) and not recompute:
+ return
+
+ bounds = get_shape(data, shape_name).bounds
+
+ data.obs[feature_keys[0]] = bounds["minx"]
+ data.obs[feature_keys[1]] = bounds["miny"]
+ data.obs[feature_keys[2]] = bounds["maxx"]
+ data.obs[feature_keys[3]] = bounds["maxy"]
+
+
+# TODO move to point_features
+def _density(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the RNA density of each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ shape_name : str
+ Key in `data.obs` that contains the shape information.
+
+ Fields
+ ------
+ .obs['{shape}_density'] : float
+ Density (molecules / shape area) of each polygon
+ """
+
+ shape_prefix = shape_name.split("_")[0]
+ feature_key = f"{shape_prefix}_density"
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ count = get_points(data).query(f"{shape_prefix} != '-1'")["cell"].value_counts()
+ _area(data, shape_name)
+
+ data.obs[feature_key] = count / data.obs[f"{shape_prefix}_area"]
+
+
+def _opening(data: AnnData, proportion: float, recompute: bool = False):
+ """Compute the opening (morphological) of distance d for each cell.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `obs['cell_open_{d}_shape']` : Polygons
+ Ratio of long / short axis for each polygon in `obs['cell_shape']`
+ """
+
+ shape_name = f"cell_open_{proportion}_shape"
+
+ if shape_name in data.obs.keys() and not recompute:
+ return
+
+ _radius(data, "cell_shape")
+
+ cells = get_shape(data, "cell_shape")
+ d = proportion * data.obs["cell_radius"]
+
+ # Opening
+ data.obs[shape_name] = cells.buffer(-d).buffer(d)
+
+
+def _second_moment_polygon(centroid, pts):
+ """
+ Calculate second moment of points with centroid as reference.
+
+ Parameters
+ ----------
+ centroid : 2D Point object
+ pts : [n x 2] float
+ """
+ if not centroid or isinstance(pts, np.ndarray):
+ return
+ centroid = np.array(centroid).reshape(1, 2)
+ radii = distance.cdist(centroid, pts)
+ second_moment = np.sum(radii * radii / len(pts))
+ return second_moment
+
+
+def _second_moment(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the second moment of each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `obs['{shape}_moment']` : float
+ The second moment for each polygon
+ """
+
+ shape_prefix = shape_name.split("_")[0]
+ feature_key = f"{shape_prefix}_moment"
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ _raster(data, shape_name, recompute=recompute)
+
+ rasters = data.obs[f"{shape_prefix}_raster"]
+ shape_centroids = get_shape(data, shape_name).centroid
+
+ moments = [
+ _second_moment_polygon(centroid, r)
+ for centroid, r in zip(shape_centroids, rasters)
+ ]
+
+ data.obs[f"{shape_prefix}_moment"] = moments
+
+
+def _raster_polygon(poly, step=1):
+ """
+ Generate a grid of points contained within the poly. The points lie on
+ a 2D grid, with vertices spaced step distance apart.
+ """
+ if not poly:
+ return
+ minx, miny, maxx, maxy = [int(i) for i in poly.bounds]
+ x, y = np.meshgrid(
+ np.arange(minx, maxx + step, step=step),
+ np.arange(miny, maxy + step, step=step),
+ )
+ x = x.flatten()
+ y = y.flatten()
+ xy = np.array([x, y]).T
+
+ poly_cell_mask = np.ones(xy.shape[0], dtype=bool)
+
+ # Add all points within the polygon; handle MultiPolygons
+ if isinstance(poly, MultiPolygon):
+ for p in poly:
+ poly_path = mplPath.Path(np.array(p.exterior.xy).T)
+ poly_cell_mask = poly_cell_mask & poly_path.contains_points(xy)
+ else:
+ poly_path = mplPath.Path(np.array(poly.exterior.xy).T)
+ poly_cell_mask = poly_path.contains_points(xy)
+ xy = xy[poly_cell_mask]
+
+ # Make sure at least a single point is returned
+ if xy.shape[0] == 0:
+ return np.array(poly.centroid.xy).reshape(1, 2)
+ return xy
+
+
+def _raster(data: AnnData, shape_name: str, step: int = 1, recompute: bool = False):
+ """Generate a grid of points contained within each shape. The points lie on
+ a 2D grid, with vertices spaced `step` distance apart.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `uns['{shape}_raster']` : np.array
+ Long DataFrame of points annotated by shape from `obs['{shape_name}']`
+ """
+
+ shape_prefix = shape_name.split("_")[0]
+ feature_key = f"{shape_prefix}_raster"
+
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ raster = data.obs[f"{shape_name}"].apply(
+ lambda poly: _raster_polygon(poly, step=step)
+ )
+
+ raster_all = []
+ for s, r in raster.items():
+ raster_df = pd.DataFrame(r, columns=["x", "y"])
+ raster_df[shape_prefix] = s
+ raster_all.append(raster_df)
+
+ # Add raster to data.obs as 2d array per cell (for point_features compatibility)
+ data.obs[feature_key] = [df[["x", "y"]].values for df in raster_all]
+
+ # Add raster to data.uns as long dataframe (for flux compatibility)
+ raster_all = pd.concat(raster_all).reset_index(drop=True)
+ data.uns[feature_key] = raster_all
+
+
+def _perimeter(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the perimeter of each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `obs['{shape}_perimeter']` : np.array
+ Perimeter of each polygon
+ """
+
+ shape_prefix = shape_name.split("_")[0]
+ feature_key = f"{shape_prefix}_perimeter"
+
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ data.obs[feature_key] = get_shape(data, shape_name).length
+
+
+def _radius(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the radius of each cell.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `obs['{shape}_radius']` : np.array
+ Radius of each polygon in `obs['cell_shape']`
+ """
+
+ shape_prefix = shape_name.split("_")[0]
+ feature_key = f"{shape_prefix}_radius"
+
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ shapes = get_shape(data, shape_name)
+
+ # Get average distance from boundary to centroid
+ shape_radius = shapes.apply(_shape_radius)
+
+ data.obs[feature_key] = shape_radius
+
+
+def _shape_radius(poly):
+ if not poly:
+ return np.nan
+
+ return distance.cdist(
+ np.array(poly.centroid).reshape(1, 2), np.array(poly.exterior.xy).T
+ ).mean()
+
+
+def _span(data: AnnData, shape_name: str, recompute: bool = False):
+ """Compute the length of the longest diagonal of each shape.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `obs['{shape}_span']` : float
+ Length of longest diagonal for each polygon
+ """
+
+ shape_prefix = shape_name.split("_")[0]
+ feature_key = f"{shape_prefix}_span"
+
+ if feature_key in data.obs.keys() and not recompute:
+ return
+
+ def get_span(poly):
+ if not poly:
+ return np.nan
+
+ shape_coo = np.array(poly.coords.xy).T
+ return int(distance_matrix(shape_coo, shape_coo).max())
+
+ span = get_shape(data, shape_name).exterior.apply(get_span)
+
+ data.obs[feature_key] = span
+
+
+def list_shape_features():
+ """Return a DataFrame of available shape features. Pulls descriptions from function docstrings.
+
+ Returns
+ -------
+ list
+ List of available shape features.
+ """
+
+ # Get shape feature descriptions from docstrings
+ df = dict()
+ for k, v in shape_features.items():
+ description = v.__doc__.split("Parameters")[0].strip()
+ df[k] = description
+
+ return df
+
+
+shape_features = dict(
+ area=_area,
+ aspect_ratio=_aspect_ratio,
+ bounds=_bounds,
+ density=_density,
+ perimeter=_perimeter,
+ radius=_radius,
+ raster=_raster,
+ second_moment=_second_moment,
+ span=_span,
+)
+
+
+def obs_stats(
+ data: AnnData,
+ feature_names: List[str] = ["area", "aspect_ratio", "density"],
+ copy=False,
+):
+ """Compute features for each cell shape. Convenient wrapper for `bento.tl.shape_features`.
+ See list of available features in `bento.tl.shape_features`.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ feature_names : list
+ List of features to compute. See list of available features in `bento.tl.shape_features`.
+ copy : bool, optional
+ Return a copy of `data` instead of writing to data, by default False.
+
+ Returns
+ -------
+ data : anndata.AnnData
+ Returns `data` if `copy=True`, otherwise adds fields to `data`:
+
+ `obs['{shape}_{feature}']` : np.array
+ Feature of each polygon
+ """
+ adata = data.copy() if copy else data
+
+ # Compute features
+ analyze_shapes(adata, "cell_shape", feature_names, copy=copy)
+ if "nucleus_shape" in adata.obs.columns:
+ analyze_shapes(adata, "nucleus_shape", feature_names, copy=copy)
+
+ return adata if copy else None
+
+
+@track
+def analyze_shapes(
+ data: AnnData,
+ shape_names: Union[str, List[str]],
+ feature_names: Union[str, List[str]],
+ feature_kws: Dict[str, Dict] = None,
+ recompute: bool = False,
+ progress: bool = True,
+ copy: bool = False,
+):
+ """Analyze features of shapes.
+
+ Parameters
+ ----------
+ data : AnnData
+ Spatial formatted AnnData
+ shape_names : list of str
+ List of shapes to analyze.
+ feature_names : list of str
+ List of features to analyze.
+ feature_kws : dict, optional (default: None)
+ Keyword arguments for each feature.
+ copy : bool, optional
+ Return a copy of `data` instead of writing to data, by default False.
+
+ Returns
+ -------
+ adata : AnnData
+ See specific feature function docs for fields added.
+
+ """
+ adata = data.copy() if copy else data
+
+ # Cast to list if not already
+ if isinstance(shape_names, str):
+ shape_names = [shape_names]
+
+ # Add _shape suffix if shape names don't have it
+ shape_names = [s if s.endswith("_shape") else f"{s}_shape" for s in shape_names]
+
+ # Cast to list if not already
+ if isinstance(feature_names, str):
+ feature_names = [feature_names]
+
+ # Generate feature x shape combinations
+ combos = [(f, s) for f in feature_names for s in shape_names]
+
+ # Set up progress bar
+ if progress:
+ combos = tqdm(combos)
+
+ # Analyze each feature x shape combination
+ for feature, shape in combos:
+ kws = dict(recompute=recompute)
+ if feature_kws and feature in feature_kws:
+ kws.update(feature_kws[feature])
+
+ shape_features[feature](adata, shape, **kws)
+
+ return adata if copy else None
+
+
+def register_shape_feature(name: str, func: Callable):
+ """Register a shape feature function. The function should take an AnnData object and a shape name as input.
+ The function should add the feature to the AnnData object as a column in AnnData.obs. This should be done in place and not return anything.
+
+ Parameters
+ ----------
+ name : str
+ Name of the feature function.
+ func : function
+ Function that takes an AnnData object and a shape name as arguments.
+ """
+ shape_features[name] = func
+
+ # TODO perform some checks on the function
+
+ print(f"Registered shape feature '{name}' to `bento.tl.shape_features`.")
diff --git a/bento/tools/_shapes.py b/bento/tools/_shapes.py
deleted file mode 100644
index 0373e62..0000000
--- a/bento/tools/_shapes.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import geopandas as gpd
-
-from .._utils import track
-
-
-@track
-def outer_edge(data, shape_name, distance=30, copy=False):
-
- adata = data.copy() if copy else data
-
- if distance <= 0:
- print("Distance must be positive.")
- return
-
- # Get region that is distance outside of specified shape but within the cell
- cell_shapes = gpd.GeoSeries(adata.obs["cell_shape"])
- query_shapes = gpd.GeoSeries(adata.obs[shape_name])
- outer_shapes = cell_shapes & (query_shapes.buffer(distance) - query_shapes)
-
- if outer_shapes[0].type == "MultiLineString":
- print("Invalid region, no area within cell.")
- return
-
- name_prefix = shape_name.split("_shape")[0]
- adata.obs[f"{name_prefix}_outer_edge_shape"] = outer_shapes
-
- return adata if copy else None
-
-
-@track
-def inner_edge(data, shape_name, distance=30, copy=False):
-
- adata = data.copy() if copy else data
-
- if distance <= 0:
- print("Distance must be positive.")
- return
-
- # Get region that is distance inside of specified shape but within the cell
- cell_shapes = gpd.GeoSeries(adata.obs["cell_shape"])
- query_shapes = gpd.GeoSeries(adata.obs[shape_name])
- inner_shapes = cell_shapes & (query_shapes - query_shapes.buffer(-distance))
-
- if inner_shapes[0].type == "MultiLineString":
- print("Invalid region, no area within cell.")
- return
-
- name_prefix = shape_name.split("_shape")[0]
- adata.obs[f"{name_prefix}_inner_edge_shape"] = inner_shapes
-
- return adata if copy else None
diff --git a/bento/tools/_tensor_tools.py b/bento/tools/_tensor_tools.py
deleted file mode 100644
index 502e4a7..0000000
--- a/bento/tools/_tensor_tools.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import cell2cell as c2c
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-from sklearn.preprocessing import MinMaxScaler, StandardScaler
-
-from .._utils import track, PATTERN_NAMES, TENSOR_DIM_NAMES
-
-
-@track
-def to_tensor(data, layers, mask=False, copy=False):
- """
- Generate tensor from data where dimensions are (layers, cells, genes).
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData
- layers : list of str
- Keys in data.layers to build tensor.
- mask : bool
- Whether to replace nans with 0, default False.
- copy : bool
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `uns['tensor']` : np.ndarray
- 3D numpy array of shape (len(layers), adata.n_obs, adata.n_vars)
-
- `uns['tensor_labels'] : dict
- Element labels across each dimension. Keys are dimension names (layers, cells, genes),
- values are lists of str
-
- """
- adata = data.copy() if copy else data
-
- cells = data.obs_names.tolist()
- genes = adata.var_names.tolist()
-
- # Build tensor from specified layers
- tensor = []
- for l in layers:
- tensor.append(adata.to_df(l).values)
- tensor = np.array(tensor)
-
- # Replace nans with 0 if mask == True
- if mask:
- tensor_mask = ~np.isnan(tensor)
- tensor[~tensor_mask] = 0
-
- # Save tensor values
- adata.uns["tensor"] = tensor
-
- # Save tensor dimension indexes
- adata.uns["tensor_labels"] = dict(zip(TENSOR_DIM_NAMES, [layers, cells, genes]))
-
- return adata
-
-
-def select_tensor_rank(data, layers, upper_rank=5, runs=3, device="auto", random_state=888, copy=False):
- """Perform `bento.tl.decompose_tensor()` up to rank `upper_rank` repeating each decomposition
- `runs` times to compute a 95% confidence interval, plotting reconstruction error for each rank.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData.
- layers : list of str
- Keys in data.layers to build tensor.
- upper_rank : int
- Maximum rank to perform decomposition, by default 10.
- runs : int
- Number of times to run decomposition for calculating the confidence interval, by default 5.
- device : str, optional
- Type of device to use, valid options include `cpu`, `gpu`, and `auto`, by default `auto`.
- Option `auto` prefers `gpu` over `cpu` if available.
- random_state : int, optional
- Random seed used for reproducibility, by default 888
- copy : bool
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- adata : anndata.AnnData
- Returns `adata` if `copy=True`, otherwise adds fields to `data`:
-
- `uns['tensor']` : np.ndarray
- 3D numpy array of shape (len(layers), adata.n_obs, adata.n_vars)
-
- `uns['tensor_labels'] : dict
- Element labels across each dimension. Keys are dimension names (layers, cells, genes),
- values are lists of str
- """
- adata = data.copy() if copy else data
-
- to_tensor(adata, layers=layers, mask=True)
- tensor_c2c = init_c2c_tensor(adata, device=device)
-
- fig, error = tensor_c2c.elbow_rank_selection(
- upper_rank=upper_rank,
- runs=runs,
- init="random",
- automatic_elbow=True,
- random_state=random_state,
- )
-
- plt.tight_layout()
-
-
-def lp_signatures(data, rank, device="auto", random_state=888, copy=False):
- """Calculate localization signatures by performing tensor decomposition on the dataset tensor.
- Wrapper for `bento.tl.decompose_tensor()`.
-
- Parameters
- ----------
- data : AnnData
- Spatial formatted AnnData.
- rank : int
- Rank to perform decomposition.
- device : str, optional
- Type of device to use, valid options include `cpu`, `gpu`, and `auto`, by default `auto`.
- Option `auto` prefers `gpu` over `cpu` if available.
- random_state : int, optional
- Random seed used for reproducibility, by default 888.
- copy : bool
- Return a copy of `data` instead of writing to data, by default False.
-
- Returns
- -------
- _type_
- _description_
- """
- return decompose_tensor(
- data, PATTERN_NAMES, rank, device=device, random_state=random_state, copy=copy
- )
-
-
-@track
-def decompose_tensor(data, layers, rank, device="auto", random_state=888, copy=False):
- """Perform tensor decomposition on the 3-dimensional tensor built from `[cell, gene, layers]`.
-
- Parameters
- ----------
- data : _type_
- _description_
- layers : _type_
- _description_
- rank : _type_
- _description_
- device : str, optional
- _description_, by default "auto"
- random_state : int, optional
- _description_, by default 888
- copy : bool, optional
- _description_, by default False
-
- Returns
- -------
- _type_
- _description_
- """
- adata = data.copy() if copy else data
-
- to_tensor(data, layers=layers, mask=True)
- tensor_c2c = init_c2c_tensor(data, device=device)
-
- tensor_c2c.compute_tensor_factorization(
- rank=rank,
- init="random",
- random_state=random_state,
- )
-
- tensor_loadings = {}
- for layer, df in tensor_c2c.factors.items():
- df.columns = df.columns.str.replace("Factor", "Signature")
- tensor_loadings[layer] = df
-
- adata.uns["tensor_loadings"] = tensor_loadings
-
- _assign_factors(data)
-
- return adata
-
-
-def _assign_factors(data, n_clusters=None, copy=False):
- adata = data.copy() if copy else data
-
- # Get tensor dimension names
- dim_names = list(adata.uns["tensor_labels"].keys())
- cell_load = adata.uns["tensor_loadings"][dim_names[1]]
- gene_load = adata.uns["tensor_loadings"][dim_names[2]]
-
- # If 1 component decomposition, don't cluster later
- cluster_factors = True
- if cell_load.shape[1] == 1:
- cluster_factors = False
-
- # Zscale for clustering
- cell_load = pd.DataFrame(
- StandardScaler().fit_transform(cell_load),
- index=cell_load.index,
- columns=cell_load.columns,
- )
- gene_load = pd.DataFrame(
- StandardScaler().fit_transform(gene_load),
- index=gene_load.index,
- columns=gene_load.columns,
- )
-
- # Get sorted cell order from clustermap
-
- iorder = sns.clustermap(
- cell_load.T, row_cluster=cluster_factors, cmap="RdBu_r", center=0
- ).dendrogram_col.reordered_ind
- plt.close()
-
- # Reorder cell names
- iorder = pd.Series(
- range(len(cell_load)), index=cell_load.index[iorder], name="td_cluster"
- )
- cell_to_factor = cell_load.join(iorder)["td_cluster"].tolist()
-
- # Save associated tensor decomposition factor to adata.obs
- adata.obs["td_cluster"] = cell_to_factor
-
- # Get sorted cell order from clustermap
- iorder = sns.clustermap(
- gene_load.T, row_cluster=cluster_factors, cmap="RdBu_r", center=0
- ).dendrogram_col.reordered_ind
- plt.close()
-
- # Reorder cell names
- iorder = pd.Series(
- range(len(gene_load)), index=gene_load.index[iorder], name="td_cluster"
- )
- gene_to_factor = gene_load.join(iorder)["td_cluster"].tolist()
-
- # Save associated tensor decomposition factor to adata.var
- adata.var["td_cluster"] = gene_to_factor
- return adata
-
-
-def init_c2c_tensor(data, device="auto"):
-
- try:
- import torch
-
- device = "cuda" if torch.cuda.is_available() else "cpu"
- except ImportError:
- device = None
-
- print(f"Device: {device}")
-
- order_labels = list(data.uns["tensor_labels"].keys())
- order_names = list(data.uns["tensor_labels"].values())
-
- tensor_c2c = c2c.tensor.PreBuiltTensor(
- data.uns["tensor"],
- order_names=order_names,
- order_labels=order_labels,
- device=device,
- )
-
- return tensor_c2c
diff --git a/bento/tools/gene_sets/__init__.py b/bento/tools/gene_sets/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bento/tools/gene_sets/fazal2019.csv b/bento/tools/gene_sets/fazal2019.csv
new file mode 100644
index 0000000..d542e88
--- /dev/null
+++ b/bento/tools/gene_sets/fazal2019.csv
@@ -0,0 +1,26305 @@
+target,source,weight
+GCLC,Nucleus,0.846217713
+NFYA,Nucleus,0.317604203
+NIPAL3,Nucleus,-0.388236784
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+SEMA3F,Nucleus,0.331924384
+CD99,Nucleus,-0.894381539
+LASP1,Nucleus,-0.481626953
+M6PR,Nucleus,0.047576545
+CFLAR,Nucleus,0.900405547
+NDUFAF7,Nucleus,0.66322515
+RBM5,Nucleus,1.760755983
+SLC7A2,Nucleus,0.686737912
+SARM1,Nucleus,0.724724678
+CAMKK1,Nucleus,0.940083255
+RECQL,Nucleus,-0.355294863
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+CDC27,Nucleus,-0.129754471
+SPPL2B,Nucleus,2.125245216
+CREBBP,Nucleus,0.351721442
+GCFC2,Nucleus,0.022367422
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+IBTK,Nucleus,0.049407747
+ZNF195,Nucleus,1.452772311
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+JHDM1D,Nucleus,1.106052437
+PHTF2,Nucleus,0.250274449
+FARP2,Nucleus,0.782347494
+IFRD1,Nucleus,1.054913434
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+CRAMP1L,Nucleus,1.064421787
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+CYB561,Nucleus,-0.502831213
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+BAZ1B,Nucleus,0.200535268
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+MBTD1,Nucleus,0.600964378
+LARS2,Nucleus,-0.388845628
+PIK3C2A,Nucleus,0.384641212
+ANLN,Nucleus,-0.18266804
+QPCTL,Nucleus,0.885235811
+MAP4K3,Nucleus,0.262865963
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+ELOVL5,Nucleus,-0.438757844
+MAP4K5,Nucleus,0.49732957
+MAN2B2,Nucleus,0.184717637
+CLK1,Nucleus,1.683731157
+ANGEL1,Nucleus,0.138726347
+DDX11,Nucleus,1.400537661
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+XYLT2,Nucleus,0.66174516
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+NRXN3,Nucleus,-0.103341715
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+SLC45A4,Nucleus,0.354369431
+ZNF839,Nucleus,1.574794251
+ZDHHC6,Nucleus,0.52068845
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+RNASET2,Nucleus,0.98534833
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+FAM13B,Nucleus,0.297245981
+PNPLA6,Nucleus,-0.795320642
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+CLEC16A,Nucleus,0.03213073
+ZFYVE16,Nucleus,0.36268231
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+PNKP,Nucleus,1.532711072
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+INPP4A,Nucleus,0.735244999
+RETSAT,Nucleus,0.155454171
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+LIMA1,Nucleus,-0.354496115
+LETMD1,Nucleus,1.368759764
+POLQ,Nucleus,0.453721518
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+MSMO1,Nucleus,-0.578589949
+TTC17,Nucleus,0.343519419
+LAMA3,Nucleus,0.425998226
+AP5M1,Nucleus,0.478232728
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+ATP9A,Nucleus,-0.22530223
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+TRAM2,Nucleus,0.563893904
+NTN1,Nucleus,0.101992123
+ADAT1,Nucleus,0.45048627
+SPEN,Nucleus,0.504681985
+MAP2K4,Nucleus,-0.31544436
+SLK,Nucleus,-0.02901927
+CYB5R4,Nucleus,-0.384182123
+ASB1,Nucleus,1.341294567
+FAM107B,Nucleus,-0.042597014
+SLC9A7,Nucleus,0.321394818
+FOXJ2,Nucleus,0.266052024
+PPP2R5A,Nucleus,-0.304138265
+ASPM,Nucleus,0.464736683
+ZBTB11,Nucleus,0.693597568
+ATXN3,Nucleus,0.858627182
+FGFR2,Nucleus,0.058661165
+LRRC40,Nucleus,-0.13611794
+ATG2B,Nucleus,0.533634927
+ARFGEF1,Nucleus,-0.674263637
+KLF6,Nucleus,0.554560754
+NEO1,Nucleus,-0.737777582
+TRAM1,Nucleus,-0.909719781
+TP53BP1,Nucleus,-0.090254609
+IARS2,Nucleus,-0.64184283
+ROCK1,Nucleus,0.088474384
+HYAL2,Nucleus,-0.706270712
+FGFR3,Nucleus,1.419012611
+MEF2A,Nucleus,0.290519154
+ACSL4,Nucleus,-0.166179106
+PRR11,Nucleus,-0.152291055
+REEP1,Nucleus,-0.494073682
+ATP11A,Nucleus,1.142405093
+POLR1A,Nucleus,0.403130674
+IP6K2,Nucleus,0.938284282
+KIF2A,Nucleus,-0.195700061
+TGFBR3,Nucleus,-0.606432704
+NEDD4,Nucleus,-0.607519146
+MAPK6,Nucleus,-0.293486357
+UFD1L,Nucleus,1.013367065
+LRP6,Nucleus,0.682001788
+NUCB2,Nucleus,-0.710961253
+SLC44A1,Nucleus,-0.603635889
+TMEM260,Nucleus,0.313829322
+SMG6,Nucleus,-0.039986711
+CLTCL1,Nucleus,1.496292637
+DGCR2,Nucleus,-0.190497108
+MNT,Nucleus,1.858093337
+ZXDC,Nucleus,1.777364718
+NDST1,Nucleus,0.378107849
+AP3M2,Nucleus,0.803836317
+RAD18,Nucleus,0.879451313
+ATP2B1,Nucleus,0.036776708
+MGAT4A,Nucleus,0.18306298
+SNX13,Nucleus,0.351547889
+VASH1,Nucleus,0.694506186
+SEL1L,Nucleus,-0.33734212
+ATP6AP1,Nucleus,-0.581835062
+DAZAP1,Nucleus,0.924737912
+CPSF1,Nucleus,0.933925775
+SLC6A15,Nucleus,-0.078621633
+RDH11,Nucleus,-0.347709332
+PRKACA,Nucleus,-0.543395286
+LPHN1,Nucleus,0.768194467
+RPS6KA6,Nucleus,0.307325087
+EPN2,Nucleus,0.548691151
+PTPN18,Nucleus,0.75336665
+TFRC,Nucleus,-0.181467359
+AFF4,Nucleus,0.573624509
+MPP5,Nucleus,-0.272478991
+HMMR,Nucleus,-0.704678582
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+TRNT1,Nucleus,0.821493465
+ACADVL,Nucleus,0.713249452
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+DERL2,Nucleus,-0.086783806
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+PVR,Nucleus,0.201183842
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+SELO,Nucleus,2.30830846
+LLGL2,Nucleus,1.601657534
+PDE8A,Nucleus,0.293769007
+SDHA,Nucleus,-0.366219306
+KDM5A,Nucleus,-0.037785262
+ADAM11,Nucleus,1.111789582
+ST6GAL1,Nucleus,-0.617110134
+GLI2,Nucleus,0.985183384
+NOTCH3,Nucleus,-0.50923819
+BCS1L,Nucleus,1.059506959
+DPP8,Nucleus,0.772812999
+SLC24A1,Nucleus,0.040584311
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+TUBE1,Nucleus,1.06877964
+SEMA3C,Nucleus,-0.519408302
+TTC38,Nucleus,0.808528246
+CELSR1,Nucleus,0.06180832
+ZNF638,Nucleus,0.187079672
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+ZNF37A,Nucleus,1.266357832
+FNDC3B,Nucleus,0.243167826
+FRYL,Nucleus,0.26391969
+TMEM131,Nucleus,-0.055987426
+WDR62,Nucleus,1.000182302
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+RBMS2,Nucleus,0.750768934
+SLC46A1,Nucleus,0.159414381
+PLXNA2,Nucleus,-1.201507692
+ANKRD13A,Nucleus,0.191212833
+PAG1,Nucleus,0.696852247
+MCAM,Nucleus,-0.113588055
+GPC4,Nucleus,-0.906475829
+MBNL3,Nucleus,0.46870208
+DGKD,Nucleus,1.044295552
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+IL4R,Nucleus,0.041934191
+LRCH4,Nucleus,1.305797278
+FAM76B,Nucleus,1.515730444
+SIRT6,Nucleus,0.312060773
+POLD3,Nucleus,0.809068504
+PHF17,Nucleus,0.251591579
+FBLN1,Nucleus,-1.246901832
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+MCCC1,Nucleus,-0.552051598
+ACER3,Nucleus,0.82714798
+N4BP2,Nucleus,0.381557542
+HOXA9,Nucleus,1.33265715
+PCM1,Nucleus,0.243309132
+TNRC6C,Nucleus,0.87143647
+ITCH,Nucleus,-0.041951632
+SDF4,Nucleus,0.263979932
+FKBP7,Nucleus,-0.632355668
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+CRTC1,Nucleus,2.003018772
+SUGP1,Nucleus,1.077893354
+SIPA1L3,Nucleus,0.374741618
+CADM4,Nucleus,-0.677866135
+SMG9,Nucleus,1.364131484
+AVL9,Nucleus,0.287299618
+CDK6,Nucleus,0.465628534
+DNAJC2,Nucleus,0.784322865
+WDR91,Nucleus,0.780952053
+CBLL1,Nucleus,0.352383705
+MTPN,Nucleus,-0.422502929
+ZC3HAV1,Nucleus,0.078440087
+OGDH,Nucleus,-0.8290997
+MET,Nucleus,-0.329787959
+LMBR1,Nucleus,-0.407828485
+HOXA3,Nucleus,1.463487737
+HOXA6,Nucleus,1.692769929
+BRAT1,Nucleus,1.039345319
+FKBP14,Nucleus,0.422644292
+NSUN5P2,Nucleus,1.956501758
+CASP2,Nucleus,0.596239087
+HSPB1,Nucleus,-1.117825265
+ZKSCAN1,Nucleus,0.194785596
+WASL,Nucleus,0.043702188
+RBM28,Nucleus,0.877008026
+C1GALT1,Nucleus,-0.542275246
+PLOD3,Nucleus,0.035549743
+CLDN15,Nucleus,2.333074153
+TMEM106B,Nucleus,-0.093125645
+CEP41,Nucleus,-0.340786461
+GLI3,Nucleus,-0.079148174
+TMEM248,Nucleus,-0.212668538
+TBL2,Nucleus,-0.351569382
+FKTN,Nucleus,0.960242161
+TMEM245,Nucleus,0.46029845
+MEGF9,Nucleus,-0.28734537
+TGFBR1,Nucleus,-0.341326814
+DNM1,Nucleus,2.046085979
+KANK1,Nucleus,0.248259506
+RAPGEF1,Nucleus,0.264362392
+NPDC1,Nucleus,2.421287849
+SETX,Nucleus,0.236802357
+CCNJ,Nucleus,0.043863702
+RAB11FIP2,Nucleus,0.358085439
+ERLIN1,Nucleus,-0.102342006
+MAPK8,Nucleus,0.688929648
+ATE1,Nucleus,-0.342110245
+PLEKHA1,Nucleus,0.734537623
+UNC5B,Nucleus,0.294448425
+BMPR1A,Nucleus,-0.461190591
+ACTA2,Nucleus,-4.868820133
+LIPA,Nucleus,-1.178268263
+LZTS2,Nucleus,1.185991934
+ARHGAP21,Nucleus,0.014486277
+ANKRD26,Nucleus,0.223945696
+LARP4B,Nucleus,0.438399535
+C10orf137,Nucleus,0.784322393
+MTPAP,Nucleus,0.276840819
+SH3PXD2A,Nucleus,0.839212881
+PITRM1,Nucleus,-0.144607269
+FAM208B,Nucleus,0.522259787
+TSPAN14,Nucleus,-0.199536031
+NUFIP2,Nucleus,1.475736278
+DHX40,Nucleus,-0.338346708
+CDK5RAP3,Nucleus,0.983278154
+RECQL5,Nucleus,0.398572686
+INTS2,Nucleus,-0.088051773
+CAMTA2,Nucleus,1.014492473
+MED13,Nucleus,0.207050315
+HOXB6,Nucleus,1.502837368
+CPD,Nucleus,-0.961972881
+GOSR1,Nucleus,-0.130683095
+CCDC47,Nucleus,-0.417992833
+AKAP10,Nucleus,0.876254337
+CYTH1,Nucleus,1.489749576
+LGALS3BP,Nucleus,-1.381282504
+EZH1,Nucleus,1.456209358
+PPP1R9B,Nucleus,-0.090220014
+LUC7L3,Nucleus,1.557681223
+DUSP3,Nucleus,-0.129503719
+EFNB3,Nucleus,-0.273255902
+DPH1,Nucleus,0.576991916
+NAT9,Nucleus,0.778432149
+TMEM104,Nucleus,0.018230108
+TMEM97,Nucleus,-0.92609798
+UNC119,Nucleus,1.009332366
+TMEM33,Nucleus,-0.107383605
+DCUN1D4,Nucleus,0.741976344
+MANBA,Nucleus,0.2664671
+ELF2,Nucleus,-0.124928948
+WFS1,Nucleus,0.196632079
+FRG1,Nucleus,-0.303754622
+CLCN3,Nucleus,-0.422639785
+GALNT7,Nucleus,-0.312026509
+TRIM2,Nucleus,0.348454749
+NEIL3,Nucleus,0.525278673
+SH3D19,Nucleus,0.141816743
+STIM2,Nucleus,0.656337153
+RAPGEF2,Nucleus,0.399793166
+UGDH,Nucleus,-0.666658588
+CCDC34,Nucleus,-0.482392181
+FNBP4,Nucleus,1.66611046
+SC5D,Nucleus,-0.520770361
+SIAE,Nucleus,-0.58228815
+EHD1,Nucleus,-0.584224722
+FOXRED1,Nucleus,1.038446229
+ST3GAL4,Nucleus,-0.38520127
+CPT1A,Nucleus,-1.169798819
+TMEM109,Nucleus,-0.657015838
+PANX1,Nucleus,-0.808810066
+UBE4A,Nucleus,-0.226523491
+DDX6,Nucleus,-0.405125293
+PVRL1,Nucleus,-0.040169579
+HIPK3,Nucleus,0.043182336
+MDK,Nucleus,-0.450746734
+AMBRA1,Nucleus,-0.237238618
+NAA40,Nucleus,0.801435175
+SLC35F2,Nucleus,-0.315072137
+LEPREL2,Nucleus,0.61359069
+CORO1C,Nucleus,-0.462153224
+ASIC1,Nucleus,1.167308997
+CAPRIN2,Nucleus,1.913530193
+SLC11A2,Nucleus,0.367121423
+MLEC,Nucleus,-0.090687596
+BCL7A,Nucleus,0.013363488
+RSRC2,Nucleus,1.251046047
+PPM1H,Nucleus,-0.232951569
+ELK3,Nucleus,-0.631546022
+MAGOHB,Nucleus,0.602178382
+ITFG2,Nucleus,1.498917548
+PARP11,Nucleus,0.239600518
+DUSP16,Nucleus,-0.228434025
+ACAD10,Nucleus,0.84136716
+NAA25,Nucleus,0.920862494
+DDX55,Nucleus,1.224474616
+SLC38A1,Nucleus,0.141673875
+C12orf49,Nucleus,-0.310024415
+MDM1,Nucleus,0.816486016
+CPSF6,Nucleus,0.778517316
+GNPTAB,Nucleus,-0.490064804
+ATN1,Nucleus,0.797216641
+C12orf57,Nucleus,-0.79223432
+LPCAT3,Nucleus,-0.281024132
+SUDS3,Nucleus,0.114891554
+GOLT1B,Nucleus,-0.27104431
+C2CD5,Nucleus,0.494621288
+RAB35,Nucleus,0.18493499
+RIC8B,Nucleus,0.786261098
+RP11-22B23.1,Nucleus,2.216410221
+DSE,Nucleus,-0.281701745
+MAN1A1,Nucleus,-0.279367353
+SERINC1,Nucleus,-0.797295945
+UST,Nucleus,-0.262149203
+KCTD20,Nucleus,-0.043603077
+RNF8,Nucleus,-0.265516042
+ICK,Nucleus,0.122657318
+RAB23,Nucleus,-0.086415982
+FBXL4,Nucleus,-0.417370555
+CCNC,Nucleus,0.094217125
+ALDH5A1,Nucleus,-0.075159916
+EYA4,Nucleus,1.007125523
+PERP,Nucleus,-0.706075481
+SLC16A10,Nucleus,0.177284172
+PHACTR2,Nucleus,0.370918149
+SLC39A7,Nucleus,-0.382761326
+PPP2R5D,Nucleus,-0.330516151
+PTK7,Nucleus,-0.029403951
+CUL9,Nucleus,0.053736555
+TMEM30A,Nucleus,-0.169761909
+SENP6,Nucleus,0.676086038
+VEGFA,Nucleus,1.701307109
+PRPF4B,Nucleus,0.884945744
+BTN2A1,Nucleus,0.451940896
+LAMA4,Nucleus,-0.208900603
+ERBB2IP,Nucleus,-0.213104182
+HARS2,Nucleus,0.3278442
+MAN2A1,Nucleus,0.241521348
+PAPD7,Nucleus,0.937446842
+NNT,Nucleus,-0.527267315
+APBB3,Nucleus,1.359708502
+SPARC,Nucleus,-2.191800433
+HMGCR,Nucleus,-0.518405688
+FAF2,Nucleus,-0.234233275
+CLK4,Nucleus,1.726998836
+ARSB,Nucleus,-0.485567923
+CNOT6,Nucleus,-0.013732613
+DROSHA,Nucleus,0.236012461
+FAM172A,Nucleus,-0.084412486
+LNPEP,Nucleus,-0.019915157
+SLC12A7,Nucleus,0.249212095
+NR3C1,Nucleus,-0.210873725
+C5orf15,Nucleus,-0.420589611
+LIFR,Nucleus,0.118060242
+TRAPPC13,Nucleus,0.634574687
+TXNDC15,Nucleus,0.137903793
+H2AFY,Nucleus,0.492468065
+TCERG1,Nucleus,0.836928186
+SMAD5,Nucleus,0.384606162
+ERGIC1,Nucleus,-0.37225579
+STC2,Nucleus,0.838364399
+ARL6,Nucleus,0.271365552
+NIT2,Nucleus,0.231950589
+UBE3A,Nucleus,0.327858232
+SLC25A36,Nucleus,1.969877643
+TFDP2,Nucleus,0.77291731
+XRN1,Nucleus,0.262463937
+WNT5A,Nucleus,0.309819257
+PFKFB4,Nucleus,0.102921438
+PRKAR2A,Nucleus,-0.143323576
+ACAP2,Nucleus,0.287335923
+CBLB,Nucleus,-0.032761141
+BBX,Nucleus,0.355637496
+GNB4,Nucleus,-0.130530718
+C3orf52,Nucleus,0.40820838
+PLXNA1,Nucleus,0.875792765
+CSPG5,Nucleus,-0.301146442
+SCAP,Nucleus,0.005438016
+HEMK1,Nucleus,1.495383438
+ACVR2B,Nucleus,0.422925208
+ABCC5,Nucleus,1.29879008
+SSR3,Nucleus,-0.424894141
+NKTR,Nucleus,2.195654705
+FOXP1,Nucleus,0.086599628
+INO80D,Nucleus,1.518383748
+ADAM23,Nucleus,-0.033221589
+MOB1A,Nucleus,-0.030462213
+LMAN2L,Nucleus,-0.509217668
+RTKN,Nucleus,0.946937184
+PIKFYVE,Nucleus,0.486343673
+FAHD2A,Nucleus,1.097909089
+SLC35F5,Nucleus,-0.390186466
+STEAP3,Nucleus,-0.208619004
+EPB41L5,Nucleus,0.433536796
+GPD2,Nucleus,-0.819893302
+ACVR1,Nucleus,-0.842702878
+MPV17,Nucleus,0.856461293
+TTC31,Nucleus,0.882904594
+NDUFS7,Nucleus,0.772168166
+SPTBN1,Nucleus,-0.044112469
+CCDC88A,Nucleus,0.236394408
+FN1,Nucleus,-0.562737491
+ELMOD3,Nucleus,1.277268601
+IGFBP5,Nucleus,-0.371560056
+USP34,Nucleus,0.080435654
+GGCX,Nucleus,-0.200076939
+CHST10,Nucleus,0.182418054
+MOB4,Nucleus,-0.065879669
+UXS1,Nucleus,-0.028120818
+PASK,Nucleus,1.621677416
+TAF1B,Nucleus,-0.025171713
+DCAF17,Nucleus,1.210533229
+SDC1,Nucleus,-1.229456477
+SLC1A4,Nucleus,-0.377875425
+SOS1,Nucleus,0.507621496
+WIPF1,Nucleus,-0.223592914
+THADA,Nucleus,-0.381642663
+TRAK2,Nucleus,0.036402169
+TIA1,Nucleus,0.461331733
+PCYOX1,Nucleus,-0.794970144
+ARID3A,Nucleus,-0.024607343
+EPHA4,Nucleus,0.264114913
+ALMS1,Nucleus,-0.242736913
+BCL9,Nucleus,0.45215343
+DHCR24,Nucleus,-0.534504889
+DNAJC16,Nucleus,-0.111004168
+RALGPS2,Nucleus,0.234769335
+CEP104,Nucleus,0.508558948
+FAM20B,Nucleus,-0.334575899
+TCEANC2,Nucleus,0.715245411
+WRAP73,Nucleus,0.850799051
+ICMT,Nucleus,-0.347005631
+QSOX1,Nucleus,-0.353102679
+AMPD2,Nucleus,1.108953063
+EDEM3,Nucleus,0.091793343
+RAP1A,Nucleus,-0.088782349
+S100PBP,Nucleus,0.041112805
+ASH1L,Nucleus,0.54833311
+SFPQ,Nucleus,1.031090732
+MEF2D,Nucleus,0.775833076
+C1orf21,Nucleus,0.805528105
+LEPR,Nucleus,0.03224301
+IVNS1ABP,Nucleus,0.703106184
+KIAA2013,Nucleus,-0.157886128
+MIIP,Nucleus,0.497860316
+SLC35D1,Nucleus,0.338705741
+WLS,Nucleus,-1.014921206
+PRDM2,Nucleus,0.746019942
+TROVE2,Nucleus,0.440179747
+SRSF11,Nucleus,1.926842147
+PHTF1,Nucleus,-0.33854559
+TMEM9,Nucleus,-0.317761
+EXOC8,Nucleus,1.205726029
+NID1,Nucleus,-0.607284065
+MTR,Nucleus,0.412108063
+BMP8B,Nucleus,-0.256560533
+RIMS3,Nucleus,1.14927281
+AKT3,Nucleus,-0.010459625
+ETV3,Nucleus,0.177742482
+LPHN2,Nucleus,-0.170317439
+RBBP5,Nucleus,-0.452035705
+ECE1,Nucleus,-0.513402729
+CD46,Nucleus,0.903813577
+APH1A,Nucleus,-0.671852058
+LEPRE1,Nucleus,-0.505416148
+SLC2A1,Nucleus,-1.041220716
+SLC19A2,Nucleus,0.594308377
+NSUN4,Nucleus,0.787403073
+TMED5,Nucleus,-0.136094554
+DR1,Nucleus,0.034973227
+PTBP2,Nucleus,1.353603168
+DARS2,Nucleus,-0.756883747
+DIEXF,Nucleus,0.520123327
+RCAN3,Nucleus,-0.110337579
+C1orf63,Nucleus,1.539960676
+SLC35A3,Nucleus,0.796022929
+RCOR3,Nucleus,1.155949351
+ARID1A,Nucleus,0.288356612
+CENPF,Nucleus,0.287544627
+ESYT2,Nucleus,-0.143802358
+CD3EAP,Nucleus,0.424594437
+MESDC2,Nucleus,-0.315721348
+CTSD,Nucleus,-0.844683174
+STK11,Nucleus,1.03712321
+KMT2A,Nucleus,0.616112962
+KPTN,Nucleus,0.816083883
+KIF14,Nucleus,0.236677103
+ATF6,Nucleus,-0.736609343
+FASTKD2,Nucleus,-0.221091138
+NRP2,Nucleus,-0.124133659
+CREB1,Nucleus,0.343154037
+B4GALT6,Nucleus,0.491801903
+ELOVL4,Nucleus,-0.488735781
+CASP8AP2,Nucleus,0.207608706
+PHF3,Nucleus,-0.116539355
+PLAGL1,Nucleus,1.096569355
+FBXO30,Nucleus,0.666935205
+TMEM5,Nucleus,-0.049541538
+ZNF430,Nucleus,0.033120888
+DCLRE1B,Nucleus,-0.32440171
+PKD2,Nucleus,1.40062111
+UBN1,Nucleus,0.526772413
+KLF12,Nucleus,0.98925253
+WDR35,Nucleus,0.371154074
+CCND2,Nucleus,0.878740471
+SATB2,Nucleus,-0.137715279
+SENP5,Nucleus,0.766459139
+C1orf198,Nucleus,-0.228188418
+HEATR1,Nucleus,0.24738893
+PTBP3,Nucleus,-0.176873297
+FAM206A,Nucleus,-0.038778362
+RBM18,Nucleus,-0.302219881
+MAPKAP1,Nucleus,-0.439365193
+KDSR,Nucleus,-0.248317889
+ONECUT2,Nucleus,1.818195336
+IRF2BPL,Nucleus,-0.076896545
+AREL1,Nucleus,-0.0947543
+ABCD4,Nucleus,1.484643899
+RBM25,Nucleus,1.463598934
+NRDE2,Nucleus,1.115015398
+KLHL29,Nucleus,1.940472079
+DNMT3A,Nucleus,1.297293154
+ATAD2B,Nucleus,0.796074414
+ATL2,Nucleus,0.376820829
+YIPF4,Nucleus,0.283599703
+AFTPH,Nucleus,0.129618713
+BCL11A,Nucleus,0.123276107
+SLC17A5,Nucleus,-0.366085747
+FAM178A,Nucleus,0.660605942
+GPAM,Nucleus,-0.231877618
+HELLS,Nucleus,0.951260421
+TCTN3,Nucleus,-0.968193649
+C10orf76,Nucleus,-0.262270558
+HOXB8,Nucleus,1.208354683
+HOXB3,Nucleus,1.901945289
+PANK3,Nucleus,1.307999179
+NUP43,Nucleus,0.660615058
+LRP11,Nucleus,-0.583401038
+MASTL,Nucleus,0.505943809
+ELF1,Nucleus,-0.25083405
+EGR1,Nucleus,-0.456042773
+NR2C1,Nucleus,0.802483156
+MTERFD3,Nucleus,1.406674782
+CLU,Nucleus,-2.994099148
+TNFRSF10B,Nucleus,-0.951108027
+TARDBP,Nucleus,0.78196669
+CRISPLD1,Nucleus,-0.13025418
+AKAP1,Nucleus,0.338427831
+TRIM25,Nucleus,0.407232726
+KIAA0922,Nucleus,-0.082948962
+PAPD5,Nucleus,0.366314241
+CEP89,Nucleus,0.634729362
+B4GALT4,Nucleus,0.27084459
+KIF18A,Nucleus,0.379305217
+CRY2,Nucleus,0.730023
+ZNF639,Nucleus,-0.203819124
+PDS5A,Nucleus,-0.158156864
+CLCC1,Nucleus,-0.072697213
+ACVR2A,Nucleus,0.015401866
+RPL21,Nucleus,1.298663625
+MTERFD2,Nucleus,0.958818286
+KIAA1191,Nucleus,-0.325752725
+RBBP6,Nucleus,0.505820031
+ZC3H7A,Nucleus,0.219956487
+FAM35A,Nucleus,0.211000512
+FAM213A,Nucleus,-0.368024617
+ODF2L,Nucleus,0.993375732
+TRMT13,Nucleus,1.229596292
+RPAP2,Nucleus,0.714057117
+FAM126A,Nucleus,0.27972342
+FKBP9,Nucleus,-0.595914688
+POLM,Nucleus,1.615946307
+SLC25A51,Nucleus,0.679623428
+DCAF10,Nucleus,0.232894393
+KIAA1549,Nucleus,0.273317577
+CALD1,Nucleus,0.283984426
+CHST3,Nucleus,0.131795841
+P4HA1,Nucleus,-0.870665163
+RBM19,Nucleus,0.815367623
+GIPC1,Nucleus,-0.23215172
+ATP7B,Nucleus,-0.012678041
+ZC3H13,Nucleus,-0.028738259
+NLN,Nucleus,0.156843984
+CENPK,Nucleus,0.559922026
+OPTN,Nucleus,-1.208054472
+SPATS2,Nucleus,0.033288029
+LRP1,Nucleus,-1.080400153
+HJURP,Nucleus,0.633700749
+USP45,Nucleus,1.346772725
+SLC36A1,Nucleus,0.514020738
+LPGAT1,Nucleus,0.043905635
+EXOSC9,Nucleus,0.742513928
+PLA2G12A,Nucleus,0.413024042
+ADCK4,Nucleus,0.060240411
+PFKFB2,Nucleus,0.80413478
+AGO2,Nucleus,0.817015027
+MXD4,Nucleus,1.040499889
+ACSL3,Nucleus,-0.543808124
+SLC12A4,Nucleus,-0.764104525
+FAM210B,Nucleus,-0.857174172
+SDC4,Nucleus,-0.741988895
+NCOA3,Nucleus,0.877336375
+PIGT,Nucleus,-0.77120191
+VAPB,Nucleus,0.276810612
+CHD6,Nucleus,0.343603963
+SRSF6,Nucleus,1.252469657
+RAB22A,Nucleus,0.722477834
+STX16,Nucleus,1.264279741
+STAMBP,Nucleus,-0.119474281
+NAGK,Nucleus,1.182288428
+PAIP2B,Nucleus,0.178728723
+ATP8A1,Nucleus,0.474543071
+BTN2A2,Nucleus,0.398830414
+ABCC10,Nucleus,0.922652448
+AARS2,Nucleus,0.179870875
+ZNF391,Nucleus,0.947466202
+CDKN1A,Nucleus,-3.210518166
+SSR1,Nucleus,-0.353827473
+NRN1,Nucleus,-0.258878781
+ATXN1,Nucleus,1.087413762
+EEF1E1,Nucleus,0.698604572
+LRRFIP1,Nucleus,0.807741218
+AHNAK,Nucleus,0.257929267
+ABCC4,Nucleus,-0.443535899
+EFNB2,Nucleus,-0.224842221
+ATP5S,Nucleus,0.93658577
+FAM193A,Nucleus,1.076989828
+GGA3,Nucleus,0.627199892
+GTF3C4,Nucleus,0.15895764
+PPP1R12C,Nucleus,1.205947043
+MBOAT7,Nucleus,0.043622228
+CCDC93,Nucleus,0.846153106
+THOC2,Nucleus,0.215154341
+MED1,Nucleus,0.100567623
+GPR108,Nucleus,-0.13406512
+GPCPD1,Nucleus,0.750034596
+PANK2,Nucleus,1.079128797
+NAPB,Nucleus,1.601166073
+TMX4,Nucleus,-0.605492339
+RRBP1,Nucleus,-0.028763511
+ZNF133,Nucleus,1.505315029
+MCM8,Nucleus,0.678895285
+NCLN,Nucleus,0.665628229
+ZNF436,Nucleus,-0.080205168
+AMOT,Nucleus,-0.689793837
+TMEM115,Nucleus,-0.540257182
+AGO3,Nucleus,1.800746606
+HECTD3,Nucleus,0.053854186
+KLC1,Nucleus,1.65870362
+XRCC3,Nucleus,1.63845235
+TUBGCP3,Nucleus,0.060972949
+PCID2,Nucleus,0.946778097
+FRMD8,Nucleus,0.400582583
+PCNXL4,Nucleus,0.28979138
+ATG14,Nucleus,1.16360453
+KTN1,Nucleus,-0.038040409
+PLEKHG3,Nucleus,1.073377452
+WDR60,Nucleus,0.97786998
+AIF1L,Nucleus,-0.108754275
+SLC10A3,Nucleus,-0.746648782
+CANX,Nucleus,-0.589718948
+CPSF3L,Nucleus,0.944150013
+TRAF2,Nucleus,0.240933766
+HELB,Nucleus,0.628472504
+DYRK2,Nucleus,0.813437588
+LRRC61,Nucleus,-0.036029641
+FGFRL1,Nucleus,0.036809753
+EMC1,Nucleus,-0.267263831
+HP1BP3,Nucleus,0.984468398
+SIN3B,Nucleus,0.924942792
+SLC35E1,Nucleus,-0.043826719
+GFER,Nucleus,0.757807075
+PKMYT1,Nucleus,1.255203377
+CHTF18,Nucleus,1.996000114
+MACF1,Nucleus,-0.095224786
+RNF6,Nucleus,0.036814553
+AKAP9,Nucleus,0.400373569
+HIP1,Nucleus,-0.291265279
+POR,Nucleus,-0.591092406
+PEX1,Nucleus,0.181141562
+LRFN1,Nucleus,0.290537043
+SRD5A3,Nucleus,-0.694710709
+PPAT,Nucleus,0.529109065
+TUBGCP6,Nucleus,1.558130408
+DGCR8,Nucleus,1.829071219
+TPST2,Nucleus,-0.321406798
+MPST,Nucleus,0.782646845
+SPECC1,Nucleus,-0.420492293
+NAA38,Nucleus,0.820701364
+PRKRIP1,Nucleus,0.826185614
+PODXL,Nucleus,0.126458624
+STRIP2,Nucleus,1.27407772
+MKLN1,Nucleus,0.575463113
+CALU,Nucleus,-0.642383236
+CCDC136,Nucleus,1.14546131
+SMO,Nucleus,0.319508675
+KLHDC10,Nucleus,0.436748707
+OSGEPL1,Nucleus,0.18976083
+HOXD10,Nucleus,2.821928279
+HOXD11,Nucleus,1.12067109
+HERC2,Nucleus,0.163275107
+TWSG1,Nucleus,-0.352740655
+MYO5C,Nucleus,0.25186714
+TMOD2,Nucleus,0.622566144
+TTBK2,Nucleus,0.751884261
+IVD,Nucleus,0.759042748
+CLN6,Nucleus,0.577812434
+ARPP19,Nucleus,0.134490001
+VPS13C,Nucleus,0.164882974
+SUMF2,Nucleus,-0.310034652
+SPCS3,Nucleus,-0.305861265
+RPAIN,Nucleus,1.16965041
+PLD2,Nucleus,0.396983406
+MPDU1,Nucleus,-0.537302084
+CCNT1,Nucleus,0.351617489
+PUS7L,Nucleus,1.321808929
+KRI1,Nucleus,0.669039441
+SLC44A2,Nucleus,-0.863984532
+BCL2L2,Nucleus,1.289995417
+PARP2,Nucleus,0.439609424
+TEP1,Nucleus,0.925844684
+MAP7D3,Nucleus,0.07584056
+ABHD17A,Nucleus,2.670958242
+ERMARD,Nucleus,0.787880413
+SAT1,Nucleus,1.294394631
+GNL3L,Nucleus,0.711096545
+SH3BP4,Nucleus,0.026925792
+LDLR,Nucleus,0.245571813
+PRKCSH,Nucleus,-0.395253233
+THEM6,Nucleus,-0.485448353
+PVRL2,Nucleus,-1.09334729
+SAFB2,Nucleus,1.075381406
+KIF1A,Nucleus,0.106932897
+COLGALT1,Nucleus,0.139997627
+MLLT1,Nucleus,0.710484088
+MLLT4,Nucleus,0.860471422
+ACTN4,Nucleus,0.087741051
+NDUFA10,Nucleus,1.068626735
+ZSWIM6,Nucleus,0.873633021
+PXDN,Nucleus,-0.961120813
+COL5A1,Nucleus,-0.915493199
+ZNF337,Nucleus,1.59545553
+TAF4,Nucleus,0.340797951
+LAMA5,Nucleus,0.508734501
+EXOSC2,Nucleus,0.756205741
+POMT1,Nucleus,1.243731896
+PRRC2B,Nucleus,0.393174871
+YIPF2,Nucleus,-0.341343987
+ZC3H4,Nucleus,0.351931969
+CLIP1,Nucleus,0.225347371
+HIP1R,Nucleus,0.677175956
+PPAN,Nucleus,1.717997388
+SLC6A8,Nucleus,1.06329231
+PLXNA3,Nucleus,1.198561417
+PRRG1,Nucleus,-0.144524585
+AKAP12,Nucleus,-0.017131541
+RBM39,Nucleus,1.035469169
+GGT7,Nucleus,1.116174903
+PPT1,Nucleus,-1.158221888
+RLIM,Nucleus,0.76176067
+ABCB7,Nucleus,-0.28646465
+MRPS25,Nucleus,1.890706571
+CAPN7,Nucleus,0.316730284
+ZFYVE20,Nucleus,0.43977221
+SLC6A6,Nucleus,1.388371123
+MGAT1,Nucleus,-0.274820264
+PSMC3IP,Nucleus,0.807160406
+DIAPH1,Nucleus,-0.320296736
+NDFIP1,Nucleus,-0.598175358
+ACAP3,Nucleus,1.941350721
+C1orf159,Nucleus,1.459135362
+MAP1B,Nucleus,0.456977104
+IL13RA1,Nucleus,-0.782768617
+WDR44,Nucleus,-0.250834784
+PRKAB2,Nucleus,0.989197795
+CLUHP3,Nucleus,2.783590515
+CHSY1,Nucleus,-0.523448521
+SNRPA1,Nucleus,1.320322551
+FBXW9,Nucleus,1.134085312
+RFX1,Nucleus,0.516321513
+CC2D1A,Nucleus,1.136592218
+NUP210,Nucleus,0.006348717
+ENOSF1,Nucleus,1.931658838
+EMILIN2,Nucleus,-2.782435487
+PRKAA1,Nucleus,1.405570335
+PNISR,Nucleus,1.989111135
+ZRANB2,Nucleus,1.613464327
+KDM6B,Nucleus,0.84673742
+GPS2,Nucleus,0.594536693
+VPS13B,Nucleus,0.501740495
+REEP2,Nucleus,-0.389389672
+PRMT7,Nucleus,1.254800417
+PCED1A,Nucleus,1.135852898
+PTPRA,Nucleus,-0.559729594
+KIAA0907,Nucleus,2.455287595
+DCAF8,Nucleus,2.194727429
+IGHMBP2,Nucleus,1.331966033
+LPIN3,Nucleus,1.287333252
+SERINC3,Nucleus,-0.522712904
+FBXO44,Nucleus,1.228666435
+USPL1,Nucleus,0.801243212
+XPO4,Nucleus,0.721656525
+SCO1,Nucleus,0.965584454
+MPRIP,Nucleus,0.794139649
+DSTYK,Nucleus,0.357660372
+SLC41A1,Nucleus,0.436229322
+GPALPP1,Nucleus,0.493516653
+IRS4,Nucleus,0.468045174
+FAM104A,Nucleus,0.064060346
+SLC39A11,Nucleus,-0.845719887
+EPHB2,Nucleus,-1.058063888
+SRRM1,Nucleus,0.277928817
+SUV420H2,Nucleus,0.784694942
+WDR74,Nucleus,0.903961711
+RTN3,Nucleus,-0.801092998
+MORC2,Nucleus,0.23082475
+LARGE,Nucleus,-0.29836643
+ADCK2,Nucleus,-1.07475662
+AGAP3,Nucleus,0.984822855
+KRBA1,Nucleus,1.500536671
+ZNF767,Nucleus,2.694905891
+ATP13A3,Nucleus,0.218550746
+TMEM254,Nucleus,-0.409046641
+TMTC1,Nucleus,-0.579226017
+KRAS,Nucleus,0.150961476
+SWAP70,Nucleus,-0.211878016
+ZFC3H1,Nucleus,0.793304753
+TEX15,Nucleus,1.044058485
+CTIF,Nucleus,0.915264616
+VHL,Nucleus,0.839647209
+ARL8B,Nucleus,-0.295517187
+EDEM1,Nucleus,0.966234835
+PRPF38B,Nucleus,1.528122217
+SORT1,Nucleus,-0.536767657
+PTGFRN,Nucleus,-0.672439558
+NOTCH2,Nucleus,-0.326395913
+CEPT1,Nucleus,0.457739035
+AP4B1,Nucleus,1.155260431
+SPIRE1,Nucleus,0.569404706
+SLC38A2,Nucleus,-0.040434436
+KIDINS220,Nucleus,-0.389413121
+ROCK2,Nucleus,0.113464492
+LPIN1,Nucleus,0.176533299
+IL6ST,Nucleus,-0.292568389
+TMEM241,Nucleus,-0.509244034
+LRP4,Nucleus,-0.112909561
+DDB2,Nucleus,-0.413353791
+ACP2,Nucleus,-0.545109301
+AGO4,Nucleus,0.743510738
+HOOK1,Nucleus,0.4435987
+DSC2,Nucleus,-0.8317792
+DSC3,Nucleus,-0.465400643
+DTNA,Nucleus,0.520512307
+FHOD3,Nucleus,0.073138235
+FADS2,Nucleus,-0.499738635
+CLOCK,Nucleus,1.03576465
+COL4A2,Nucleus,-0.368858444
+DZIP1,Nucleus,0.623736651
+UBAC2,Nucleus,-0.876065435
+ARGLU1,Nucleus,1.783470799
+BIVM,Nucleus,0.824458998
+ARHGAP32,Nucleus,0.19480363
+TMED7,Nucleus,-0.749188042
+APC,Nucleus,0.124378907
+WDR36,Nucleus,0.727851057
+NAA35,Nucleus,0.115994501
+TMEM2,Nucleus,-0.675754317
+GOLM1,Nucleus,-0.402433252
+TAOK3,Nucleus,0.082836889
+DMTF1,Nucleus,1.952997585
+TMEM243,Nucleus,0.287295076
+PNPLA8,Nucleus,-0.742785792
+MDFIC,Nucleus,-0.436179664
+ANKRD6,Nucleus,0.325719345
+KIAA1009,Nucleus,0.851240747
+SNX14,Nucleus,-0.619745227
+EPHA7,Nucleus,0.223956702
+DNAJC14,Nucleus,-0.541566456
+GDF11,Nucleus,0.334861992
+TROAP,Nucleus,1.027343096
+TSPAN31,Nucleus,-1.064868217
+TFCP2,Nucleus,0.000117913
+PAN2,Nucleus,0.58096336
+HNRNPA1,Nucleus,1.386149358
+ACVR1B,Nucleus,0.327201854
+OS9,Nucleus,-0.703496924
+MAP7,Nucleus,-0.147279097
+CD164,Nucleus,-0.158635793
+NHSL1,Nucleus,0.385844288
+AHI1,Nucleus,0.420694568
+SEMA4F,Nucleus,-0.516955832
+RAB11FIP5,Nucleus,0.977351557
+CCDC142,Nucleus,1.678476731
+GNS,Nucleus,-0.800781633
+MDM2,Nucleus,0.058172197
+KLHL36,Nucleus,0.579556533
+DYNC1LI2,Nucleus,1.358613174
+EGLN1,Nucleus,-0.043586649
+ABCB10,Nucleus,-0.144214382
+TAF5L,Nucleus,0.341057415
+STX6,Nucleus,-0.184025626
+CEP350,Nucleus,0.099435307
+LAMC1,Nucleus,-0.577604612
+RC3H1,Nucleus,0.947691805
+TTLL4,Nucleus,0.430121898
+USP37,Nucleus,0.011636688
+ITM2C,Nucleus,-0.720771445
+SERPINE2,Nucleus,-0.495230201
+TMEM127,Nucleus,-0.645288309
+GCC2,Nucleus,0.025721139
+C2orf49,Nucleus,0.059566085
+EPC2,Nucleus,0.02785397
+ARHGEF4,Nucleus,1.043976272
+ALDH1L2,Nucleus,0.547666932
+CKAP4,Nucleus,-0.889938946
+NEK3,Nucleus,0.605779787
+RCBTB1,Nucleus,0.532239674
+COG3,Nucleus,0.679465524
+SCRN1,Nucleus,-0.184287252
+CHST12,Nucleus,0.889385492
+KDELR2,Nucleus,-0.604694127
+NUPL2,Nucleus,1.051563826
+DBNL,Nucleus,0.864070625
+TTYH3,Nucleus,0.141191323
+IREB2,Nucleus,0.229904885
+RSAD1,Nucleus,0.88734485
+VEZF1,Nucleus,0.296315326
+TEX2,Nucleus,-0.125351825
+BRIP1,Nucleus,0.818037953
+SKIL,Nucleus,0.776449676
+RPS6KC1,Nucleus,-0.560490519
+BIN1,Nucleus,0.704411848
+HS6ST1,Nucleus,0.013025079
+UGGT1,Nucleus,-0.084822225
+DNAJC1,Nucleus,-1.33750182
+LRRC8A,Nucleus,-0.523474735
+CDK9,Nucleus,-0.106956805
+TOR1B,Nucleus,-0.168707857
+SMC2,Nucleus,-0.218047295
+TOR1A,Nucleus,-0.474286995
+RALGPS1,Nucleus,1.367251254
+FAM129B,Nucleus,0.35005151
+SLC2A8,Nucleus,1.064932329
+SLC31A1,Nucleus,-0.308391631
+ZNF189,Nucleus,0.362361398
+STX17,Nucleus,0.697763852
+TSTD2,Nucleus,0.771696535
+LMX1B,Nucleus,0.751288552
+RANBP6,Nucleus,0.340450157
+TLN1,Nucleus,-0.31265593
+ALDH1B1,Nucleus,-0.696142448
+CNPY3,Nucleus,-0.769971287
+TMEM63B,Nucleus,0.079728852
+TJAP1,Nucleus,0.894869335
+SLC22A23,Nucleus,0.465890064
+FOXF2,Nucleus,0.023616204
+RIPK1,Nucleus,-0.269520714
+ATAT1,Nucleus,1.676200763
+NRM,Nucleus,-0.764698073
+VARS2,Nucleus,1.130266907
+FAM8A1,Nucleus,-0.130466969
+PRKRIR,Nucleus,-0.070546079
+CREBZF,Nucleus,1.91674105
+PRCP,Nucleus,-1.119610528
+RNF121,Nucleus,0.019219107
+SULF1,Nucleus,-0.199012276
+SORL1,Nucleus,-0.083425991
+YAP1,Nucleus,-0.409091912
+RDX,Nucleus,-0.477977082
+MAP2K5,Nucleus,-0.68175487
+MAPKBP1,Nucleus,1.253343151
+CASC5,Nucleus,0.146153117
+HAUS2,Nucleus,0.293531719
+PARP6,Nucleus,1.261504781
+TUBGCP4,Nucleus,0.812474276
+RMDN3,Nucleus,0.335064999
+UACA,Nucleus,0.606127351
+SMAD6,Nucleus,1.96723164
+ADAM10,Nucleus,-0.474918699
+TTLL7,Nucleus,0.211634947
+FNBP1L,Nucleus,0.657015839
+RABGGTB,Nucleus,1.580629602
+ARHGAP29,Nucleus,0.354333386
+SLC44A5,Nucleus,1.184297404
+DBT,Nucleus,0.337898195
+EPT1,Nucleus,0.46666589
+ADCY3,Nucleus,0.538070605
+PNPT1,Nucleus,0.170711633
+THUMPD2,Nucleus,0.932185999
+PREPL,Nucleus,-0.194229509
+ACTR1A,Nucleus,-0.568948335
+TMEM180,Nucleus,1.076876576
+ATAD1,Nucleus,-0.085636914
+KIF20B,Nucleus,0.143519989
+TET1,Nucleus,-0.136311797
+DNA2,Nucleus,0.165128833
+BARD1,Nucleus,0.566140218
+NAB1,Nucleus,0.636460316
+PPIG,Nucleus,0.475303643
+FASTKD1,Nucleus,0.518140163
+SSFA2,Nucleus,0.198403837
+ITGAV,Nucleus,0.164974224
+SLC35A5,Nucleus,-0.723618302
+SECISBP2L,Nucleus,-0.02336187
+SPPL2A,Nucleus,-0.252284328
+GLCE,Nucleus,-0.284007217
+PPCDC,Nucleus,0.791268694
+PCDH10,Nucleus,-1.16291604
+AP1AR,Nucleus,0.309665713
+FGF2,Nucleus,0.961083665
+KIAA1109,Nucleus,0.346681891
+LARP1B,Nucleus,0.226511966
+BMP2K,Nucleus,0.929657801
+FRAS1,Nucleus,0.027095817
+SCARB2,Nucleus,-0.682251507
+USO1,Nucleus,-0.45421138
+CENPE,Nucleus,0.716587747
+GSTCD,Nucleus,0.107625302
+LEF1,Nucleus,0.236299203
+PPP3CA,Nucleus,-0.378319847
+FBN2,Nucleus,-0.531643752
+MAPK8IP3,Nucleus,2.243438978
+B4GALNT3,Nucleus,0.599677339
+AEBP2,Nucleus,0.012837332
+ETNK1,Nucleus,0.598487911
+CLSTN3,Nucleus,-0.945446412
+SCAF11,Nucleus,0.534039681
+COL2A1,Nucleus,-0.325842287
+LRIG3,Nucleus,0.533091289
+TMEM19,Nucleus,-0.778833371
+POC1B,Nucleus,-0.551683711
+TMTC3,Nucleus,-0.067013597
+GAS2L3,Nucleus,0.491467242
+SLC15A4,Nucleus,0.55514533
+TDG,Nucleus,0.231693629
+NUPL1,Nucleus,0.479065748
+MTMR6,Nucleus,-0.159436778
+SLC7A1,Nucleus,0.195129607
+BRCA2,Nucleus,0.766537698
+CERS5,Nucleus,-0.705375382
+ESYT1,Nucleus,-0.771993769
+TMBIM6,Nucleus,-0.837862772
+ANKRD52,Nucleus,1.044629623
+ZNF740,Nucleus,0.646329551
+HNRNPA1L2,Nucleus,0.81012717
+SBNO1,Nucleus,0.158866914
+SETD1B,Nucleus,0.716575449
+RBM26,Nucleus,1.112847025
+ZIC5,Nucleus,0.82502329
+TMX1,Nucleus,-0.314447249
+NAA30,Nucleus,0.030042011
+DCAF5,Nucleus,-0.113092238
+RAB15,Nucleus,0.070480829
+NIPA2,Nucleus,-0.065403601
+ZSCAN29,Nucleus,0.640010783
+BNIP2,Nucleus,0.139034757
+MAN2C1,Nucleus,2.129014319
+MESDC1,Nucleus,-0.615328263
+IGF1R,Nucleus,0.322088772
+ARRDC4,Nucleus,0.096081352
+PML,Nucleus,0.324827518
+LINS,Nucleus,1.142722388
+PCSK6,Nucleus,-0.079190292
+SCAMP2,Nucleus,-0.503063119
+POLG,Nucleus,0.453945531
+ABHD2,Nucleus,-0.205312772
+TICRR,Nucleus,0.665445715
+MFGE8,Nucleus,0.518410894
+FURIN,Nucleus,-0.329074806
+IQGAP1,Nucleus,-0.645619903
+CRTC3,Nucleus,0.539063989
+FTO,Nucleus,-0.865344666
+MBTPS1,Nucleus,0.15829579
+RHOT2,Nucleus,1.19412673
+PDPK1,Nucleus,0.654553169
+TCF25,Nucleus,1.224630036
+GALNS,Nucleus,0.60314036
+GAS8,Nucleus,1.370199855
+MED9,Nucleus,0.00721139
+GID4,Nucleus,0.041974092
+KSR1,Nucleus,-0.197093641
+SGSM2,Nucleus,1.201337497
+SSH2,Nucleus,0.454695496
+PTRH2,Nucleus,0.984019361
+SS18,Nucleus,0.782795723
+SLC39A6,Nucleus,-0.462249301
+GALNT1,Nucleus,-0.413185233
+ESCO1,Nucleus,-0.127300132
+GREB1L,Nucleus,0.079417179
+NPC1,Nucleus,-0.398184897
+MINK1,Nucleus,0.753238356
+TTYH2,Nucleus,0.792473585
+CSNK1D,Nucleus,1.126374311
+FOXK2,Nucleus,0.332933091
+TRIM65,Nucleus,1.187242009
+RNF157,Nucleus,-0.232782234
+CBX4,Nucleus,0.364609655
+MBD1,Nucleus,0.779170037
+ZCCHC2,Nucleus,0.564737827
+LEPREL4,Nucleus,-0.457643695
+FAM134C,Nucleus,-0.735138216
+ERBB2,Nucleus,-0.966490147
+FKBP10,Nucleus,0.562537817
+PRDM15,Nucleus,1.451951835
+DUS3L,Nucleus,1.072715283
+ATHL1,Nucleus,1.899203142
+COL6A1,Nucleus,-0.072085578
+IFNAR1,Nucleus,-0.425318802
+COL6A2,Nucleus,-0.445217295
+TMEM50B,Nucleus,0.528193768
+APP,Nucleus,-0.816039293
+URB1,Nucleus,0.192549115
+CAPN10,Nucleus,1.666374943
+ERVK3-1,Nucleus,2.790781568
+SLC47A1,Nucleus,-0.075069729
+RERE,Nucleus,0.750773803
+EPHA2,Nucleus,-0.062564465
+KIAA0319L,Nucleus,-0.07410354
+PLK4,Nucleus,0.527973699
+GPN2,Nucleus,1.164123652
+PIGK,Nucleus,-0.838509825
+PTPRF,Nucleus,-0.027422842
+SYPL2,Nucleus,0.021714373
+IGSF3,Nucleus,-0.540435297
+CELSR2,Nucleus,0.018856693
+ATP1B1,Nucleus,-0.878327999
+CREG1,Nucleus,-0.653245601
+POU2F1,Nucleus,1.124883106
+PPOX,Nucleus,1.282745064
+USP21,Nucleus,0.982066577
+PIGM,Nucleus,0.448460079
+ABL2,Nucleus,0.570092724
+XPR1,Nucleus,-0.722962773
+TOR1AIP1,Nucleus,-0.630259766
+TUFT1,Nucleus,0.79660866
+TARS2,Nucleus,-0.251459268
+CERS2,Nucleus,-0.80302169
+SEMA6C,Nucleus,1.24712044
+ATP8B2,Nucleus,-0.142935249
+ADAM15,Nucleus,0.502820498
+SLC39A1,Nucleus,-0.576868275
+GATAD2B,Nucleus,0.11449678
+HCN3,Nucleus,1.389392174
+GALNT2,Nucleus,-0.475005164
+TTC13,Nucleus,0.369592083
+MLK4,Nucleus,1.068409267
+CEP170,Nucleus,0.111190453
+SDE2,Nucleus,-0.399307338
+FBXO28,Nucleus,0.181170937
+CDC42BPA,Nucleus,0.262428391
+MBOAT2,Nucleus,0.529935763
+PSEN2,Nucleus,-0.039806948
+LBR,Nucleus,-0.421024788
+RHOB,Nucleus,-0.378141963
+ASXL2,Nucleus,0.625899483
+ETAA1,Nucleus,0.679859196
+ZNF514,Nucleus,1.753987725
+SFXN5,Nucleus,1.050215853
+TEX261,Nucleus,0.025341255
+RALB,Nucleus,-0.58476605
+SLC20A1,Nucleus,-0.012196545
+ZC3H8,Nucleus,0.95550973
+UBXN4,Nucleus,0.162724925
+AMMECR1L,Nucleus,0.163564255
+GALNT13,Nucleus,0.076726915
+SCRN3,Nucleus,0.428674853
+KIAA1715,Nucleus,-0.485539789
+CDCA7,Nucleus,0.750253498
+DLX1,Nucleus,0.886523748
+GULP1,Nucleus,0.578860655
+FAM171B,Nucleus,-0.272674866
+CCDC150,Nucleus,1.474582622
+SUMF1,Nucleus,-0.868133365
+RHBDD1,Nucleus,-0.105290774
+FAM134A,Nucleus,-0.34671338
+CTDSP1,Nucleus,-0.136281028
+EAF1,Nucleus,0.510393882
+GOLGA4,Nucleus,0.89340867
+IQSEC1,Nucleus,0.709810182
+PTPRG,Nucleus,-0.500967224
+IL17RD,Nucleus,-0.417343835
+ARL6IP5,Nucleus,-1.050950777
+TMF1,Nucleus,0.498189972
+LRIG1,Nucleus,0.057893838
+LIMD1,Nucleus,0.922520342
+NXPE3,Nucleus,-0.485013544
+SRPRB,Nucleus,-0.648143077
+TCTA,Nucleus,-0.678157969
+VPRBP,Nucleus,0.241826285
+SLIT2,Nucleus,-0.574356139
+DGKQ,Nucleus,1.919136272
+ATP10D,Nucleus,-0.447241433
+SCD5,Nucleus,-1.081388525
+ENOPH1,Nucleus,-0.123022817
+TRMT10A,Nucleus,-0.056777476
+KLHL8,Nucleus,-0.00174377
+USP53,Nucleus,0.786329459
+MARCH6,Nucleus,0.287008525
+FAM105A,Nucleus,-0.893316269
+PIK3R1,Nucleus,0.312618204
+LHFPL2,Nucleus,-0.092516752
+IQGAP2,Nucleus,-0.919360062
+PPIP5K2,Nucleus,0.268457703
+PAM,Nucleus,-0.945792956
+BDP1,Nucleus,0.550641124
+SLC30A5,Nucleus,-0.12918507
+ATG12,Nucleus,0.773041504
+YIPF5,Nucleus,-0.564767171
+RNF145,Nucleus,-0.0337138
+FBXO38,Nucleus,0.513028475
+PCYOX1L,Nucleus,0.524006722
+TNIP1,Nucleus,-0.352880375
+ZNF300,Nucleus,0.785813588
+GFOD1,Nucleus,1.713697282
+TRIM41,Nucleus,1.2286502
+FAM193B,Nucleus,1.337626533
+RNF44,Nucleus,1.323297357
+MUT,Nucleus,-0.704121592
+PHIP,Nucleus,0.801116929
+MMS22L,Nucleus,1.126472953
+PM20D2,Nucleus,0.019916318
+RNF217,Nucleus,1.642063027
+AIG1,Nucleus,-0.769460184
+TMEM181,Nucleus,0.689267363
+SDK1,Nucleus,-0.627742128
+RBAK,Nucleus,1.167999678
+CREB5,Nucleus,1.592164316
+PURB,Nucleus,1.194953573
+GBAS,Nucleus,-0.090911538
+ZNF92,Nucleus,0.419867314
+TMEM168,Nucleus,-0.026205165
+C7orf43,Nucleus,0.990910356
+SLC12A9,Nucleus,0.480070712
+GIGYF1,Nucleus,1.978612739
+TMEM209,Nucleus,-0.422756887
+NOM1,Nucleus,1.397910963
+SH3KBP1,Nucleus,-0.154429322
+CASK,Nucleus,-0.233705893
+SLC16A2,Nucleus,-1.28761352
+OGT,Nucleus,1.977021753
+ZNF711,Nucleus,0.689490625
+DIAPH2,Nucleus,-0.20443781
+CXorf57,Nucleus,0.898395557
+GPC3,Nucleus,-0.995932458
+BIN3,Nucleus,0.883288007
+SLC25A37,Nucleus,2.169335298
+CHMP7,Nucleus,-0.080341819
+ERLIN2,Nucleus,-0.453444177
+TACC1,Nucleus,0.503694875
+WHSC1L1,Nucleus,0.47162347
+TERF1,Nucleus,-0.056825745
+MTDH,Nucleus,-0.354018881
+LRP12,Nucleus,-0.879424773
+EBAG9,Nucleus,-0.101110445
+UTP23,Nucleus,0.094560568
+ZNF7,Nucleus,1.272416173
+ARHGAP39,Nucleus,1.197624833
+NAPRT1,Nucleus,1.200237754
+UHRF2,Nucleus,1.545660005
+ZCCHC7,Nucleus,-0.387576542
+SIGMAR1,Nucleus,-0.662543639
+CEP78,Nucleus,0.693912068
+HIATL1,Nucleus,-0.172252937
+INIP,Nucleus,-0.43946187
+UGCG,Nucleus,-0.412875303
+STOM,Nucleus,-0.656321119
+MRRF,Nucleus,0.836823729
+NR6A1,Nucleus,1.185176548
+SURF4,Nucleus,-0.508777651
+MED22,Nucleus,0.933223987
+SH3GLB2,Nucleus,1.46068961
+FAM73B,Nucleus,1.20390715
+GPR107,Nucleus,-0.295861448
+C9orf142,Nucleus,0.242118516
+INPP5E,Nucleus,1.626635613
+DPH7,Nucleus,0.894913182
+NOTCH1,Nucleus,0.487323114
+NACC2,Nucleus,1.13697543
+USP6NL,Nucleus,-0.030609686
+FAM171A1,Nucleus,-0.152701242
+PARD3,Nucleus,-0.420207098
+POLR3A,Nucleus,-0.055995547
+FRA10AC1,Nucleus,0.625403278
+ADD3,Nucleus,-0.104570962
+DNAJB12,Nucleus,0.412263379
+EIF4EBP2,Nucleus,-0.281423452
+MKI67,Nucleus,0.769532415
+MTG1,Nucleus,1.857999023
+PPRC1,Nucleus,1.23486668
+ITPRIP,Nucleus,-0.656306153
+CNNM2,Nucleus,-0.325992694
+PDCD11,Nucleus,-0.090704774
+LIN7C,Nucleus,0.427994703
+DGKZ,Nucleus,1.267975375
+TNKS1BP1,Nucleus,0.871329252
+SLC43A1,Nucleus,0.649092162
+PTPRJ,Nucleus,-0.416161923
+CELF1,Nucleus,1.134499707
+SESN3,Nucleus,0.594205413
+ENDOD1,Nucleus,-0.773252693
+SERPINH1,Nucleus,-1.296009903
+NCAM1,Nucleus,-0.058790377
+NPAT,Nucleus,0.325279568
+ATM,Nucleus,0.907314491
+GLB1L2,Nucleus,0.535379985
+HYOU1,Nucleus,-0.359374286
+DAK,Nucleus,0.362547729
+TMEM138,Nucleus,1.282043738
+FADS1,Nucleus,-0.643993868
+EML3,Nucleus,1.250698308
+B3GAT3,Nucleus,0.103263032
+SIDT2,Nucleus,0.627857873
+SOGA1,Nucleus,0.209876455
+LSM14B,Nucleus,0.832707826
+ORAOV1,Nucleus,2.19653468
+TAOK2,Nucleus,1.293172263
+ITGB1,Nucleus,-1.078933232
+ARID5B,Nucleus,-0.359380023
+TMCO3,Nucleus,0.130277261
+LATS2,Nucleus,-0.317769171
+LPHN3,Nucleus,-0.808297782
+PRSS23,Nucleus,-0.731126663
+PIP4K2A,Nucleus,-0.575957953
+FREM2,Nucleus,-0.071308437
+CRIM1,Nucleus,-0.318564881
+IPMK,Nucleus,0.16984373
+PLBD2,Nucleus,0.026699216
+GXYLT1,Nucleus,-0.27737595
+CSNK1G3,Nucleus,-0.123483952
+MIPOL1,Nucleus,-0.129389102
+EXT2,Nucleus,-1.083734554
+TMEM18,Nucleus,0.998762318
+NEK7,Nucleus,0.059397085
+FER,Nucleus,0.55178046
+VIPAS39,Nucleus,-0.600952009
+ANKRD50,Nucleus,0.43865617
+UPF2,Nucleus,-0.080069444
+EPS8,Nucleus,-0.08309161
+FAM160B1,Nucleus,1.24136166
+ADAM17,Nucleus,-0.350971981
+WWC2,Nucleus,0.048373592
+BICD1,Nucleus,0.712295046
+NBAS,Nucleus,-0.762102649
+GUF1,Nucleus,0.611101091
+SACS,Nucleus,1.147279813
+PABPC3,Nucleus,-0.228217747
+DST,Nucleus,0.318158965
+TIAL1,Nucleus,0.948340971
+TMEM56,Nucleus,-0.281499447
+FAM168B,Nucleus,0.150484359
+AC093838.4,Nucleus,2.261334873
+MGAT5,Nucleus,0.075838521
+GPATCH11,Nucleus,0.148089238
+POU4F1,Nucleus,1.071488344
+RNF219,Nucleus,0.039585004
+EPG5,Nucleus,0.563542968
+C18orf25,Nucleus,0.883893095
+PDK1,Nucleus,0.535522261
+PDE3B,Nucleus,-0.045190279
+TGOLN2,Nucleus,0.158679022
+UHMK1,Nucleus,1.004703255
+TADA1,Nucleus,0.284093224
+CWF19L2,Nucleus,-0.215773029
+JMY,Nucleus,0.85641908
+HOMER1,Nucleus,0.142500964
+USP12,Nucleus,-0.089300901
+CCDC50,Nucleus,0.172489608
+PAN3,Nucleus,0.465789887
+TMEM123,Nucleus,-0.313440897
+GJA1,Nucleus,-0.591539767
+SLC30A6,Nucleus,-0.346321338
+SAR1B,Nucleus,-0.186821125
+GPR180,Nucleus,0.127062198
+UTRN,Nucleus,-0.296922312
+PTPRK,Nucleus,-0.529366894
+PLOD2,Nucleus,-0.64957453
+GPR125,Nucleus,0.051674427
+SREK1IP1,Nucleus,0.576639409
+TXNDC11,Nucleus,-0.287493708
+BCL2L11,Nucleus,1.201605232
+CLGN,Nucleus,-0.732717841
+RASSF3,Nucleus,-0.392900443
+RANBP2,Nucleus,0.094155909
+TMEM87B,Nucleus,-0.069604107
+RBMS1,Nucleus,0.509725575
+LPCAT1,Nucleus,0.029626685
+UBALD1,Nucleus,2.535568917
+RMND5A,Nucleus,-0.275435347
+ZDHHC7,Nucleus,-0.189580121
+TRIP12,Nucleus,-0.067663541
+CEBPG,Nucleus,0.214976076
+SREK1,Nucleus,1.71179846
+CHD1,Nucleus,0.643769366
+DGKE,Nucleus,1.803994466
+HS2ST1,Nucleus,-0.397764823
+MSI2,Nucleus,0.81407171
+CACNA2D1,Nucleus,0.216290153
+NUS1,Nucleus,-0.032532286
+IMPACT,Nucleus,0.043752766
+TBCEL,Nucleus,0.571882702
+FAM105B,Nucleus,0.490190617
+TBRG1,Nucleus,1.526161223
+CC2D1B,Nucleus,0.752271967
+MIA3,Nucleus,0.019359776
+TRIM11,Nucleus,1.115746492
+CCSAP,Nucleus,-0.272342048
+CXADR,Nucleus,-0.5528504
+GABPA,Nucleus,0.409049493
+ADAMTS1,Nucleus,0.078445898
+TSEN2,Nucleus,1.390560644
+FLCN,Nucleus,0.778937345
+SKA1,Nucleus,-0.488860214
+RAB6B,Nucleus,0.566438862
+ACSS1,Nucleus,0.224097751
+ANKRD40,Nucleus,0.220908367
+VOPP1,Nucleus,-0.770277365
+APOOL,Nucleus,0.157087779
+CYP2U1,Nucleus,-0.701154366
+AGPAT5,Nucleus,-0.013049031
+MARVELD1,Nucleus,-0.211148102
+ZFYVE27,Nucleus,0.715839694
+SLC25A28,Nucleus,1.552539632
+HSPA13,Nucleus,-0.769199999
+USP25,Nucleus,0.428537332
+RHOC,Nucleus,-1.333561884
+SLC16A1,Nucleus,-0.621351755
+LARP1,Nucleus,-0.003131441
+MIER3,Nucleus,0.425402703
+ZKSCAN2,Nucleus,0.18575291
+PDIA4,Nucleus,-0.47355107
+FAM126B,Nucleus,1.271970224
+FZD7,Nucleus,-0.397710494
+FMN2,Nucleus,-0.599639186
+PPARGC1B,Nucleus,1.699475102
+SLC26A2,Nucleus,0.136834978
+LSM11,Nucleus,1.101583064
+PSD3,Nucleus,0.428053948
+DCK,Nucleus,-0.425003391
+ADAMTS3,Nucleus,0.717081111
+DPY19L4,Nucleus,-0.071049356
+NDUFAF6,Nucleus,1.021921018
+N6AMT1,Nucleus,1.1577933
+CDK20,Nucleus,0.99446517
+PCGF6,Nucleus,-0.077077991
+ANKRD9,Nucleus,1.389225107
+SFXN2,Nucleus,0.861000819
+PTDSS1,Nucleus,-0.674611173
+SUPV3L1,Nucleus,0.465540229
+TYSND1,Nucleus,1.252282912
+CD109,Nucleus,-0.307648777
+ZDHHC5,Nucleus,-0.308280682
+ZFAND3,Nucleus,-0.529956813
+NPTN,Nucleus,-0.403448001
+KAT6B,Nucleus,-0.063649467
+SAMD8,Nucleus,0.282713029
+BAG4,Nucleus,-0.341176224
+ATAD2,Nucleus,0.060678748
+PHKG2,Nucleus,1.160662885
+SASS6,Nucleus,0.344352897
+ZIC3,Nucleus,0.587925933
+EXOG,Nucleus,1.880173469
+SMG1,Nucleus,0.959243803
+FCHO2,Nucleus,0.099407423
+C1orf27,Nucleus,0.655754576
+LRP8,Nucleus,1.557235604
+PAXIP1,Nucleus,0.842791496
+SSBP3,Nucleus,0.227741099
+CLDN12,Nucleus,-0.083695368
+GATAD1,Nucleus,0.513150803
+ST3GAL2,Nucleus,0.300346396
+FUK,Nucleus,1.539612765
+KIT,Nucleus,0.080194373
+AASDH,Nucleus,0.418621878
+DYRK1A,Nucleus,-0.127980316
+TSPAN18,Nucleus,-0.525261815
+SLC35B2,Nucleus,-0.649826468
+TMEM164,Nucleus,-0.024783695
+TAB3,Nucleus,0.855999152
+SLC38A10,Nucleus,-0.159792909
+ZNF618,Nucleus,0.597361324
+C9orf91,Nucleus,0.075494211
+UBN2,Nucleus,1.778397568
+BRAF,Nucleus,0.686849901
+SLC37A3,Nucleus,0.159892817
+DPYSL5,Nucleus,-0.006046752
+FAM213B,Nucleus,0.173125495
+C12orf43,Nucleus,-0.422946031
+RER1,Nucleus,0.766957535
+UBXN11,Nucleus,0.992963022
+RHPN1,Nucleus,2.040403253
+CNNM4,Nucleus,-0.288734525
+EYA3,Nucleus,0.426171707
+MRAS,Nucleus,-0.647798713
+COLEC12,Nucleus,-0.357461576
+CUL4B,Nucleus,-0.421929304
+MITD1,Nucleus,0.790058463
+EIF5B,Nucleus,0.188062322
+TSPAN33,Nucleus,-0.78819685
+AHCYL2,Nucleus,-0.055916084
+B4GALT5,Nucleus,-0.163088978
+TSR2,Nucleus,0.192556699
+ZC3H18,Nucleus,0.489788386
+TMED4,Nucleus,0.253772127
+PPP1R15B,Nucleus,-0.174607113
+AGPAT6,Nucleus,0.086493353
+ZSCAN12,Nucleus,0.953054706
+ELK4,Nucleus,1.983278501
+F11R,Nucleus,-0.759940371
+ZNF276,Nucleus,2.315877369
+PINK1,Nucleus,-1.154196312
+B4GALT3,Nucleus,-0.330800481
+FAM160B2,Nucleus,1.208748262
+CACHD1,Nucleus,-0.005004952
+PAXBP1,Nucleus,1.960919121
+IFNAR2,Nucleus,0.123385169
+SON,Nucleus,0.752672714
+SV2A,Nucleus,-0.136045417
+HLCS,Nucleus,0.063468184
+ADPGK,Nucleus,0.870645025
+ALDH4A1,Nucleus,0.330575758
+STARD9,Nucleus,0.985588052
+UBR1,Nucleus,0.231087083
+AMFR,Nucleus,-0.717152207
+RSPRY1,Nucleus,-0.140887829
+ARHGAP35,Nucleus,0.089318043
+CALM3,Nucleus,-0.550682483
+IQCC,Nucleus,0.231882141
+BSDC1,Nucleus,1.237973176
+ATAD3B,Nucleus,1.331895711
+VMA21,Nucleus,0.128712156
+WDR4,Nucleus,0.858911336
+CBS,Nucleus,1.629852677
+PDXK,Nucleus,1.188908933
+G6PD,Nucleus,-0.281662913
+AGPAT3,Nucleus,0.764790444
+C21orf2,Nucleus,1.78490997
+LRRC3,Nucleus,0.440426651
+LSS,Nucleus,1.174443033
+VAV2,Nucleus,0.772340423
+MCM3AP,Nucleus,0.599735154
+C21orf58,Nucleus,1.653314148
+PCNT,Nucleus,0.61184612
+DIP2A,Nucleus,0.648672112
+ZNF714,Nucleus,0.825404589
+PKN3,Nucleus,1.244677224
+TAOK1,Nucleus,0.532262443
+SIK3,Nucleus,0.619517699
+PCSK7,Nucleus,1.148547481
+CHTOP,Nucleus,1.239151443
+ZBTB7B,Nucleus,0.624533746
+NLRX1,Nucleus,0.303937002
+ANO10,Nucleus,-1.250273803
+SLC25A44,Nucleus,0.405211211
+NBEAL2,Nucleus,1.907857608
+IER2,Nucleus,0.299073145
+ZNF394,Nucleus,0.427258268
+CPSF4,Nucleus,1.118992962
+TONSL,Nucleus,1.497024574
+MUM1,Nucleus,1.113933686
+RECQL4,Nucleus,1.411571661
+LRRC14,Nucleus,1.462914015
+PPP1R16A,Nucleus,1.266223362
+C5orf45,Nucleus,2.40618907
+MFSD12,Nucleus,1.227404183
+FDXR,Nucleus,-0.360016355
+ALDH16A1,Nucleus,0.775208529
+ITGA5,Nucleus,-0.644135992
+ZNF385A,Nucleus,-0.38917716
+MPP3,Nucleus,1.985267591
+EMC10,Nucleus,-0.239602509
+FAM171A2,Nucleus,-0.550249652
+DBF4B,Nucleus,1.303723755
+LARP4,Nucleus,0.248271155
+LEMD2,Nucleus,0.798345028
+WDR90,Nucleus,2.492325701
+C16orf59,Nucleus,0.343686056
+AMDHD2,Nucleus,2.075867548
+PAQR4,Nucleus,0.105847761
+ADCY9,Nucleus,-0.138295284
+CLPB,Nucleus,-0.416719953
+NEU3,Nucleus,1.018107696
+CYB561A3,Nucleus,0.082768457
+TAF6L,Nucleus,0.768415771
+LRP5,Nucleus,0.213180507
+ZYG11B,Nucleus,0.251005479
+PPAP2B,Nucleus,-0.60461316
+PRKAA2,Nucleus,0.583717782
+KLHL21,Nucleus,0.905554832
+GMEB1,Nucleus,-0.15303601
+SEPN1,Nucleus,-0.206750511
+AK4,Nucleus,-0.03602182
+RAVER2,Nucleus,0.754077637
+PDPN,Nucleus,-0.847580372
+SDC3,Nucleus,0.202593766
+KIAA1522,Nucleus,0.403893802
+C1orf86,Nucleus,1.521836474
+NFIA,Nucleus,0.103274308
+OMA1,Nucleus,-0.524402908
+MYSM1,Nucleus,2.186046071
+FUBP1,Nucleus,1.324639188
+DNAJB4,Nucleus,0.050639098
+FAM102B,Nucleus,-0.224865866
+ATXN7L2,Nucleus,1.644498069
+ZNF326,Nucleus,0.867384697
+EXTL2,Nucleus,0.186081603
+SLC30A7,Nucleus,0.183091813
+PEA15,Nucleus,-0.024600113
+NCSTN,Nucleus,-0.384748125
+VANGL2,Nucleus,0.026092228
+FLVCR1,Nucleus,0.908766923
+RBM15,Nucleus,0.519314188
+BPNT1,Nucleus,-0.309251171
+BROX,Nucleus,-0.43711824
+ACP6,Nucleus,1.174607493
+PPP1R21,Nucleus,-0.168635815
+B3GALNT2,Nucleus,0.612816221
+C2orf47,Nucleus,-0.293034052
+ARL5A,Nucleus,0.053052244
+SGCB,Nucleus,-0.744800854
+SMARCAD1,Nucleus,-0.116536219
+RNF149,Nucleus,0.841298734
+FZD5,Nucleus,0.492514861
+DCAF16,Nucleus,1.280933912
+PAQR3,Nucleus,-0.192963615
+ANTXR2,Nucleus,-1.172568232
+PBXIP1,Nucleus,-0.583631615
+PYGO2,Nucleus,0.245028711
+HIPK1,Nucleus,0.13866882
+KBTBD8,Nucleus,-0.140020127
+EOGT,Nucleus,0.731138813
+POGLUT1,Nucleus,0.717628895
+ATP1A1,Nucleus,-0.918864296
+EIF4E3,Nucleus,-0.387318391
+LRRC58,Nucleus,0.729694655
+FSTL1,Nucleus,-0.970990695
+KRTCAP2,Nucleus,0.969752157
+KIAA1524,Nucleus,0.294224841
+TGFBR2,Nucleus,-0.563731645
+ANKZF1,Nucleus,1.431146556
+STT3B,Nucleus,-0.149330812
+PPM1L,Nucleus,1.056311819
+RYBP,Nucleus,0.020160314
+PPP4R2,Nucleus,0.097544364
+C3orf17,Nucleus,0.5069702
+SPICE1,Nucleus,0.468452059
+WDFY3,Nucleus,0.242369407
+ATXN7,Nucleus,1.131010007
+PPM1K,Nucleus,0.713771911
+CCNL1,Nucleus,2.29255782
+RPP14,Nucleus,0.990709831
+ABHD6,Nucleus,-0.567928626
+CRELD1,Nucleus,0.1310995
+U2SURP,Nucleus,0.719453486
+TTC14,Nucleus,0.971128882
+SNRK,Nucleus,0.216912999
+SLC4A1AP,Nucleus,0.096635609
+ZDHHC3,Nucleus,0.37153765
+FYCO1,Nucleus,0.516990421
+YEATS2,Nucleus,0.285895896
+SNIP1,Nucleus,0.183065331
+TMEM41A,Nucleus,0.071586314
+RPN1,Nucleus,-1.18563316
+SFMBT1,Nucleus,0.361119738
+PBRM1,Nucleus,-0.21776574
+FAM208A,Nucleus,0.043438523
+ARHGEF3,Nucleus,-0.500739637
+UBXN7,Nucleus,0.647098577
+ZNF691,Nucleus,0.660604885
+SGMS2,Nucleus,-0.130766403
+DNAJB14,Nucleus,0.738475445
+ZNF589,Nucleus,1.473563991
+SHISA5,Nucleus,-0.81009852
+INTU,Nucleus,0.396151842
+RNF123,Nucleus,0.776142855
+MFSD8,Nucleus,0.859161029
+C4orf29,Nucleus,0.944659127
+RAD54L2,Nucleus,0.618230536
+MAP9,Nucleus,0.250736783
+CEP44,Nucleus,0.701775602
+ABCE1,Nucleus,-0.017351575
+TMEM184C,Nucleus,-0.283010176
+TMEM161B,Nucleus,1.478654106
+ELOVL7,Nucleus,-0.05492927
+LMBRD2,Nucleus,-0.009011134
+NIPBL,Nucleus,-0.062619035
+SLC25A46,Nucleus,-0.04783326
+STARD4,Nucleus,0.453710253
+PGGT1B,Nucleus,1.018892929
+NDUFS4,Nucleus,-0.810897334
+ARSK,Nucleus,-0.266223771
+GPX8,Nucleus,-0.725177534
+SERINC5,Nucleus,0.087112427
+GFM2,Nucleus,-0.103798587
+CCDC127,Nucleus,0.349673811
+SEPT8,Nucleus,0.155069441
+DCBLD1,Nucleus,-0.25693167
+PDSS2,Nucleus,-0.29888952
+STXBP5,Nucleus,0.610401677
+DAGLB,Nucleus,0.32906773
+GALNT10,Nucleus,0.004708732
+ZNF12,Nucleus,1.16215888
+USP49,Nucleus,1.892515358
+ZNF704,Nucleus,1.12319313
+LMTK2,Nucleus,0.195303173
+CTSB,Nucleus,-1.214502402
+ADCY1,Nucleus,0.515833971
+EN2,Nucleus,0.431514876
+SUN1,Nucleus,0.761066195
+OXR1,Nucleus,-0.059177522
+SLC4A2,Nucleus,-0.234702033
+FASTK,Nucleus,1.635153196
+TMUB1,Nucleus,0.315979644
+C7orf55,Nucleus,-0.780588086
+FOXK1,Nucleus,1.417354374
+FZD6,Nucleus,0.036270113
+KIAA1429,Nucleus,-0.340789183
+TMEM67,Nucleus,0.362496753
+SNAPC3,Nucleus,0.271941015
+KIAA1161,Nucleus,-0.60853107
+METTL2B,Nucleus,0.438830901
+HGSNAT,Nucleus,0.45653585
+RASEF,Nucleus,0.526261838
+ANKS6,Nucleus,1.274072422
+TMEM246,Nucleus,-0.812114164
+ZHX1,Nucleus,0.056039541
+KIAA1958,Nucleus,1.245693504
+PIGA,Nucleus,0.72050835
+WNK2,Nucleus,1.182452382
+ATP7A,Nucleus,-0.70118303
+PIGO,Nucleus,-0.246877694
+BRWD3,Nucleus,0.212344704
+SLITRK5,Nucleus,0.746013259
+DDX26B,Nucleus,1.009391522
+MARCH8,Nucleus,-0.231165118
+GTF2A1,Nucleus,-0.24556737
+ZCCHC24,Nucleus,-0.218711967
+REEP3,Nucleus,-0.227238837
+MICU2,Nucleus,-0.182762919
+PCF11,Nucleus,1.047469158
+PKNOX2,Nucleus,0.176162872
+ZNF22,Nucleus,-0.424867206
+RPUSD4,Nucleus,1.111469589
+ARF6,Nucleus,-0.140666486
+TTC8,Nucleus,-0.11746287
+CDX2,Nucleus,-0.139736477
+BEND7,Nucleus,0.909310189
+TAF3,Nucleus,-0.158719571
+PDZD8,Nucleus,0.105497834
+ZNF503,Nucleus,-0.309604377
+FAM175B,Nucleus,-0.045813467
+QSOX2,Nucleus,1.001248405
+NSD1,Nucleus,0.267724147
+SNAPC4,Nucleus,1.562971332
+PMPCA,Nucleus,-0.090529404
+SDCCAG3,Nucleus,1.05599293
+TSC1,Nucleus,0.709269249
+FAM69B,Nucleus,1.56203426
+KIAA1462,Nucleus,-0.236694937
+ZNF219,Nucleus,0.099065891
+METTL3,Nucleus,0.971920971
+HSPA12A,Nucleus,1.129349322
+TC2N,Nucleus,0.196981124
+CPSF2,Nucleus,-0.103047061
+ARL5B,Nucleus,0.555280843
+TAF1D,Nucleus,1.407969566
+HTRA1,Nucleus,-1.110595845
+CEP57,Nucleus,0.480272031
+JAM3,Nucleus,-0.134475781
+HIF1AN,Nucleus,0.689194802
+ZFYVE19,Nucleus,1.152533467
+FBN1,Nucleus,0.01845551
+BAG5,Nucleus,0.589870736
+GABRB3,Nucleus,-0.127482396
+SGPL1,Nucleus,-0.397334472
+FRS2,Nucleus,0.379822264
+ZNF202,Nucleus,1.192903968
+STXBP4,Nucleus,0.543563234
+CUL5,Nucleus,0.46370587
+WBP1L,Nucleus,-1.325954023
+TRIM44,Nucleus,0.60006949
+TPP1,Nucleus,-0.473310008
+C11orf74,Nucleus,-0.370391224
+TUB,Nucleus,2.185133768
+RNF169,Nucleus,1.651044141
+PRTG,Nucleus,0.965503592
+TMEM41B,Nucleus,0.219315279
+TMX3,Nucleus,0.224751589
+WEE1,Nucleus,0.666584735
+ZNF3,Nucleus,0.84496263
+RIMKLB,Nucleus,1.808429866
+TMED3,Nucleus,-0.251378887
+NDEL1,Nucleus,0.929882794
+BLCAP,Nucleus,0.245727577
+CASC4,Nucleus,-0.628755081
+AP1G1,Nucleus,-0.260765221
+KIF7,Nucleus,0.80911529
+PEX11A,Nucleus,-0.037249512
+ZBTB39,Nucleus,0.714515379
+TMEM194A,Nucleus,0.399611323
+SMAD3,Nucleus,0.613036271
+MAP1A,Nucleus,-0.258245254
+MBD6,Nucleus,0.901325363
+PDIA3,Nucleus,-0.833260726
+ACSF2,Nucleus,0.159861424
+COQ4,Nucleus,1.870459099
+SLC27A4,Nucleus,-0.164535656
+CERCAM,Nucleus,0.947859179
+DOLPP1,Nucleus,-0.469268548
+GPRC5B,Nucleus,-0.621430815
+CRK,Nucleus,-0.110712494
+FBXO22,Nucleus,1.171156295
+TBC1D2B,Nucleus,1.059084549
+CDK12,Nucleus,0.211115241
+ENGASE,Nucleus,1.833797905
+TBC1D16,Nucleus,0.608870139
+ENTHD2,Nucleus,1.351049256
+STIM1,Nucleus,-0.329005738
+IRGQ,Nucleus,1.003439687
+PPP2R3B,Nucleus,1.444391434
+ZNF646,Nucleus,0.929692033
+MIDN,Nucleus,0.764993137
+MVD,Nucleus,0.816360244
+ANKRD11,Nucleus,0.980540937
+SPATA33,Nucleus,1.093321585
+ZNF641,Nucleus,0.969709656
+DHRS13,Nucleus,-1.159522808
+TP53I13,Nucleus,-0.452445412
+KMT2D,Nucleus,0.967634535
+C19orf55,Nucleus,1.272020657
+LENG8,Nucleus,2.618614083
+ZNF146,Nucleus,0.198263014
+ZNF444,Nucleus,1.922446183
+FAM57A,Nucleus,-0.665679087
+SLC43A2,Nucleus,0.123374351
+SRR,Nucleus,-1.085102939
+GHDC,Nucleus,-0.792306038
+ITFG3,Nucleus,-0.857738081
+ZNF598,Nucleus,1.182594582
+E4F1,Nucleus,1.819177416
+ABCA3,Nucleus,-0.308186387
+SRRM2,Nucleus,1.18923281
+LTBP3,Nucleus,1.011668009
+SAC3D1,Nucleus,-0.739681444
+SF1,Nucleus,0.970598481
+PAFAH1B2,Nucleus,-0.223892697
+ANKS3,Nucleus,1.345742696
+SETD5,Nucleus,0.946383748
+HOOK3,Nucleus,0.6634523
+RBPJ,Nucleus,-0.027962347
+TTC39C,Nucleus,0.399510903
+KIF5C,Nucleus,-0.861636507
+MGAT2,Nucleus,-0.576591105
+BMI1,Nucleus,-0.129008762
+KCTD6,Nucleus,0.206089865
+TAP1,Nucleus,-0.905016685
+ING5,Nucleus,0.893507215
+ATG4B,Nucleus,0.928668181
+SOGA2,Nucleus,0.6307921
+SNRNP48,Nucleus,0.844218166
+SLC20A2,Nucleus,-0.003078814
+TMUB2,Nucleus,-0.051607638
+STAT3,Nucleus,-0.735167815
+ADAM9,Nucleus,-0.598539052
+PKIG,Nucleus,-0.342717591
+SEMA4C,Nucleus,0.955403443
+CNNM3,Nucleus,0.046057404
+TET2,Nucleus,0.477369888
+TCTN2,Nucleus,-0.479822642
+TSPAN5,Nucleus,-0.478843899
+ZBTB5,Nucleus,0.936094544
+SNTB2,Nucleus,1.068271269
+ZNF507,Nucleus,0.902899403
+STX18,Nucleus,0.589328861
+GFM1,Nucleus,-0.294473979
+ANKRD49,Nucleus,0.830643607
+MAT2A,Nucleus,1.912216751
+ZNF608,Nucleus,-0.134847137
+LETM1,Nucleus,0.491085736
+TMEM129,Nucleus,0.469488265
+FEM1B,Nucleus,0.357915436
+HNRNPH1,Nucleus,1.655738918
+MECP2,Nucleus,1.281733967
+UPF3A,Nucleus,2.026810711
+CHST14,Nucleus,-0.906534015
+PARM1,Nucleus,-0.241478883
+CSNK1G1,Nucleus,0.653684752
+ZBTB43,Nucleus,0.980400914
+GPRIN1,Nucleus,-0.187316684
+MRPL1,Nucleus,-0.303173667
+SLC33A1,Nucleus,-0.008122273
+SDC2,Nucleus,-0.814727437
+MMGT1,Nucleus,-0.600058546
+CLIC4,Nucleus,-0.570394831
+CCDC8,Nucleus,-0.075972457
+INO80E,Nucleus,1.374516093
+DFFB,Nucleus,0.810256728
+ANTXR1,Nucleus,-0.524579627
+CKAP2L,Nucleus,-0.402689297
+C15orf40,Nucleus,0.97169734
+HIC2,Nucleus,0.919893184
+LUZP1,Nucleus,0.581387865
+HEXDC,Nucleus,2.083483838
+LRRC45,Nucleus,1.474229245
+ASPSCR1,Nucleus,1.808041337
+TAPT1,Nucleus,-0.014723532
+CSGALNACT2,Nucleus,0.468814124
+PCDH7,Nucleus,-0.192566347
+ROBO1,Nucleus,-0.308813259
+P2RY1,Nucleus,1.091967246
+TPST1,Nucleus,-0.254724527
+TOR1AIP2,Nucleus,0.165489006
+OTUD3,Nucleus,1.558610311
+GUSB,Nucleus,0.755152299
+BRD3,Nucleus,0.355968698
+MAP3K2,Nucleus,1.198140901
+NLGN2,Nucleus,1.09106324
+ALCAM,Nucleus,-0.223928903
+YWHAG,Nucleus,-0.204576067
+TMEM192,Nucleus,0.063842225
+ZNF778,Nucleus,0.209891062
+NIPA1,Nucleus,0.835192881
+SIK2,Nucleus,0.289760008
+RNF150,Nucleus,0.899915986
+ZNF212,Nucleus,0.761682034
+FAM161A,Nucleus,0.663192662
+CRTAP,Nucleus,-0.487457218
+PRDM10,Nucleus,0.498930373
+FOS,Nucleus,-0.967085325
+TMED10,Nucleus,-0.957806985
+SLC30A1,Nucleus,-0.184640825
+DNAJC18,Nucleus,-0.360003242
+RALGAPB,Nucleus,0.723867861
+LONRF2,Nucleus,1.493806502
+ELOVL6,Nucleus,0.38897755
+ARL6IP1,Nucleus,-0.614217632
+CDH2,Nucleus,-0.549022863
+EMB,Nucleus,0.024036094
+STAT2,Nucleus,1.173687528
+TRABD,Nucleus,0.909321508
+POLH,Nucleus,-0.035039079
+KIF5B,Nucleus,-0.151859435
+AKAP13,Nucleus,0.393100408
+CHCHD7,Nucleus,0.562841034
+GPR27,Nucleus,-0.404748804
+KBTBD2,Nucleus,0.211474288
+KIAA0232,Nucleus,0.343325046
+TMEM43,Nucleus,0.221227
+RNF139,Nucleus,-0.665992947
+PAQR8,Nucleus,-1.240555133
+TANC2,Nucleus,0.484242025
+DNAJC24,Nucleus,1.239381703
+HS6ST2,Nucleus,-0.401810113
+INSR,Nucleus,-0.506293127
+ATP6V0E2,Nucleus,-0.468234488
+ZNF692,Nucleus,1.745562037
+NETO2,Nucleus,-0.327245807
+NPTX1,Nucleus,1.061493386
+FAM98B,Nucleus,0.899107193
+GAA,Nucleus,1.218313487
+CANT1,Nucleus,-0.338616799
+CHST11,Nucleus,-0.379319836
+CLCN5,Nucleus,0.062326789
+ZBTB26,Nucleus,0.798263844
+ZNF562,Nucleus,1.18701501
+ZNF318,Nucleus,0.187440897
+WIPF2,Nucleus,-0.109878049
+LRRC8C,Nucleus,-0.311685023
+LRRC8D,Nucleus,-0.292114124
+ETFDH,Nucleus,0.075548446
+LPAR3,Nucleus,-0.026392545
+CLSTN1,Nucleus,-0.063476871
+BPTF,Nucleus,0.295835567
+ATF7IP,Nucleus,0.076909077
+TCEA2,Nucleus,1.155834086
+ANO5,Nucleus,0.024113159
+MLLT3,Nucleus,-0.392117935
+PRNP,Nucleus,-0.460973494
+ZNF217,Nucleus,0.197497598
+JMJD1C,Nucleus,0.377813252
+THOP1,Nucleus,0.881593112
+ORMDL3,Nucleus,-0.009515227
+KLF11,Nucleus,0.242399627
+MTBP,Nucleus,0.870392483
+ZNF131,Nucleus,0.402851216
+BSG,Nucleus,-0.736698521
+CERS6,Nucleus,-0.219454539
+TP53RK,Nucleus,0.122328387
+FAM195A,Nucleus,1.414273882
+GTPBP2,Nucleus,0.841392923
+ZNF24,Nucleus,0.9805414
+MANEA,Nucleus,-0.438628073
+RAD9A,Nucleus,1.082968712
+FAM21C,Nucleus,0.317735098
+CORO1B,Nucleus,-0.516098384
+LRRC20,Nucleus,-0.796536798
+NAA16,Nucleus,1.798874204
+DCP2,Nucleus,0.047774764
+CES3,Nucleus,1.493110799
+CES2,Nucleus,0.416097657
+PDP2,Nucleus,0.548950108
+SP3,Nucleus,-0.064555992
+METAP1D,Nucleus,1.366206837
+ZNF621,Nucleus,2.080463139
+NADSYN1,Nucleus,1.054381218
+DHCR7,Nucleus,-0.348211918
+NBEA,Nucleus,-0.27235369
+ANKRD13D,Nucleus,1.536962298
+LCLAT1,Nucleus,-0.322326532
+TADA2B,Nucleus,0.07256525
+HECTD4,Nucleus,0.756449314
+ESRRA,Nucleus,0.644818505
+AHSA2,Nucleus,2.306755017
+VANGL1,Nucleus,-0.457214962
+IQCB1,Nucleus,0.983407288
+GOLGB1,Nucleus,0.579402221
+TNKS,Nucleus,1.034643049
+ZBTB21,Nucleus,0.580441635
+STOX2,Nucleus,0.805667404
+DAG1,Nucleus,-0.701484096
+RNF26,Nucleus,-0.453129507
+PEAK1,Nucleus,0.672645293
+TNFRSF10D,Nucleus,-1.112870353
+MOB1B,Nucleus,0.832473835
+SNX33,Nucleus,0.378474772
+CHD2,Nucleus,1.293834818
+CCDC41,Nucleus,1.067391911
+PC,Nucleus,-0.332484207
+SUSD5,Nucleus,-0.43401367
+HEG1,Nucleus,-0.226997656
+TOMM20,Nucleus,-0.24428152
+CNP,Nucleus,-0.763543128
+DPY19L1,Nucleus,-0.582372859
+ZNF791,Nucleus,1.280568084
+PHC3,Nucleus,1.72042915
+GOLIM4,Nucleus,-0.253873201
+XXYLT1,Nucleus,-0.58790978
+UBXN2A,Nucleus,0.150762608
+CCS,Nucleus,0.509674434
+FAM3C2,Nucleus,-0.131964809
+CTSF,Nucleus,-0.806918366
+MSRB3,Nucleus,-0.121712313
+LEMD3,Nucleus,-0.238863185
+RGMB,Nucleus,0.347962822
+ZDHHC24,Nucleus,-1.10106614
+MGA,Nucleus,0.382661192
+PIGG,Nucleus,1.32783478
+ADCY6,Nucleus,0.297851135
+ZBTB4,Nucleus,0.762556708
+ZHX3,Nucleus,1.177293937
+RALGAPA1,Nucleus,0.670176437
+ATP2A2,Nucleus,0.383463182
+CNTNAP2,Nucleus,0.002163451
+DENND4A,Nucleus,0.056442538
+MSL2,Nucleus,0.366027801
+UGT8,Nucleus,-0.922557091
+ZNF266,Nucleus,1.356986303
+SLC29A2,Nucleus,1.353527956
+BRSK2,Nucleus,1.32121727
+B3GNT1,Nucleus,-0.718191313
+TMEM167A,Nucleus,0.240421028
+CEP135,Nucleus,0.70280114
+FZD4,Nucleus,0.369447899
+PDE12,Nucleus,-0.103956672
+GLMN,Nucleus,0.610508102
+SEZ6L2,Nucleus,-0.735426511
+KLC2,Nucleus,0.616808053
+GK5,Nucleus,1.373313896
+VCPIP1,Nucleus,0.401265346
+PCCA,Nucleus,-0.955152944
+GOLGA8A,Nucleus,2.056274951
+TP53I11,Nucleus,0.376986043
+PHYKPL,Nucleus,1.959716064
+ARL10,Nucleus,1.018822893
+CCDC14,Nucleus,1.635240334
+ALG10B,Nucleus,0.960256619
+RAB6A,Nucleus,-0.260071386
+ERCC4,Nucleus,0.390283765
+RMI2,Nucleus,-0.488522298
+TOM1L2,Nucleus,0.076613846
+MLXIP,Nucleus,1.005184121
+SLC35E3,Nucleus,0.926817827
+ARL4D,Nucleus,-1.367982129
+LYSMD3,Nucleus,0.18335448
+B3GALT6,Nucleus,-0.550489367
+MBLAC2,Nucleus,0.245261858
+TPRN,Nucleus,1.365457738
+YES1,Nucleus,-0.027314344
+TMEM39A,Nucleus,0.273872297
+CCDC57,Nucleus,2.116060313
+FOXG1,Nucleus,0.406436286
+ATAD5,Nucleus,-0.013215701
+ANAPC2,Nucleus,1.897335179
+SPRYD4,Nucleus,1.027352457
+CLK2,Nucleus,1.432852853
+LPCAT4,Nucleus,1.472438583
+B3GNT5,Nucleus,0.245249627
+LMNB2,Nucleus,0.085482183
+RMDN1,Nucleus,0.484199997
+ACSF3,Nucleus,1.296001598
+ANKLE2,Nucleus,0.962681023
+NFATC2IP,Nucleus,1.561286079
+SMCR8,Nucleus,0.758462505
+MTX3,Nucleus,1.561992933
+FBXO46,Nucleus,0.732585325
+WDR73,Nucleus,0.851584011
+ANO6,Nucleus,-0.297484284
+ZBTB34,Nucleus,0.577275574
+FAM210A,Nucleus,-0.300020852
+ULK1,Nucleus,1.53399258
+RPS6KA3,Nucleus,0.120390972
+PUS1,Nucleus,1.284442416
+CHD9,Nucleus,0.512455015
+PDDC1,Nucleus,2.249083094
+TOP3A,Nucleus,0.589318937
+NR2C2,Nucleus,1.329664164
+ZBTB33,Nucleus,0.380930517
+SLC25A22,Nucleus,0.861539756
+PIDD,Nucleus,1.349505059
+GBA,Nucleus,-0.994301372
+IL17RA,Nucleus,-0.120213981
+THAP5,Nucleus,0.112931423
+PVRL3,Nucleus,-0.731012365
+KIAA0195,Nucleus,0.203436776
+SOX12,Nucleus,0.539489474
+CHID1,Nucleus,-0.48663147
+ZNF518A,Nucleus,0.547677953
+ZBTB41,Nucleus,0.147111612
+DMAP1,Nucleus,1.613366618
+C2orf69,Nucleus,0.551341367
+SH2B1,Nucleus,1.537207362
+KDELC2,Nucleus,-0.468548198
+GALNT11,Nucleus,-0.319303682
+WDR6,Nucleus,1.240775776
+GEN1,Nucleus,2.223568628
+GLDC,Nucleus,-0.748980976
+CTNNBIP1,Nucleus,-0.364608539
+ERN1,Nucleus,0.446965919
+KCTD12,Nucleus,-0.194807264
+DHFRL1,Nucleus,0.09242231
+FAM132B,Nucleus,1.494164145
+FAM219B,Nucleus,1.544147098
+DPY19L3,Nucleus,0.844804065
+PFAS,Nucleus,0.286285083
+C17orf62,Nucleus,1.100346834
+ZBTB7A,Nucleus,1.655037852
+SPTY2D1,Nucleus,-0.180364441
+FUCA1,Nucleus,-1.381133275
+CALR,Nucleus,-1.007704947
+LDLRAD3,Nucleus,-0.288037192
+CLK3,Nucleus,1.249345094
+PACS2,Nucleus,1.560341001
+ELMOD2,Nucleus,-0.05443424
+FJX1,Nucleus,-0.255737991
+ZBTB18,Nucleus,0.750923384
+GCC1,Nucleus,-0.28358873
+PLD6,Nucleus,0.693393328
+CDC42EP4,Nucleus,-0.670505698
+PCBP1-AS1,Nucleus,1.971348621
+MYADM,Nucleus,-0.523043747
+SERTAD2,Nucleus,0.531725304
+BBS10,Nucleus,0.123598473
+SOCS4,Nucleus,1.378333475
+ZADH2,Nucleus,0.460221898
+EXOC3,Nucleus,0.982528283
+C7orf41,Nucleus,0.401101935
+ZNF609,Nucleus,0.199413496
+CCDC66,Nucleus,1.309315383
+MCFD2,Nucleus,-0.30565285
+GAS1,Nucleus,-0.963611639
+FAM73A,Nucleus,0.371712766
+NRIP1,Nucleus,0.490726474
+PCGF5,Nucleus,0.072791951
+YOD1,Nucleus,0.786578605
+SLC36A4,Nucleus,0.579320895
+ZDHHC20,Nucleus,-0.201935716
+PSMG4,Nucleus,2.019269319
+PDIA3P,Nucleus,-0.740305206
+CUEDC1,Nucleus,1.021360307
+KCTD2,Nucleus,-0.233458265
+D2HGDH,Nucleus,1.838026615
+FKRP,Nucleus,0.602918576
+SLC26A11,Nucleus,0.788831302
+F2R,Nucleus,-0.291423053
+DHTKD1,Nucleus,-0.578268288
+ZNF746,Nucleus,0.605621074
+TMEM136,Nucleus,0.478830902
+ZNF322,Nucleus,-0.142130661
+OGFOD3,Nucleus,0.417082743
+ZNF678,Nucleus,0.549290091
+ZBTB2,Nucleus,0.029997738
+SGSH,Nucleus,1.019480348
+SETD2,Nucleus,0.069296176
+YIPF6,Nucleus,0.194563212
+IBA57,Nucleus,1.659057164
+C5orf24,Nucleus,0.893063725
+ADO,Nucleus,-0.158459799
+CREB3L2,Nucleus,0.046089422
+UNC5C,Nucleus,0.375938176
+RGMA,Nucleus,-0.347843091
+EXT1,Nucleus,-0.666288701
+ATP6AP2,Nucleus,-0.906171964
+BACE2,Nucleus,-0.779847142
+FIGN,Nucleus,0.736133274
+B4GALNT4,Nucleus,1.553187614
+AP1S2,Nucleus,0.558542495
+FBXL6,Nucleus,1.73358931
+YBEY,Nucleus,-0.094802264
+CLN8,Nucleus,1.25114667
+PLCXD1,Nucleus,1.545473407
+EXOC7,Nucleus,0.631987844
+CEP97,Nucleus,0.934436266
+MXRA7,Nucleus,-0.635955649
+SATB1,Nucleus,0.199443709
+PLCB1,Nucleus,0.080971308
+TTC3,Nucleus,-0.022066525
+COL18A1,Nucleus,0.039113347
+ZNF721,Nucleus,1.229024323
+SRPR,Nucleus,-0.247658583
+EWSR1,Nucleus,1.803846015
+GJC1,Nucleus,0.52540521
+MTA1,Nucleus,1.768946278
+CADM1,Nucleus,-0.697536618
+LYSMD4,Nucleus,1.433418615
+NKX2-5,Nucleus,-0.107772092
+GPC6,Nucleus,-0.789514685
+PTTG1IP,Nucleus,-0.574775909
+ZNF623,Nucleus,0.714336044
+BCOR,Nucleus,-0.054936436
+ASB7,Nucleus,-0.012624389
+EP400,Nucleus,0.482454732
+COA5,Nucleus,1.100923328
+PRR14L,Nucleus,0.391443792
+ZNRF3,Nucleus,0.447249491
+TRMT12,Nucleus,-0.215756722
+FAM101B,Nucleus,-0.178311854
+TRIM52,Nucleus,1.882582206
+CMTM4,Nucleus,0.816286945
+TMEM50A,Nucleus,-0.727111307
+CBX6,Nucleus,1.678043597
+KREMEN1,Nucleus,-0.173414908
+TRAIP,Nucleus,0.755371123
+EMILIN3,Nucleus,-0.465064267
+RBM12B,Nucleus,1.00296103
+BTBD9,Nucleus,-0.260534221
+KIRREL,Nucleus,0.573865779
+IQGAP3,Nucleus,0.657171639
+PRKX,Nucleus,0.543673375
+SMTN,Nucleus,0.451964558
+TBX1,Nucleus,1.324688672
+TSPYL2,Nucleus,1.157385479
+C22orf46,Nucleus,0.495060675
+PCDH9,Nucleus,0.645215569
+OAF,Nucleus,0.521095986
+ZDHHC23,Nucleus,1.276770912
+EFNA5,Nucleus,-0.300903777
+SS18L1,Nucleus,1.236046282
+KNTC1,Nucleus,0.746083789
+WDR27,Nucleus,2.581258289
+FOXO4,Nucleus,-0.391815816
+POU3F2,Nucleus,0.169704608
+PROS1,Nucleus,-0.850434467
+ZFP1,Nucleus,-0.7787374
+XPOT,Nucleus,0.251198941
+SNN,Nucleus,-0.322481588
+AMER1,Nucleus,0.900900373
+ZBTB40,Nucleus,1.328039383
+ATL3,Nucleus,-0.423751769
+UBE2G2,Nucleus,1.163501593
+TMED9,Nucleus,-0.724368898
+RBM33,Nucleus,1.954445356
+JAG2,Nucleus,1.267797953
+ZFP90,Nucleus,0.467380423
+SIVA1,Nucleus,0.788518624
+BRI3BP,Nucleus,-0.176920027
+ROBO2,Nucleus,-0.49191637
+BRF1,Nucleus,0.349823296
+MANEAL,Nucleus,-0.548650636
+PURA,Nucleus,0.548243675
+DDX51,Nucleus,1.393722672
+NOMO2,Nucleus,-0.891859941
+NRBP2,Nucleus,1.868012907
+ZNF445,Nucleus,1.592467426
+PRPF39,Nucleus,1.082885993
+CDK10,Nucleus,2.14292214
+ATP6V0A2,Nucleus,0.703289402
+C14orf80,Nucleus,2.224302481
+HGS,Nucleus,0.910063248
+MRPL30,Nucleus,-0.183988744
+METTL7A,Nucleus,-1.620740715
+NR2F2,Nucleus,0.110391745
+SP1,Nucleus,0.545246526
+PCGF3,Nucleus,0.628237519
+P4HB,Nucleus,-0.458249008
+PBX1,Nucleus,0.004305809
+BRWD1,Nucleus,1.164295552
+EP400NL,Nucleus,1.853088836
+MYBL1,Nucleus,1.364003836
+DMWD,Nucleus,1.136491742
+SLC52A2,Nucleus,-0.646941368
+NAT8L,Nucleus,1.416094428
+GNB1L,Nucleus,0.633469262
+LAMP1,Nucleus,-0.371916371
+KLHDC8B,Nucleus,-0.064570536
+SETD4,Nucleus,1.651961676
+RNPC3,Nucleus,1.704279504
+BICD2,Nucleus,0.36543568
+LRCH3,Nucleus,0.61245636
+ZNF529,Nucleus,1.840454872
+AIDA,Nucleus,-0.135295832
+ZBTB6,Nucleus,0.485420292
+BCL9L,Nucleus,0.525509063
+KIF18B,Nucleus,0.759272937
+MKL2,Nucleus,0.843654128
+CA5BP1,Nucleus,0.998535907
+BACE1,Nucleus,-0.134333639
+KPNA4,Nucleus,-0.256461165
+ZNF197,Nucleus,0.381522223
+BTN3A2,Nucleus,0.154051859
+INSIG1,Nucleus,0.071372892
+TMEM222,Nucleus,0.653211711
+SMYD4,Nucleus,1.493454364
+GPATCH8,Nucleus,0.443612367
+LYRM7,Nucleus,0.456783868
+ZNF397,Nucleus,1.729619776
+ZSCAN30,Nucleus,1.290179327
+TPCN1,Nucleus,1.015849234
+POFUT2,Nucleus,1.1889686
+ZDHHC17,Nucleus,1.112410968
+PPARA,Nucleus,0.446372188
+TEAD1,Nucleus,0.49350697
+ENTPD5,Nucleus,0.678760744
+KIAA1598,Nucleus,0.787983426
+TSPYL4,Nucleus,0.687936957
+FNBP1,Nucleus,-0.045832565
+BCAM,Nucleus,0.601719696
+COL4A1,Nucleus,-0.521840004
+SIRT7,Nucleus,2.10235145
+TET3,Nucleus,1.063960183
+ZNF286A,Nucleus,0.818850101
+SAMD11,Nucleus,0.566226077
+B3GALTL,Nucleus,-0.617776465
+TCEA1,Nucleus,-0.365157293
+FANCA,Nucleus,0.807421673
+SEMA4D,Nucleus,1.343461764
+LIN28B,Nucleus,-0.094400847
+FANCM,Nucleus,0.333808787
+FAM122A,Nucleus,0.333228331
+ARHGAP11B,Nucleus,2.189499163
+CYHR1,Nucleus,2.555291748
+KLHL17,Nucleus,1.67808969
+ANKRD19P,Nucleus,1.714100141
+ARL4C,Nucleus,-0.415397758
+C11orf95,Nucleus,1.10519332
+MAPK12,Nucleus,1.261204068
+COL4A5,Nucleus,-0.115606744
+NCR3LG1,Nucleus,1.34784761
+HES4,Nucleus,1.10788637
+CHM,Nucleus,0.014227402
+H2AFX,Nucleus,-0.527738176
+SRSF10,Nucleus,1.048208901
+NDOR1,Nucleus,1.6822558
+FAM72B,Nucleus,0.113014576
+LDOC1L,Nucleus,-0.443885802
+PTAR1,Nucleus,2.093677651
+ZDHHC9,Nucleus,-0.546909146
+ZBED6CL,Nucleus,1.022549227
+TMEM120B,Nucleus,1.788137232
+MTF1,Nucleus,0.800206744
+TMEM201,Nucleus,0.363247709
+NHLRC3,Nucleus,1.982264881
+BEND4,Nucleus,0.521799243
+MSL1,Nucleus,0.669054793
+DHFRP1,Nucleus,-0.465540121
+ZNF292,Nucleus,1.010670409
+ADAT2,Nucleus,1.927096337
+H1F0,Nucleus,-0.799982163
+LITAF,Nucleus,-0.741530104
+ARID2,Nucleus,0.055875419
+S100A13,Nucleus,0.759923811
+ZNF33A,Nucleus,1.303001914
+LIN54,Nucleus,-0.018335092
+KAZN,Nucleus,1.171096012
+SLC35E2B,Nucleus,0.081097897
+KIAA0895L,Nucleus,1.267054279
+PLEKHG4,Nucleus,0.838311453
+ACADSB,Nucleus,0.626466184
+TMEM63A,Nucleus,1.192410292
+MPHOSPH8,Nucleus,0.417743463
+FAM217B,Nucleus,0.070378279
+LCOR,Nucleus,1.003266875
+POM121,Nucleus,0.258330035
+ZBTB44,Nucleus,1.017071295
+SLC35F1,Nucleus,-0.490136936
+PTPN1,Nucleus,-0.043155493
+EVL,Nucleus,1.661241298
+EPHB4,Nucleus,-0.092836798
+PPP1R26,Nucleus,0.17437377
+TSC22D2,Nucleus,0.698551944
+PIK3R4,Nucleus,-0.20457963
+GDAP2,Nucleus,-0.394289359
+AFAP1,Nucleus,0.597284248
+MAN2A2,Nucleus,1.012569609
+CACNA1H,Nucleus,0.567148335
+SULF2,Nucleus,-0.776362915
+PLXNB2,Nucleus,-0.203844236
+XRCC2,Nucleus,2.684314697
+MYO6,Nucleus,0.065483414
+TCF4,Nucleus,0.56418575
+RABL6,Nucleus,2.03349474
+ZKSCAN5,Nucleus,0.104986726
+ZFP62,Nucleus,0.959912815
+ERI2,Nucleus,0.116830003
+ZNF33B,Nucleus,1.121469472
+ZNF512B,Nucleus,1.58912255
+ZNF431,Nucleus,2.03714416
+NF1,Nucleus,0.127085865
+VKORC1L1,Nucleus,0.161060414
+COL27A1,Nucleus,1.584435553
+GM2A,Nucleus,-0.880920277
+SNHG17,Nucleus,2.189396841
+CD47,Nucleus,-0.116240135
+CTBP1-AS2,Nucleus,0.448959771
+C6orf106,Nucleus,-0.072859577
+NHLRC2,Nucleus,1.282281941
+KPNA5,Nucleus,0.485285366
+ZNF252P,Nucleus,0.166392662
+PDLIM7,Nucleus,0.955362792
+SLC39A10,Nucleus,-0.587597251
+ZNF100,Nucleus,0.666144867
+ZNF398,Nucleus,1.001029407
+GMFB,Nucleus,-0.235816771
+ZMYM1,Nucleus,0.531668946
+MAFG,Nucleus,2.060944692
+ARRDC1,Nucleus,1.298255213
+KIAA1671,Nucleus,0.258979917
+IGF2R,Nucleus,-0.445044246
+SLC25A29,Nucleus,1.110830179
+PGAP1,Nucleus,0.977620924
+SRC,Nucleus,0.333759646
+PCNXL3,Nucleus,0.64474686
+LRRC8B,Nucleus,0.379898694
+ABCB8,Nucleus,0.913030339
+SND1,Nucleus,-0.608537385
+ENTPD4,Nucleus,0.865344469
+KANK2,Nucleus,-0.408098065
+FITM2,Nucleus,-0.134556918
+DDI2,Nucleus,0.520560649
+TRIM33,Nucleus,0.564595774
+LRP10,Nucleus,-0.520282063
+ZNF655,Nucleus,1.20769559
+SLC22A5,Nucleus,1.055818309
+ADARB1,Nucleus,1.710409304
+OGDHL,Nucleus,1.407011188
+STMN3,Nucleus,-0.133453204
+SIPA1L1,Nucleus,0.042361596
+PIGN,Nucleus,-0.805032768
+COL4A6,Nucleus,0.17034462
+GPX1P1,Nucleus,-0.619668416
+ENTPD6,Nucleus,0.940144671
+ENPP1,Nucleus,-0.011978096
+PHF2,Nucleus,0.282131123
+RPS26,Nucleus,0.956200212
+PSAP,Nucleus,-1.015588274
+HOXC6,Nucleus,0.948135797
+EME2,Nucleus,3.032342845
+ZNF780A,Nucleus,0.768795095
+SLC9A8,Nucleus,1.095886222
+OCLN,Nucleus,-0.608232688
+GPAA1,Nucleus,-0.401123931
+SPG7,Nucleus,1.447787041
+ERO1L,Nucleus,-0.350732269
+ZNF121,Nucleus,2.220283635
+MPZL1,Nucleus,-0.338105398
+VPS13A,Nucleus,1.182137534
+AKAP17A,Nucleus,1.194732351
+ELOVL2,Nucleus,0.044307375
+SNHG12,Nucleus,0.68082267
+NOL8,Nucleus,0.865859925
+MRPL42,Nucleus,0.173042601
+ENTPD7,Nucleus,-0.914142299
+ZNF84,Nucleus,1.161631154
+SIRPA,Nucleus,0.110358753
+CD2AP,Nucleus,0.140246895
+NUP62CL,Nucleus,0.212294935
+SFI1,Nucleus,1.888925508
+ZNF248,Nucleus,1.005702427
+ZNF770,Nucleus,0.351909793
+HMGN5,Nucleus,0.390430634
+MIER1,Nucleus,0.298078867
+MAN1A2,Nucleus,0.106670513
+RPS6KL1,Nucleus,1.904214542
+DDX42,Nucleus,1.041080223
+STYX,Nucleus,0.222141262
+UCKL1,Nucleus,1.139823668
+ZKSCAN8,Nucleus,1.687247003
+HOXC4,Nucleus,0.964098083
+ASPH,Nucleus,-0.266193781
+WWP2,Nucleus,-0.447553355
+GFPT1,Nucleus,0.143887919
+ITSN2,Nucleus,-0.099996585
+MGEA5,Nucleus,0.937587833
+FAM115A,Nucleus,-0.235992397
+ZNF587,Nucleus,2.460019184
+MAFK,Nucleus,2.00706937
+DDX39B,Nucleus,2.119045878
+NUDT16,Nucleus,0.647708881
+TLK1,Nucleus,-0.225573932
+MDM4,Nucleus,2.158566028
+KLHL9,Nucleus,0.085831245
+C6orf89,Nucleus,0.078365817
+SLC9A6,Nucleus,-0.39133393
+FAN1,Nucleus,1.050537853
+CEP290,Nucleus,0.712884515
+C1orf85,Nucleus,-0.55115934
+SMOC1,Nucleus,-0.486449579
+ZNF652,Nucleus,0.992652784
+SMURF1,Nucleus,1.039291414
+SLC5A3,Nucleus,0.387180303
+COLGALT2,Nucleus,0.679725997
+TMEM184B,Nucleus,0.636315453
+LRIG2,Nucleus,0.914497654
+FOXJ3,Nucleus,1.037531422
+ARHGAP11A,Nucleus,0.211247737
+DENND4B,Nucleus,0.950424441
+R3HDM4,Nucleus,0.089512576
+RUNDC1,Nucleus,0.73207004
+TYW1,Nucleus,-0.565988855
+SFMBT2,Nucleus,1.231793844
+ITPRIPL1,Nucleus,-0.360785848
+SMC5,Nucleus,0.595973362
+SREBF2,Nucleus,-0.143285508
+DZIP3,Nucleus,0.031648862
+KIAA0753,Nucleus,0.705656847
+ATG9A,Nucleus,-0.292318521
+TBKBP1,Nucleus,1.57047263
+NAGA,Nucleus,-0.764079096
+SGMS1,Nucleus,-0.192366883
+COX20,Nucleus,1.056126545
+CHML,Nucleus,0.5171659
+TATDN3,Nucleus,-0.128029873
+RBM20,Nucleus,1.223155949
+PCMTD2,Nucleus,0.849349969
+EFCAB7,Nucleus,0.232040491
+ZYG11A,Nucleus,1.323352953
+SYS1,Nucleus,-0.531534897
+TRAF3IP1,Nucleus,0.235957756
+PHACTR4,Nucleus,0.212485714
+ZDHHC18,Nucleus,0.184813078
+TMEM57,Nucleus,-0.230116646
+ZDBF2,Nucleus,1.131674705
+BMPR2,Nucleus,-0.051227292
+OXLD1,Nucleus,0.705844379
+MRPL38,Nucleus,1.550173946
+SDHD,Nucleus,-0.378702888
+NEU1,Nucleus,-1.152641822
+HSPA1B,Nucleus,-0.233302368
+HLA-C,Nucleus,-1.24794407
+DDR1,Nucleus,-1.134736848
+HLA-E,Nucleus,-0.512323127
+ZNF616,Nucleus,-0.286306348
+CRHR1-IT1,Nucleus,1.160314242
+GABBR1,Nucleus,2.164956351
+RANBP17,Nucleus,0.916239526
+ZNF204P,Nucleus,0.697475285
+TCTN1,Nucleus,0.665106152
+ZBTB48,Nucleus,1.650948454
+C11orf83,Nucleus,-0.435794463
+ZNF783,Nucleus,1.063285307
+SLC35B4,Nucleus,0.280542715
+TRIQK,Nucleus,-0.870164974
+PSENEN,Nucleus,-0.838883555
+LGR4,Nucleus,0.019892839
+PDE7A,Nucleus,0.490633412
+TMEM170B,Nucleus,0.268353755
+TECPR1,Nucleus,0.39675922
+ITPRIPL2,Nucleus,1.042464642
+CRYZL1,Nucleus,0.280100159
+C5orf51,Nucleus,0.223051201
+ZNF316,Nucleus,0.914436764
+NYNRIN,Nucleus,-0.158696399
+AP000525.9,Nucleus,1.52898313
+PTPLB,Nucleus,-0.187320283
+WDR52,Nucleus,1.81252833
+SETD5-AS1,Nucleus,2.179637919
+SACM1L,Nucleus,0.301214615
+C17orf51,Nucleus,1.896874785
+ZNF580,Nucleus,1.151399583
+DENND1B,Nucleus,0.695982919
+SFT2D2,Nucleus,0.253230146
+FGFR1OP,Nucleus,1.638249976
+KLHL23,Nucleus,0.179289709
+TRIM59,Nucleus,0.268608485
+NRAS,Nucleus,-0.401416029
+QTRT1,Nucleus,1.389315071
+CHUK,Nucleus,0.544067606
+COG8,Nucleus,1.128743899
+RBMXL1,Nucleus,-0.174157277
+LEPROT,Nucleus,0.61269315
+ATF6B,Nucleus,0.072332784
+SLC35F6,Nucleus,-0.762921158
+ZNF134,Nucleus,0.700897235
+EMP2,Nucleus,-0.682221728
+DNASE1,Nucleus,0.473607246
+CSNK1E,Nucleus,0.789793868
+GALT,Nucleus,1.244898706
+ITGA1,Nucleus,-0.127747211
+AP1G2,Nucleus,1.307316284
+CARKD,Nucleus,1.507336587
+TTLL3,Nucleus,2.690828773
+CPNE1,Nucleus,0.034490187
+RCN1P2,Nucleus,-0.896150577
+ZBED1,Nucleus,-0.144148501
+DDX12P,Nucleus,2.041073448
+C22orf29,Nucleus,1.990873091
+NEURL4,Nucleus,0.891531444
+TTC3P1,Nucleus,-0.294865233
+PEX26,Nucleus,0.886814405
+GOLGA8B,Nucleus,1.816355604
+MIR17HG,Nucleus,2.726577989
+TMEM242,Nucleus,0.102468575
+TMEM167B,Nucleus,0.269853146
+CYB5RL,Nucleus,1.237607731
+CROCCP2,Nucleus,3.27696104
+FNIP1,Nucleus,0.703355055
+RP11-204C16.4,Nucleus,-0.539386203
+TENM3,Nucleus,-0.496984194
+AC007390.5,Nucleus,0.059896428
+NBPF1,Nucleus,0.875139441
+CCNL2,Nucleus,2.188910788
+PPT2,Nucleus,0.013115839
+CKMT1A,Nucleus,0.465115002
+NSUN5P1,Nucleus,2.453209025
+RP11-54O7.3,Nucleus,2.897770792
+AFG3L1P,Nucleus,2.33028944
+EPB41L4A-AS1,Nucleus,0.88947965
+ATXN1L,Nucleus,1.729992185
+SMIM13,Nucleus,0.048843021
+CTD-2228K2.7,Nucleus,3.202888889
+PLEKHM1,Nucleus,0.366713388
+FAM195B,Nucleus,-0.349324379
+IPO7P2,Nucleus,-0.51992799
+SLC26A6,Nucleus,1.350263405
+FGD5-AS1,Nucleus,0.669167895
+TLK2P1,Nucleus,0.074547832
+BAIAP2-AS1,Nucleus,-0.036976734
+RP11-31F15.1,Nucleus,2.409155728
+TMEM185B,Nucleus,-0.151481099
+C14orf132,Nucleus,0.970771863
+PARG,Nucleus,-0.11156937
+TP73-AS1,Nucleus,1.026282893
+SCAMP4,Nucleus,0.352670535
+SEC63P1,Nucleus,-0.357445687
+HCG11,Nucleus,0.248414602
+DHFR,Nucleus,-0.244770297
+RP11-206L10.11,Nucleus,1.089075497
+XIST,Nucleus,3.128981268
+RP5-1180C10.2,Nucleus,0.88056738
+HCG18,Nucleus,1.015203107
+CTD-2619J13.14,Nucleus,1.186161791
+SNHG15,Nucleus,1.693404564
+SNHG7,Nucleus,0.959647807
+HOTAIRM1,Nucleus,1.706069444
+RP4-775C13.1,Nucleus,-0.4297231
+ZNF37BP,Nucleus,1.711259582
+ZNF736,Nucleus,1.557571517
+MAGI2-AS3,Nucleus,0.888336057
+JRK,Nucleus,2.647388513
+GAS5,Nucleus,1.273542611
+LINC00338,Nucleus,1.754900326
+PPP1R3E,Nucleus,2.456618719
+RP5-827C21.1,Nucleus,-0.715928288
+NUS1P1,Nucleus,-0.345477721
+ZBTB22,Nucleus,0.143861626
+AD000090.2,Nucleus,2.17895316
+ZBED5,Nucleus,1.141894307
+RNF103,Nucleus,0.92951049
+TMEM189,Nucleus,0.049814061
+RPL36A,Nucleus,2.582466229
+CD302,Nucleus,-0.674912188
+SNHG3,Nucleus,1.2235952
+C15orf38,Nucleus,0.130181231
+AP5Z1,Nucleus,1.583806673
+MICAL3,Nucleus,0.295660113
+KCTD7,Nucleus,1.024352449
+SCARF2,Nucleus,1.223255157
+APOBEC3C,Nucleus,-0.623289673
+N4BP2L2,Nucleus,0.798210114
+NEAT1,Nucleus,2.742054077
+CRNDE,Nucleus,1.070797015
+OIP5-AS1,Nucleus,0.4918
+MARS2,Nucleus,0.16447254
+HAUS5,Nucleus,0.818737729
+PDCD6,Nucleus,1.063163425
+THAP9-AS1,Nucleus,2.04178026
+SHANK3,Nucleus,1.672407002
+RTN3P1,Nucleus,-0.97165033
+MALAT1,Nucleus,2.270640544
+TUG1,Nucleus,1.052586921
+GS1-251I9.4,Nucleus,0.038738184
+UTP14C,Nucleus,-0.078775894
+ZNF260,Nucleus,0.740217698
+PABPC4L,Nucleus,0.708844803
+FPGT,Nucleus,0.090335169
+MEX3A,Nucleus,1.165024449
+POLR2M,Nucleus,-0.377396071
+SNHG1,Nucleus,1.22191254
+POLG2,Nucleus,2.009705224
+KIAA1147,Nucleus,0.961927538
+RP11-349A22.5,Nucleus,2.079908701
+LINC00641,Nucleus,1.609092458
+RP4-773N10.4,Nucleus,2.15349162
+CEP95,Nucleus,1.593950628
+LINC00657,Nucleus,0.532861334
+RP1-239B22.5,Nucleus,1.432060729
+RP6-24A23.6,Nucleus,-0.305658328
+ERVK13-1,Nucleus,2.60731306
+GS1-358P8.4,Nucleus,0.493226272
+VPS9D1-AS1,Nucleus,1.148508817
+RP6-24A23.7,Nucleus,2.035204644
+GAN,Nucleus,1.140276891
+SPON1,Nucleus,-0.603828378
+RP11-159D12.2,Nucleus,2.644292439
+OTUD7B,Nucleus,0.218141758
+RNF115,Nucleus,1.002602491
+BAHCC1,Nucleus,1.330461339
+NBPF15,Nucleus,1.957548923
+RP11-242D8.1,Nucleus,2.42348371
+RP11-18I14.10,Nucleus,1.603835761
+NBPF9,Nucleus,0.999182828
+EGLN2,Nucleus,1.691792683
+NUDT3,Nucleus,1.062108758
+KMT2B,Nucleus,1.23736839
+POM121C,Nucleus,0.261729908
+RP11-504P24.8,Nucleus,2.067277882
+DCP1A,Nucleus,0.671402769
+EPOP,Nucleus,-0.555983636
+NOL12,Nucleus,2.188891713
+SOCS7,Nucleus,0.866069322
+PI4KAP1,Nucleus,2.321454934
+ZNF280B,Nucleus,0.51913608
+MLLT6,Nucleus,1.092475622
+SYNRG,Nucleus,-0.668929183
+TADA2A,Nucleus,0.114912889
+PIP4K2B,Nucleus,-0.343501121
+AL133325.3,Nucleus,3.353893448
+DDX52,Nucleus,0.33340888
+MYO19,Nucleus,0.881258683
+DHRS11,Nucleus,1.340386765
+ACACA,Nucleus,-0.172745557
+AC005332.6,Nucleus,0.240529047
+RP11-574K11,Nucleus,0.983359099
+AL035425.4,Nucleus,3.916195435
+EBLN3P,Nucleus,0.484352823
+GCLC,Nucleolus,0.55266492
+NFYA,Nucleolus,0.760535411
+NIPAL3,Nucleolus,0.348581394
+ENPP4,Nucleolus,-0.870506896
+SEMA3F,Nucleolus,1.144999435
+CD99,Nucleolus,-1.070904049
+LASP1,Nucleolus,0.500574644
+M6PR,Nucleolus,-1.137941405
+CFLAR,Nucleolus,0.877963204
+NDUFAF7,Nucleolus,1.229705474
+RBM5,Nucleolus,1.172042535
+SLC7A2,Nucleolus,0.748067649
+SARM1,Nucleolus,0.574644643
+CAMKK1,Nucleolus,0.158223431
+RECQL,Nucleolus,0.836649765
+ARHGAP33,Nucleolus,-0.290297552
+CDC27,Nucleolus,0.762420091
+SPPL2B,Nucleolus,1.23818981
+CREBBP,Nucleolus,0.695239107
+GCFC2,Nucleolus,1.417824604
+RHBDD2,Nucleolus,-0.811651871
+IBTK,Nucleolus,0.670927938
+ZNF195,Nucleolus,0.968341722
+MYCBP2,Nucleolus,0.887339421
+ZFX,Nucleolus,0.837205981
+LAMP2,Nucleolus,-0.262306208
+GDE1,Nucleolus,-0.707873963
+TMEM98,Nucleolus,-0.687288246
+TMEM132A,Nucleolus,0.537683652
+ZNF263,Nucleolus,0.286304761
+MAP3K9,Nucleolus,0.409668503
+JHDM1D,Nucleolus,0.751531965
+PHTF2,Nucleolus,1.173522011
+FARP2,Nucleolus,-0.05243891
+IFRD1,Nucleolus,0.61817939
+ARHGAP44,Nucleolus,0.318449436
+ELAC2,Nucleolus,-0.319612803
+ADIPOR2,Nucleolus,-0.190353054
+PAFAH1B1,Nucleolus,0.933075338
+KIAA0100,Nucleolus,-0.226133102
+PAX6,Nucleolus,1.044655972
+LUC7L,Nucleolus,0.418785356
+CACNA2D2,Nucleolus,-0.229310149
+PIGQ,Nucleolus,0.583129929
+CRAMP1L,Nucleolus,0.679230594
+JARID2,Nucleolus,-0.340625827
+ADAM22,Nucleolus,0.32424183
+CYB561,Nucleolus,0.205489859
+SPAG9,Nucleolus,0.512924651
+CELSR3,Nucleolus,1.051344229
+AASS,Nucleolus,0.86456917
+PKD1,Nucleolus,0.750518939
+SEC62,Nucleolus,-0.02973419
+REV3L,Nucleolus,-0.262451876
+POMT2,Nucleolus,-0.419403627
+BAZ1B,Nucleolus,0.246180457
+ZNF207,Nucleolus,0.849587067
+IFFO1,Nucleolus,0.124078782
+NISCH,Nucleolus,0.54799335
+IDS,Nucleolus,0.510765265
+CLCN6,Nucleolus,1.468213061
+MRC2,Nucleolus,-0.415991286
+TSPAN9,Nucleolus,0.678424933
+BTBD7,Nucleolus,1.347994329
+MBTD1,Nucleolus,0.103549141
+LARS2,Nucleolus,0.036883782
+PIK3C2A,Nucleolus,0.868265025
+ANLN,Nucleolus,0.343030056
+QPCTL,Nucleolus,0.975835654
+MAP4K3,Nucleolus,1.111013703
+BRCA1,Nucleolus,0.954209027
+MBTPS2,Nucleolus,-0.026348571
+EXTL3,Nucleolus,-0.136027562
+ELOVL5,Nucleolus,-0.498307025
+MAP4K5,Nucleolus,0.921313541
+MAN2B2,Nucleolus,1.175483139
+CLK1,Nucleolus,0.667858104
+ANGEL1,Nucleolus,0.543677614
+DDX11,Nucleolus,0.845245802
+UFL1,Nucleolus,0.321761089
+SLC30A9,Nucleolus,0.05918813
+COX15,Nucleolus,-0.240125351
+ZMYND11,Nucleolus,1.004978118
+XYLT2,Nucleolus,-0.337437722
+NUDCD3,Nucleolus,-0.405908414
+CHDH,Nucleolus,0.147527559
+GLT8D1,Nucleolus,0.363702894
+ATP2C1,Nucleolus,-0.037954517
+RALBP1,Nucleolus,0.031584512
+CNTN1,Nucleolus,0.276607003
+PHLDB1,Nucleolus,-0.464454105
+MRE11A,Nucleolus,0.955632495
+SPAST,Nucleolus,1.136050019
+NRXN3,Nucleolus,0.948898629
+CPS1,Nucleolus,-0.365515115
+SLC45A4,Nucleolus,-1.464050539
+ZNF839,Nucleolus,0.68918034
+ZDHHC6,Nucleolus,0.713370668
+RNH1,Nucleolus,-0.947969482
+RB1CC1,Nucleolus,0.98553661
+ERP44,Nucleolus,-0.228273723
+AKAP11,Nucleolus,0.174195863
+GCLM,Nucleolus,0.594378222
+DEPDC1,Nucleolus,0.298342724
+SEC63,Nucleolus,-0.037540549
+FAS,Nucleolus,-2.936326233
+RNASET2,Nucleolus,-0.24371448
+AGPAT4,Nucleolus,0.963777932
+MIPEP,Nucleolus,-1.061630129
+VEZT,Nucleolus,-0.561793828
+BRD9,Nucleolus,0.656067962
+SNX1,Nucleolus,0.175344673
+BCLAF1,Nucleolus,0.871712864
+SLC39A9,Nucleolus,0.105702464
+RABEP1,Nucleolus,0.839048728
+FAM13B,Nucleolus,0.238775886
+PNPLA6,Nucleolus,-0.969890775
+ZCCHC8,Nucleolus,1.085461532
+CHPF2,Nucleolus,-0.943602264
+FUT8,Nucleolus,-1.36515182
+UBA6,Nucleolus,0.855591798
+ATP6V0A1,Nucleolus,0.405161867
+SLC4A7,Nucleolus,0.599716603
+VCL,Nucleolus,0.071935314
+ADSS,Nucleolus,0.765532234
+TIMP2,Nucleolus,-0.998965041
+RFC1,Nucleolus,0.109351544
+ZZZ3,Nucleolus,0.804789534
+MFAP3,Nucleolus,0.143624073
+MRI1,Nucleolus,0.292698356
+AGA,Nucleolus,0.814859068
+BOD1L1,Nucleolus,1.222935753
+TRIO,Nucleolus,0.269890397
+VCAN,Nucleolus,0.007351323
+CLEC16A,Nucleolus,-0.074457285
+ZFYVE16,Nucleolus,0.723091677
+RAI14,Nucleolus,0.53131568
+PNKP,Nucleolus,0.612901304
+PQLC2,Nucleolus,-1.455020271
+CTNS,Nucleolus,-0.433426399
+INPP4A,Nucleolus,0.705468898
+RETSAT,Nucleolus,-0.414580649
+CUL7,Nucleolus,-0.753568478
+PHKA2,Nucleolus,0.723152748
+DSG2,Nucleolus,-0.131364033
+OFD1,Nucleolus,-0.471270378
+GPM6B,Nucleolus,-0.188035844
+YTHDC2,Nucleolus,1.01562478
+TPR,Nucleolus,0.86882352
+SCML1,Nucleolus,0.698275922
+MAP4,Nucleolus,0.206172538
+GOPC,Nucleolus,0.784822985
+ZNF800,Nucleolus,0.022281754
+SNX29,Nucleolus,-1.967843626
+KITLG,Nucleolus,-1.385051307
+H6PD,Nucleolus,1.283637484
+LTBP1,Nucleolus,-0.716634073
+RCN1,Nucleolus,0.07885123
+PTCD2,Nucleolus,1.237882578
+LIMA1,Nucleolus,-0.389753463
+LETMD1,Nucleolus,1.220626268
+POLQ,Nucleolus,0.733502163
+MPHOSPH9,Nucleolus,0.572549452
+PLEKHA5,Nucleolus,-1.495122424
+SIKE1,Nucleolus,0.966350614
+MSMO1,Nucleolus,-0.879384564
+TTC17,Nucleolus,-0.140171787
+LAMA3,Nucleolus,0.100762865
+AP5M1,Nucleolus,0.626206021
+ANAPC4,Nucleolus,-0.469621986
+ARID4B,Nucleolus,1.192074089
+SDCCAG8,Nucleolus,0.997348468
+FOXC1,Nucleolus,0.315161774
+PLEKHH1,Nucleolus,0.557852796
+ATP9A,Nucleolus,0.064595526
+FAM168A,Nucleolus,0.228127591
+RELT,Nucleolus,-0.368106478
+NOP58,Nucleolus,0.404458754
+TAB2,Nucleolus,0.57293632
+USP36,Nucleolus,0.294467425
+KMT2C,Nucleolus,0.709949144
+MCOLN3,Nucleolus,1.937523639
+PUM2,Nucleolus,0.77061146
+RC3H2,Nucleolus,0.989176645
+DCBLD2,Nucleolus,0.157039631
+SOAT1,Nucleolus,-0.252112126
+ATP11B,Nucleolus,0.363894669
+SEC61A1,Nucleolus,-0.560209329
+PPP1R12A,Nucleolus,0.845835319
+POLR3E,Nucleolus,0.328680887
+ATP2B4,Nucleolus,0.366258838
+ZC3H11A,Nucleolus,0.85363215
+NDC1,Nucleolus,-0.283523274
+UNKL,Nucleolus,0.735077548
+ALDH18A1,Nucleolus,-0.741070933
+TARBP1,Nucleolus,1.276251007
+WNK1,Nucleolus,0.380376574
+CCAR1,Nucleolus,1.007588773
+PTPRU,Nucleolus,-0.548469851
+QSER1,Nucleolus,0.412936146
+BCAT1,Nucleolus,1.1581262
+PRDM6,Nucleolus,0.712475865
+TNK2,Nucleolus,0.74063069
+MON2,Nucleolus,0.333742997
+GPBP1,Nucleolus,0.88925865
+WAPAL,Nucleolus,0.250233347
+VMP1,Nucleolus,-0.517870417
+APPBP2,Nucleolus,0.863769988
+AHRR,Nucleolus,0.894654627
+ZNF275,Nucleolus,0.394747609
+MTMR1,Nucleolus,0.300383392
+GPC1,Nucleolus,-1.779827289
+TM7SF3,Nucleolus,-0.273823832
+CDON,Nucleolus,0.575132855
+HIPK2,Nucleolus,1.15949009
+SUGP2,Nucleolus,0.690279797
+SLC12A2,Nucleolus,-0.447530999
+HMG20B,Nucleolus,-0.030145919
+UHRF1BP1,Nucleolus,0.833985853
+PKN2,Nucleolus,0.514494228
+TRAM2,Nucleolus,0.965396728
+NTN1,Nucleolus,-1.458109516
+ADAT1,Nucleolus,1.477128418
+SPEN,Nucleolus,0.710156608
+MAP2K4,Nucleolus,0.190333225
+SLK,Nucleolus,0.356147608
+CYB5R4,Nucleolus,1.163051608
+ASB1,Nucleolus,1.155500702
+FAM107B,Nucleolus,-0.588500112
+SLC9A7,Nucleolus,-0.451622751
+FOXJ2,Nucleolus,1.237313996
+PPP2R5A,Nucleolus,0.776025111
+ASPM,Nucleolus,0.868776688
+ZBTB11,Nucleolus,1.160969906
+ATXN3,Nucleolus,1.488640568
+FGFR2,Nucleolus,-0.281394085
+LRRC40,Nucleolus,0.92623725
+ATG2B,Nucleolus,0.935504407
+ARFGEF1,Nucleolus,0.3982695
+KLF6,Nucleolus,0.561475126
+NEO1,Nucleolus,0.130849096
+TRAM1,Nucleolus,-0.087842943
+TP53BP1,Nucleolus,0.442033879
+IARS2,Nucleolus,-0.031774805
+ROCK1,Nucleolus,1.182366188
+HYAL2,Nucleolus,-1.567685019
+FGFR3,Nucleolus,0.846373269
+MEF2A,Nucleolus,0.191125852
+ACSL4,Nucleolus,0.953308822
+PRR11,Nucleolus,0.813573964
+REEP1,Nucleolus,-0.193525701
+ATP11A,Nucleolus,-0.05416616
+POLR1A,Nucleolus,1.150333566
+IP6K2,Nucleolus,0.496330399
+KIF2A,Nucleolus,0.805792827
+TGFBR3,Nucleolus,0.768170715
+NEDD4,Nucleolus,-0.655785066
+MAPK6,Nucleolus,-0.097821611
+UFD1L,Nucleolus,0.615906098
+LRP6,Nucleolus,0.695053594
+NUCB2,Nucleolus,-0.77545367
+SLC44A1,Nucleolus,0.329127843
+TMEM260,Nucleolus,0.78529969
+SMG6,Nucleolus,0.23859945
+CLTCL1,Nucleolus,-0.273823447
+DGCR2,Nucleolus,-0.467181958
+MNT,Nucleolus,0.75548449
+ZXDC,Nucleolus,1.024291574
+NDST1,Nucleolus,0.213044236
+AP3M2,Nucleolus,-0.203512761
+RAD18,Nucleolus,0.913230208
+ATP2B1,Nucleolus,0.722747167
+MGAT4A,Nucleolus,1.098397587
+SNX13,Nucleolus,0.173511583
+VASH1,Nucleolus,0.038434082
+SEL1L,Nucleolus,0.745443168
+ATP6AP1,Nucleolus,-1.253225947
+DAZAP1,Nucleolus,0.189995871
+CPSF1,Nucleolus,0.061253285
+SLC6A15,Nucleolus,0.313831012
+RDH11,Nucleolus,-0.103956273
+PRKACA,Nucleolus,-1.124470014
+LPHN1,Nucleolus,0.443767178
+RPS6KA6,Nucleolus,1.380152925
+EPN2,Nucleolus,0.800853874
+PTPN18,Nucleolus,0.52918293
+TFRC,Nucleolus,0.661916392
+AFF4,Nucleolus,1.022288184
+MPP5,Nucleolus,1.150007838
+HMMR,Nucleolus,0.393485028
+P4HA2,Nucleolus,-0.225276487
+TRNT1,Nucleolus,1.729764946
+ACADVL,Nucleolus,0.168115754
+EVC,Nucleolus,-1.964185832
+DERL2,Nucleolus,0.105161028
+NDE1,Nucleolus,-0.374264321
+PVR,Nucleolus,-0.272401533
+SCARB1,Nucleolus,0.177481913
+SELO,Nucleolus,0.427183507
+LLGL2,Nucleolus,0.349443052
+PDE8A,Nucleolus,1.080386773
+SDHA,Nucleolus,-0.474184877
+KDM5A,Nucleolus,0.20442605
+ADAM11,Nucleolus,1.064466107
+ST6GAL1,Nucleolus,0.098222224
+GLI2,Nucleolus,0.312588625
+NOTCH3,Nucleolus,-0.464317586
+BCS1L,Nucleolus,-0.446156454
+DPP8,Nucleolus,1.071593533
+SLC24A1,Nucleolus,-0.337740059
+LMAN1,Nucleolus,0.574353304
+PTPLAD1,Nucleolus,-0.300047391
+TUBE1,Nucleolus,1.57437927
+SEMA3C,Nucleolus,0.672053676
+TTC38,Nucleolus,-0.442353482
+CELSR1,Nucleolus,-0.026153531
+ZNF638,Nucleolus,0.353726608
+SLC25A40,Nucleolus,1.10332726
+RASAL2,Nucleolus,-0.639161321
+ZNF37A,Nucleolus,1.828339016
+FNDC3B,Nucleolus,-3.429702366
+FRYL,Nucleolus,0.483778012
+TMEM131,Nucleolus,0.004629519
+WDR62,Nucleolus,0.725638349
+BCAP29,Nucleolus,-0.063206672
+SEC31B,Nucleolus,1.045714633
+RBMS2,Nucleolus,1.052499776
+SLC46A1,Nucleolus,-0.95504953
+PLXNA2,Nucleolus,-1.301743771
+ANKRD13A,Nucleolus,0.868412722
+PAG1,Nucleolus,1.246968953
+MCAM,Nucleolus,-1.442248132
+GPC4,Nucleolus,-0.055156124
+MBNL3,Nucleolus,1.116518068
+DGKD,Nucleolus,-0.824619367
+TM9SF3,Nucleolus,0.523318258
+PPP1R12B,Nucleolus,1.535392218
+DNAJC10,Nucleolus,0.329586284
+GTF3C1,Nucleolus,0.107212607
+IL4R,Nucleolus,0.832990142
+LRCH4,Nucleolus,-0.096735904
+FAM76B,Nucleolus,1.952104163
+SIRT6,Nucleolus,-0.905272728
+POLD3,Nucleolus,0.397085188
+PHF17,Nucleolus,0.83057487
+FBLN1,Nucleolus,-1.547087656
+ITGA8,Nucleolus,-0.415806549
+MCCC1,Nucleolus,0.861873395
+ACER3,Nucleolus,0.540603654
+N4BP2,Nucleolus,1.214580118
+HOXA9,Nucleolus,0.614958956
+PCM1,Nucleolus,0.80209668
+TNRC6C,Nucleolus,1.125519301
+ITCH,Nucleolus,0.948717953
+SDF4,Nucleolus,-0.467273095
+FKBP7,Nucleolus,-1.125557974
+SLC1A3,Nucleolus,-0.212955246
+SAR1A,Nucleolus,0.060719795
+PAFAH1B3,Nucleolus,-1.612755196
+MOXD1,Nucleolus,-0.244260598
+STX7,Nucleolus,0.524006556
+RABL2B,Nucleolus,0.55463957
+SLC35C2,Nucleolus,-0.413011923
+CRYBG3,Nucleolus,1.149062406
+RIF1,Nucleolus,0.912741531
+PSEN1,Nucleolus,-0.003119906
+RBL1,Nucleolus,0.433839299
+RSBN1,Nucleolus,1.621057691
+MAGI3,Nucleolus,0.754967446
+OSTM1,Nucleolus,-0.588594574
+EXD2,Nucleolus,-1.396361894
+MEF2C,Nucleolus,1.194112687
+UBA5,Nucleolus,0.739724188
+STK17B,Nucleolus,-0.50707639
+ZNF510,Nucleolus,0.981814561
+LRP2,Nucleolus,-1.588365701
+DUSP12,Nucleolus,-0.080617678
+KIAA0141,Nucleolus,0.299007788
+PHLPP1,Nucleolus,-0.344592036
+SMARCD3,Nucleolus,0.508550993
+C5orf22,Nucleolus,0.951729593
+CCNT2,Nucleolus,0.76996199
+NFE2L1,Nucleolus,-0.263997582
+GSK3B,Nucleolus,0.37967597
+ITGB5,Nucleolus,-0.824272146
+ERC1,Nucleolus,-0.112692511
+RNF13,Nucleolus,0.08615184
+LYRM2,Nucleolus,0.202360954
+KAT6A,Nucleolus,0.193516286
+PLOD1,Nucleolus,-0.58359791
+TDRD3,Nucleolus,-1.317939114
+PDS5B,Nucleolus,1.255940941
+OXCT1,Nucleolus,-0.467655223
+RRAGB,Nucleolus,1.254126915
+FAT1,Nucleolus,-0.123442948
+YTHDC1,Nucleolus,0.996772639
+ZMPSTE24,Nucleolus,-0.535766536
+REST,Nucleolus,0.197665009
+APLP2,Nucleolus,-0.160609019
+KIAA1467,Nucleolus,0.253995002
+TXLNA,Nucleolus,0.578815954
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+AGBL5,Nucleolus,-0.594952048
+CD59,Nucleolus,-1.036284223
+ATRX,Nucleolus,0.928378603
+SCAMP1,Nucleolus,0.270536774
+HACE1,Nucleolus,-1.396856799
+WDFY1,Nucleolus,-0.113742786
+MTIF2,Nucleolus,0.729325853
+ATG16L1,Nucleolus,-0.311556674
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+B4GALT1,Nucleolus,-0.115317495
+IPO11,Nucleolus,0.886675486
+TMED2,Nucleolus,-0.255877406
+ERO1LB,Nucleolus,0.309283995
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+NLK,Nucleolus,0.926909671
+PIGS,Nucleolus,-0.401563219
+ATXN7L3,Nucleolus,0.384168293
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+SH3BP2,Nucleolus,-0.24822445
+ADD1,Nucleolus,-0.29289305
+TXNDC16,Nucleolus,0.432943612
+NID2,Nucleolus,-1.599409489
+KLHL42,Nucleolus,0.671828501
+ERGIC2,Nucleolus,0.297470604
+METTL2A,Nucleolus,0.408072785
+PTPN4,Nucleolus,1.020580505
+KHSRP,Nucleolus,-0.056506633
+EPB41L1,Nucleolus,0.558319836
+ANKRD10,Nucleolus,0.952143361
+DOCK3,Nucleolus,1.011319172
+PPP1R13B,Nucleolus,0.554342945
+ATRN,Nucleolus,0.592166362
+ZNF343,Nucleolus,-1.9699837
+MAVS,Nucleolus,1.033180656
+LZTS3,Nucleolus,0.289515746
+SNX5,Nucleolus,0.936024418
+MAPKAPK5,Nucleolus,0.772336377
+ESF1,Nucleolus,0.88818375
+SLC23A2,Nucleolus,-0.145398191
+KIF16B,Nucleolus,1.180980635
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+FUS,Nucleolus,0.344915104
+ZNF302,Nucleolus,0.272263613
+GRAMD1A,Nucleolus,-0.249034659
+GANAB,Nucleolus,-0.51470151
+RBM41,Nucleolus,0.425939701
+GPATCH2L,Nucleolus,0.935775693
+SLC9A1,Nucleolus,0.750862766
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+PAPOLA,Nucleolus,0.617644183
+MUL1,Nucleolus,-0.961696122
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+GOLGA3,Nucleolus,1.107416129
+EFNB1,Nucleolus,-1.059595481
+PDPR,Nucleolus,0.576966021
+GLG1,Nucleolus,-0.039296895
+TNRC6A,Nucleolus,0.143122556
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+RBM27,Nucleolus,1.201818175
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+NRCAM,Nucleolus,0.305294742
+LAMB1,Nucleolus,0.416863314
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+ITGA6,Nucleolus,-0.961820089
+SEL1L3,Nucleolus,-11.38748956
+ORC6,Nucleolus,1.341361936
+TMEM101,Nucleolus,-0.49149507
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+G2E3,Nucleolus,0.77852115
+HECTD1,Nucleolus,0.457184528
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+TRPM7,Nucleolus,0.462252461
+TYRO3,Nucleolus,-1.179390729
+AGO1,Nucleolus,0.499121195
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+GPATCH2,Nucleolus,0.327605133
+NUP50,Nucleolus,0.64246813
+LRRFIP2,Nucleolus,0.768043656
+SEC22C,Nucleolus,0.712427125
+XYLB,Nucleolus,0.628705881
+HDAC6,Nucleolus,0.087209304
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+SUCO,Nucleolus,0.738285551
+HOOK2,Nucleolus,0.323973798
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+WAC,Nucleolus,0.734230264
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+EFHC1,Nucleolus,0.724178838
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+POLRMT,Nucleolus,-0.22305698
+ARVCF,Nucleolus,0.138236651
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+LZTR1,Nucleolus,0.549387738
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+CLPTM1,Nucleolus,-0.370461491
+CLASRP,Nucleolus,0.326608279
+FCGRT,Nucleolus,0.121816993
+ERCC2,Nucleolus,0.149885667
+DOT1L,Nucleolus,0.97910476
+SF3A2,Nucleolus,-0.138254524
+AMH,Nucleolus,1.172373543
+DMPK,Nucleolus,0.122060227
+TIMM44,Nucleolus,0.441547371
+AKAP8,Nucleolus,0.622049433
+AKT2,Nucleolus,-0.005181488
+PLD3,Nucleolus,-0.615137291
+FSD1,Nucleolus,-0.539271138
+APLP1,Nucleolus,-2.751485887
+CACTIN,Nucleolus,0.702100455
+TYK2,Nucleolus,0.140321272
+PTPRS,Nucleolus,-0.059198202
+MEGF8,Nucleolus,0.211710928
+KDELR1,Nucleolus,-0.84683617
+CYTH2,Nucleolus,0.493031611
+LIG1,Nucleolus,0.057839788
+BCAT2,Nucleolus,-0.419693599
+TNPO2,Nucleolus,0.108066368
+DNASE2,Nucleolus,-1.19104206
+ISYNA1,Nucleolus,-0.158160099
+CRTC1,Nucleolus,1.214120306
+SUGP1,Nucleolus,-0.050439472
+SIPA1L3,Nucleolus,1.712055386
+CADM4,Nucleolus,-3.461333314
+SMG9,Nucleolus,0.155810034
+AVL9,Nucleolus,0.206348321
+CDK6,Nucleolus,0.920554334
+DNAJC2,Nucleolus,0.714091445
+WDR91,Nucleolus,-1.016280725
+CBLL1,Nucleolus,0.91461054
+MTPN,Nucleolus,0.453326764
+ZC3HAV1,Nucleolus,0.581244537
+OGDH,Nucleolus,-0.838564123
+MET,Nucleolus,0.080673265
+LMBR1,Nucleolus,0.052283416
+HOXA3,Nucleolus,0.263332192
+HOXA6,Nucleolus,0.934743018
+BRAT1,Nucleolus,-0.266157653
+FKBP14,Nucleolus,0.057755947
+NSUN5P2,Nucleolus,1.390820932
+CASP2,Nucleolus,0.063688639
+HSPB1,Nucleolus,-2.4800486
+ZKSCAN1,Nucleolus,0.667458155
+WASL,Nucleolus,0.208669503
+RBM28,Nucleolus,0.781714218
+C1GALT1,Nucleolus,-0.84468518
+PLOD3,Nucleolus,-1.238753941
+CLDN15,Nucleolus,1.523473923
+TMEM106B,Nucleolus,0.994542823
+CEP41,Nucleolus,-1.313212749
+GLI3,Nucleolus,0.411043109
+TMEM248,Nucleolus,0.305252362
+TBL2,Nucleolus,-0.528695602
+FKTN,Nucleolus,0.668021266
+TMEM245,Nucleolus,0.371701948
+MEGF9,Nucleolus,0.742724013
+TGFBR1,Nucleolus,0.686813258
+DNM1,Nucleolus,1.749835703
+KANK1,Nucleolus,-1.437897739
+RAPGEF1,Nucleolus,-0.99592759
+NPDC1,Nucleolus,0.728847719
+SETX,Nucleolus,0.735240781
+CCNJ,Nucleolus,0.265253695
+RAB11FIP2,Nucleolus,0.603951441
+ERLIN1,Nucleolus,-0.263921748
+MAPK8,Nucleolus,0.040117884
+ATE1,Nucleolus,-0.443426728
+PLEKHA1,Nucleolus,0.918798907
+UNC5B,Nucleolus,0.332630663
+BMPR1A,Nucleolus,0.673913997
+ACTA2,Nucleolus,-1.892779029
+LIPA,Nucleolus,-0.274636932
+LZTS2,Nucleolus,1.069192947
+ARHGAP21,Nucleolus,0.644655056
+ANKRD26,Nucleolus,1.019360838
+LARP4B,Nucleolus,0.814210188
+C10orf137,Nucleolus,-0.060155573
+MTPAP,Nucleolus,1.286123964
+SH3PXD2A,Nucleolus,0.472756092
+PITRM1,Nucleolus,-0.465707004
+FAM208B,Nucleolus,0.293807594
+TSPAN14,Nucleolus,-1.352973284
+NUFIP2,Nucleolus,1.213949427
+DHX40,Nucleolus,0.892959628
+CDK5RAP3,Nucleolus,0.532485562
+RECQL5,Nucleolus,0.967471427
+INTS2,Nucleolus,0.940225262
+CAMTA2,Nucleolus,0.899166294
+MED13,Nucleolus,0.540579329
+HOXB6,Nucleolus,0.877491377
+CPD,Nucleolus,0.482392374
+GOSR1,Nucleolus,0.244985444
+CCDC47,Nucleolus,-0.121990992
+AKAP10,Nucleolus,0.570602121
+CYTH1,Nucleolus,0.581156523
+LGALS3BP,Nucleolus,-0.508732265
+EZH1,Nucleolus,0.811838341
+PPP1R9B,Nucleolus,0.385775752
+LUC7L3,Nucleolus,1.446820499
+DUSP3,Nucleolus,0.171656377
+EFNB3,Nucleolus,-0.929043346
+DPH1,Nucleolus,0.049125142
+NAT9,Nucleolus,-0.07960161
+TMEM104,Nucleolus,-0.697592478
+TMEM97,Nucleolus,-0.417975604
+UNC119,Nucleolus,0.321616329
+TMEM33,Nucleolus,0.676793813
+DCUN1D4,Nucleolus,0.459859515
+MANBA,Nucleolus,0.556120694
+ELF2,Nucleolus,0.906301085
+WFS1,Nucleolus,-0.93140174
+FRG1,Nucleolus,0.907634717
+CLCN3,Nucleolus,0.082464279
+GALNT7,Nucleolus,-0.114931998
+TRIM2,Nucleolus,1.163013649
+NEIL3,Nucleolus,0.305218451
+SH3D19,Nucleolus,0.34134972
+STIM2,Nucleolus,0.180708731
+RAPGEF2,Nucleolus,0.9500633
+UGDH,Nucleolus,0.91821345
+CCDC34,Nucleolus,1.247163898
+FNBP4,Nucleolus,1.129417675
+SC5D,Nucleolus,-0.718757566
+SIAE,Nucleolus,-0.716255127
+EHD1,Nucleolus,0.385035955
+FOXRED1,Nucleolus,0.132575312
+ST3GAL4,Nucleolus,-1.775766799
+CPT1A,Nucleolus,-0.577784698
+TMEM109,Nucleolus,-0.767131923
+PANX1,Nucleolus,-0.431669677
+UBE4A,Nucleolus,-0.031263237
+DDX6,Nucleolus,0.804175202
+PVRL1,Nucleolus,0.091139316
+HIPK3,Nucleolus,1.188227299
+MDK,Nucleolus,-1.438103604
+AMBRA1,Nucleolus,-0.256444304
+NAA40,Nucleolus,0.324377856
+SLC35F2,Nucleolus,0.397262617
+LEPREL2,Nucleolus,-2.203922042
+CORO1C,Nucleolus,-0.297211163
+ASIC1,Nucleolus,1.193270483
+CAPRIN2,Nucleolus,-0.332932013
+SLC11A2,Nucleolus,0.215076106
+MLEC,Nucleolus,-0.153626484
+BCL7A,Nucleolus,0.138635774
+RSRC2,Nucleolus,1.201811695
+PPM1H,Nucleolus,1.232758945
+ELK3,Nucleolus,-0.527908146
+MAGOHB,Nucleolus,0.757999114
+ITFG2,Nucleolus,-0.081417045
+PARP11,Nucleolus,-2.568683948
+DUSP16,Nucleolus,0.336165265
+ACAD10,Nucleolus,0.795273998
+NAA25,Nucleolus,1.229698203
+DDX55,Nucleolus,0.469350528
+SLC38A1,Nucleolus,0.718571571
+C12orf49,Nucleolus,-0.232824755
+MDM1,Nucleolus,1.435940457
+CPSF6,Nucleolus,0.484815639
+GNPTAB,Nucleolus,0.11620158
+ATN1,Nucleolus,0.143191652
+C12orf57,Nucleolus,-1.43453284
+LPCAT3,Nucleolus,-1.834731883
+SUDS3,Nucleolus,0.411141142
+GOLT1B,Nucleolus,0.55507424
+C2CD5,Nucleolus,1.098035881
+RAB35,Nucleolus,0.014656935
+RIC8B,Nucleolus,0.603820449
+RP11-22B23.1,Nucleolus,1.569117342
+DSE,Nucleolus,-0.227744591
+MAN1A1,Nucleolus,0.559323292
+SERINC1,Nucleolus,-0.194102365
+UST,Nucleolus,0.47607404
+KCTD20,Nucleolus,0.601649388
+RNF8,Nucleolus,1.086634233
+ICK,Nucleolus,1.348739582
+RAB23,Nucleolus,0.94303601
+FBXL4,Nucleolus,0.30616489
+CCNC,Nucleolus,0.971414015
+ALDH5A1,Nucleolus,-0.188635107
+EYA4,Nucleolus,0.460331996
+PERP,Nucleolus,-0.015978613
+SLC16A10,Nucleolus,0.451777961
+PHACTR2,Nucleolus,0.486875402
+SLC39A7,Nucleolus,-0.912878797
+PPP2R5D,Nucleolus,-0.470621301
+PTK7,Nucleolus,-0.542649553
+CUL9,Nucleolus,-0.347920004
+TMEM30A,Nucleolus,0.193698065
+SENP6,Nucleolus,0.988560545
+VEGFA,Nucleolus,1.024874894
+PRPF4B,Nucleolus,0.396160965
+BTN2A1,Nucleolus,-0.201591103
+LAMA4,Nucleolus,0.904343158
+ERBB2IP,Nucleolus,0.279436812
+HARS2,Nucleolus,0.757864933
+MAN2A1,Nucleolus,-0.028575885
+PAPD7,Nucleolus,-0.195675589
+NNT,Nucleolus,0.04493996
+APBB3,Nucleolus,0.501942757
+SPARC,Nucleolus,-11.05293609
+HMGCR,Nucleolus,-0.161130983
+FAF2,Nucleolus,-0.377198827
+CLK4,Nucleolus,1.916716497
+ARSB,Nucleolus,0.251593619
+CNOT6,Nucleolus,0.778977294
+DROSHA,Nucleolus,0.782376676
+FAM172A,Nucleolus,0.560776876
+LNPEP,Nucleolus,1.173574476
+SLC12A7,Nucleolus,-1.086526938
+NR3C1,Nucleolus,0.415129496
+C5orf15,Nucleolus,-0.185865374
+LIFR,Nucleolus,1.03466261
+TRAPPC13,Nucleolus,0.317401364
+TXNDC15,Nucleolus,-0.254442929
+H2AFY,Nucleolus,0.127174813
+TCERG1,Nucleolus,0.974432146
+SMAD5,Nucleolus,0.386973666
+ERGIC1,Nucleolus,-0.473741223
+STC2,Nucleolus,0.730458107
+ARL6,Nucleolus,1.278422486
+NIT2,Nucleolus,1.017869804
+UBE3A,Nucleolus,1.070809815
+SLC25A36,Nucleolus,1.091196123
+TFDP2,Nucleolus,0.400346029
+XRN1,Nucleolus,1.1233458
+WNT5A,Nucleolus,0.378806194
+PFKFB4,Nucleolus,0.843532939
+PRKAR2A,Nucleolus,0.195467888
+ACAP2,Nucleolus,0.003070136
+CBLB,Nucleolus,-2.988917704
+BBX,Nucleolus,0.294418725
+GNB4,Nucleolus,0.960071569
+C3orf52,Nucleolus,-0.353474532
+PLXNA1,Nucleolus,0.077396607
+CSPG5,Nucleolus,-0.601738258
+SCAP,Nucleolus,-0.08198334
+HEMK1,Nucleolus,1.63463373
+ACVR2B,Nucleolus,0.064596011
+ABCC5,Nucleolus,0.080446281
+SSR3,Nucleolus,0.31475214
+NKTR,Nucleolus,1.766407347
+FOXP1,Nucleolus,1.057632806
+INO80D,Nucleolus,-0.463817456
+ADAM23,Nucleolus,-0.688291372
+MOB1A,Nucleolus,0.509162632
+LMAN2L,Nucleolus,-1.609314976
+RTKN,Nucleolus,-0.717243676
+PIKFYVE,Nucleolus,1.169958176
+FAHD2A,Nucleolus,0.588093957
+SLC35F5,Nucleolus,0.530640009
+STEAP3,Nucleolus,-0.128690996
+EPB41L5,Nucleolus,-0.470663664
+GPD2,Nucleolus,-0.782907863
+ACVR1,Nucleolus,-0.262281146
+MPV17,Nucleolus,0.495734473
+TTC31,Nucleolus,0.853212483
+NDUFS7,Nucleolus,0.089897517
+SPTBN1,Nucleolus,0.474031504
+CCDC88A,Nucleolus,0.5706379
+FN1,Nucleolus,-0.064522295
+ELMOD3,Nucleolus,0.457921768
+IGFBP5,Nucleolus,0.576690202
+USP34,Nucleolus,0.906429982
+GGCX,Nucleolus,-0.944264232
+CHST10,Nucleolus,-1.961511922
+MOB4,Nucleolus,0.919313724
+UXS1,Nucleolus,0.12942812
+PASK,Nucleolus,1.129726447
+TAF1B,Nucleolus,0.832168094
+DCAF17,Nucleolus,0.925423842
+SDC1,Nucleolus,-0.55641813
+SLC1A4,Nucleolus,-0.498182328
+SOS1,Nucleolus,0.179176279
+WIPF1,Nucleolus,1.183925178
+THADA,Nucleolus,1.067800749
+TRAK2,Nucleolus,0.740806887
+TIA1,Nucleolus,0.335455661
+PCYOX1,Nucleolus,-0.167043054
+ARID3A,Nucleolus,0.153139908
+EPHA4,Nucleolus,0.347745624
+ALMS1,Nucleolus,0.059852583
+BCL9,Nucleolus,-0.305774208
+DHCR24,Nucleolus,-0.835839003
+DNAJC16,Nucleolus,0.277345254
+RALGPS2,Nucleolus,1.159403722
+CEP104,Nucleolus,1.119652762
+FAM20B,Nucleolus,0.311416057
+TCEANC2,Nucleolus,0.869917264
+WRAP73,Nucleolus,0.403199196
+ICMT,Nucleolus,-0.333853373
+QSOX1,Nucleolus,-0.361902181
+AMPD2,Nucleolus,0.561995401
+EDEM3,Nucleolus,0.56415913
+RAP1A,Nucleolus,0.832105089
+S100PBP,Nucleolus,1.015374686
+ASH1L,Nucleolus,0.801285125
+SFPQ,Nucleolus,0.524722683
+MEF2D,Nucleolus,-0.057943976
+C1orf21,Nucleolus,0.48972224
+LEPR,Nucleolus,0.545246627
+IVNS1ABP,Nucleolus,0.487675074
+KIAA2013,Nucleolus,-0.380316424
+MIIP,Nucleolus,0.226401899
+SLC35D1,Nucleolus,0.499185565
+WLS,Nucleolus,-0.598217163
+PRDM2,Nucleolus,0.816859741
+TROVE2,Nucleolus,1.143582035
+SRSF11,Nucleolus,1.398489505
+PHTF1,Nucleolus,0.528352438
+TMEM9,Nucleolus,-0.469838641
+EXOC8,Nucleolus,0.570885042
+NID1,Nucleolus,-0.219883755
+MTR,Nucleolus,1.094211154
+BMP8B,Nucleolus,0.380425654
+RIMS3,Nucleolus,-0.006408814
+AKT3,Nucleolus,0.963050397
+ETV3,Nucleolus,1.169106913
+LPHN2,Nucleolus,0.470532128
+RBBP5,Nucleolus,-0.068076199
+ECE1,Nucleolus,-0.42690886
+CD46,Nucleolus,0.620747105
+APH1A,Nucleolus,-0.691662303
+LEPRE1,Nucleolus,-1.766881389
+SLC2A1,Nucleolus,-0.9607556
+SLC19A2,Nucleolus,1.195644852
+NSUN4,Nucleolus,0.626268211
+TMED5,Nucleolus,0.013975919
+DR1,Nucleolus,0.782546454
+PTBP2,Nucleolus,1.737356387
+DARS2,Nucleolus,0.200529309
+DIEXF,Nucleolus,0.843339774
+RCAN3,Nucleolus,0.310230251
+C1orf63,Nucleolus,0.576161677
+SLC35A3,Nucleolus,0.530744844
+RCOR3,Nucleolus,0.549921578
+ARID1A,Nucleolus,0.296681543
+CENPF,Nucleolus,0.808180802
+ESYT2,Nucleolus,-0.361302565
+CD3EAP,Nucleolus,0.125826914
+MESDC2,Nucleolus,-0.280268437
+CTSD,Nucleolus,-1.617158405
+STK11,Nucleolus,0.894337682
+KMT2A,Nucleolus,0.611726303
+KPTN,Nucleolus,0.40410278
+KIF14,Nucleolus,1.106935355
+ATF6,Nucleolus,-0.631106548
+FASTKD2,Nucleolus,0.797118068
+NRP2,Nucleolus,-1.156334799
+CREB1,Nucleolus,0.761770792
+B4GALT6,Nucleolus,0.212648175
+ELOVL4,Nucleolus,-0.555563059
+CASP8AP2,Nucleolus,1.010844224
+PHF3,Nucleolus,1.11553838
+PLAGL1,Nucleolus,0.874280755
+FBXO30,Nucleolus,0.363024815
+TMEM5,Nucleolus,1.645985549
+ZNF430,Nucleolus,0.581529312
+DCLRE1B,Nucleolus,-1.319299652
+PKD2,Nucleolus,1.274632238
+UBN1,Nucleolus,0.497639069
+KLF12,Nucleolus,0.209193013
+WDR35,Nucleolus,1.055248692
+CCND2,Nucleolus,1.141700523
+SATB2,Nucleolus,-0.195347259
+SENP5,Nucleolus,0.970552284
+C1orf198,Nucleolus,-1.050953087
+HEATR1,Nucleolus,0.873066144
+PTBP3,Nucleolus,1.035614621
+FAM206A,Nucleolus,0.924471841
+RBM18,Nucleolus,-0.785300278
+MAPKAP1,Nucleolus,0.202662026
+KDSR,Nucleolus,-0.439550796
+ONECUT2,Nucleolus,0.834917796
+IRF2BPL,Nucleolus,-1.569927593
+AREL1,Nucleolus,0.238026295
+ABCD4,Nucleolus,-0.072820469
+RBM25,Nucleolus,1.346292217
+NRDE2,Nucleolus,0.711902314
+KLHL29,Nucleolus,0.962635075
+DNMT3A,Nucleolus,1.918248982
+ATAD2B,Nucleolus,0.57261033
+ATL2,Nucleolus,0.407760254
+YIPF4,Nucleolus,0.788236772
+AFTPH,Nucleolus,1.072479989
+BCL11A,Nucleolus,0.878166057
+SLC17A5,Nucleolus,-0.020704074
+FAM178A,Nucleolus,0.023534399
+GPAM,Nucleolus,-0.588011321
+HELLS,Nucleolus,0.896682663
+TCTN3,Nucleolus,-1.379575665
+C10orf76,Nucleolus,-0.4266106
+HOXB8,Nucleolus,-0.810248244
+HOXB3,Nucleolus,1.274156149
+PANK3,Nucleolus,1.460880398
+NUP43,Nucleolus,0.923000388
+LRP11,Nucleolus,-0.54778116
+MASTL,Nucleolus,0.486796173
+ELF1,Nucleolus,0.34995435
+EGR1,Nucleolus,-2.266084072
+NR2C1,Nucleolus,0.373611419
+MTERFD3,Nucleolus,1.326017883
+CLU,Nucleolus,-2.55134513
+TNFRSF10B,Nucleolus,-1.176531618
+TARDBP,Nucleolus,0.386302701
+CRISPLD1,Nucleolus,1.070966876
+AKAP1,Nucleolus,-0.035763924
+TRIM25,Nucleolus,0.394323721
+KIAA0922,Nucleolus,-0.386496833
+PAPD5,Nucleolus,0.704882569
+CEP89,Nucleolus,0.655091422
+B4GALT4,Nucleolus,0.159423484
+KIF18A,Nucleolus,1.290827803
+CRY2,Nucleolus,-6.850937775
+ZNF639,Nucleolus,1.27570932
+PDS5A,Nucleolus,0.646912785
+CLCC1,Nucleolus,0.291309927
+ACVR2A,Nucleolus,0.361170227
+RPL21,Nucleolus,-0.110495929
+MTERFD2,Nucleolus,0.606268084
+KIAA1191,Nucleolus,-0.281373048
+RBBP6,Nucleolus,0.978314078
+ZC3H7A,Nucleolus,1.040858513
+FAM35A,Nucleolus,0.360021164
+FAM213A,Nucleolus,-0.671988078
+ODF2L,Nucleolus,1.189710424
+TRMT13,Nucleolus,1.175793401
+RPAP2,Nucleolus,0.862017175
+FAM126A,Nucleolus,-0.017438303
+FKBP9,Nucleolus,0.010334314
+POLM,Nucleolus,-0.13317147
+SLC25A51,Nucleolus,0.8471395
+DCAF10,Nucleolus,0.838624272
+KIAA1549,Nucleolus,0.407814948
+CALD1,Nucleolus,0.074035847
+CHST3,Nucleolus,-2.03521787
+P4HA1,Nucleolus,-0.598311599
+RBM19,Nucleolus,0.527939185
+GIPC1,Nucleolus,-1.332409668
+ATP7B,Nucleolus,0.335868783
+ZC3H13,Nucleolus,0.702773617
+NLN,Nucleolus,0.944786462
+CENPK,Nucleolus,0.796286244
+OPTN,Nucleolus,-5.358128353
+SPATS2,Nucleolus,0.848684494
+LRP1,Nucleolus,-1.660060952
+HJURP,Nucleolus,-0.301803649
+USP45,Nucleolus,0.913300859
+SLC36A1,Nucleolus,0.076977428
+LPGAT1,Nucleolus,0.897328178
+EXOSC9,Nucleolus,1.1071098
+PLA2G12A,Nucleolus,0.606266728
+ADCK4,Nucleolus,-0.515231901
+PFKFB2,Nucleolus,1.066645798
+AGO2,Nucleolus,0.742566215
+MXD4,Nucleolus,0.27981196
+ACSL3,Nucleolus,-0.022413529
+SLC12A4,Nucleolus,-0.78392326
+FAM210B,Nucleolus,-0.45915786
+SDC4,Nucleolus,-1.008574936
+NCOA3,Nucleolus,1.139564211
+PIGT,Nucleolus,-0.744209225
+VAPB,Nucleolus,0.229024956
+CHD6,Nucleolus,0.769143081
+SRSF6,Nucleolus,0.754452305
+RAB22A,Nucleolus,1.366309627
+STX16,Nucleolus,0.879813988
+STAMBP,Nucleolus,0.769776783
+NAGK,Nucleolus,-0.868517632
+PAIP2B,Nucleolus,0.569886514
+ATP8A1,Nucleolus,0.909066881
+BTN2A2,Nucleolus,0.345153962
+ABCC10,Nucleolus,-0.520854901
+AARS2,Nucleolus,-1.225867457
+ZNF391,Nucleolus,-0.10864286
+CDKN1A,Nucleolus,-3.16503486
+SSR1,Nucleolus,0.204912297
+NRN1,Nucleolus,0.397246399
+ATXN1,Nucleolus,0.534856455
+EEF1E1,Nucleolus,0.248386723
+LRRFIP1,Nucleolus,0.615483004
+AHNAK,Nucleolus,0.150832419
+ABCC4,Nucleolus,-0.175054482
+EFNB2,Nucleolus,-0.341046155
+ATP5S,Nucleolus,1.213617353
+FAM193A,Nucleolus,0.647740017
+GGA3,Nucleolus,-0.314737344
+GTF3C4,Nucleolus,0.553898599
+PPP1R12C,Nucleolus,-0.288302794
+MBOAT7,Nucleolus,-0.681985398
+CCDC93,Nucleolus,0.80209123
+THOC2,Nucleolus,0.756591937
+MED1,Nucleolus,0.045705669
+GPR108,Nucleolus,-1.469354331
+GPCPD1,Nucleolus,0.738713037
+PANK2,Nucleolus,0.514546921
+NAPB,Nucleolus,1.67112543
+TMX4,Nucleolus,0.133177553
+RRBP1,Nucleolus,-0.086181994
+ZNF133,Nucleolus,0.397670178
+MCM8,Nucleolus,0.166078195
+NCLN,Nucleolus,-0.745198288
+ZNF436,Nucleolus,0.49857668
+AMOT,Nucleolus,0.070672748
+TMEM115,Nucleolus,-0.357239557
+AGO3,Nucleolus,1.828388033
+HECTD3,Nucleolus,0.044269813
+KLC1,Nucleolus,1.025956032
+XRCC3,Nucleolus,0.640651791
+TUBGCP3,Nucleolus,0.821713423
+PCID2,Nucleolus,0.690361835
+FRMD8,Nucleolus,-0.904379273
+PCNXL4,Nucleolus,0.85001955
+ATG14,Nucleolus,1.29723796
+KTN1,Nucleolus,0.549853669
+PLEKHG3,Nucleolus,0.686457388
+WDR60,Nucleolus,1.255336823
+AIF1L,Nucleolus,0.032451689
+SLC10A3,Nucleolus,-3.839349501
+CANX,Nucleolus,0.450254759
+CPSF3L,Nucleolus,1.183588499
+TRAF2,Nucleolus,-2.172343593
+HELB,Nucleolus,1.201978576
+DYRK2,Nucleolus,0.472000247
+LRRC61,Nucleolus,0.257539255
+FGFRL1,Nucleolus,-0.215197375
+EMC1,Nucleolus,-0.436154116
+HP1BP3,Nucleolus,0.411135274
+SIN3B,Nucleolus,0.087486817
+SLC35E1,Nucleolus,0.155221032
+GFER,Nucleolus,-0.3446325
+PKMYT1,Nucleolus,0.438702334
+CHTF18,Nucleolus,0.784015733
+MACF1,Nucleolus,-0.435241966
+RNF6,Nucleolus,0.192556449
+AKAP9,Nucleolus,0.881533704
+HIP1,Nucleolus,-0.4432705
+POR,Nucleolus,-0.93603078
+PEX1,Nucleolus,1.152616611
+LRFN1,Nucleolus,-0.726691607
+SRD5A3,Nucleolus,0.927632371
+PPAT,Nucleolus,0.82486349
+TUBGCP6,Nucleolus,1.357114894
+DGCR8,Nucleolus,1.201717593
+TPST2,Nucleolus,-1.607188537
+MPST,Nucleolus,0.023045037
+SPECC1,Nucleolus,-0.451849174
+NAA38,Nucleolus,-0.524745117
+PRKRIP1,Nucleolus,0.278878321
+PODXL,Nucleolus,0.009582794
+STRIP2,Nucleolus,-1.384437438
+MKLN1,Nucleolus,1.060355276
+CALU,Nucleolus,-0.189696278
+CCDC136,Nucleolus,-0.239084543
+SMO,Nucleolus,-0.537071541
+KLHDC10,Nucleolus,0.558653358
+OSGEPL1,Nucleolus,1.025242389
+HOXD10,Nucleolus,2.144383975
+HOXD11,Nucleolus,0.486509506
+HERC2,Nucleolus,0.760241637
+TWSG1,Nucleolus,0.31760173
+MYO5C,Nucleolus,1.048552861
+TMOD2,Nucleolus,-1.473590173
+TTBK2,Nucleolus,0.61117121
+IVD,Nucleolus,0.526374328
+CLN6,Nucleolus,-0.139793105
+ARPP19,Nucleolus,0.258492909
+VPS13C,Nucleolus,0.974532113
+SUMF2,Nucleolus,0.232122316
+SPCS3,Nucleolus,0.230787517
+RPAIN,Nucleolus,0.636773112
+PLD2,Nucleolus,-0.502707946
+MPDU1,Nucleolus,-1.304500156
+CCNT1,Nucleolus,0.42886468
+PUS7L,Nucleolus,1.449626125
+KRI1,Nucleolus,-0.191753406
+SLC44A2,Nucleolus,-0.561861759
+BCL2L2,Nucleolus,1.083573801
+PARP2,Nucleolus,0.869489281
+TEP1,Nucleolus,-0.693647509
+MAP7D3,Nucleolus,0.496449
+ABHD17A,Nucleolus,1.54249005
+ERMARD,Nucleolus,0.906267278
+SAT1,Nucleolus,0.952936586
+GNL3L,Nucleolus,1.137125259
+SH3BP4,Nucleolus,0.699961164
+LDLR,Nucleolus,0.630893193
+PRKCSH,Nucleolus,-0.684152227
+THEM6,Nucleolus,-0.64315137
+PVRL2,Nucleolus,-0.470960645
+SAFB2,Nucleolus,0.31608442
+KIF1A,Nucleolus,-0.380345017
+COLGALT1,Nucleolus,-0.466713562
+MLLT1,Nucleolus,-0.021316236
+MLLT4,Nucleolus,0.951704805
+ACTN4,Nucleolus,-0.330095246
+NDUFA10,Nucleolus,0.210768309
+ZSWIM6,Nucleolus,0.690510732
+PXDN,Nucleolus,-0.145418001
+COL5A1,Nucleolus,-0.252550777
+ZNF337,Nucleolus,1.303286233
+TAF4,Nucleolus,1.142906243
+LAMA5,Nucleolus,-0.268615108
+EXOSC2,Nucleolus,0.24293452
+POMT1,Nucleolus,0.806753373
+PRRC2B,Nucleolus,0.187050557
+YIPF2,Nucleolus,-0.816044622
+ZC3H4,Nucleolus,0.518863409
+CLIP1,Nucleolus,0.226549188
+HIP1R,Nucleolus,-1.319339768
+PPAN,Nucleolus,1.050135656
+SLC6A8,Nucleolus,0.047030379
+PLXNA3,Nucleolus,1.233936066
+PRRG1,Nucleolus,0.670633229
+AKAP12,Nucleolus,0.49780253
+RBM39,Nucleolus,0.617911067
+GGT7,Nucleolus,0.645600875
+PPT1,Nucleolus,-0.313384372
+RLIM,Nucleolus,0.661300163
+ABCB7,Nucleolus,0.302506286
+MRPS25,Nucleolus,0.52205704
+CAPN7,Nucleolus,0.973902303
+ZFYVE20,Nucleolus,0.925914388
+SLC6A6,Nucleolus,0.779690518
+MGAT1,Nucleolus,-0.356062363
+PSMC3IP,Nucleolus,1.025335299
+DIAPH1,Nucleolus,-0.134648129
+NDFIP1,Nucleolus,-0.736500954
+ACAP3,Nucleolus,0.507332219
+C1orf159,Nucleolus,0.240685715
+MAP1B,Nucleolus,0.416745597
+IL13RA1,Nucleolus,-0.086846121
+WDR44,Nucleolus,0.331274997
+PRKAB2,Nucleolus,0.092062516
+CLUHP3,Nucleolus,1.733488439
+CHSY1,Nucleolus,0.138317006
+SNRPA1,Nucleolus,1.388772922
+FBXW9,Nucleolus,1.106516393
+RFX1,Nucleolus,-1.586834747
+CC2D1A,Nucleolus,-0.569859142
+NUP210,Nucleolus,-0.212467545
+ENOSF1,Nucleolus,1.770339849
+EMILIN2,Nucleolus,-2.920723588
+PRKAA1,Nucleolus,1.126392016
+PNISR,Nucleolus,1.644423972
+ZRANB2,Nucleolus,1.239740161
+KDM6B,Nucleolus,0.173965098
+GPS2,Nucleolus,1.53733504
+VPS13B,Nucleolus,0.47714373
+REEP2,Nucleolus,0.193580522
+PRMT7,Nucleolus,0.282538168
+PCED1A,Nucleolus,0.203156309
+PTPRA,Nucleolus,-0.37364919
+KIAA0907,Nucleolus,1.727392324
+DCAF8,Nucleolus,1.802630296
+IGHMBP2,Nucleolus,-0.420566772
+LPIN3,Nucleolus,-1.554592047
+SERINC3,Nucleolus,0.273891011
+FBXO44,Nucleolus,-0.935288771
+USPL1,Nucleolus,1.030137916
+XPO4,Nucleolus,0.762971927
+SCO1,Nucleolus,0.958584717
+MPRIP,Nucleolus,0.557216975
+DSTYK,Nucleolus,0.43998099
+SLC41A1,Nucleolus,-0.453423447
+GPALPP1,Nucleolus,1.490378656
+IRS4,Nucleolus,0.095038735
+FAM104A,Nucleolus,1.787304671
+SLC39A11,Nucleolus,-1.347525947
+EPHB2,Nucleolus,-0.658178329
+SRRM1,Nucleolus,1.123437392
+SUV420H2,Nucleolus,-0.495424938
+WDR74,Nucleolus,0.470629324
+RTN3,Nucleolus,-0.325879076
+MORC2,Nucleolus,0.790932642
+LARGE,Nucleolus,0.427050995
+ADCK2,Nucleolus,-1.512402749
+AGAP3,Nucleolus,0.035882086
+KRBA1,Nucleolus,0.760626004
+ZNF767,Nucleolus,1.299636053
+ATP13A3,Nucleolus,0.604163654
+TMEM254,Nucleolus,-0.521494203
+TMTC1,Nucleolus,0.047018885
+KRAS,Nucleolus,0.83596992
+SWAP70,Nucleolus,0.923626608
+ZFC3H1,Nucleolus,1.038138734
+TEX15,Nucleolus,1.083923817
+CTIF,Nucleolus,0.378354507
+VHL,Nucleolus,0.519158325
+ARL8B,Nucleolus,-0.576019753
+EDEM1,Nucleolus,0.115397497
+PRPF38B,Nucleolus,0.850982886
+SORT1,Nucleolus,-0.489243251
+PTGFRN,Nucleolus,-0.061581791
+NOTCH2,Nucleolus,0.215695146
+CEPT1,Nucleolus,0.67647869
+AP4B1,Nucleolus,0.648975776
+SPIRE1,Nucleolus,0.488711795
+SLC38A2,Nucleolus,0.254492998
+KIDINS220,Nucleolus,0.034216303
+ROCK2,Nucleolus,0.610796722
+LPIN1,Nucleolus,0.288107686
+IL6ST,Nucleolus,-0.216056503
+TMEM241,Nucleolus,1.198622754
+LRP4,Nucleolus,0.588141714
+DDB2,Nucleolus,-1.102011059
+ACP2,Nucleolus,-1.248289545
+AGO4,Nucleolus,0.136331725
+HOOK1,Nucleolus,0.605967037
+DSC2,Nucleolus,0.541451933
+DSC3,Nucleolus,0.320660625
+DTNA,Nucleolus,0.431096865
+FHOD3,Nucleolus,1.068566273
+FADS2,Nucleolus,-0.50246354
+CLOCK,Nucleolus,-0.191344779
+COL4A2,Nucleolus,-0.51711284
+DZIP1,Nucleolus,0.891684017
+UBAC2,Nucleolus,-1.04753599
+ARGLU1,Nucleolus,1.411872642
+BIVM,Nucleolus,1.34426173
+ARHGAP32,Nucleolus,0.305002364
+TMED7,Nucleolus,0.431773654
+APC,Nucleolus,0.533046693
+WDR36,Nucleolus,0.860951381
+NAA35,Nucleolus,0.8959681
+TMEM2,Nucleolus,0.222366945
+GOLM1,Nucleolus,-0.128874922
+TAOK3,Nucleolus,1.477342915
+DMTF1,Nucleolus,1.521097894
+TMEM243,Nucleolus,0.825168652
+PNPLA8,Nucleolus,0.260266216
+MDFIC,Nucleolus,-0.417779372
+ANKRD6,Nucleolus,1.615665924
+KIAA1009,Nucleolus,1.554467939
+SNX14,Nucleolus,-0.878785623
+EPHA7,Nucleolus,-0.324142431
+DNAJC14,Nucleolus,-0.419156894
+GDF11,Nucleolus,-0.095813659
+TROAP,Nucleolus,0.585032122
+TSPAN31,Nucleolus,-0.353655408
+TFCP2,Nucleolus,1.11934915
+PAN2,Nucleolus,1.040709055
+HNRNPA1,Nucleolus,0.802347071
+ACVR1B,Nucleolus,0.222771674
+OS9,Nucleolus,-1.358371156
+MAP7,Nucleolus,-0.353983933
+CD164,Nucleolus,0.16642099
+NHSL1,Nucleolus,1.266596578
+AHI1,Nucleolus,1.098618477
+SEMA4F,Nucleolus,-1.060960041
+RAB11FIP5,Nucleolus,-0.487682574
+CCDC142,Nucleolus,0.481572382
+GNS,Nucleolus,0.34819076
+MDM2,Nucleolus,0.265150842
+KLHL36,Nucleolus,0.32083656
+DYNC1LI2,Nucleolus,0.80937517
+EGLN1,Nucleolus,0.781270206
+ABCB10,Nucleolus,0.247001958
+TAF5L,Nucleolus,0.535568171
+STX6,Nucleolus,0.540097079
+CEP350,Nucleolus,0.644448758
+LAMC1,Nucleolus,0.231352848
+RC3H1,Nucleolus,1.128487501
+TTLL4,Nucleolus,0.740269511
+USP37,Nucleolus,0.396626804
+ITM2C,Nucleolus,-0.99850203
+SERPINE2,Nucleolus,-0.850889662
+TMEM127,Nucleolus,0.312483516
+GCC2,Nucleolus,0.511190623
+C2orf49,Nucleolus,0.343529851
+EPC2,Nucleolus,1.083592121
+ARHGEF4,Nucleolus,1.050999079
+ALDH1L2,Nucleolus,0.952476648
+CKAP4,Nucleolus,-0.593928136
+NEK3,Nucleolus,0.573917278
+RCBTB1,Nucleolus,0.375356887
+COG3,Nucleolus,-0.06093385
+SCRN1,Nucleolus,-0.293146199
+CHST12,Nucleolus,0.358365481
+KDELR2,Nucleolus,-0.379349794
+NUPL2,Nucleolus,0.313670297
+DBNL,Nucleolus,0.199177222
+TTYH3,Nucleolus,-0.37372738
+IREB2,Nucleolus,1.026770693
+RSAD1,Nucleolus,0.794025349
+VEZF1,Nucleolus,0.545492075
+TEX2,Nucleolus,-0.321307055
+BRIP1,Nucleolus,1.031732524
+SKIL,Nucleolus,1.068972797
+RPS6KC1,Nucleolus,-0.657002965
+BIN1,Nucleolus,-1.068538647
+HS6ST1,Nucleolus,-0.227867875
+UGGT1,Nucleolus,0.259318794
+DNAJC1,Nucleolus,-0.512515668
+LRRC8A,Nucleolus,-0.440590656
+CDK9,Nucleolus,-1.122387175
+TOR1B,Nucleolus,-0.757936238
+SMC2,Nucleolus,0.794558683
+TOR1A,Nucleolus,-0.200581996
+RALGPS1,Nucleolus,1.389557446
+FAM129B,Nucleolus,-0.249767035
+SLC2A8,Nucleolus,0.447250764
+SLC31A1,Nucleolus,0.253237952
+ZNF189,Nucleolus,1.11384538
+STX17,Nucleolus,0.541102173
+TSTD2,Nucleolus,0.336888989
+LMX1B,Nucleolus,0.866271729
+RANBP6,Nucleolus,0.343681672
+TLN1,Nucleolus,-0.490355599
+ALDH1B1,Nucleolus,-0.445197603
+CNPY3,Nucleolus,-0.112396641
+TMEM63B,Nucleolus,-0.04529481
+TJAP1,Nucleolus,-0.010577676
+SLC22A23,Nucleolus,-0.058997519
+FOXF2,Nucleolus,-0.178795834
+RIPK1,Nucleolus,-0.131945323
+ATAT1,Nucleolus,1.11654703
+NRM,Nucleolus,-0.791687336
+VARS2,Nucleolus,0.438446366
+FAM8A1,Nucleolus,0.167858732
+PRKRIR,Nucleolus,0.512794909
+CREBZF,Nucleolus,1.417807876
+PRCP,Nucleolus,-0.276109318
+RNF121,Nucleolus,-0.26353332
+SULF1,Nucleolus,0.585471719
+SORL1,Nucleolus,-1.268493674
+YAP1,Nucleolus,-0.120221666
+RDX,Nucleolus,0.539858603
+MAP2K5,Nucleolus,1.044629517
+MAPKBP1,Nucleolus,0.664361415
+CASC5,Nucleolus,0.122737868
+HAUS2,Nucleolus,-0.363478533
+PARP6,Nucleolus,1.036126437
+TUBGCP4,Nucleolus,1.035792806
+RMDN3,Nucleolus,-0.410382809
+UACA,Nucleolus,0.904541689
+SMAD6,Nucleolus,1.9001454
+ADAM10,Nucleolus,0.906239183
+TTLL7,Nucleolus,1.45195989
+FNBP1L,Nucleolus,-0.033771918
+RABGGTB,Nucleolus,0.547794999
+ARHGAP29,Nucleolus,1.248554124
+SLC44A5,Nucleolus,0.044011282
+DBT,Nucleolus,0.730144476
+EPT1,Nucleolus,0.342044351
+ADCY3,Nucleolus,0.200420418
+PNPT1,Nucleolus,-0.080570233
+THUMPD2,Nucleolus,0.417835504
+PREPL,Nucleolus,0.304371941
+ACTR1A,Nucleolus,-0.234825323
+TMEM180,Nucleolus,-0.089535841
+ATAD1,Nucleolus,0.727209104
+KIF20B,Nucleolus,1.168131825
+TET1,Nucleolus,-0.072818302
+DNA2,Nucleolus,1.126311325
+BARD1,Nucleolus,0.402566368
+NAB1,Nucleolus,0.122827706
+PPIG,Nucleolus,0.820179938
+FASTKD1,Nucleolus,1.33619839
+SSFA2,Nucleolus,0.974933887
+ITGAV,Nucleolus,0.217383734
+SLC35A5,Nucleolus,0.453716767
+SECISBP2L,Nucleolus,0.612893261
+SPPL2A,Nucleolus,-0.717066517
+GLCE,Nucleolus,0.326102055
+PPCDC,Nucleolus,0.115868241
+PCDH10,Nucleolus,-0.354370845
+AP1AR,Nucleolus,0.895985806
+FGF2,Nucleolus,0.860522276
+KIAA1109,Nucleolus,0.726243117
+LARP1B,Nucleolus,0.873359794
+BMP2K,Nucleolus,0.912868226
+FRAS1,Nucleolus,0.306495539
+SCARB2,Nucleolus,-0.253738906
+USO1,Nucleolus,0.498657065
+CENPE,Nucleolus,1.234574084
+GSTCD,Nucleolus,0.121546467
+LEF1,Nucleolus,0.611042488
+PPP3CA,Nucleolus,1.02398351
+FBN2,Nucleolus,-0.355601516
+MAPK8IP3,Nucleolus,1.634926253
+B4GALNT3,Nucleolus,0.307214755
+AEBP2,Nucleolus,1.148525576
+ETNK1,Nucleolus,0.563328069
+CLSTN3,Nucleolus,-0.574927933
+SCAF11,Nucleolus,1.039118906
+COL2A1,Nucleolus,-0.575214184
+LRIG3,Nucleolus,0.362329599
+TMEM19,Nucleolus,-0.676221928
+POC1B,Nucleolus,1.257981889
+TMTC3,Nucleolus,0.856648162
+GAS2L3,Nucleolus,1.816859317
+SLC15A4,Nucleolus,0.374920669
+TDG,Nucleolus,0.90119641
+NUPL1,Nucleolus,0.813526493
+MTMR6,Nucleolus,-0.076571459
+SLC7A1,Nucleolus,0.527728048
+BRCA2,Nucleolus,1.651720408
+CERS5,Nucleolus,-0.633990881
+ESYT1,Nucleolus,-0.744382523
+TMBIM6,Nucleolus,-0.549269893
+ANKRD52,Nucleolus,0.369391151
+ZNF740,Nucleolus,0.293163132
+HNRNPA1L2,Nucleolus,-0.690826077
+SBNO1,Nucleolus,1.115426571
+SETD1B,Nucleolus,-0.406532997
+RBM26,Nucleolus,1.210012488
+ZIC5,Nucleolus,0.583049148
+TMX1,Nucleolus,0.623995915
+NAA30,Nucleolus,0.950717793
+DCAF5,Nucleolus,0.539252039
+RAB15,Nucleolus,-0.558788265
+NIPA2,Nucleolus,0.244649996
+ZSCAN29,Nucleolus,-1.00329634
+BNIP2,Nucleolus,0.782963149
+MAN2C1,Nucleolus,1.236572642
+MESDC1,Nucleolus,0.376137068
+IGF1R,Nucleolus,0.567973977
+ARRDC4,Nucleolus,1.109488295
+PML,Nucleolus,-3.120519721
+LINS,Nucleolus,1.269633514
+PCSK6,Nucleolus,-0.248120042
+SCAMP2,Nucleolus,-1.357479205
+POLG,Nucleolus,0.41997438
+ABHD2,Nucleolus,-0.236912191
+TICRR,Nucleolus,-0.357328026
+MFGE8,Nucleolus,0.826903789
+FURIN,Nucleolus,-0.916461799
+IQGAP1,Nucleolus,0.252082637
+CRTC3,Nucleolus,0.106484452
+FTO,Nucleolus,-0.35393459
+MBTPS1,Nucleolus,-0.298242051
+RHOT2,Nucleolus,-0.022996577
+PDPK1,Nucleolus,0.292179476
+TCF25,Nucleolus,0.716920068
+GALNS,Nucleolus,-0.5782657
+GAS8,Nucleolus,1.405043643
+MED9,Nucleolus,0.373609737
+GID4,Nucleolus,1.011344125
+KSR1,Nucleolus,-0.554490211
+SGSM2,Nucleolus,0.963017616
+SSH2,Nucleolus,0.900832613
+PTRH2,Nucleolus,0.568145764
+SS18,Nucleolus,0.556048883
+SLC39A6,Nucleolus,0.05823821
+GALNT1,Nucleolus,0.243453074
+ESCO1,Nucleolus,1.009310592
+GREB1L,Nucleolus,0.718040069
+NPC1,Nucleolus,-1.286126717
+MINK1,Nucleolus,0.330423161
+TTYH2,Nucleolus,0.846868687
+CSNK1D,Nucleolus,0.885295228
+FOXK2,Nucleolus,-0.147564694
+TRIM65,Nucleolus,0.798387977
+RNF157,Nucleolus,-0.310170415
+CBX4,Nucleolus,-0.643240832
+MBD1,Nucleolus,0.183562793
+ZCCHC2,Nucleolus,0.329132103
+LEPREL4,Nucleolus,0.199495634
+FAM134C,Nucleolus,0.012574471
+ERBB2,Nucleolus,-0.833813778
+FKBP10,Nucleolus,-0.016451374
+PRDM15,Nucleolus,0.573579524
+DUS3L,Nucleolus,-0.910341636
+ATHL1,Nucleolus,1.333474514
+COL6A1,Nucleolus,-0.750211581
+IFNAR1,Nucleolus,-0.047295344
+COL6A2,Nucleolus,-0.394018797
+TMEM50B,Nucleolus,0.415072648
+APP,Nucleolus,-0.256834849
+URB1,Nucleolus,0.547740901
+CAPN10,Nucleolus,0.493189027
+ERVK3-1,Nucleolus,1.429404551
+SLC47A1,Nucleolus,-0.036270526
+RERE,Nucleolus,0.5380102
+EPHA2,Nucleolus,-0.14379985
+KIAA0319L,Nucleolus,-0.175497979
+PLK4,Nucleolus,1.293463151
+GPN2,Nucleolus,0.614636311
+PIGK,Nucleolus,-0.067007543
+PTPRF,Nucleolus,-0.403809893
+SYPL2,Nucleolus,0.518434566
+IGSF3,Nucleolus,-0.300135611
+CELSR2,Nucleolus,-0.187264454
+ATP1B1,Nucleolus,-0.835206687
+CREG1,Nucleolus,-0.467900526
+POU2F1,Nucleolus,0.734260693
+PPOX,Nucleolus,0.333475629
+USP21,Nucleolus,0.350280095
+PIGM,Nucleolus,0.607926905
+ABL2,Nucleolus,0.445392476
+XPR1,Nucleolus,0.23111406
+TOR1AIP1,Nucleolus,-0.582463006
+TUFT1,Nucleolus,-0.693008732
+TARS2,Nucleolus,-0.006830229
+CERS2,Nucleolus,-0.942733353
+SEMA6C,Nucleolus,0.74548701
+ATP8B2,Nucleolus,0.0178811
+ADAM15,Nucleolus,-1.26605848
+SLC39A1,Nucleolus,-1.03692814
+GATAD2B,Nucleolus,0.84605476
+HCN3,Nucleolus,-0.804603859
+GALNT2,Nucleolus,-0.12049374
+TTC13,Nucleolus,0.223937365
+MLK4,Nucleolus,1.041291984
+CEP170,Nucleolus,1.142856738
+SDE2,Nucleolus,0.4519192
+FBXO28,Nucleolus,0.721012372
+CDC42BPA,Nucleolus,1.021057717
+MBOAT2,Nucleolus,0.430235564
+PSEN2,Nucleolus,-0.536181153
+LBR,Nucleolus,-0.303930928
+RHOB,Nucleolus,-0.185944613
+ASXL2,Nucleolus,0.852096405
+ETAA1,Nucleolus,1.048339175
+ZNF514,Nucleolus,1.32060723
+SFXN5,Nucleolus,0.207701447
+TEX261,Nucleolus,-0.33924723
+RALB,Nucleolus,-2.256866559
+SLC20A1,Nucleolus,-0.334498443
+ZC3H8,Nucleolus,1.519428554
+UBXN4,Nucleolus,0.146185722
+AMMECR1L,Nucleolus,0.256223684
+GALNT13,Nucleolus,1.141162286
+SCRN3,Nucleolus,0.55140868
+KIAA1715,Nucleolus,0.265699791
+CDCA7,Nucleolus,0.152295245
+DLX1,Nucleolus,0.087703674
+GULP1,Nucleolus,-0.889157974
+FAM171B,Nucleolus,1.33608128
+CCDC150,Nucleolus,0.650403481
+SUMF1,Nucleolus,-0.667185677
+RHBDD1,Nucleolus,0.867599289
+FAM134A,Nucleolus,-0.682461898
+CTDSP1,Nucleolus,-0.95033757
+EAF1,Nucleolus,0.464918878
+GOLGA4,Nucleolus,0.671914502
+IQSEC1,Nucleolus,-0.023485197
+PTPRG,Nucleolus,0.122593956
+IL17RD,Nucleolus,-0.726815423
+ARL6IP5,Nucleolus,-0.51949491
+TMF1,Nucleolus,1.274220842
+LRIG1,Nucleolus,0.722580332
+LIMD1,Nucleolus,0.341728738
+NXPE3,Nucleolus,0.328886446
+SRPRB,Nucleolus,-0.085794091
+TCTA,Nucleolus,-0.546281256
+VPRBP,Nucleolus,1.005906989
+SLIT2,Nucleolus,0.076785093
+DGKQ,Nucleolus,0.608026129
+ATP10D,Nucleolus,0.790296056
+SCD5,Nucleolus,-1.600278491
+ENOPH1,Nucleolus,0.821011508
+TRMT10A,Nucleolus,1.043255238
+KLHL8,Nucleolus,0.090128119
+USP53,Nucleolus,1.023094844
+MARCH6,Nucleolus,0.287606374
+FAM105A,Nucleolus,-0.212587118
+PIK3R1,Nucleolus,0.697261115
+LHFPL2,Nucleolus,0.653350802
+IQGAP2,Nucleolus,0.673544633
+PPIP5K2,Nucleolus,0.7991488
+PAM,Nucleolus,-0.699244266
+BDP1,Nucleolus,0.780874727
+SLC30A5,Nucleolus,0.181042489
+ATG12,Nucleolus,0.357623051
+YIPF5,Nucleolus,-1.108156673
+RNF145,Nucleolus,-1.254504933
+FBXO38,Nucleolus,0.913298739
+PCYOX1L,Nucleolus,1.288831494
+TNIP1,Nucleolus,-0.744241728
+ZNF300,Nucleolus,0.431823707
+GFOD1,Nucleolus,0.631367382
+TRIM41,Nucleolus,0.640147316
+FAM193B,Nucleolus,0.656747505
+RNF44,Nucleolus,0.342069044
+MUT,Nucleolus,-1.304999051
+PHIP,Nucleolus,0.624166263
+MMS22L,Nucleolus,1.10207722
+PM20D2,Nucleolus,0.876006783
+RNF217,Nucleolus,1.120894698
+AIG1,Nucleolus,-1.31759202
+TMEM181,Nucleolus,0.52514123
+SDK1,Nucleolus,-1.144234668
+RBAK,Nucleolus,1.38831386
+CREB5,Nucleolus,1.170973351
+PURB,Nucleolus,1.490080655
+GBAS,Nucleolus,0.771601822
+ZNF92,Nucleolus,1.551567236
+TMEM168,Nucleolus,-0.282110235
+C7orf43,Nucleolus,0.076190666
+SLC12A9,Nucleolus,-1.153732896
+GIGYF1,Nucleolus,0.854206988
+TMEM209,Nucleolus,0.284699593
+NOM1,Nucleolus,0.902371426
+SH3KBP1,Nucleolus,1.001798826
+CASK,Nucleolus,0.212375971
+SLC16A2,Nucleolus,-0.311391613
+OGT,Nucleolus,1.258565819
+ZNF711,Nucleolus,0.441193826
+DIAPH2,Nucleolus,0.443342603
+CXorf57,Nucleolus,0.765764346
+GPC3,Nucleolus,-1.462867094
+BIN3,Nucleolus,-0.009879037
+SLC25A37,Nucleolus,1.343698994
+CHMP7,Nucleolus,0.77572201
+ERLIN2,Nucleolus,0.324159324
+TACC1,Nucleolus,0.147639511
+WHSC1L1,Nucleolus,0.773111781
+TERF1,Nucleolus,0.22559604
+MTDH,Nucleolus,0.384256875
+LRP12,Nucleolus,0.141086987
+EBAG9,Nucleolus,0.770440757
+UTP23,Nucleolus,1.273871567
+ZNF7,Nucleolus,1.042805233
+ARHGAP39,Nucleolus,-0.71520994
+NAPRT1,Nucleolus,0.323172668
+UHRF2,Nucleolus,1.308284073
+ZCCHC7,Nucleolus,1.036785512
+SIGMAR1,Nucleolus,-0.787759805
+CEP78,Nucleolus,0.803662007
+HIATL1,Nucleolus,-0.504567163
+INIP,Nucleolus,0.219927405
+UGCG,Nucleolus,0.763103805
+STOM,Nucleolus,-0.594648359
+MRRF,Nucleolus,0.268886547
+NR6A1,Nucleolus,0.545924871
+SURF4,Nucleolus,-0.267898095
+MED22,Nucleolus,-0.187196458
+SH3GLB2,Nucleolus,-0.095027171
+FAM73B,Nucleolus,-0.151694988
+GPR107,Nucleolus,0.286709605
+C9orf142,Nucleolus,-1.960697561
+INPP5E,Nucleolus,0.363064552
+DPH7,Nucleolus,-0.956626719
+NOTCH1,Nucleolus,-0.497432237
+NACC2,Nucleolus,0.632773976
+USP6NL,Nucleolus,0.25440428
+FAM171A1,Nucleolus,-0.125820993
+PARD3,Nucleolus,-0.025797512
+POLR3A,Nucleolus,0.497064734
+FRA10AC1,Nucleolus,1.021412145
+ADD3,Nucleolus,0.788797416
+DNAJB12,Nucleolus,0.154757638
+EIF4EBP2,Nucleolus,0.266511179
+MKI67,Nucleolus,1.031229705
+MTG1,Nucleolus,1.234328952
+PPRC1,Nucleolus,0.602224653
+ITPRIP,Nucleolus,0.493010405
+CNNM2,Nucleolus,-3.087222405
+PDCD11,Nucleolus,0.381764483
+LIN7C,Nucleolus,1.177882745
+DGKZ,Nucleolus,0.736862263
+TNKS1BP1,Nucleolus,0.393492361
+SLC43A1,Nucleolus,1.296826565
+PTPRJ,Nucleolus,-0.344115544
+CELF1,Nucleolus,0.636806258
+SESN3,Nucleolus,0.329338119
+ENDOD1,Nucleolus,-1.689741396
+SERPINH1,Nucleolus,-2.557386003
+NCAM1,Nucleolus,0.569180423
+NPAT,Nucleolus,1.150337918
+ATM,Nucleolus,1.124030824
+GLB1L2,Nucleolus,0.410329851
+HYOU1,Nucleolus,-0.256195449
+DAK,Nucleolus,0.351172886
+TMEM138,Nucleolus,0.724927343
+FADS1,Nucleolus,0.080474651
+EML3,Nucleolus,1.25551504
+B3GAT3,Nucleolus,-0.355658833
+SIDT2,Nucleolus,-1.560565568
+SOGA1,Nucleolus,1.649874399
+LSM14B,Nucleolus,0.35547769
+ORAOV1,Nucleolus,1.394653053
+TAOK2,Nucleolus,0.832259402
+ITGB1,Nucleolus,0.240798664
+ARID5B,Nucleolus,-4.444562829
+TMCO3,Nucleolus,0.148504534
+LATS2,Nucleolus,1.048982055
+LPHN3,Nucleolus,0.744721002
+PRSS23,Nucleolus,-1.71987457
+PIP4K2A,Nucleolus,0.458902247
+FREM2,Nucleolus,0.642499449
+CRIM1,Nucleolus,0.483678328
+IPMK,Nucleolus,0.503628803
+PLBD2,Nucleolus,0.205184444
+GXYLT1,Nucleolus,0.084010398
+CSNK1G3,Nucleolus,1.082830105
+MIPOL1,Nucleolus,1.331386588
+EXT2,Nucleolus,-1.113669113
+TMEM18,Nucleolus,1.029496698
+NEK7,Nucleolus,0.39373959
+FER,Nucleolus,0.710787138
+VIPAS39,Nucleolus,1.008604542
+ANKRD50,Nucleolus,0.668730787
+UPF2,Nucleolus,0.359981617
+EPS8,Nucleolus,0.781278293
+FAM160B1,Nucleolus,1.697835755
+ADAM17,Nucleolus,-0.023359859
+WWC2,Nucleolus,1.008590914
+BICD1,Nucleolus,0.558378168
+NBAS,Nucleolus,0.334215805
+GUF1,Nucleolus,0.866744081
+SACS,Nucleolus,1.409109533
+PABPC3,Nucleolus,0.353493265
+DST,Nucleolus,0.40434475
+TIAL1,Nucleolus,0.867823161
+TMEM56,Nucleolus,-0.924333127
+FAM168B,Nucleolus,0.168408983
+AC093838.4,Nucleolus,1.721307894
+MGAT5,Nucleolus,0.731196742
+GPATCH11,Nucleolus,0.167951901
+POU4F1,Nucleolus,0.604238219
+RNF219,Nucleolus,0.221616336
+EPG5,Nucleolus,0.342421814
+C18orf25,Nucleolus,0.847992299
+PDK1,Nucleolus,1.020211962
+PDE3B,Nucleolus,0.746931273
+TGOLN2,Nucleolus,0.21987448
+UHMK1,Nucleolus,0.843715579
+TADA1,Nucleolus,0.8172027
+CWF19L2,Nucleolus,1.110053291
+JMY,Nucleolus,0.257535076
+HOMER1,Nucleolus,1.261982494
+USP12,Nucleolus,0.485226609
+CCDC50,Nucleolus,1.323138561
+PAN3,Nucleolus,0.756991109
+TMEM123,Nucleolus,0.381364754
+GJA1,Nucleolus,-0.256979773
+SLC30A6,Nucleolus,-0.129327829
+SAR1B,Nucleolus,-0.751522226
+GPR180,Nucleolus,0.421325189
+UTRN,Nucleolus,0.286765328
+PTPRK,Nucleolus,-0.263929134
+PLOD2,Nucleolus,0.128704084
+GPR125,Nucleolus,0.303252635
+SREK1IP1,Nucleolus,0.881120244
+TXNDC11,Nucleolus,0.131097854
+BCL2L11,Nucleolus,0.439726793
+CLGN,Nucleolus,0.003688379
+RASSF3,Nucleolus,0.387070239
+RANBP2,Nucleolus,0.891289412
+TMEM87B,Nucleolus,0.084683118
+RBMS1,Nucleolus,0.179004587
+LPCAT1,Nucleolus,-0.208359439
+UBALD1,Nucleolus,1.442275221
+RMND5A,Nucleolus,0.485864308
+ZDHHC7,Nucleolus,-0.193134422
+TRIP12,Nucleolus,0.387911869
+CEBPG,Nucleolus,0.626727854
+SREK1,Nucleolus,1.269316145
+CHD1,Nucleolus,0.878724718
+DGKE,Nucleolus,1.002830973
+HS2ST1,Nucleolus,0.514915413
+MSI2,Nucleolus,0.643121476
+CACNA2D1,Nucleolus,0.813077352
+NUS1,Nucleolus,0.933551155
+IMPACT,Nucleolus,1.231811298
+TBCEL,Nucleolus,-0.031900352
+FAM105B,Nucleolus,0.357670244
+TBRG1,Nucleolus,1.315428704
+CC2D1B,Nucleolus,-1.069545861
+MIA3,Nucleolus,0.555167734
+TRIM11,Nucleolus,0.389585506
+CCSAP,Nucleolus,1.157823639
+CXADR,Nucleolus,0.521157661
+GABPA,Nucleolus,-0.0657086
+ADAMTS1,Nucleolus,0.112298733
+TSEN2,Nucleolus,0.720481705
+FLCN,Nucleolus,1.035453845
+SKA1,Nucleolus,0.04620718
+RAB6B,Nucleolus,0.218535725
+ACSS1,Nucleolus,0.828126187
+ANKRD40,Nucleolus,0.772302736
+VOPP1,Nucleolus,-1.415585566
+APOOL,Nucleolus,0.865925758
+CYP2U1,Nucleolus,0.84650341
+AGPAT5,Nucleolus,0.41735065
+MARVELD1,Nucleolus,-0.75845835
+ZFYVE27,Nucleolus,0.149971692
+SLC25A28,Nucleolus,1.310020843
+HSPA13,Nucleolus,0.595759747
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+RHOC,Nucleolus,-1.176854286
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+LARP1,Nucleolus,0.607608892
+MIER3,Nucleolus,0.345244071
+ZKSCAN2,Nucleolus,0.733044103
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+FAM126B,Nucleolus,-0.066661353
+FZD7,Nucleolus,1.124669467
+FMN2,Nucleolus,0.578344298
+PPARGC1B,Nucleolus,1.532309606
+SLC26A2,Nucleolus,0.355331781
+LSM11,Nucleolus,1.602999054
+PSD3,Nucleolus,-0.637168932
+DCK,Nucleolus,0.762924801
+ADAMTS3,Nucleolus,-0.761179306
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+N6AMT1,Nucleolus,1.37177572
+CDK20,Nucleolus,0.208640014
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+ANKRD9,Nucleolus,0.335327244
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+TYSND1,Nucleolus,-0.092645084
+CD109,Nucleolus,0.697347465
+ZDHHC5,Nucleolus,0.202758629
+ZFAND3,Nucleolus,-0.611757446
+NPTN,Nucleolus,-0.222657547
+KAT6B,Nucleolus,0.190676271
+SAMD8,Nucleolus,0.859745089
+BAG4,Nucleolus,0.195575285
+ATAD2,Nucleolus,0.836815439
+PHKG2,Nucleolus,0.374555583
+SASS6,Nucleolus,0.31932341
+ZIC3,Nucleolus,-1.805583758
+EXOG,Nucleolus,0.524657312
+SMG1,Nucleolus,0.789434523
+FCHO2,Nucleolus,1.321250788
+C1orf27,Nucleolus,1.258512544
+LRP8,Nucleolus,0.786074258
+PAXIP1,Nucleolus,0.736724301
+SSBP3,Nucleolus,-1.239987174
+CLDN12,Nucleolus,0.46510663
+GATAD1,Nucleolus,1.266371841
+ST3GAL2,Nucleolus,0.54506616
+FUK,Nucleolus,-1.296077411
+KIT,Nucleolus,-0.499763409
+AASDH,Nucleolus,1.096524567
+DYRK1A,Nucleolus,1.046335425
+TSPAN18,Nucleolus,0.6203193
+SLC35B2,Nucleolus,-1.078675467
+TMEM164,Nucleolus,0.423063747
+TAB3,Nucleolus,0.632374168
+SLC38A10,Nucleolus,-0.595600337
+ZNF618,Nucleolus,0.276270774
+C9orf91,Nucleolus,0.682839806
+UBN2,Nucleolus,1.501374644
+BRAF,Nucleolus,0.79669638
+SLC37A3,Nucleolus,0.023906425
+DPYSL5,Nucleolus,-0.155766208
+FAM213B,Nucleolus,-0.954815176
+C12orf43,Nucleolus,0.247485725
+RER1,Nucleolus,-0.496117518
+UBXN11,Nucleolus,-0.673427026
+RHPN1,Nucleolus,1.629872348
+CNNM4,Nucleolus,-0.300916306
+EYA3,Nucleolus,0.885667596
+MRAS,Nucleolus,-0.281868254
+COLEC12,Nucleolus,0.008431114
+CUL4B,Nucleolus,0.869913186
+MITD1,Nucleolus,-0.096007443
+EIF5B,Nucleolus,0.814272435
+TSPAN33,Nucleolus,-1.554421993
+AHCYL2,Nucleolus,0.289759204
+B4GALT5,Nucleolus,-0.208986328
+TSR2,Nucleolus,-0.376253305
+ZC3H18,Nucleolus,0.238414504
+TMED4,Nucleolus,-0.225644001
+PPP1R15B,Nucleolus,0.453601567
+AGPAT6,Nucleolus,0.265992428
+ZSCAN12,Nucleolus,1.473142418
+ELK4,Nucleolus,1.428088863
+F11R,Nucleolus,-2.268242156
+ZNF276,Nucleolus,1.269737646
+PINK1,Nucleolus,-0.721045084
+B4GALT3,Nucleolus,-0.892492482
+FAM160B2,Nucleolus,0.390290087
+CACHD1,Nucleolus,-0.453300589
+PAXBP1,Nucleolus,1.612212036
+IFNAR2,Nucleolus,0.029586284
+SON,Nucleolus,0.588407573
+SV2A,Nucleolus,0.246303442
+HLCS,Nucleolus,0.339437877
+ADPGK,Nucleolus,0.75992682
+ALDH4A1,Nucleolus,0.289601604
+STARD9,Nucleolus,0.62364486
+UBR1,Nucleolus,0.134811658
+AMFR,Nucleolus,-0.144292311
+RSPRY1,Nucleolus,0.836827524
+ARHGAP35,Nucleolus,-0.093921454
+CALM3,Nucleolus,-0.67034695
+IQCC,Nucleolus,1.121257348
+BSDC1,Nucleolus,1.174206325
+ATAD3B,Nucleolus,0.309499729
+VMA21,Nucleolus,0.30527603
+WDR4,Nucleolus,0.071030306
+CBS,Nucleolus,0.425533785
+PDXK,Nucleolus,1.032911247
+G6PD,Nucleolus,-1.586864844
+AGPAT3,Nucleolus,0.488127115
+C21orf2,Nucleolus,1.528048849
+LRRC3,Nucleolus,0.01390123
+LSS,Nucleolus,0.041055566
+VAV2,Nucleolus,-0.119928082
+MCM3AP,Nucleolus,0.85223974
+C21orf58,Nucleolus,1.465595369
+PCNT,Nucleolus,-0.224700606
+DIP2A,Nucleolus,0.020258999
+ZNF714,Nucleolus,1.342421355
+PKN3,Nucleolus,0.686259548
+TAOK1,Nucleolus,0.929606263
+SIK3,Nucleolus,0.896609428
+PCSK7,Nucleolus,1.003107285
+CHTOP,Nucleolus,-0.016508076
+ZBTB7B,Nucleolus,-0.459257178
+NLRX1,Nucleolus,-0.247254066
+ANO10,Nucleolus,-0.611017077
+SLC25A44,Nucleolus,-0.223326949
+NBEAL2,Nucleolus,0.573674824
+IER2,Nucleolus,-1.223499933
+ZNF394,Nucleolus,1.206903948
+CPSF4,Nucleolus,0.033721621
+TONSL,Nucleolus,0.258905685
+MUM1,Nucleolus,0.513259842
+RECQL4,Nucleolus,0.593187532
+LRRC14,Nucleolus,1.089702771
+PPP1R16A,Nucleolus,-3.583177173
+C5orf45,Nucleolus,1.092810271
+MFSD12,Nucleolus,-0.485047815
+FDXR,Nucleolus,-1.340405551
+ALDH16A1,Nucleolus,-0.803749535
+ITGA5,Nucleolus,-4.309184582
+ZNF385A,Nucleolus,-0.428334205
+MPP3,Nucleolus,1.791872787
+EMC10,Nucleolus,-0.73122206
+FAM171A2,Nucleolus,-2.702149975
+DBF4B,Nucleolus,0.532471604
+LARP4,Nucleolus,1.39837315
+LEMD2,Nucleolus,0.081449749
+WDR90,Nucleolus,1.325238431
+C16orf59,Nucleolus,-0.143499225
+AMDHD2,Nucleolus,1.427331217
+PAQR4,Nucleolus,-0.806937676
+ADCY9,Nucleolus,-0.713292253
+CLPB,Nucleolus,-0.267892506
+NEU3,Nucleolus,0.736970939
+CYB561A3,Nucleolus,-0.128132721
+TAF6L,Nucleolus,0.30232692
+LRP5,Nucleolus,-0.926789958
+ZYG11B,Nucleolus,0.490008665
+PPAP2B,Nucleolus,0.111020351
+PRKAA2,Nucleolus,1.14203867
+KLHL21,Nucleolus,0.390748452
+GMEB1,Nucleolus,0.523292973
+SEPN1,Nucleolus,-0.693250456
+AK4,Nucleolus,0.83132357
+RAVER2,Nucleolus,0.512895634
+PDPN,Nucleolus,-0.570212531
+SDC3,Nucleolus,0.263054552
+KIAA1522,Nucleolus,0.265885093
+C1orf86,Nucleolus,0.662482047
+NFIA,Nucleolus,0.630023591
+OMA1,Nucleolus,0.532606844
+MYSM1,Nucleolus,1.649162384
+FUBP1,Nucleolus,0.876165352
+DNAJB4,Nucleolus,-1.083778026
+FAM102B,Nucleolus,0.601287811
+ATXN7L2,Nucleolus,-0.426049548
+ZNF326,Nucleolus,0.341174531
+EXTL2,Nucleolus,-0.705113947
+SLC30A7,Nucleolus,-0.30126071
+PEA15,Nucleolus,-0.285804784
+NCSTN,Nucleolus,-0.533464965
+VANGL2,Nucleolus,-0.044255568
+FLVCR1,Nucleolus,0.716590791
+RBM15,Nucleolus,0.149658708
+BPNT1,Nucleolus,0.784808932
+BROX,Nucleolus,0.780248657
+ACP6,Nucleolus,-0.266871382
+PPP1R21,Nucleolus,1.417168466
+B3GALNT2,Nucleolus,1.209566078
+C2orf47,Nucleolus,1.080515975
+ARL5A,Nucleolus,0.459407123
+SGCB,Nucleolus,-0.517847893
+SMARCAD1,Nucleolus,0.981607913
+RNF149,Nucleolus,0.723974289
+FZD5,Nucleolus,0.645222448
+DCAF16,Nucleolus,0.987735483
+PAQR3,Nucleolus,0.762713254
+ANTXR2,Nucleolus,0.23024891
+PBXIP1,Nucleolus,0.028874084
+PYGO2,Nucleolus,0.35926623
+HIPK1,Nucleolus,-0.436058691
+KBTBD8,Nucleolus,0.594922741
+EOGT,Nucleolus,0.95680201
+POGLUT1,Nucleolus,1.126150783
+ATP1A1,Nucleolus,-0.207717257
+EIF4E3,Nucleolus,0.105591989
+LRRC58,Nucleolus,1.084742318
+FSTL1,Nucleolus,-0.022118729
+KRTCAP2,Nucleolus,-0.516643539
+KIAA1524,Nucleolus,0.865722038
+TGFBR2,Nucleolus,-1.464839195
+ANKZF1,Nucleolus,0.521124981
+STT3B,Nucleolus,-0.20335471
+PPM1L,Nucleolus,1.02052586
+RYBP,Nucleolus,0.20134527
+PPP4R2,Nucleolus,1.113942839
+C3orf17,Nucleolus,-0.231297801
+SPICE1,Nucleolus,-0.16878966
+WDFY3,Nucleolus,0.535837365
+ATXN7,Nucleolus,1.272294015
+PPM1K,Nucleolus,0.457249153
+CCNL1,Nucleolus,1.567091773
+RPP14,Nucleolus,0.719859381
+ABHD6,Nucleolus,-2.502039638
+CRELD1,Nucleolus,-1.042021454
+U2SURP,Nucleolus,1.013132877
+TTC14,Nucleolus,0.699285519
+SNRK,Nucleolus,0.736819453
+SLC4A1AP,Nucleolus,0.804276406
+ZDHHC3,Nucleolus,-0.006545916
+FYCO1,Nucleolus,0.936303262
+YEATS2,Nucleolus,0.313946597
+SNIP1,Nucleolus,0.861704079
+TMEM41A,Nucleolus,-0.005168329
+RPN1,Nucleolus,-0.799010821
+SFMBT1,Nucleolus,-0.483690419
+PBRM1,Nucleolus,0.075568799
+FAM208A,Nucleolus,1.135223864
+ARHGEF3,Nucleolus,-2.061666972
+UBXN7,Nucleolus,0.757550752
+ZNF691,Nucleolus,-7.00331692
+SGMS2,Nucleolus,0.086697901
+DNAJB14,Nucleolus,0.538580063
+ZNF589,Nucleolus,0.749573493
+SHISA5,Nucleolus,-0.272113163
+INTU,Nucleolus,-0.926652601
+RNF123,Nucleolus,0.268134534
+MFSD8,Nucleolus,-0.735459165
+C4orf29,Nucleolus,1.254174632
+RAD54L2,Nucleolus,0.764303687
+MAP9,Nucleolus,0.693399116
+CEP44,Nucleolus,0.456532709
+ABCE1,Nucleolus,0.775270734
+TMEM184C,Nucleolus,0.578028311
+TMEM161B,Nucleolus,-0.091101234
+ELOVL7,Nucleolus,-0.07815708
+LMBRD2,Nucleolus,1.363002658
+NIPBL,Nucleolus,0.484786642
+SLC25A46,Nucleolus,0.204167112
+STARD4,Nucleolus,0.441371671
+PGGT1B,Nucleolus,0.901054962
+NDUFS4,Nucleolus,0.772951342
+ARSK,Nucleolus,0.921047585
+GPX8,Nucleolus,0.104996244
+SERINC5,Nucleolus,-3.336234489
+GFM2,Nucleolus,0.351047832
+CCDC127,Nucleolus,0.225836897
+SEPT8,Nucleolus,-0.186699633
+DCBLD1,Nucleolus,-0.732382016
+PDSS2,Nucleolus,0.978432251
+STXBP5,Nucleolus,1.962773089
+DAGLB,Nucleolus,0.196107467
+GALNT10,Nucleolus,-0.171454931
+ZNF12,Nucleolus,1.539999842
+USP49,Nucleolus,1.316099824
+ZNF704,Nucleolus,0.625533433
+LMTK2,Nucleolus,0.477917969
+CTSB,Nucleolus,-0.860391079
+ADCY1,Nucleolus,0.622372985
+EN2,Nucleolus,0.783869148
+SUN1,Nucleolus,0.129881959
+OXR1,Nucleolus,0.327314118
+SLC4A2,Nucleolus,-0.732861086
+FASTK,Nucleolus,0.677633001
+TMUB1,Nucleolus,-1.454456149
+C7orf55,Nucleolus,-0.159925331
+FOXK1,Nucleolus,1.258490317
+FZD6,Nucleolus,-0.261355243
+KIAA1429,Nucleolus,0.94200368
+TMEM67,Nucleolus,1.584957122
+SNAPC3,Nucleolus,1.12258624
+KIAA1161,Nucleolus,-0.402890024
+METTL2B,Nucleolus,0.489672399
+HGSNAT,Nucleolus,0.231763823
+RASEF,Nucleolus,0.556186911
+ANKS6,Nucleolus,0.245603314
+TMEM246,Nucleolus,0.328516837
+ZHX1,Nucleolus,0.427769863
+KIAA1958,Nucleolus,0.910665017
+PIGA,Nucleolus,1.263197209
+WNK2,Nucleolus,0.186105736
+ATP7A,Nucleolus,0.285225736
+PIGO,Nucleolus,-0.153213603
+BRWD3,Nucleolus,0.752896446
+SLITRK5,Nucleolus,0.063089943
+DDX26B,Nucleolus,1.170192186
+MARCH8,Nucleolus,-0.120195897
+GTF2A1,Nucleolus,1.013476161
+ZCCHC24,Nucleolus,0.106687741
+REEP3,Nucleolus,-0.189812755
+MICU2,Nucleolus,0.81028455
+PCF11,Nucleolus,1.047235326
+PKNOX2,Nucleolus,-3.822361319
+ZNF22,Nucleolus,1.017857405
+RPUSD4,Nucleolus,-0.976766774
+ARF6,Nucleolus,0.7564025
+TTC8,Nucleolus,0.769202201
+CDX2,Nucleolus,0.60694435
+BEND7,Nucleolus,-0.042008355
+TAF3,Nucleolus,0.265262531
+PDZD8,Nucleolus,1.06897061
+ZNF503,Nucleolus,-0.919971375
+FAM175B,Nucleolus,0.668511393
+QSOX2,Nucleolus,0.375341289
+NSD1,Nucleolus,0.71968455
+SNAPC4,Nucleolus,0.326243634
+PMPCA,Nucleolus,0.013731198
+SDCCAG3,Nucleolus,0.759696386
+TSC1,Nucleolus,1.309270234
+FAM69B,Nucleolus,0.374795404
+KIAA1462,Nucleolus,-0.099025701
+ZNF219,Nucleolus,-0.427780771
+METTL3,Nucleolus,0.53235941
+HSPA12A,Nucleolus,1.349241368
+TC2N,Nucleolus,1.241172182
+CPSF2,Nucleolus,0.775919293
+ARL5B,Nucleolus,1.016157301
+TAF1D,Nucleolus,0.694226779
+HTRA1,Nucleolus,0.213044231
+CEP57,Nucleolus,0.382032023
+JAM3,Nucleolus,-0.272403966
+HIF1AN,Nucleolus,0.676342485
+ZFYVE19,Nucleolus,0.638996159
+FBN1,Nucleolus,-0.190265575
+BAG5,Nucleolus,0.434222542
+GABRB3,Nucleolus,0.846268403
+SGPL1,Nucleolus,-0.503711354
+FRS2,Nucleolus,0.454012808
+ZNF202,Nucleolus,-1.828786255
+STXBP4,Nucleolus,1.095530708
+CUL5,Nucleolus,1.16002057
+WBP1L,Nucleolus,-0.922583587
+TRIM44,Nucleolus,0.34929885
+TPP1,Nucleolus,-0.357853601
+C11orf74,Nucleolus,0.974270417
+TUB,Nucleolus,1.430025108
+RNF169,Nucleolus,1.141176389
+PRTG,Nucleolus,0.532276635
+TMEM41B,Nucleolus,0.517862321
+TMX3,Nucleolus,0.731061513
+WEE1,Nucleolus,1.09015504
+ZNF3,Nucleolus,0.798873307
+RIMKLB,Nucleolus,1.222175859
+TMED3,Nucleolus,-0.41898825
+NDEL1,Nucleolus,-0.308360053
+BLCAP,Nucleolus,0.241725453
+CASC4,Nucleolus,-0.054432172
+AP1G1,Nucleolus,0.341560402
+KIF7,Nucleolus,0.101586498
+PEX11A,Nucleolus,1.01277314
+ZBTB39,Nucleolus,0.754021478
+TMEM194A,Nucleolus,0.780352835
+SMAD3,Nucleolus,0.317494125
+MAP1A,Nucleolus,0.036136591
+MBD6,Nucleolus,0.169427874
+PDIA3,Nucleolus,-0.246219983
+ACSF2,Nucleolus,0.423783404
+COQ4,Nucleolus,0.31470315
+SLC27A4,Nucleolus,-1.004609617
+CERCAM,Nucleolus,-0.723655341
+DOLPP1,Nucleolus,-0.086473739
+GPRC5B,Nucleolus,0.719890302
+CRK,Nucleolus,0.547358656
+FBXO22,Nucleolus,0.900199745
+TBC1D2B,Nucleolus,0.67562919
+CDK12,Nucleolus,0.698599042
+ENGASE,Nucleolus,0.63089596
+TBC1D16,Nucleolus,-0.177278376
+ENTHD2,Nucleolus,0.12012216
+STIM1,Nucleolus,-0.495818068
+IRGQ,Nucleolus,1.416235767
+PPP2R3B,Nucleolus,0.080902941
+ZNF646,Nucleolus,0.389226709
+MIDN,Nucleolus,-0.289158077
+MVD,Nucleolus,-1.532434662
+ANKRD11,Nucleolus,0.218471135
+SPATA33,Nucleolus,-0.256348949
+ZNF641,Nucleolus,0.618747658
+DHRS13,Nucleolus,-0.051025459
+TP53I13,Nucleolus,-1.167444935
+KMT2D,Nucleolus,0.720824334
+C19orf55,Nucleolus,0.827050269
+LENG8,Nucleolus,1.624358014
+ZNF146,Nucleolus,0.895380782
+ZNF444,Nucleolus,1.124437774
+FAM57A,Nucleolus,-1.181270626
+SLC43A2,Nucleolus,-0.846970114
+SRR,Nucleolus,1.431582524
+GHDC,Nucleolus,-0.828141927
+ITFG3,Nucleolus,-0.953770999
+ZNF598,Nucleolus,0.135925441
+E4F1,Nucleolus,1.176141046
+ABCA3,Nucleolus,-0.427571311
+SRRM2,Nucleolus,0.8965995
+LTBP3,Nucleolus,0.748794218
+SAC3D1,Nucleolus,-1.851338791
+SF1,Nucleolus,-0.046194881
+PAFAH1B2,Nucleolus,0.340694377
+ANKS3,Nucleolus,1.281444687
+SETD5,Nucleolus,0.536307585
+HOOK3,Nucleolus,1.649573775
+RBPJ,Nucleolus,1.417286075
+TTC39C,Nucleolus,1.348071748
+KIF5C,Nucleolus,-0.941683086
+MGAT2,Nucleolus,-0.126133514
+BMI1,Nucleolus,0.763386226
+KCTD6,Nucleolus,1.424614838
+TAP1,Nucleolus,-0.794106869
+ING5,Nucleolus,0.546893117
+ATG4B,Nucleolus,-0.137494667
+SOGA2,Nucleolus,0.464867896
+SNRNP48,Nucleolus,0.621524322
+SLC20A2,Nucleolus,-0.410546381
+TMUB2,Nucleolus,0.611063455
+STAT3,Nucleolus,-0.490010548
+ADAM9,Nucleolus,-0.103735926
+PKIG,Nucleolus,-5.072867205
+SEMA4C,Nucleolus,0.619765728
+CNNM3,Nucleolus,-0.744002187
+TET2,Nucleolus,1.138468746
+TCTN2,Nucleolus,-0.340339085
+TSPAN5,Nucleolus,-0.299749291
+ZBTB5,Nucleolus,0.736639002
+SNTB2,Nucleolus,1.138209926
+ZNF507,Nucleolus,1.166730791
+STX18,Nucleolus,0.354262225
+GFM1,Nucleolus,0.216209714
+ANKRD49,Nucleolus,-0.7130385
+MAT2A,Nucleolus,1.234220619
+ZNF608,Nucleolus,-1.834150369
+LETM1,Nucleolus,0.00112151
+TMEM129,Nucleolus,0.317063565
+FEM1B,Nucleolus,1.014265787
+HNRNPH1,Nucleolus,0.811048517
+MECP2,Nucleolus,0.279670071
+UPF3A,Nucleolus,1.69742118
+CHST14,Nucleolus,-0.50472387
+PARM1,Nucleolus,0.206177645
+CSNK1G1,Nucleolus,0.409432262
+ZBTB43,Nucleolus,-0.166902902
+GPRIN1,Nucleolus,-1.19456679
+MRPL1,Nucleolus,0.837500874
+SLC33A1,Nucleolus,0.00905179
+SDC2,Nucleolus,0.688466233
+MMGT1,Nucleolus,0.133702062
+CLIC4,Nucleolus,0.368675051
+CCDC8,Nucleolus,-0.283151721
+INO80E,Nucleolus,0.612184184
+DFFB,Nucleolus,-0.272518655
+ANTXR1,Nucleolus,0.298990687
+CKAP2L,Nucleolus,0.46258371
+C15orf40,Nucleolus,-0.013970426
+HIC2,Nucleolus,0.33747684
+LUZP1,Nucleolus,1.325099485
+HEXDC,Nucleolus,1.942981422
+LRRC45,Nucleolus,0.787919701
+ASPSCR1,Nucleolus,1.299354708
+TAPT1,Nucleolus,-0.042557309
+CSGALNACT2,Nucleolus,0.469259749
+PCDH7,Nucleolus,-0.105531419
+ROBO1,Nucleolus,-0.21935146
+P2RY1,Nucleolus,1.453343119
+TPST1,Nucleolus,-2.422214892
+TOR1AIP2,Nucleolus,0.63123952
+OTUD3,Nucleolus,0.685114303
+GUSB,Nucleolus,0.101354887
+BRD3,Nucleolus,0.29884612
+MAP3K2,Nucleolus,1.436623591
+NLGN2,Nucleolus,-0.221759019
+ALCAM,Nucleolus,0.141394484
+YWHAG,Nucleolus,0.011972449
+TMEM192,Nucleolus,0.358110558
+ZNF778,Nucleolus,-1.153074688
+NIPA1,Nucleolus,0.391802377
+SIK2,Nucleolus,0.996545383
+RNF150,Nucleolus,0.020272279
+ZNF212,Nucleolus,0.534102036
+FAM161A,Nucleolus,1.482121958
+CRTAP,Nucleolus,0.063821414
+PRDM10,Nucleolus,0.741984603
+FOS,Nucleolus,-11.0353118
+TMED10,Nucleolus,-0.35387073
+SLC30A1,Nucleolus,-0.267033505
+DNAJC18,Nucleolus,-2.079141529
+RALGAPB,Nucleolus,0.705090796
+LONRF2,Nucleolus,0.947711342
+ELOVL6,Nucleolus,0.681737193
+ARL6IP1,Nucleolus,-0.22559642
+CDH2,Nucleolus,-0.2935502
+EMB,Nucleolus,0.930165351
+STAT2,Nucleolus,0.898851747
+TRABD,Nucleolus,-0.311442991
+POLH,Nucleolus,0.386850094
+KIF5B,Nucleolus,0.732343326
+AKAP13,Nucleolus,0.689980693
+CHCHD7,Nucleolus,0.821823518
+GPR27,Nucleolus,0.260909047
+KBTBD2,Nucleolus,0.837831523
+KIAA0232,Nucleolus,0.290680738
+TMEM43,Nucleolus,-0.792817032
+RNF139,Nucleolus,-0.263698587
+PAQR8,Nucleolus,-1.510220974
+TANC2,Nucleolus,0.537878923
+DNAJC24,Nucleolus,0.903718685
+HS6ST2,Nucleolus,-0.001082244
+INSR,Nucleolus,-0.323587399
+ATP6V0E2,Nucleolus,-1.654954466
+ZNF692,Nucleolus,-0.16941576
+NETO2,Nucleolus,0.332070378
+NPTX1,Nucleolus,0.996569832
+FAM98B,Nucleolus,0.907777005
+GAA,Nucleolus,0.051880929
+CANT1,Nucleolus,-0.314403809
+CHST11,Nucleolus,0.615785995
+CLCN5,Nucleolus,0.160249471
+ZBTB26,Nucleolus,-0.643952165
+ZNF562,Nucleolus,0.434664122
+ZNF318,Nucleolus,0.870451649
+WIPF2,Nucleolus,-0.631872134
+LRRC8C,Nucleolus,0.686764836
+LRRC8D,Nucleolus,0.273846701
+ETFDH,Nucleolus,0.743964682
+LPAR3,Nucleolus,-0.760225318
+CLSTN1,Nucleolus,-0.131707988
+BPTF,Nucleolus,0.791745848
+ATF7IP,Nucleolus,0.272307844
+TCEA2,Nucleolus,-0.263563901
+ANO5,Nucleolus,1.046224704
+MLLT3,Nucleolus,-0.156366709
+PRNP,Nucleolus,-0.108493877
+ZNF217,Nucleolus,0.990263102
+JMJD1C,Nucleolus,0.720267825
+THOP1,Nucleolus,-0.074325693
+ORMDL3,Nucleolus,0.015578865
+KLF11,Nucleolus,1.281955427
+MTBP,Nucleolus,0.993768482
+ZNF131,Nucleolus,1.227674891
+BSG,Nucleolus,-0.942737929
+CERS6,Nucleolus,-0.015207628
+TP53RK,Nucleolus,0.013305788
+FAM195A,Nucleolus,0.612368695
+GTPBP2,Nucleolus,0.058714096
+ZNF24,Nucleolus,0.735818405
+MANEA,Nucleolus,0.296407239
+RAD9A,Nucleolus,0.910492388
+FAM21C,Nucleolus,0.7812174
+CORO1B,Nucleolus,-0.594730528
+LRRC20,Nucleolus,-0.317238458
+NAA16,Nucleolus,0.930438831
+DCP2,Nucleolus,0.985751173
+CES3,Nucleolus,-0.379634836
+CES2,Nucleolus,0.320725599
+PDP2,Nucleolus,0.985032972
+SP3,Nucleolus,1.038431311
+METAP1D,Nucleolus,1.007709046
+ZNF621,Nucleolus,1.818404399
+NADSYN1,Nucleolus,-0.107471669
+DHCR7,Nucleolus,-0.714267377
+NBEA,Nucleolus,1.23752691
+ANKRD13D,Nucleolus,1.001578434
+LCLAT1,Nucleolus,0.383912721
+TADA2B,Nucleolus,-0.35878058
+HECTD4,Nucleolus,0.051177721
+ESRRA,Nucleolus,1.16784806
+AHSA2,Nucleolus,2.033330682
+VANGL1,Nucleolus,-0.176903847
+IQCB1,Nucleolus,0.860002127
+GOLGB1,Nucleolus,0.73802919
+TNKS,Nucleolus,1.088758215
+ZBTB21,Nucleolus,0.855908452
+STOX2,Nucleolus,0.185413781
+DAG1,Nucleolus,-0.622283854
+RNF26,Nucleolus,-0.867246268
+PEAK1,Nucleolus,0.990474916
+TNFRSF10D,Nucleolus,-1.585385431
+MOB1B,Nucleolus,1.160980537
+SNX33,Nucleolus,0.166011088
+CHD2,Nucleolus,0.546416126
+CCDC41,Nucleolus,1.536529409
+PC,Nucleolus,0.382476692
+SUSD5,Nucleolus,-0.527972015
+HEG1,Nucleolus,-0.929494042
+TOMM20,Nucleolus,0.052594894
+CNP,Nucleolus,-0.571631593
+DPY19L1,Nucleolus,-0.409387946
+ZNF791,Nucleolus,1.008060367
+PHC3,Nucleolus,1.341251344
+GOLIM4,Nucleolus,0.340072966
+XXYLT1,Nucleolus,-1.301453336
+UBXN2A,Nucleolus,0.773487083
+CCS,Nucleolus,-0.566000717
+FAM3C2,Nucleolus,-0.551264553
+CTSF,Nucleolus,-10.10314678
+MSRB3,Nucleolus,0.462138337
+LEMD3,Nucleolus,-0.228106374
+RGMB,Nucleolus,-0.261686143
+ZDHHC24,Nucleolus,-2.450517297
+MGA,Nucleolus,1.267240168
+PIGG,Nucleolus,0.894545457
+ADCY6,Nucleolus,0.272188838
+ZBTB4,Nucleolus,0.970120874
+ZHX3,Nucleolus,0.766929517
+RALGAPA1,Nucleolus,0.89365679
+ATP2A2,Nucleolus,-0.031570294
+CNTNAP2,Nucleolus,-0.0021282
+DENND4A,Nucleolus,0.951993743
+MSL2,Nucleolus,-0.085333538
+UGT8,Nucleolus,-0.629887088
+ZNF266,Nucleolus,0.937223256
+SLC29A2,Nucleolus,0.469650255
+BRSK2,Nucleolus,0.932273682
+B3GNT1,Nucleolus,-1.221280625
+TMEM167A,Nucleolus,0.239886759
+CEP135,Nucleolus,1.378960806
+FZD4,Nucleolus,-0.875565944
+PDE12,Nucleolus,0.226323479
+GLMN,Nucleolus,0.056624808
+SEZ6L2,Nucleolus,-1.74366505
+KLC2,Nucleolus,-0.081537468
+GK5,Nucleolus,1.265512837
+VCPIP1,Nucleolus,1.467652358
+PCCA,Nucleolus,-0.245041686
+GOLGA8A,Nucleolus,1.823384094
+TP53I11,Nucleolus,0.002701828
+PHYKPL,Nucleolus,1.656336499
+ARL10,Nucleolus,1.135435399
+CCDC14,Nucleolus,1.123259513
+ALG10B,Nucleolus,1.584603705
+RAB6A,Nucleolus,0.986900451
+ERCC4,Nucleolus,0.956347507
+RMI2,Nucleolus,0.809440934
+TOM1L2,Nucleolus,0.283921625
+MLXIP,Nucleolus,0.38767469
+SLC35E3,Nucleolus,1.273323979
+ARL4D,Nucleolus,-3.119506049
+LYSMD3,Nucleolus,0.877032981
+B3GALT6,Nucleolus,-1.705621631
+MBLAC2,Nucleolus,0.465219341
+TPRN,Nucleolus,0.286884416
+YES1,Nucleolus,1.077506085
+TMEM39A,Nucleolus,0.637500066
+CCDC57,Nucleolus,1.587716416
+FOXG1,Nucleolus,-0.629705339
+ATAD5,Nucleolus,0.936963034
+ANAPC2,Nucleolus,0.311019431
+SPRYD4,Nucleolus,0.063666327
+CLK2,Nucleolus,1.110544605
+LPCAT4,Nucleolus,1.167325788
+B3GNT5,Nucleolus,0.241258626
+LMNB2,Nucleolus,-0.358479241
+RMDN1,Nucleolus,1.000699874
+ACSF3,Nucleolus,0.74603667
+ANKLE2,Nucleolus,0.884695922
+NFATC2IP,Nucleolus,0.943513861
+SMCR8,Nucleolus,1.116186855
+MTX3,Nucleolus,1.183527458
+FBXO46,Nucleolus,0.668593077
+WDR73,Nucleolus,0.562025023
+ANO6,Nucleolus,-0.035417666
+ZBTB34,Nucleolus,1.357477241
+FAM210A,Nucleolus,1.589745784
+ULK1,Nucleolus,0.540559958
+RPS6KA3,Nucleolus,0.788919816
+PUS1,Nucleolus,0.172855814
+CHD9,Nucleolus,-0.277943435
+PDDC1,Nucleolus,1.364121526
+TOP3A,Nucleolus,0.371037802
+NR2C2,Nucleolus,0.970307031
+ZBTB33,Nucleolus,0.946186188
+SLC25A22,Nucleolus,-0.541456829
+PIDD,Nucleolus,0.797103861
+GBA,Nucleolus,0.259106666
+IL17RA,Nucleolus,-0.110858117
+THAP5,Nucleolus,0.92733646
+PVRL3,Nucleolus,-0.66250419
+KIAA0195,Nucleolus,-0.19921159
+SOX12,Nucleolus,-0.96445672
+CHID1,Nucleolus,-0.465721585
+ZNF518A,Nucleolus,1.691175
+ZBTB41,Nucleolus,0.603768862
+DMAP1,Nucleolus,1.215383551
+C2orf69,Nucleolus,0.343691658
+SH2B1,Nucleolus,0.307629446
+KDELC2,Nucleolus,0.933041216
+GALNT11,Nucleolus,-0.456724809
+WDR6,Nucleolus,0.386317357
+GEN1,Nucleolus,1.165570113
+GLDC,Nucleolus,0.340393425
+CTNNBIP1,Nucleolus,-2.26459654
+ERN1,Nucleolus,-0.450834386
+KCTD12,Nucleolus,0.842445557
+DHFRL1,Nucleolus,-0.885174094
+FAM132B,Nucleolus,-0.003191086
+FAM219B,Nucleolus,0.108221535
+DPY19L3,Nucleolus,0.986165821
+PFAS,Nucleolus,0.076094972
+C17orf62,Nucleolus,0.178075442
+ZBTB7A,Nucleolus,-0.266987147
+SPTY2D1,Nucleolus,0.068595313
+FUCA1,Nucleolus,0.682390154
+CALR,Nucleolus,-0.915551237
+LDLRAD3,Nucleolus,-0.559193582
+CLK3,Nucleolus,0.401153627
+PACS2,Nucleolus,1.040239469
+ELMOD2,Nucleolus,0.922769962
+FJX1,Nucleolus,2.123881052
+ZBTB18,Nucleolus,0.307695221
+GCC1,Nucleolus,0.44968788
+PLD6,Nucleolus,0.736156015
+CDC42EP4,Nucleolus,-0.090308581
+PCBP1-AS1,Nucleolus,0.940877096
+MYADM,Nucleolus,-1.769510955
+SERTAD2,Nucleolus,1.125726557
+BBS10,Nucleolus,0.075509935
+SOCS4,Nucleolus,2.182527634
+ZADH2,Nucleolus,0.116849156
+EXOC3,Nucleolus,-0.036360637
+C7orf41,Nucleolus,-0.272456093
+ZNF609,Nucleolus,-0.117778662
+CCDC66,Nucleolus,1.579655625
+MCFD2,Nucleolus,-0.107134382
+GAS1,Nucleolus,-0.323726158
+FAM73A,Nucleolus,0.809855773
+NRIP1,Nucleolus,1.150985575
+PCGF5,Nucleolus,0.771383163
+YOD1,Nucleolus,0.818289069
+SLC36A4,Nucleolus,0.930367403
+ZDHHC20,Nucleolus,0.583796457
+PSMG4,Nucleolus,1.028022676
+PDIA3P,Nucleolus,-0.222063583
+CUEDC1,Nucleolus,-0.111957955
+KCTD2,Nucleolus,0.253621422
+D2HGDH,Nucleolus,0.039192745
+FKRP,Nucleolus,0.271425536
+SLC26A11,Nucleolus,-1.566174032
+F2R,Nucleolus,0.684846064
+DHTKD1,Nucleolus,-0.733905095
+ZNF746,Nucleolus,-0.404313922
+TMEM136,Nucleolus,0.753811112
+ZNF322,Nucleolus,0.680698756
+OGFOD3,Nucleolus,0.223122373
+ZNF678,Nucleolus,0.72993086
+ZBTB2,Nucleolus,-0.824431656
+SGSH,Nucleolus,0.692979504
+SETD2,Nucleolus,0.902219912
+YIPF6,Nucleolus,-0.424778441
+IBA57,Nucleolus,0.955965186
+C5orf24,Nucleolus,1.341079087
+ADO,Nucleolus,1.175639902
+CREB3L2,Nucleolus,0.210337484
+UNC5C,Nucleolus,-0.569121289
+RGMA,Nucleolus,-0.725993437
+EXT1,Nucleolus,-0.747163959
+ATP6AP2,Nucleolus,-0.334370695
+BACE2,Nucleolus,-2.23572928
+FIGN,Nucleolus,0.291212823
+B4GALNT4,Nucleolus,-0.573374335
+AP1S2,Nucleolus,0.851603016
+FBXL6,Nucleolus,0.102473701
+YBEY,Nucleolus,-7.653004324
+CLN8,Nucleolus,-0.191371436
+PLCXD1,Nucleolus,0.618260306
+EXOC7,Nucleolus,-0.405395475
+CEP97,Nucleolus,1.35693312
+MXRA7,Nucleolus,-0.081379805
+SATB1,Nucleolus,1.43472269
+PLCB1,Nucleolus,1.748653416
+TTC3,Nucleolus,0.623930002
+COL18A1,Nucleolus,-0.378496426
+ZNF721,Nucleolus,0.445543103
+SRPR,Nucleolus,-0.638324843
+EWSR1,Nucleolus,0.599674947
+GJC1,Nucleolus,0.558236146
+MTA1,Nucleolus,0.879319276
+CADM1,Nucleolus,-0.300340608
+LYSMD4,Nucleolus,1.336403975
+NKX2-5,Nucleolus,-2.149943002
+GPC6,Nucleolus,0.305038522
+PTTG1IP,Nucleolus,-0.578797614
+ZNF623,Nucleolus,0.910865818
+BCOR,Nucleolus,0.089577326
+ASB7,Nucleolus,1.257466917
+EP400,Nucleolus,0.9942076
+COA5,Nucleolus,1.926236215
+PRR14L,Nucleolus,0.934193384
+ZNRF3,Nucleolus,-0.599986432
+TRMT12,Nucleolus,-1.059176157
+FAM101B,Nucleolus,0.624470679
+TRIM52,Nucleolus,1.569563712
+CMTM4,Nucleolus,0.686288997
+TMEM50A,Nucleolus,-0.785193725
+CBX6,Nucleolus,0.559045205
+KREMEN1,Nucleolus,-0.264197604
+TRAIP,Nucleolus,-0.017598564
+EMILIN3,Nucleolus,-1.126593114
+RBM12B,Nucleolus,0.921873839
+BTBD9,Nucleolus,-0.525856068
+KIRREL,Nucleolus,-0.099213575
+IQGAP3,Nucleolus,0.829762095
+PRKX,Nucleolus,0.850811407
+SMTN,Nucleolus,0.614422855
+TBX1,Nucleolus,-0.448082413
+TSPYL2,Nucleolus,0.066235279
+C22orf46,Nucleolus,0.806015753
+PCDH9,Nucleolus,-0.105655302
+OAF,Nucleolus,0.276385572
+ZDHHC23,Nucleolus,0.979212939
+EFNA5,Nucleolus,-1.538000445
+SS18L1,Nucleolus,-0.277675353
+KNTC1,Nucleolus,0.796702206
+WDR27,Nucleolus,1.704314521
+FOXO4,Nucleolus,-0.889741212
+POU3F2,Nucleolus,-0.504205643
+PROS1,Nucleolus,-0.158858318
+ZFP1,Nucleolus,1.22926056
+XPOT,Nucleolus,0.804196535
+SNN,Nucleolus,-1.498453817
+AMER1,Nucleolus,0.099626294
+ZBTB40,Nucleolus,0.613264925
+ATL3,Nucleolus,0.36470406
+UBE2G2,Nucleolus,0.690439102
+TMED9,Nucleolus,-1.086683986
+RBM33,Nucleolus,0.940245408
+JAG2,Nucleolus,0.81195747
+ZFP90,Nucleolus,1.577824802
+SIVA1,Nucleolus,-1.284700919
+BRI3BP,Nucleolus,0.132007879
+ROBO2,Nucleolus,-0.548438088
+BRF1,Nucleolus,-0.11026265
+MANEAL,Nucleolus,-0.933967812
+PURA,Nucleolus,0.678894632
+DDX51,Nucleolus,0.208700695
+NOMO2,Nucleolus,-0.10422511
+NRBP2,Nucleolus,0.476035588
+ZNF445,Nucleolus,1.623905128
+PRPF39,Nucleolus,1.521418696
+CDK10,Nucleolus,1.119800694
+ATP6V0A2,Nucleolus,0.396877195
+C14orf80,Nucleolus,1.145923403
+HGS,Nucleolus,0.56218777
+MRPL30,Nucleolus,0.805288654
+METTL7A,Nucleolus,-0.082437005
+NR2F2,Nucleolus,0.431678548
+SP1,Nucleolus,0.377188781
+PCGF3,Nucleolus,0.850211867
+P4HB,Nucleolus,-0.671282466
+PBX1,Nucleolus,0.43529263
+BRWD1,Nucleolus,0.79752768
+EP400NL,Nucleolus,0.696593883
+MYBL1,Nucleolus,0.913538638
+DMWD,Nucleolus,-0.109108348
+SLC52A2,Nucleolus,-0.407088224
+NAT8L,Nucleolus,0.737503482
+GNB1L,Nucleolus,-0.617567907
+LAMP1,Nucleolus,-1.115128674
+KLHDC8B,Nucleolus,-2.678196433
+SETD4,Nucleolus,1.591335375
+RNPC3,Nucleolus,1.409117096
+BICD2,Nucleolus,-0.225542359
+LRCH3,Nucleolus,0.74130049
+ZNF529,Nucleolus,1.09674137
+AIDA,Nucleolus,-3.749933415
+ZBTB6,Nucleolus,0.00503295
+BCL9L,Nucleolus,-4.106341454
+KIF18B,Nucleolus,0.312316319
+MKL2,Nucleolus,0.589401689
+CA5BP1,Nucleolus,0.730868676
+BACE1,Nucleolus,-1.416938877
+KPNA4,Nucleolus,0.015179859
+ZNF197,Nucleolus,0.92662556
+BTN3A2,Nucleolus,1.421828063
+INSIG1,Nucleolus,0.182117745
+TMEM222,Nucleolus,0.516632774
+SMYD4,Nucleolus,1.269393583
+GPATCH8,Nucleolus,0.7612558
+LYRM7,Nucleolus,1.033158056
+ZNF397,Nucleolus,1.983616633
+ZSCAN30,Nucleolus,1.125979925
+TPCN1,Nucleolus,0.084352712
+POFUT2,Nucleolus,-0.166764249
+ZDHHC17,Nucleolus,0.531688905
+PPARA,Nucleolus,-0.073606346
+TEAD1,Nucleolus,0.787193476
+ENTPD5,Nucleolus,0.246044744
+KIAA1598,Nucleolus,0.33057466
+TSPYL4,Nucleolus,-0.205674959
+FNBP1,Nucleolus,0.409331569
+BCAM,Nucleolus,0.184265063
+COL4A1,Nucleolus,-0.295438541
+SIRT7,Nucleolus,1.277167714
+TET3,Nucleolus,0.404629405
+ZNF286A,Nucleolus,-0.171443259
+SAMD11,Nucleolus,0.119575002
+B3GALTL,Nucleolus,-0.304651959
+TCEA1,Nucleolus,0.993857127
+FANCA,Nucleolus,0.127942806
+SEMA4D,Nucleolus,0.233543686
+LIN28B,Nucleolus,0.344828713
+FANCM,Nucleolus,0.946027731
+FAM122A,Nucleolus,-0.392871871
+ARHGAP11B,Nucleolus,1.286525659
+CYHR1,Nucleolus,0.725361098
+KLHL17,Nucleolus,1.053784616
+ANKRD19P,Nucleolus,0.780740293
+ARL4C,Nucleolus,-1.564339917
+C11orf95,Nucleolus,0.469484826
+MAPK12,Nucleolus,0.493208431
+COL4A5,Nucleolus,-0.932678311
+NCR3LG1,Nucleolus,1.054751828
+HES4,Nucleolus,0.491447891
+CHM,Nucleolus,0.477403126
+H2AFX,Nucleolus,-1.230137413
+SRSF10,Nucleolus,0.674225485
+NDOR1,Nucleolus,0.692096039
+FAM72B,Nucleolus,1.029591845
+LDOC1L,Nucleolus,-0.140362643
+PTAR1,Nucleolus,0.937843741
+ZDHHC9,Nucleolus,-0.366966079
+ZBED6CL,Nucleolus,0.740394213
+TMEM120B,Nucleolus,1.116967665
+MTF1,Nucleolus,1.396526995
+TMEM201,Nucleolus,0.422883637
+NHLRC3,Nucleolus,1.309775121
+BEND4,Nucleolus,0.995671059
+MSL1,Nucleolus,0.941166724
+DHFRP1,Nucleolus,-0.157349126
+ZNF292,Nucleolus,1.235252494
+ADAT2,Nucleolus,1.542196044
+H1F0,Nucleolus,-1.295950779
+LITAF,Nucleolus,-0.178044102
+ARID2,Nucleolus,0.452284712
+S100A13,Nucleolus,0.619527461
+ZNF33A,Nucleolus,1.079535058
+LIN54,Nucleolus,0.891552939
+KAZN,Nucleolus,0.849634809
+SLC35E2B,Nucleolus,-1.442228735
+KIAA0895L,Nucleolus,0.841668993
+PLEKHG4,Nucleolus,0.73277958
+ACADSB,Nucleolus,0.582462952
+TMEM63A,Nucleolus,-0.157258166
+MPHOSPH8,Nucleolus,1.063975605
+FAM217B,Nucleolus,-0.292187416
+LCOR,Nucleolus,1.750285208
+POM121,Nucleolus,-0.232241827
+ZBTB44,Nucleolus,0.820348096
+SLC35F1,Nucleolus,-0.911579125
+PTPN1,Nucleolus,-0.203996421
+EVL,Nucleolus,1.306933384
+EPHB4,Nucleolus,-1.368658704
+PPP1R26,Nucleolus,0.990008912
+TSC22D2,Nucleolus,1.005184723
+PIK3R4,Nucleolus,0.150983245
+GDAP2,Nucleolus,1.013164293
+AFAP1,Nucleolus,0.974374652
+MAN2A2,Nucleolus,0.533566997
+CACNA1H,Nucleolus,-0.111026288
+SULF2,Nucleolus,-0.477609191
+PLXNB2,Nucleolus,-0.21689555
+XRCC2,Nucleolus,2.512201131
+MYO6,Nucleolus,0.850275999
+TCF4,Nucleolus,0.919835675
+RABL6,Nucleolus,1.425545718
+ZKSCAN5,Nucleolus,0.905212304
+ZFP62,Nucleolus,1.084143802
+ERI2,Nucleolus,0.204998432
+ZNF33B,Nucleolus,0.567777307
+ZNF512B,Nucleolus,0.881266705
+ZNF431,Nucleolus,1.276630662
+NF1,Nucleolus,1.212130522
+VKORC1L1,Nucleolus,0.104249833
+COL27A1,Nucleolus,1.364018053
+GM2A,Nucleolus,0.05956042
+SNHG17,Nucleolus,1.163731262
+CD47,Nucleolus,0.188893801
+CTBP1-AS2,Nucleolus,1.249307461
+C6orf106,Nucleolus,-0.396245096
+NHLRC2,Nucleolus,1.379311016
+KPNA5,Nucleolus,0.998405229
+ZNF252P,Nucleolus,1.169905667
+PDLIM7,Nucleolus,0.049967253
+SLC39A10,Nucleolus,-0.201952832
+ZNF100,Nucleolus,1.78348596
+ZNF398,Nucleolus,0.577376563
+GMFB,Nucleolus,-0.357273686
+ZMYM1,Nucleolus,1.287389515
+MAFG,Nucleolus,0.950323034
+ARRDC1,Nucleolus,0.894132361
+KIAA1671,Nucleolus,1.096639755
+IGF2R,Nucleolus,0.062377129
+SLC25A29,Nucleolus,0.253115262
+PGAP1,Nucleolus,0.994851986
+SRC,Nucleolus,-0.343470922
+PCNXL3,Nucleolus,0.146238056
+LRRC8B,Nucleolus,0.756774426
+ABCB8,Nucleolus,0.313652753
+SND1,Nucleolus,-0.085978106
+ENTPD4,Nucleolus,1.202407018
+KANK2,Nucleolus,0.282428574
+FITM2,Nucleolus,0.607093251
+DDI2,Nucleolus,0.556294883
+TRIM33,Nucleolus,0.95998041
+LRP10,Nucleolus,-0.686950419
+ZNF655,Nucleolus,1.359804334
+SLC22A5,Nucleolus,-0.327161087
+ADARB1,Nucleolus,1.187672057
+OGDHL,Nucleolus,0.517905017
+STMN3,Nucleolus,-0.580217499
+SIPA1L1,Nucleolus,-0.327262461
+PIGN,Nucleolus,-0.224061573
+COL4A6,Nucleolus,0.469097513
+GPX1P1,Nucleolus,-3.212247967
+ENTPD6,Nucleolus,0.054633963
+ENPP1,Nucleolus,0.445788639
+PHF2,Nucleolus,-0.406671924
+RPS26,Nucleolus,-0.335276909
+PSAP,Nucleolus,-1.15907686
+HOXC6,Nucleolus,-1.357310249
+EME2,Nucleolus,1.446506263
+ZNF780A,Nucleolus,0.595132344
+SLC9A8,Nucleolus,-0.597004681
+OCLN,Nucleolus,0.054634388
+GPAA1,Nucleolus,-1.346262589
+SPG7,Nucleolus,0.994446545
+ERO1L,Nucleolus,0.837388097
+ZNF121,Nucleolus,2.086389651
+MPZL1,Nucleolus,-0.023380754
+VPS13A,Nucleolus,0.950048403
+AKAP17A,Nucleolus,0.491700408
+ELOVL2,Nucleolus,0.238101008
+SNHG12,Nucleolus,-1.369201832
+NOL8,Nucleolus,0.759824229
+MRPL42,Nucleolus,1.055471866
+ENTPD7,Nucleolus,-0.361509724
+ZNF84,Nucleolus,0.854422608
+SIRPA,Nucleolus,0.370563722
+CD2AP,Nucleolus,0.648988232
+NUP62CL,Nucleolus,1.086837893
+SFI1,Nucleolus,1.520964994
+ZNF248,Nucleolus,0.39768952
+ZNF770,Nucleolus,0.718709333
+HMGN5,Nucleolus,1.016395211
+MIER1,Nucleolus,0.79192931
+MAN1A2,Nucleolus,0.50183097
+RPS6KL1,Nucleolus,0.308433519
+DDX42,Nucleolus,0.721971983
+STYX,Nucleolus,0.452898342
+UCKL1,Nucleolus,0.01383715
+ZKSCAN8,Nucleolus,1.565588072
+HOXC4,Nucleolus,1.52576639
+ASPH,Nucleolus,0.334116398
+WWP2,Nucleolus,0.20491859
+GFPT1,Nucleolus,1.380890143
+ITSN2,Nucleolus,-0.347150922
+MGEA5,Nucleolus,0.889285545
+FAM115A,Nucleolus,0.315172399
+ZNF587,Nucleolus,2.483086067
+MAFK,Nucleolus,0.592474291
+DDX39B,Nucleolus,0.967434913
+NUDT16,Nucleolus,0.331377583
+TLK1,Nucleolus,0.822328673
+MDM4,Nucleolus,1.814665863
+KLHL9,Nucleolus,0.943472567
+C6orf89,Nucleolus,0.179729622
+SLC9A6,Nucleolus,-0.072968815
+FAN1,Nucleolus,1.00321203
+CEP290,Nucleolus,1.255213527
+C1orf85,Nucleolus,-0.422457818
+SMOC1,Nucleolus,-0.169423114
+ZNF652,Nucleolus,1.58075078
+SMURF1,Nucleolus,0.570366665
+SLC5A3,Nucleolus,0.432370066
+COLGALT2,Nucleolus,0.748013528
+TMEM184B,Nucleolus,0.198710377
+LRIG2,Nucleolus,0.313062708
+FOXJ3,Nucleolus,0.455949855
+ARHGAP11A,Nucleolus,0.105463656
+DENND4B,Nucleolus,-0.214072488
+R3HDM4,Nucleolus,-1.327309524
+RUNDC1,Nucleolus,1.074184265
+TYW1,Nucleolus,-0.137571629
+SFMBT2,Nucleolus,0.840539057
+ITPRIPL1,Nucleolus,-0.068620607
+SMC5,Nucleolus,0.006468662
+SREBF2,Nucleolus,-1.38731429
+DZIP3,Nucleolus,0.988591142
+KIAA0753,Nucleolus,0.978081341
+ATG9A,Nucleolus,-0.369887077
+TBKBP1,Nucleolus,1.026316252
+NAGA,Nucleolus,-0.254327143
+SGMS1,Nucleolus,0.117379206
+COX20,Nucleolus,0.894740627
+CHML,Nucleolus,1.423846734
+TATDN3,Nucleolus,0.838088642
+RBM20,Nucleolus,0.521793934
+PCMTD2,Nucleolus,1.230133902
+EFCAB7,Nucleolus,0.850077602
+ZYG11A,Nucleolus,2.127806755
+SYS1,Nucleolus,-1.596660753
+TRAF3IP1,Nucleolus,1.030762185
+PHACTR4,Nucleolus,0.913715698
+ZDHHC18,Nucleolus,-0.566465436
+TMEM57,Nucleolus,-0.609530583
+ZDBF2,Nucleolus,0.790907294
+BMPR2,Nucleolus,0.431570634
+OXLD1,Nucleolus,0.934740758
+MRPL38,Nucleolus,0.691664339
+SDHD,Nucleolus,0.90084678
+NEU1,Nucleolus,-1.369052248
+HSPA1B,Nucleolus,-0.766902398
+HLA-C,Nucleolus,-1.584665635
+DDR1,Nucleolus,-1.618357733
+HLA-E,Nucleolus,-0.28385975
+ZNF616,Nucleolus,1.922474678
+CRHR1-IT1,Nucleolus,0.673480217
+GABBR1,Nucleolus,1.33730108
+RANBP17,Nucleolus,-0.866638919
+ZNF204P,Nucleolus,1.158310904
+TCTN1,Nucleolus,-0.001490137
+ZBTB48,Nucleolus,1.685918308
+C11orf83,Nucleolus,-2.281055785
+ZNF783,Nucleolus,1.029193597
+SLC35B4,Nucleolus,0.717045805
+TRIQK,Nucleolus,1.009600025
+PSENEN,Nucleolus,-1.395870529
+LGR4,Nucleolus,0.040476572
+PDE7A,Nucleolus,1.357498173
+TMEM170B,Nucleolus,0.679355912
+TECPR1,Nucleolus,0.880425337
+ITPRIPL2,Nucleolus,-0.076986295
+CRYZL1,Nucleolus,1.000915088
+C5orf51,Nucleolus,1.047349377
+ZNF316,Nucleolus,0.525065756
+NYNRIN,Nucleolus,-0.53814008
+AP000525.9,Nucleolus,1.399336596
+PTPLB,Nucleolus,-0.352459605
+WDR52,Nucleolus,2.037956978
+SETD5-AS1,Nucleolus,1.311234957
+SACM1L,Nucleolus,0.198340461
+C17orf51,Nucleolus,0.947542426
+ZNF580,Nucleolus,0.640807303
+DENND1B,Nucleolus,1.250262867
+SFT2D2,Nucleolus,0.58814633
+FGFR1OP,Nucleolus,1.578450826
+KLHL23,Nucleolus,0.885868563
+TRIM59,Nucleolus,0.943904798
+NRAS,Nucleolus,0.383577556
+QTRT1,Nucleolus,1.055181632
+CHUK,Nucleolus,0.767422656
+COG8,Nucleolus,0.844732474
+RBMXL1,Nucleolus,0.321028906
+LEPROT,Nucleolus,-0.096506622
+ATF6B,Nucleolus,0.06054106
+SLC35F6,Nucleolus,0.67135226
+ZNF134,Nucleolus,0.659688202
+EMP2,Nucleolus,-0.438854238
+DNASE1,Nucleolus,0.14512113
+CSNK1E,Nucleolus,0.325236
+GALT,Nucleolus,-0.115011726
+ITGA1,Nucleolus,0.165873604
+AP1G2,Nucleolus,0.888993511
+CARKD,Nucleolus,1.303651543
+TTLL3,Nucleolus,2.044508779
+CPNE1,Nucleolus,0.757624708
+RCN1P2,Nucleolus,-0.126919793
+ZBED1,Nucleolus,-0.239836946
+DDX12P,Nucleolus,1.487319399
+C22orf29,Nucleolus,1.000783611
+NEURL4,Nucleolus,-0.060728333
+TTC3P1,Nucleolus,-0.613688368
+PEX26,Nucleolus,0.924960216
+GOLGA8B,Nucleolus,1.468582398
+MIR17HG,Nucleolus,1.232149141
+TMEM242,Nucleolus,0.641784206
+TMEM167B,Nucleolus,-0.37793189
+CYB5RL,Nucleolus,0.788770905
+CROCCP2,Nucleolus,1.745885889
+FNIP1,Nucleolus,1.216122569
+RP11-204C16.4,Nucleolus,1.139582627
+TENM3,Nucleolus,0.306628498
+AC007390.5,Nucleolus,0.112759311
+NBPF1,Nucleolus,0.344937153
+CCNL2,Nucleolus,1.569480239
+PPT2,Nucleolus,-1.212662682
+CKMT1A,Nucleolus,0.893027696
+NSUN5P1,Nucleolus,1.695199662
+RP11-54O7.3,Nucleolus,1.624730462
+AFG3L1P,Nucleolus,1.578457108
+EPB41L4A-AS1,Nucleolus,-0.123739465
+ATXN1L,Nucleolus,0.748073774
+SMIM13,Nucleolus,0.305515489
+CTD-2228K2.7,Nucleolus,2.295053464
+PLEKHM1,Nucleolus,-1.515859841
+FAM195B,Nucleolus,-1.186958716
+IPO7P2,Nucleolus,1.316714409
+SLC26A6,Nucleolus,-0.022662832
+FGD5-AS1,Nucleolus,0.720805985
+TLK2P1,Nucleolus,0.579812426
+BAIAP2-AS1,Nucleolus,-1.410368291
+RP11-31F15.1,Nucleolus,1.463671449
+TMEM185B,Nucleolus,-0.586170454
+C14orf132,Nucleolus,0.933947303
+PARG,Nucleolus,0.908525733
+TP73-AS1,Nucleolus,0.094278881
+SCAMP4,Nucleolus,-0.423422339
+SEC63P1,Nucleolus,1.165866546
+HCG11,Nucleolus,0.296738867
+DHFR,Nucleolus,0.022736397
+RP11-206L10.11,Nucleolus,1.220700521
+XIST,Nucleolus,2.152473648
+RP5-1180C10.2,Nucleolus,0.260576975
+HCG18,Nucleolus,0.514016889
+CTD-2619J13.14,Nucleolus,1.583410692
+SNHG15,Nucleolus,0.793009335
+SNHG7,Nucleolus,-0.089489735
+HOTAIRM1,Nucleolus,0.827609445
+RP4-775C13.1,Nucleolus,1.179599436
+ZNF37BP,Nucleolus,1.703809228
+ZNF736,Nucleolus,1.180980329
+MAGI2-AS3,Nucleolus,0.971779941
+JRK,Nucleolus,1.473455186
+GAS5,Nucleolus,0.172369194
+LINC00338,Nucleolus,1.605819308
+PPP1R3E,Nucleolus,1.627856714
+RP5-827C21.1,Nucleolus,-1.774393041
+NUS1P1,Nucleolus,1.080480126
+ZBTB22,Nucleolus,-2.218733351
+AD000090.2,Nucleolus,1.59744343
+ZBED5,Nucleolus,0.438112157
+RNF103,Nucleolus,0.104442341
+TMEM189,Nucleolus,-0.348460136
+RPL36A,Nucleolus,2.224599632
+CD302,Nucleolus,-1.84744037
+SNHG3,Nucleolus,0.780640333
+C15orf38,Nucleolus,-0.0103522
+AP5Z1,Nucleolus,0.841943251
+MICAL3,Nucleolus,0.774224679
+KCTD7,Nucleolus,0.35205029
+SCARF2,Nucleolus,-1.119502338
+APOBEC3C,Nucleolus,0.201963824
+N4BP2L2,Nucleolus,1.448422665
+NEAT1,Nucleolus,1.864516865
+CRNDE,Nucleolus,-0.150563527
+OIP5-AS1,Nucleolus,0.543100864
+MARS2,Nucleolus,0.142521672
+HAUS5,Nucleolus,0.044366381
+PDCD6,Nucleolus,-0.206490836
+THAP9-AS1,Nucleolus,1.325974078
+SHANK3,Nucleolus,0.429472918
+RTN3P1,Nucleolus,-0.82860098
+MALAT1,Nucleolus,1.847984223
+TUG1,Nucleolus,1.046912636
+GS1-251I9.4,Nucleolus,1.243008782
+UTP14C,Nucleolus,1.025262171
+ZNF260,Nucleolus,1.301103824
+PABPC4L,Nucleolus,1.314497031
+FPGT,Nucleolus,0.845986845
+MEX3A,Nucleolus,1.233005567
+POLR2M,Nucleolus,1.173923041
+SNHG1,Nucleolus,-0.017216284
+POLG2,Nucleolus,1.814370784
+KIAA1147,Nucleolus,0.734656539
+RP11-349A22.5,Nucleolus,1.086451776
+LINC00641,Nucleolus,0.655154827
+RP4-773N10.4,Nucleolus,1.899656526
+CEP95,Nucleolus,0.408981586
+LINC00657,Nucleolus,0.913436904
+RP1-239B22.5,Nucleolus,1.764725894
+RP6-24A23.6,Nucleolus,1.371216317
+ERVK13-1,Nucleolus,1.469767192
+GS1-358P8.4,Nucleolus,1.748836492
+VPS9D1-AS1,Nucleolus,-0.181257477
+RP6-24A23.7,Nucleolus,1.310104051
+GAN,Nucleolus,1.264767458
+SPON1,Nucleolus,0.41913083
+RP11-159D12.2,Nucleolus,2.029559943
+OTUD7B,Nucleolus,0.061727534
+RNF115,Nucleolus,0.9774747
+BAHCC1,Nucleolus,0.582812463
+NBPF15,Nucleolus,1.958751674
+RP11-242D8.1,Nucleolus,2.368555584
+RP11-18I14.10,Nucleolus,1.842228585
+NBPF9,Nucleolus,1.325573865
+EGLN2,Nucleolus,1.453319918
+NUDT3,Nucleolus,1.323924913
+KMT2B,Nucleolus,0.452509291
+POM121C,Nucleolus,-0.224114957
+RP11-504P24.8,Nucleolus,1.742904506
+DCP1A,Nucleolus,0.849613343
+EPOP,Nucleolus,-0.812611208
+NOL12,Nucleolus,1.371133709
+SOCS7,Nucleolus,0.5314399
+PI4KAP1,Nucleolus,1.860263519
+ZNF280B,Nucleolus,1.040657444
+MLLT6,Nucleolus,0.970464398
+SYNRG,Nucleolus,-0.235480532
+TADA2A,Nucleolus,1.067138678
+PIP4K2B,Nucleolus,-0.125289807
+AL133325.3,Nucleolus,2.315770207
+DDX52,Nucleolus,1.062837347
+MYO19,Nucleolus,0.569895502
+DHRS11,Nucleolus,0.444274404
+ACACA,Nucleolus,0.269882594
+AC005332.6,Nucleolus,-0.147554787
+RP11-574K11,Nucleolus,1.034940292
+AL035425.4,Nucleolus,2.421027524
+EBLN3P,Nucleolus,0.934240479
+GCLC,Lamina,0.792826826
+NFYA,Lamina,0.05603596
+NIPAL3,Lamina,0.411312938
+ENPP4,Lamina,0.558327187
+SEMA3F,Lamina,0.065755877
+CD99,Lamina,-0.811157109
+LASP1,Lamina,-0.781575095
+M6PR,Lamina,0.152332279
+CFLAR,Lamina,1.316672054
+NDUFAF7,Lamina,0.842292677
+RBM5,Lamina,1.853688158
+SLC7A2,Lamina,0.820220305
+SARM1,Lamina,0.726036972
+CAMKK1,Lamina,0.560787281
+RECQL,Lamina,-0.149653763
+ARHGAP33,Lamina,0.544020775
+CDC27,Lamina,0.042354969
+SPPL2B,Lamina,1.52459368
+CREBBP,Lamina,0.041826575
+GCFC2,Lamina,-0.098888156
+RHBDD2,Lamina,-0.947563948
+IBTK,Lamina,0.485198168
+ZNF195,Lamina,0.734671041
+MYCBP2,Lamina,0.584352314
+ZFX,Lamina,0.438515298
+LAMP2,Lamina,-0.510394638
+GDE1,Lamina,-0.784567069
+TMEM98,Lamina,-0.485754175
+TMEM132A,Lamina,0.361301319
+ZNF263,Lamina,0.426693049
+MAP3K9,Lamina,1.550475164
+JHDM1D,Lamina,1.268163449
+PHTF2,Lamina,0.871460726
+FARP2,Lamina,0.592383846
+IFRD1,Lamina,1.191606469
+ARHGAP44,Lamina,0.680512426
+ELAC2,Lamina,-0.754945994
+ADIPOR2,Lamina,-0.121163389
+PAFAH1B1,Lamina,0.218974659
+KIAA0100,Lamina,-0.800208611
+PAX6,Lamina,0.75474322
+LUC7L,Lamina,0.806396546
+CACNA2D2,Lamina,0.052337623
+PIGQ,Lamina,0.471399025
+CRAMP1L,Lamina,0.869869349
+JARID2,Lamina,0.537274543
+ADAM22,Lamina,1.475647778
+CYB561,Lamina,-0.112999147
+SPAG9,Lamina,0.175343206
+CELSR3,Lamina,1.017064532
+AASS,Lamina,0.44397838
+PKD1,Lamina,1.161378944
+SEC62,Lamina,-0.355833221
+REV3L,Lamina,0.796332938
+POMT2,Lamina,0.199979936
+BAZ1B,Lamina,0.054423005
+ZNF207,Lamina,0.503307379
+IFFO1,Lamina,0.809307297
+NISCH,Lamina,1.466353634
+IDS,Lamina,0.155178254
+CLCN6,Lamina,1.403079064
+MRC2,Lamina,0.397626976
+TSPAN9,Lamina,0.157770857
+BTBD7,Lamina,0.603871731
+MBTD1,Lamina,0.831143513
+LARS2,Lamina,-0.401830269
+PIK3C2A,Lamina,0.402890289
+ANLN,Lamina,-0.156346773
+QPCTL,Lamina,0.658141624
+MAP4K3,Lamina,0.564568482
+BRCA1,Lamina,0.205574753
+MBTPS2,Lamina,-0.226971725
+EXTL3,Lamina,-0.315953824
+ELOVL5,Lamina,-0.286362637
+MAP4K5,Lamina,0.655051542
+MAN2B2,Lamina,0.140375252
+CLK1,Lamina,1.325973874
+ANGEL1,Lamina,0.245478691
+DDX11,Lamina,0.777902992
+UFL1,Lamina,0.332386201
+SLC30A9,Lamina,-0.2866759
+COX15,Lamina,-0.054444591
+ZMYND11,Lamina,0.803305626
+XYLT2,Lamina,0.719485233
+NUDCD3,Lamina,0.422743188
+CHDH,Lamina,0.771726024
+GLT8D1,Lamina,0.034142933
+ATP2C1,Lamina,-0.523395521
+RALBP1,Lamina,-0.314081803
+CNTN1,Lamina,-0.559150092
+PHLDB1,Lamina,0.249048548
+MRE11A,Lamina,0.754268383
+SPAST,Lamina,-0.481915762
+NRXN3,Lamina,0.277120766
+CPS1,Lamina,-1.059692178
+SLC45A4,Lamina,-0.010985547
+ZNF839,Lamina,1.708308121
+ZDHHC6,Lamina,0.57645863
+RNH1,Lamina,-0.204982998
+RB1CC1,Lamina,0.011338666
+ERP44,Lamina,-0.538296588
+AKAP11,Lamina,1.05738876
+GCLM,Lamina,0.249391407
+DEPDC1,Lamina,0.347290608
+SEC63,Lamina,-0.109750198
+FAS,Lamina,-1.082130209
+RNASET2,Lamina,1.059450776
+AGPAT4,Lamina,2.008651407
+MIPEP,Lamina,-0.29077926
+VEZT,Lamina,-0.261209106
+BRD9,Lamina,1.052981301
+SNX1,Lamina,0.612924215
+BCLAF1,Lamina,0.223011452
+SLC39A9,Lamina,0.480038587
+RABEP1,Lamina,-0.073680271
+FAM13B,Lamina,-0.004282208
+PNPLA6,Lamina,-0.647827528
+ZCCHC8,Lamina,0.913002622
+CHPF2,Lamina,0.541318269
+FUT8,Lamina,-0.662179159
+UBA6,Lamina,0.07319229
+ATP6V0A1,Lamina,0.589593214
+SLC4A7,Lamina,1.310447102
+VCL,Lamina,-0.350463493
+ADSS,Lamina,0.089183224
+TIMP2,Lamina,-1.023117878
+RFC1,Lamina,-0.136099042
+ZZZ3,Lamina,0.13609831
+MFAP3,Lamina,0.308263133
+MRI1,Lamina,0.891757842
+AGA,Lamina,-0.583263349
+BOD1L1,Lamina,0.925651606
+TRIO,Lamina,0.856894152
+VCAN,Lamina,-0.180332752
+CLEC16A,Lamina,0.037253586
+ZFYVE16,Lamina,0.430069827
+RAI14,Lamina,-0.017487533
+PNKP,Lamina,0.816867083
+PQLC2,Lamina,-0.300451777
+CTNS,Lamina,-0.337149432
+INPP4A,Lamina,0.918381814
+RETSAT,Lamina,0.343255808
+CUL7,Lamina,0.027646555
+PHKA2,Lamina,0.874693859
+DSG2,Lamina,-0.093784479
+OFD1,Lamina,0.708587879
+GPM6B,Lamina,-0.032291221
+YTHDC2,Lamina,0.860925713
+TPR,Lamina,0.379647519
+SCML1,Lamina,1.831040122
+MAP4,Lamina,-0.108535577
+GOPC,Lamina,0.642225821
+ZNF800,Lamina,0.413692338
+SNX29,Lamina,-0.019209654
+KITLG,Lamina,-0.51240957
+H6PD,Lamina,1.51983464
+LTBP1,Lamina,-0.749441385
+RCN1,Lamina,-0.02163399
+PTCD2,Lamina,2.40791227
+LIMA1,Lamina,-0.305936308
+LETMD1,Lamina,1.466280992
+POLQ,Lamina,0.81751714
+MPHOSPH9,Lamina,0.958299931
+PLEKHA5,Lamina,0.053127753
+SIKE1,Lamina,1.143928756
+MSMO1,Lamina,-0.549536402
+TTC17,Lamina,0.959908444
+LAMA3,Lamina,0.941083333
+AP5M1,Lamina,0.792711004
+ANAPC4,Lamina,0.806563532
+ARID4B,Lamina,0.611655364
+SDCCAG8,Lamina,0.172940577
+FOXC1,Lamina,0.039087452
+PLEKHH1,Lamina,1.166769762
+ATP9A,Lamina,-0.242667061
+FAM168A,Lamina,1.199559783
+RELT,Lamina,1.060043076
+NOP58,Lamina,0.258951382
+TAB2,Lamina,-0.072290098
+USP36,Lamina,0.36609101
+KMT2C,Lamina,0.965029825
+MCOLN3,Lamina,1.91429891
+PUM2,Lamina,0.46592831
+RC3H2,Lamina,0.683347725
+DCBLD2,Lamina,0.093458549
+SOAT1,Lamina,-0.160611836
+ATP11B,Lamina,0.481906738
+SEC61A1,Lamina,-0.779561512
+PPP1R12A,Lamina,0.336821478
+POLR3E,Lamina,0.539083337
+ATP2B4,Lamina,0.541983617
+ZC3H11A,Lamina,1.060802426
+NDC1,Lamina,-0.345860863
+UNKL,Lamina,1.19265557
+ALDH18A1,Lamina,-0.789264966
+TARBP1,Lamina,2.012612351
+WNK1,Lamina,0.012463396
+CCAR1,Lamina,0.603028465
+PTPRU,Lamina,-0.018234529
+QSER1,Lamina,0.229385598
+BCAT1,Lamina,0.520174488
+PRDM6,Lamina,2.325332803
+TNK2,Lamina,1.50268266
+MON2,Lamina,0.825316529
+GPBP1,Lamina,0.324858808
+WAPAL,Lamina,-0.056865119
+VMP1,Lamina,-0.840223404
+APPBP2,Lamina,-0.026715056
+AHRR,Lamina,1.420539872
+ZNF275,Lamina,1.435509887
+MTMR1,Lamina,0.252303332
+GPC1,Lamina,0.108765434
+TM7SF3,Lamina,-0.598548494
+CDON,Lamina,0.501061384
+HIPK2,Lamina,0.786649868
+SUGP2,Lamina,1.545465164
+SLC12A2,Lamina,0.043045688
+HMG20B,Lamina,0.566028075
+UHRF1BP1,Lamina,0.888630693
+PKN2,Lamina,0.266674733
+TRAM2,Lamina,0.791806139
+NTN1,Lamina,0.095456478
+ADAT1,Lamina,0.648947169
+SPEN,Lamina,0.912336294
+MAP2K4,Lamina,-0.140859928
+SLK,Lamina,-0.01110268
+CYB5R4,Lamina,0.243794343
+ASB1,Lamina,1.761944645
+FAM107B,Lamina,0.063511664
+SLC9A7,Lamina,0.642799122
+FOXJ2,Lamina,0.370802164
+PPP2R5A,Lamina,0.003606342
+ASPM,Lamina,1.2055256
+ZBTB11,Lamina,1.014085428
+ATXN3,Lamina,1.077645488
+FGFR2,Lamina,0.238205979
+LRRC40,Lamina,0.076778977
+ATG2B,Lamina,1.01651691
+ARFGEF1,Lamina,-0.204623375
+KLF6,Lamina,1.059879391
+NEO1,Lamina,-0.45125103
+TRAM1,Lamina,-0.813458992
+TP53BP1,Lamina,-0.145890412
+IARS2,Lamina,-0.459397897
+ROCK1,Lamina,0.096585197
+HYAL2,Lamina,-0.947498719
+FGFR3,Lamina,0.917948606
+MEF2A,Lamina,0.345276655
+ACSL4,Lamina,0.098755915
+PRR11,Lamina,-0.378124705
+REEP1,Lamina,-0.655593098
+ATP11A,Lamina,1.186289968
+POLR1A,Lamina,0.374819667
+IP6K2,Lamina,0.717491419
+KIF2A,Lamina,-0.005796832
+TGFBR3,Lamina,0.119990807
+NEDD4,Lamina,-0.182304688
+MAPK6,Lamina,-0.380412017
+UFD1L,Lamina,0.809764334
+LRP6,Lamina,0.941992021
+NUCB2,Lamina,-0.451977345
+SLC44A1,Lamina,-0.207299168
+TMEM260,Lamina,1.078085856
+SMG6,Lamina,-0.112895624
+CLTCL1,Lamina,1.31709017
+DGCR2,Lamina,-0.21415379
+MNT,Lamina,1.931411332
+ZXDC,Lamina,1.504659048
+NDST1,Lamina,0.29711593
+AP3M2,Lamina,0.853071458
+RAD18,Lamina,0.701419843
+ATP2B1,Lamina,0.238214878
+MGAT4A,Lamina,0.682067228
+SNX13,Lamina,0.807426653
+VASH1,Lamina,1.333699914
+SEL1L,Lamina,-0.031732261
+ATP6AP1,Lamina,-0.639873244
+DAZAP1,Lamina,0.658361434
+CPSF1,Lamina,0.209190208
+SLC6A15,Lamina,0.221469658
+RDH11,Lamina,-0.264721662
+PRKACA,Lamina,-0.686110806
+LPHN1,Lamina,0.395043213
+RPS6KA6,Lamina,0.593505891
+EPN2,Lamina,0.635961279
+PTPN18,Lamina,0.49778495
+TFRC,Lamina,-0.181450172
+AFF4,Lamina,0.812730679
+MPP5,Lamina,-0.054946336
+HMMR,Lamina,-0.33994075
+P4HA2,Lamina,-0.919550488
+TRNT1,Lamina,1.016612561
+ACADVL,Lamina,0.054144646
+EVC,Lamina,0.495242448
+DERL2,Lamina,-0.050691422
+NDE1,Lamina,-0.314735415
+PVR,Lamina,0.530686775
+SCARB1,Lamina,0.633912198
+SELO,Lamina,1.989398863
+LLGL2,Lamina,1.379862618
+PDE8A,Lamina,0.895811717
+SDHA,Lamina,-0.378328527
+KDM5A,Lamina,-0.035505984
+ADAM11,Lamina,0.530041187
+ST6GAL1,Lamina,0.165430804
+GLI2,Lamina,1.163718423
+NOTCH3,Lamina,0.066357884
+BCS1L,Lamina,0.530666216
+DPP8,Lamina,1.249023625
+SLC24A1,Lamina,0.863850502
+LMAN1,Lamina,0.044175758
+PTPLAD1,Lamina,-0.307663645
+TUBE1,Lamina,1.731298464
+SEMA3C,Lamina,-0.421514423
+TTC38,Lamina,0.161750372
+CELSR1,Lamina,0.322221111
+ZNF638,Lamina,0.29856108
+SLC25A40,Lamina,1.121603022
+RASAL2,Lamina,1.164405318
+ZNF37A,Lamina,2.020691715
+FNDC3B,Lamina,0.305733834
+FRYL,Lamina,0.377471217
+TMEM131,Lamina,0.059417824
+WDR62,Lamina,0.328281526
+BCAP29,Lamina,0.246168939
+SEC31B,Lamina,1.273452844
+RBMS2,Lamina,0.534077192
+SLC46A1,Lamina,0.398661268
+PLXNA2,Lamina,-0.628077243
+ANKRD13A,Lamina,0.073882215
+PAG1,Lamina,1.516728554
+MCAM,Lamina,-0.110330319
+GPC4,Lamina,-0.643522589
+MBNL3,Lamina,1.247651611
+DGKD,Lamina,0.794118597
+TM9SF3,Lamina,-0.094122764
+PPP1R12B,Lamina,2.157243461
+DNAJC10,Lamina,0.021572074
+GTF3C1,Lamina,-0.368603481
+IL4R,Lamina,0.550503197
+LRCH4,Lamina,0.725135191
+FAM76B,Lamina,1.390510382
+SIRT6,Lamina,-0.135222887
+POLD3,Lamina,0.550912261
+PHF17,Lamina,0.715038222
+FBLN1,Lamina,-0.742400723
+ITGA8,Lamina,-0.360085463
+MCCC1,Lamina,0.145615775
+ACER3,Lamina,0.995085106
+N4BP2,Lamina,0.649960432
+HOXA9,Lamina,1.372001674
+PCM1,Lamina,0.314680381
+TNRC6C,Lamina,0.604454537
+ITCH,Lamina,-0.050920311
+SDF4,Lamina,0.013239455
+FKBP7,Lamina,-0.535385055
+SLC1A3,Lamina,0.230323783
+SAR1A,Lamina,-0.40847071
+PAFAH1B3,Lamina,-0.370227098
+MOXD1,Lamina,-0.903123002
+STX7,Lamina,0.824075674
+RABL2B,Lamina,0.741517274
+SLC35C2,Lamina,0.324184728
+CRYBG3,Lamina,1.412067061
+RIF1,Lamina,0.848777214
+PSEN1,Lamina,-0.379927607
+RBL1,Lamina,0.162702109
+RSBN1,Lamina,0.717808358
+MAGI3,Lamina,0.579370967
+OSTM1,Lamina,-0.5555750779999999
+EXD2,Lamina,-0.231446736
+MEF2C,Lamina,0.255254445
+UBA5,Lamina,1.037965841
+STK17B,Lamina,0.79939927
+ZNF510,Lamina,1.447101304
+LRP2,Lamina,0.282945215
+DUSP12,Lamina,0.439005626
+KIAA0141,Lamina,0.88075135
+PHLPP1,Lamina,-0.722292892
+SMARCD3,Lamina,0.185092293
+C5orf22,Lamina,0.194354052
+CCNT2,Lamina,1.70492655
+NFE2L1,Lamina,-0.410277002
+GSK3B,Lamina,0.191619989
+ITGB5,Lamina,-0.750690184
+ERC1,Lamina,-0.173636906
+RNF13,Lamina,-0.291111886
+LYRM2,Lamina,-0.244298404
+KAT6A,Lamina,-0.028051831
+PLOD1,Lamina,-0.939989175
+TDRD3,Lamina,-0.278682061
+PDS5B,Lamina,0.020858109
+OXCT1,Lamina,-0.10696658
+RRAGB,Lamina,0.661354972
+FAT1,Lamina,-0.427605184
+YTHDC1,Lamina,0.3703978
+ZMPSTE24,Lamina,-0.479087174
+REST,Lamina,0.867467236
+APLP2,Lamina,-0.426240181
+KIAA1467,Lamina,0.428636939
+TXLNA,Lamina,0.507795633
+NCOA1,Lamina,-0.381741209
+AGBL5,Lamina,-0.816951096
+CD59,Lamina,-0.491302845
+ATRX,Lamina,0.586752412
+SCAMP1,Lamina,-0.708763915
+HACE1,Lamina,1.145807963
+WDFY1,Lamina,0.086393017
+MTIF2,Lamina,-0.100429631
+ATG16L1,Lamina,0.371186746
+POMGNT1,Lamina,-0.388325618
+B4GALT1,Lamina,-0.139269997
+IPO11,Lamina,-0.621022327
+TMED2,Lamina,-0.545438761
+ERO1LB,Lamina,0.643952335
+PPP1R15A,Lamina,-1.146307709
+NLK,Lamina,-0.028222526
+PIGS,Lamina,-0.600091229
+ATXN7L3,Lamina,-0.161249385
+PGS1,Lamina,1.021668876
+SH3BP2,Lamina,1.131988342
+ADD1,Lamina,0.093550835
+TXNDC16,Lamina,0.183124503
+NID2,Lamina,0.082562207
+KLHL42,Lamina,1.724841342
+ERGIC2,Lamina,-0.00110545
+METTL2A,Lamina,-0.258723911
+PTPN4,Lamina,1.429513404
+KHSRP,Lamina,-0.525074988
+EPB41L1,Lamina,-0.462437382
+ANKRD10,Lamina,2.474603539
+DOCK3,Lamina,1.613306761
+PPP1R13B,Lamina,0.957902324
+ATRN,Lamina,0.163835601
+ZNF343,Lamina,0.794626164
+MAVS,Lamina,1.368271223
+LZTS3,Lamina,0.423795274
+SNX5,Lamina,1.198911105
+MAPKAPK5,Lamina,0.648404215
+ESF1,Lamina,0.243395524
+SLC23A2,Lamina,0.385556899
+KIF16B,Lamina,0.223435127
+ERP29,Lamina,-0.597920864
+FUS,Lamina,1.183754842
+ZNF302,Lamina,1.171524851
+GRAMD1A,Lamina,0.281165034
+GANAB,Lamina,-0.778276109
+RBM41,Lamina,1.500913427
+GPATCH2L,Lamina,1.777179724
+SLC9A1,Lamina,-0.103352439
+SPTLC1,Lamina,-0.493974541
+PAPOLA,Lamina,-0.328025605
+MUL1,Lamina,-0.417960442
+RAB11FIP3,Lamina,0.599822194
+GOLGA3,Lamina,0.621287464
+EFNB1,Lamina,-0.382740463
+PDPR,Lamina,1.167897091
+GLG1,Lamina,-0.411794427
+TNRC6A,Lamina,0.686247355
+PLEKHG2,Lamina,0.080319385
+NAT14,Lamina,-0.386128104
+RBM27,Lamina,-0.159327509
+OSBPL8,Lamina,0.099268009
+NRCAM,Lamina,0.063635094
+LAMB1,Lamina,0.037238719
+CMTM6,Lamina,-0.745323992
+ITGA6,Lamina,-0.185360855
+SEL1L3,Lamina,-0.310074739
+ORC6,Lamina,0.950871396
+TMEM101,Lamina,-0.660792197
+OSGEP,Lamina,0.73929388
+G2E3,Lamina,0.520677691
+HECTD1,Lamina,0.180678095
+SEMA6A,Lamina,0.810618842
+TRPM7,Lamina,0.451638054
+TYRO3,Lamina,-0.238113421
+AGO1,Lamina,1.073349624
+MFSD11,Lamina,0.197537621
+GPATCH2,Lamina,0.904592004
+NUP50,Lamina,0.485061671
+LRRFIP2,Lamina,0.284871535
+SEC22C,Lamina,1.06473341
+XYLB,Lamina,0.840821264
+HDAC6,Lamina,-0.150586298
+CBX5,Lamina,1.239116259
+SUCO,Lamina,0.039422249
+HOOK2,Lamina,0.543679103
+ARCN1,Lamina,-0.560955137
+TMEM38B,Lamina,-0.35626597
+BTAF1,Lamina,0.99177635
+IKZF5,Lamina,0.766231912
+WAC,Lamina,0.234254541
+CREM,Lamina,-0.685742232
+BRPF3,Lamina,0.038243293
+EFHC1,Lamina,1.832901903
+ABL1,Lamina,-0.384203571
+SH3GLB1,Lamina,0.485120346
+SCD,Lamina,-0.091191226
+ABLIM1,Lamina,0.637617239
+ERMP1,Lamina,-0.13441829
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+MZF1,Lamina,2.180312801
+FBXL19,Lamina,0.082182074
+MTAP,Lamina,0.776773197
+CEP170B,Lamina,0.66941128
+POLRMT,Lamina,0.003643856
+ARVCF,Lamina,1.07918924
+TRMT2A,Lamina,0.556510917
+ZDHHC8,Lamina,1.189562048
+KLHL22,Lamina,1.204246486
+CRKL,Lamina,0.539907924
+LZTR1,Lamina,0.276545734
+CECR2,Lamina,1.069427101
+DERL3,Lamina,0.881555627
+PPM1F,Lamina,0.995993183
+TOP3B,Lamina,1.071187372
+CRYBB2P1,Lamina,1.302956296
+ADRBK2,Lamina,0.664931157
+GGA1,Lamina,0.999649944
+HPS4,Lamina,0.693993361
+TTC28,Lamina,0.509552721
+SEPT3,Lamina,-0.001403059
+KDELR3,Lamina,-0.162200159
+DDX17,Lamina,2.305871441
+TCF20,Lamina,0.270472946
+TIMP3,Lamina,0.344473431
+PPP6R2,Lamina,0.521282771
+SUN2,Lamina,-0.088782006
+ARSA,Lamina,0.744345744
+MYH9,Lamina,-0.399888075
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+TNRC6B,Lamina,1.062553553
+SGSM3,Lamina,0.261359546
+IFT27,Lamina,2.192883313
+KIAA0930,Lamina,-0.787817121
+EP300,Lamina,0.232710879
+ZC3H7B,Lamina,0.160436318
+ACO2,Lamina,-1.208452808
+TRMU,Lamina,0.58555663
+ZBED4,Lamina,0.293329344
+ABHD4,Lamina,-0.450368103
+KHNYN,Lamina,0.803166473
+NIN,Lamina,0.621868777
+GNPNAT1,Lamina,-0.059460693
+DDHD1,Lamina,1.338572614
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+TMED8,Lamina,1.37093634
+SPTLC2,Lamina,-0.139874661
+PPM1A,Lamina,0.800745876
+SIX4,Lamina,0.75831478
+GALNT16,Lamina,1.272109682
+KIAA0247,Lamina,0.73952257
+SRSF5,Lamina,0.752939374
+DICER1,Lamina,0.6855736
+ZFYVE21,Lamina,1.13643477
+TELO2,Lamina,0.290021787
+PCNX,Lamina,0.309021242
+GSKIP,Lamina,-0.112286987
+SMEK1,Lamina,0.049242664
+TRIP11,Lamina,0.353581496
+PABPN1,Lamina,0.782528029
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+CHD8,Lamina,-0.161792718
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+PNN,Lamina,1.139922564
+PLTP,Lamina,-1.166934764
+ABHD12,Lamina,-0.252369126
+GINS1,Lamina,-0.282064977
+RIMS4,Lamina,0.349499817
+PABPC1L,Lamina,1.617727604
+STK4,Lamina,0.406196256
+BMP7,Lamina,-0.421666397
+DNAJC5,Lamina,0.006329266
+SLCO4A1,Lamina,0.39394275
+DIDO1,Lamina,1.441816288
+ARFGAP1,Lamina,1.035650502
+ARFRP1,Lamina,1.403428055
+CDS2,Lamina,0.07508018
+TM9SF4,Lamina,-0.293450531
+POFUT1,Lamina,-0.041616821
+SAMHD1,Lamina,-0.723732735
+KIF3B,Lamina,-0.067920637
+E2F1,Lamina,0.106709748
+APMAP,Lamina,-0.46337302
+ZNF516,Lamina,1.385323364
+LPIN2,Lamina,0.470212808
+SMCHD1,Lamina,0.429519957
+LAMA1,Lamina,0.370364692
+RNF125,Lamina,1.151392315
+ANKRD12,Lamina,1.621368911
+MIB1,Lamina,0.639864209
+MID1,Lamina,1.66303919
+WDR13,Lamina,-0.024236872
+XIAP,Lamina,0.725069124
+ATP11C,Lamina,0.403537104
+SYP,Lamina,0.26765203
+FMR1,Lamina,0.268924304
+SLC35A2,Lamina,-0.207991209
+TAZ,Lamina,0.92165665
+MAGT1,Lamina,-0.417633043
+CD99L2,Lamina,-0.098424654
+EEA1,Lamina,0.721347023
+NDFIP2,Lamina,-0.192859778
+DNAJC3,Lamina,-0.221916849
+UGGT2,Lamina,0.798676301
+ARHGEF7,Lamina,0.289041228
+PARP4,Lamina,0.252819752
+FLT1,Lamina,-0.572998157
+VWA8,Lamina,0.379470583
+DGKH,Lamina,2.620274489
+INTS6,Lamina,0.142954331
+CLN5,Lamina,-0.004805578
+MGRN1,Lamina,0.431858377
+ZNF629,Lamina,0.431013257
+CENPT,Lamina,0.559115729
+NFAT5,Lamina,1.855368359
+SETD6,Lamina,1.653733894
+SLC38A7,Lamina,0.356183806
+SLC7A6OS,Lamina,1.333459691
+SLC7A6,Lamina,0.700777336
+WDR59,Lamina,0.801222156
+TAF1C,Lamina,1.026660099
+TSC2,Lamina,-0.006687429
+ZNF500,Lamina,1.587516783
+ABCC1,Lamina,-0.052167075
+NOMO3,Lamina,-1.280181858
+NARFL,Lamina,0.386536184
+MTHFSD,Lamina,1.319231041
+CLCN7,Lamina,0.4276417
+SLC7A5,Lamina,-0.209882183
+FBXO31,Lamina,0.4633061
+EEF2K,Lamina,0.705840952
+CAPN15,Lamina,0.880699894
+PIEZO1,Lamina,0.842847328
+BFAR,Lamina,-0.346759382
+NOMO1,Lamina,-0.658077799
+CCP110,Lamina,0.689222577
+RNF40,Lamina,-0.491292512
+LACTB,Lamina,-0.069124154
+CD276,Lamina,-0.325792657
+HOMER2,Lamina,0.662601288
+TMEM87A,Lamina,-0.242536866
+ZNF106,Lamina,0.673863151
+CEP152,Lamina,0.98992366
+TJP1,Lamina,-0.29848777
+VPS18,Lamina,-0.673378615
+MYEF2,Lamina,0.771314793
+CSPP1,Lamina,1.029064421
+ZFAND1,Lamina,0.756117062
+FZD3,Lamina,1.396712834
+EYA1,Lamina,-0.605152641
+NBN,Lamina,0.110867639
+IMPAD1,Lamina,0.384894128
+UBE2W,Lamina,0.916695881
+IKBKB,Lamina,0.726679272
+PLAT,Lamina,-0.695691328
+JPH1,Lamina,1.162385848
+TRPS1,Lamina,1.556573467
+PYCRL,Lamina,-0.358182354
+EEF1D,Lamina,0.406172378
+SQLE,Lamina,-0.439146415
+SLC39A14,Lamina,-0.076442433
+MTMR9,Lamina,1.397152154
+LEPROTL1,Lamina,-0.365565602
+PPP2CB,Lamina,-0.236448979
+KLHDC4,Lamina,0.879769043
+KCTD9,Lamina,0.454286363
+MAN2B1,Lamina,0.045640504
+NUCB1,Lamina,-0.711941854
+SARS2,Lamina,0.594923272
+SNRNP70,Lamina,0.521351774
+CLPTM1,Lamina,-0.432524939
+CLASRP,Lamina,0.376401254
+FCGRT,Lamina,-0.657235347
+ERCC2,Lamina,0.136742881
+DOT1L,Lamina,0.693954089
+SF3A2,Lamina,0.164895868
+AMH,Lamina,1.615793061
+DMPK,Lamina,1.414043979
+TIMM44,Lamina,0.070392309
+AKAP8,Lamina,0.390395998
+AKT2,Lamina,0.557086314
+PLD3,Lamina,-0.383555308
+FSD1,Lamina,0.151351188
+APLP1,Lamina,-1.259439949
+CACTIN,Lamina,0.447357787
+TYK2,Lamina,0.549415138
+PTPRS,Lamina,0.12482541
+MEGF8,Lamina,-0.123791392
+KDELR1,Lamina,-0.774209187
+CYTH2,Lamina,0.888877005
+LIG1,Lamina,0.573112635
+BCAT2,Lamina,0.025529029
+TNPO2,Lamina,0.433136515
+DNASE2,Lamina,-0.633638522
+ISYNA1,Lamina,0.289342934
+CRTC1,Lamina,1.57375245
+SUGP1,Lamina,0.718455862
+SIPA1L3,Lamina,0.200659198
+CADM4,Lamina,-0.32192732
+SMG9,Lamina,0.995556834
+AVL9,Lamina,0.596866323
+CDK6,Lamina,0.775880569
+DNAJC2,Lamina,0.577756927
+WDR91,Lamina,0.571123003
+CBLL1,Lamina,0.364281717
+MTPN,Lamina,-0.527094865
+ZC3HAV1,Lamina,-0.194858107
+OGDH,Lamina,-0.912413019
+MET,Lamina,-0.303890554
+LMBR1,Lamina,-0.327386816
+HOXA3,Lamina,1.333291166
+HOXA6,Lamina,1.702964535
+BRAT1,Lamina,0.202488257
+FKBP14,Lamina,0.328081505
+NSUN5P2,Lamina,1.294035591
+CASP2,Lamina,0.066133305
+HSPB1,Lamina,-0.79185684
+ZKSCAN1,Lamina,0.575243819
+WASL,Lamina,-0.213367192
+RBM28,Lamina,0.75295898
+C1GALT1,Lamina,0.29403784
+PLOD3,Lamina,-0.042790712
+CLDN15,Lamina,1.653175326
+TMEM106B,Lamina,0.504540021
+CEP41,Lamina,-0.325740888
+GLI3,Lamina,0.141849613
+TMEM248,Lamina,-0.439036576
+TBL2,Lamina,-0.368103134
+FKTN,Lamina,1.473714086
+TMEM245,Lamina,0.964627918
+MEGF9,Lamina,-0.028836427
+TGFBR1,Lamina,-0.132265365
+DNM1,Lamina,1.77480044
+KANK1,Lamina,0.336010314
+RAPGEF1,Lamina,0.280383792
+NPDC1,Lamina,1.75814819
+SETX,Lamina,0.434877573
+CCNJ,Lamina,0.567130226
+RAB11FIP2,Lamina,1.012892558
+ERLIN1,Lamina,0.088766007
+MAPK8,Lamina,0.881045381
+ATE1,Lamina,0.223006516
+PLEKHA1,Lamina,1.1461879
+UNC5B,Lamina,0.686270348
+BMPR1A,Lamina,-0.395862395
+ACTA2,Lamina,-4.118160538
+LIPA,Lamina,-0.48548067
+LZTS2,Lamina,1.335584827
+ARHGAP21,Lamina,0.348603342
+ANKRD26,Lamina,0.59852827
+LARP4B,Lamina,0.398132324
+C10orf137,Lamina,0.717293884
+MTPAP,Lamina,0.583048061
+SH3PXD2A,Lamina,1.3478475
+PITRM1,Lamina,-0.474471946
+FAM208B,Lamina,0.475308465
+TSPAN14,Lamina,0.089200267
+NUFIP2,Lamina,2.473190144
+DHX40,Lamina,-0.084468558
+CDK5RAP3,Lamina,0.664331427
+RECQL5,Lamina,0.411675314
+INTS2,Lamina,-0.177171673
+CAMTA2,Lamina,0.734039497
+MED13,Lamina,0.332444196
+HOXB6,Lamina,1.594536315
+CPD,Lamina,-0.780904707
+GOSR1,Lamina,-0.244445261
+CCDC47,Lamina,-0.61281013
+AKAP10,Lamina,0.932803747
+CYTH1,Lamina,1.488336273
+LGALS3BP,Lamina,-1.054646077
+EZH1,Lamina,1.805732547
+PPP1R9B,Lamina,-0.396235619
+LUC7L3,Lamina,1.386327701
+DUSP3,Lamina,0.041937039
+EFNB3,Lamina,-0.108668504
+DPH1,Lamina,-0.001601101
+NAT9,Lamina,0.075046281
+TMEM104,Lamina,-0.100574238
+TMEM97,Lamina,-0.696803693
+UNC119,Lamina,0.692806393
+TMEM33,Lamina,-0.101851702
+DCUN1D4,Lamina,1.144282611
+MANBA,Lamina,0.739999835
+ELF2,Lamina,0.080486319
+WFS1,Lamina,0.173329072
+FRG1,Lamina,-0.509478277
+CLCN3,Lamina,-0.269077868
+GALNT7,Lamina,-0.057221745
+TRIM2,Lamina,0.463036853
+NEIL3,Lamina,0.980060187
+SH3D19,Lamina,0.555649618
+STIM2,Lamina,0.563487079
+RAPGEF2,Lamina,0.40286493
+UGDH,Lamina,-1.285581803
+CCDC34,Lamina,-0.522214011
+FNBP4,Lamina,1.687093575
+SC5D,Lamina,-0.518289311
+SIAE,Lamina,0.094095012
+EHD1,Lamina,-0.411955971
+FOXRED1,Lamina,0.288586228
+ST3GAL4,Lamina,-0.310295882
+CPT1A,Lamina,-1.046218763
+TMEM109,Lamina,-0.593146461
+PANX1,Lamina,-0.47093855
+UBE4A,Lamina,-0.15725139
+DDX6,Lamina,-0.245736953
+PVRL1,Lamina,-0.080038366
+HIPK3,Lamina,0.153431665
+MDK,Lamina,-0.786047049
+AMBRA1,Lamina,-0.512468449
+NAA40,Lamina,0.531894234
+SLC35F2,Lamina,-0.403151745
+LEPREL2,Lamina,0.232219135
+CORO1C,Lamina,-0.680314128
+ASIC1,Lamina,0.948830186
+CAPRIN2,Lamina,1.753756144
+SLC11A2,Lamina,0.46795277
+MLEC,Lamina,0.047528358
+BCL7A,Lamina,0.053971113
+RSRC2,Lamina,1.112284013
+PPM1H,Lamina,0.464925492
+ELK3,Lamina,0.048379694
+MAGOHB,Lamina,0.340258497
+ITFG2,Lamina,1.111173417
+PARP11,Lamina,0.399847233
+DUSP16,Lamina,0.019716871
+ACAD10,Lamina,0.532612765
+NAA25,Lamina,0.776278164
+DDX55,Lamina,0.767693712
+SLC38A1,Lamina,0.642781857
+C12orf49,Lamina,-0.231008253
+MDM1,Lamina,1.146088422
+CPSF6,Lamina,0.570689251
+GNPTAB,Lamina,-0.064051319
+ATN1,Lamina,0.394985346
+C12orf57,Lamina,-1.389279976
+LPCAT3,Lamina,-0.171213247
+SUDS3,Lamina,0.195607042
+GOLT1B,Lamina,-0.001733211
+C2CD5,Lamina,1.104948775
+RAB35,Lamina,-0.289727503
+RIC8B,Lamina,1.241434698
+RP11-22B23.1,Lamina,1.526583498
+DSE,Lamina,0.340469622
+MAN1A1,Lamina,0.020808586
+SERINC1,Lamina,-0.367358841
+UST,Lamina,-0.42725103
+KCTD20,Lamina,0.089232541
+RNF8,Lamina,-0.022129615
+ICK,Lamina,0.38878728
+RAB23,Lamina,-0.029618399
+FBXL4,Lamina,0.211345194
+CCNC,Lamina,-0.006282969
+ALDH5A1,Lamina,0.307043286
+EYA4,Lamina,1.312431432
+PERP,Lamina,-0.670730562
+SLC16A10,Lamina,0.971183304
+PHACTR2,Lamina,0.874860637
+SLC39A7,Lamina,-0.636798358
+PPP2R5D,Lamina,-0.653271233
+PTK7,Lamina,-0.208046684
+CUL9,Lamina,-0.083403071
+TMEM30A,Lamina,-0.208018961
+SENP6,Lamina,0.503737399
+VEGFA,Lamina,1.707517193
+PRPF4B,Lamina,0.614674235
+BTN2A1,Lamina,0.46115507
+LAMA4,Lamina,0.11705835
+ERBB2IP,Lamina,-0.135621437
+HARS2,Lamina,0.229826708
+MAN2A1,Lamina,0.493445593
+PAPD7,Lamina,1.05167809
+NNT,Lamina,-0.129764562
+APBB3,Lamina,1.243550546
+SPARC,Lamina,-1.260295045
+HMGCR,Lamina,-0.630182683
+FAF2,Lamina,-0.265704146
+CLK4,Lamina,1.895281959
+ARSB,Lamina,-0.22033203
+CNOT6,Lamina,-0.006528221
+DROSHA,Lamina,0.293974178
+FAM172A,Lamina,0.081476103
+LNPEP,Lamina,0.448632036
+SLC12A7,Lamina,-0.04767095
+NR3C1,Lamina,0.283324093
+C5orf15,Lamina,-0.266154751
+LIFR,Lamina,0.626077025
+TRAPPC13,Lamina,0.815282368
+TXNDC15,Lamina,0.193183906
+H2AFY,Lamina,0.855938367
+TCERG1,Lamina,0.809963422
+SMAD5,Lamina,0.699196704
+ERGIC1,Lamina,-0.432564167
+STC2,Lamina,1.192828485
+ARL6,Lamina,0.732982314
+NIT2,Lamina,0.461725653
+UBE3A,Lamina,0.002473357
+SLC25A36,Lamina,2.53339976
+TFDP2,Lamina,1.119539239
+XRN1,Lamina,0.936284056
+WNT5A,Lamina,0.748334037
+PFKFB4,Lamina,0.180572469
+PRKAR2A,Lamina,-0.279085363
+ACAP2,Lamina,0.866914872
+CBLB,Lamina,0.844589283
+BBX,Lamina,0.635142119
+GNB4,Lamina,-0.037641401
+C3orf52,Lamina,0.575975517
+PLXNA1,Lamina,0.543057036
+CSPG5,Lamina,-0.0082569
+SCAP,Lamina,-0.373711923
+HEMK1,Lamina,0.706446886
+ACVR2B,Lamina,0.881192184
+ABCC5,Lamina,1.283770592
+SSR3,Lamina,-0.566708009
+NKTR,Lamina,2.302548899
+FOXP1,Lamina,-0.045899053
+INO80D,Lamina,1.977442074
+ADAM23,Lamina,0.25495724
+MOB1A,Lamina,-0.021076207
+LMAN2L,Lamina,-0.758190345
+RTKN,Lamina,0.215896837
+PIKFYVE,Lamina,0.637816161
+FAHD2A,Lamina,1.334443619
+SLC35F5,Lamina,0.188109533
+STEAP3,Lamina,0.304161879
+EPB41L5,Lamina,0.232877674
+GPD2,Lamina,-0.713531777
+ACVR1,Lamina,-0.625039392
+MPV17,Lamina,0.3676319
+TTC31,Lamina,0.732199347
+NDUFS7,Lamina,0.138135695
+SPTBN1,Lamina,-0.261412817
+CCDC88A,Lamina,0.326127325
+FN1,Lamina,-0.665755582
+ELMOD3,Lamina,1.560937948
+IGFBP5,Lamina,0.004204176
+USP34,Lamina,0.339915019
+GGCX,Lamina,-0.110416331
+CHST10,Lamina,0.16918309
+MOB4,Lamina,-0.241707336
+UXS1,Lamina,0.137960154
+PASK,Lamina,1.24699627
+TAF1B,Lamina,0.153820305
+DCAF17,Lamina,1.619587159
+SDC1,Lamina,-0.604625624
+SLC1A4,Lamina,-0.365704454
+SOS1,Lamina,0.757712329
+WIPF1,Lamina,0.24603484
+THADA,Lamina,-0.553321893
+TRAK2,Lamina,0.282506627
+TIA1,Lamina,0.762544947
+PCYOX1,Lamina,-0.442979441
+ARID3A,Lamina,0.304812774
+EPHA4,Lamina,0.630234555
+ALMS1,Lamina,-0.159266742
+BCL9,Lamina,0.128507921
+DHCR24,Lamina,-0.757763652
+DNAJC16,Lamina,0.151828969
+RALGPS2,Lamina,0.525546033
+CEP104,Lamina,0.272119704
+FAM20B,Lamina,-0.140065585
+TCEANC2,Lamina,1.538438805
+WRAP73,Lamina,0.446745472
+ICMT,Lamina,-0.560121628
+QSOX1,Lamina,-0.18002581
+AMPD2,Lamina,0.698903097
+EDEM3,Lamina,0.304192133
+RAP1A,Lamina,-0.068605638
+S100PBP,Lamina,0.41897325
+ASH1L,Lamina,1.067342264
+SFPQ,Lamina,0.737023616
+MEF2D,Lamina,0.920989833
+C1orf21,Lamina,1.344973034
+LEPR,Lamina,0.517342583
+IVNS1ABP,Lamina,0.816642653
+KIAA2013,Lamina,-0.41774242
+MIIP,Lamina,0.152527117
+SLC35D1,Lamina,0.813276743
+WLS,Lamina,-0.612263681
+PRDM2,Lamina,1.007600142
+TROVE2,Lamina,0.636408335
+SRSF11,Lamina,1.823436616
+PHTF1,Lamina,0.129157594
+TMEM9,Lamina,-0.155276686
+EXOC8,Lamina,2.129532653
+NID1,Lamina,-0.320651862
+MTR,Lamina,0.599647385
+BMP8B,Lamina,0.041984506
+RIMS3,Lamina,1.896782404
+AKT3,Lamina,0.105457355
+ETV3,Lamina,0.582366729
+LPHN2,Lamina,0.01399608
+RBBP5,Lamina,-0.257595762
+ECE1,Lamina,-0.444735803
+CD46,Lamina,1.19027801
+APH1A,Lamina,-0.626627935
+LEPRE1,Lamina,-0.697726609
+SLC2A1,Lamina,-0.555848989
+SLC19A2,Lamina,0.89053186
+NSUN4,Lamina,0.893724827
+TMED5,Lamina,-0.08673556
+DR1,Lamina,0.35964227
+PTBP2,Lamina,1.355367783
+DARS2,Lamina,-0.786215672
+DIEXF,Lamina,0.736204726
+RCAN3,Lamina,0.045147517
+C1orf63,Lamina,1.858350223
+SLC35A3,Lamina,1.197786389
+RCOR3,Lamina,1.347208317
+ARID1A,Lamina,0.165954746
+CENPF,Lamina,0.81921909
+ESYT2,Lamina,0.050399928
+CD3EAP,Lamina,-0.035881772
+MESDC2,Lamina,-0.143877065
+CTSD,Lamina,-0.675359324
+STK11,Lamina,0.599175503
+KMT2A,Lamina,0.873315245
+KPTN,Lamina,0.069154948
+KIF14,Lamina,0.583138968
+ATF6,Lamina,-0.39364553
+FASTKD2,Lamina,-0.131876311
+NRP2,Lamina,0.312124239
+CREB1,Lamina,0.856956038
+B4GALT6,Lamina,0.670630914
+ELOVL4,Lamina,-0.432570997
+CASP8AP2,Lamina,0.452530471
+PHF3,Lamina,0.110865126
+PLAGL1,Lamina,1.830361682
+FBXO30,Lamina,1.471578678
+TMEM5,Lamina,0.056804884
+ZNF430,Lamina,0.435893283
+DCLRE1B,Lamina,-0.962810936
+PKD2,Lamina,2.024037526
+UBN1,Lamina,0.366426169
+KLF12,Lamina,1.693967316
+WDR35,Lamina,0.743258455
+CCND2,Lamina,1.466175468
+SATB2,Lamina,0.133151642
+SENP5,Lamina,0.978131568
+C1orf198,Lamina,-0.335015332
+HEATR1,Lamina,0.15446282
+PTBP3,Lamina,0.093057768
+FAM206A,Lamina,-0.144784684
+RBM18,Lamina,-0.215493633
+MAPKAP1,Lamina,-0.547147029
+KDSR,Lamina,0.101898307
+ONECUT2,Lamina,2.945961786
+IRF2BPL,Lamina,0.415348784
+AREL1,Lamina,-0.200705754
+ABCD4,Lamina,1.101998731
+RBM25,Lamina,1.318308191
+NRDE2,Lamina,1.186133605
+KLHL29,Lamina,1.574176164
+DNMT3A,Lamina,1.227987343
+ATAD2B,Lamina,0.782746112
+ATL2,Lamina,0.342082511
+YIPF4,Lamina,0.366719214
+AFTPH,Lamina,0.297421258
+BCL11A,Lamina,0.889574573
+SLC17A5,Lamina,-0.069247959
+FAM178A,Lamina,0.742741299
+GPAM,Lamina,-0.07566756
+HELLS,Lamina,1.246272161
+TCTN3,Lamina,-0.618037205
+C10orf76,Lamina,-0.602076841
+HOXB8,Lamina,1.506313554
+HOXB3,Lamina,1.862032792
+PANK3,Lamina,1.86522809
+NUP43,Lamina,0.59278039
+LRP11,Lamina,0.075206098
+MASTL,Lamina,1.019175942
+ELF1,Lamina,-0.335296914
+EGR1,Lamina,0.036569169
+NR2C1,Lamina,1.248613181
+MTERFD3,Lamina,1.809157937
+CLU,Lamina,-2.057276735
+TNFRSF10B,Lamina,-0.748847082
+TARDBP,Lamina,0.523416724
+CRISPLD1,Lamina,0.014688354
+AKAP1,Lamina,0.24827301
+TRIM25,Lamina,0.269213009
+KIAA0922,Lamina,0.073229578
+PAPD5,Lamina,0.507055971
+CEP89,Lamina,0.988331429
+B4GALT4,Lamina,0.357435087
+KIF18A,Lamina,0.644590506
+CRY2,Lamina,1.009422231
+ZNF639,Lamina,0.063436087
+PDS5A,Lamina,-0.188620279
+CLCC1,Lamina,0.05084933
+ACVR2A,Lamina,0.297029927
+RPL21,Lamina,0.359714259
+MTERFD2,Lamina,1.023634917
+KIAA1191,Lamina,-0.358348413
+RBBP6,Lamina,0.568861641
+ZC3H7A,Lamina,0.037487022
+FAM35A,Lamina,0.234460179
+FAM213A,Lamina,-0.034981277
+ODF2L,Lamina,1.507798461
+TRMT13,Lamina,1.42653851
+RPAP2,Lamina,1.135954431
+FAM126A,Lamina,0.289005729
+FKBP9,Lamina,-0.587754493
+POLM,Lamina,1.415962681
+SLC25A51,Lamina,1.19724018
+DCAF10,Lamina,0.859426963
+KIAA1549,Lamina,0.416163457
+CALD1,Lamina,0.662562244
+CHST3,Lamina,0.469778349
+P4HA1,Lamina,-0.633812813
+RBM19,Lamina,0.412542909
+GIPC1,Lamina,-0.647004721
+ATP7B,Lamina,0.136876752
+ZC3H13,Lamina,0.060824518
+NLN,Lamina,0.1126037
+CENPK,Lamina,0.942143212
+OPTN,Lamina,-0.727453806
+SPATS2,Lamina,0.076052272
+LRP1,Lamina,-0.345071684
+HJURP,Lamina,0.837980434
+USP45,Lamina,1.675120503
+SLC36A1,Lamina,0.930007992
+LPGAT1,Lamina,0.291274186
+EXOSC9,Lamina,0.480519019
+PLA2G12A,Lamina,0.844057562
+ADCK4,Lamina,0.069252018
+PFKFB2,Lamina,1.277830328
+AGO2,Lamina,1.302453578
+MXD4,Lamina,0.511036467
+ACSL3,Lamina,-0.384654833
+SLC12A4,Lamina,-0.486705752
+FAM210B,Lamina,-0.350790768
+SDC4,Lamina,-0.320668309
+NCOA3,Lamina,0.829871507
+PIGT,Lamina,-0.605670861
+VAPB,Lamina,0.66850469
+CHD6,Lamina,0.569114391
+SRSF6,Lamina,0.961578769
+RAB22A,Lamina,0.764572327
+STX16,Lamina,1.481476222
+STAMBP,Lamina,-0.342376709
+NAGK,Lamina,0.839233881
+PAIP2B,Lamina,0.901433147
+ATP8A1,Lamina,1.240632175
+BTN2A2,Lamina,0.687929898
+ABCC10,Lamina,0.455380003
+AARS2,Lamina,-0.195066313
+ZNF391,Lamina,1.484776557
+CDKN1A,Lamina,-2.668847284
+SSR1,Lamina,-0.11318953
+NRN1,Lamina,0.34085478
+ATXN1,Lamina,1.683398212
+EEF1E1,Lamina,0.850578186
+LRRFIP1,Lamina,0.843438593
+AHNAK,Lamina,1.504471677
+ABCC4,Lamina,-0.103394711
+EFNB2,Lamina,0.001331245
+ATP5S,Lamina,1.59380367
+FAM193A,Lamina,0.796223981
+GGA3,Lamina,0.272485082
+GTF3C4,Lamina,0.563714575
+PPP1R12C,Lamina,0.457703661
+MBOAT7,Lamina,-0.174683438
+CCDC93,Lamina,1.412704822
+THOC2,Lamina,0.246987817
+MED1,Lamina,0.404034415
+GPR108,Lamina,-0.569824463
+GPCPD1,Lamina,1.092177933
+PANK2,Lamina,0.900630344
+NAPB,Lamina,1.797122295
+TMX4,Lamina,-0.294016025
+RRBP1,Lamina,0.070883594
+ZNF133,Lamina,1.999494324
+MCM8,Lamina,0.871550342
+NCLN,Lamina,0.165878291
+ZNF436,Lamina,0.21222618
+AMOT,Lamina,-0.345104421
+TMEM115,Lamina,-0.497405682
+AGO3,Lamina,2.136511207
+HECTD3,Lamina,-0.454835538
+KLC1,Lamina,1.662876844
+XRCC3,Lamina,1.215969379
+TUBGCP3,Lamina,0.197137236
+PCID2,Lamina,0.847315215
+FRMD8,Lamina,0.041599832
+PCNXL4,Lamina,0.595224251
+ATG14,Lamina,1.293309174
+KTN1,Lamina,-0.135177233
+PLEKHG3,Lamina,0.823077175
+WDR60,Lamina,1.205220703
+AIF1L,Lamina,-0.275898328
+SLC10A3,Lamina,-0.742118292
+CANX,Lamina,-0.592530296
+CPSF3L,Lamina,0.238859874
+TRAF2,Lamina,0.417855895
+HELB,Lamina,1.411910058
+DYRK2,Lamina,1.537420752
+LRRC61,Lamina,-0.10993959
+FGFRL1,Lamina,0.051758525
+EMC1,Lamina,-0.449807797
+HP1BP3,Lamina,1.071383983
+SIN3B,Lamina,0.799440796
+SLC35E1,Lamina,0.121442081
+GFER,Lamina,0.379571539
+PKMYT1,Lamina,0.564604997
+CHTF18,Lamina,0.916305953
+MACF1,Lamina,0.223322823
+RNF6,Lamina,-0.144596306
+AKAP9,Lamina,0.865020624
+HIP1,Lamina,-0.056383553
+POR,Lamina,-0.843655228
+PEX1,Lamina,0.465673918
+LRFN1,Lamina,-0.474574702
+SRD5A3,Lamina,-0.268437555
+PPAT,Lamina,0.310877383
+TUBGCP6,Lamina,1.079951353
+DGCR8,Lamina,1.43240185
+TPST2,Lamina,-0.05867191
+MPST,Lamina,0.823366651
+SPECC1,Lamina,-0.300694883
+NAA38,Lamina,0.780894577
+PRKRIP1,Lamina,0.82266772
+PODXL,Lamina,-0.011215262
+STRIP2,Lamina,0.991582647
+MKLN1,Lamina,1.167654948
+CALU,Lamina,-0.567549908
+CCDC136,Lamina,1.35646089
+SMO,Lamina,0.029880873
+KLHDC10,Lamina,1.054198025
+OSGEPL1,Lamina,0.338310354
+HOXD10,Lamina,3.243284387
+HOXD11,Lamina,1.31391594
+HERC2,Lamina,0.549966383
+TWSG1,Lamina,-0.171465378
+MYO5C,Lamina,0.287118177
+TMOD2,Lamina,1.55854475
+TTBK2,Lamina,1.520233204
+IVD,Lamina,0.365309699
+CLN6,Lamina,0.419250974
+ARPP19,Lamina,0.27447858
+VPS13C,Lamina,0.828648
+SUMF2,Lamina,-0.259644126
+SPCS3,Lamina,-0.123718968
+RPAIN,Lamina,0.962606225
+PLD2,Lamina,0.117335933
+MPDU1,Lamina,-0.805654786
+CCNT1,Lamina,0.802751824
+PUS7L,Lamina,1.951643003
+KRI1,Lamina,0.246523657
+SLC44A2,Lamina,-0.641156478
+BCL2L2,Lamina,1.421021932
+PARP2,Lamina,0.384133495
+TEP1,Lamina,1.016643682
+MAP7D3,Lamina,0.192334445
+ABHD17A,Lamina,1.32803378
+ERMARD,Lamina,1.464346664
+SAT1,Lamina,0.70030152
+GNL3L,Lamina,0.794907091
+SH3BP4,Lamina,-0.22434223
+LDLR,Lamina,-0.288607647
+PRKCSH,Lamina,-0.467605686
+THEM6,Lamina,-0.350642337
+PVRL2,Lamina,-0.735810524
+SAFB2,Lamina,0.154119099
+KIF1A,Lamina,-0.101229629
+COLGALT1,Lamina,-0.25500244
+MLLT1,Lamina,0.325850512
+MLLT4,Lamina,0.706573779
+ACTN4,Lamina,-0.388345762
+NDUFA10,Lamina,0.878102493
+ZSWIM6,Lamina,0.828767968
+PXDN,Lamina,0.129323336
+COL5A1,Lamina,-0.172924091
+ZNF337,Lamina,2.025198847
+TAF4,Lamina,0.140577536
+LAMA5,Lamina,0.347718986
+EXOSC2,Lamina,0.364974907
+POMT1,Lamina,0.880843631
+PRRC2B,Lamina,0.343406928
+YIPF2,Lamina,-0.293574081
+ZC3H4,Lamina,-0.189326091
+CLIP1,Lamina,0.12381292
+HIP1R,Lamina,0.105989416
+PPAN,Lamina,0.770095221
+SLC6A8,Lamina,0.371137778
+PLXNA3,Lamina,0.670800701
+PRRG1,Lamina,0.84136201
+AKAP12,Lamina,0.859440091
+RBM39,Lamina,0.943413773
+GGT7,Lamina,0.490789144
+PPT1,Lamina,-0.753980778
+RLIM,Lamina,1.224170359
+ABCB7,Lamina,-0.139070588
+MRPS25,Lamina,2.011781703
+CAPN7,Lamina,0.182995315
+ZFYVE20,Lamina,0.207894548
+SLC6A6,Lamina,1.628559427
+MGAT1,Lamina,-0.135779894
+PSMC3IP,Lamina,0.21886638
+DIAPH1,Lamina,-0.620347706
+NDFIP1,Lamina,-0.498325594
+ACAP3,Lamina,1.159544028
+C1orf159,Lamina,1.033124262
+MAP1B,Lamina,1.192451006
+IL13RA1,Lamina,-0.488538086
+WDR44,Lamina,-0.316860918
+PRKAB2,Lamina,1.286082621
+CLUHP3,Lamina,2.8646668
+CHSY1,Lamina,-0.05078976
+SNRPA1,Lamina,1.047870896
+FBXW9,Lamina,0.702002789
+RFX1,Lamina,0.202898997
+CC2D1A,Lamina,0.916522216
+NUP210,Lamina,0.060998435
+ENOSF1,Lamina,2.194489688
+EMILIN2,Lamina,-1.267901067
+PRKAA1,Lamina,1.591760274
+PNISR,Lamina,1.973628506
+ZRANB2,Lamina,1.603692303
+KDM6B,Lamina,0.626766369
+GPS2,Lamina,0.305797766
+VPS13B,Lamina,0.817598808
+REEP2,Lamina,-0.267666604
+PRMT7,Lamina,0.672208862
+PCED1A,Lamina,1.02914038
+PTPRA,Lamina,-0.317769421
+KIAA0907,Lamina,2.022091835
+DCAF8,Lamina,1.937652961
+IGHMBP2,Lamina,0.909168938
+LPIN3,Lamina,0.607339342
+SERINC3,Lamina,-0.288143832
+FBXO44,Lamina,0.807494249
+USPL1,Lamina,1.081228092
+XPO4,Lamina,0.774929274
+SCO1,Lamina,1.189914485
+MPRIP,Lamina,0.980963452
+DSTYK,Lamina,1.118225087
+SLC41A1,Lamina,0.381469027
+GPALPP1,Lamina,0.80513505
+IRS4,Lamina,1.024599536
+FAM104A,Lamina,0.22165129
+SLC39A11,Lamina,-0.598018414
+EPHB2,Lamina,-0.332769117
+SRRM1,Lamina,-0.056458866
+SUV420H2,Lamina,0.465999539
+WDR74,Lamina,-0.344163331
+RTN3,Lamina,-0.717365834
+MORC2,Lamina,0.835529754
+LARGE,Lamina,-0.102967233
+ADCK2,Lamina,-0.798574987
+AGAP3,Lamina,0.515556899
+KRBA1,Lamina,0.951303002
+ZNF767,Lamina,2.356524146
+ATP13A3,Lamina,0.248440445
+TMEM254,Lamina,0.176770868
+TMTC1,Lamina,-0.04588681
+KRAS,Lamina,0.355691341
+SWAP70,Lamina,-0.253535733
+ZFC3H1,Lamina,0.891776831
+TEX15,Lamina,2.191676622
+CTIF,Lamina,1.257402269
+VHL,Lamina,0.845902687
+ARL8B,Lamina,-0.263757667
+EDEM1,Lamina,0.918213191
+PRPF38B,Lamina,1.338436121
+SORT1,Lamina,-0.372205526
+PTGFRN,Lamina,-0.442012691
+NOTCH2,Lamina,-0.288864634
+CEPT1,Lamina,0.816579215
+AP4B1,Lamina,0.996435504
+SPIRE1,Lamina,0.818945259
+SLC38A2,Lamina,0.105773581
+KIDINS220,Lamina,0.041796525
+ROCK2,Lamina,0.236692613
+LPIN1,Lamina,0.767005482
+IL6ST,Lamina,-0.064087466
+TMEM241,Lamina,-0.747281083
+LRP4,Lamina,0.235616183
+DDB2,Lamina,-0.388913754
+ACP2,Lamina,-0.76847719
+AGO4,Lamina,1.022386467
+HOOK1,Lamina,0.58750037
+DSC2,Lamina,-0.547306639
+DSC3,Lamina,-0.133436556
+DTNA,Lamina,0.981521585
+FHOD3,Lamina,0.040774245
+FADS2,Lamina,-0.478852516
+CLOCK,Lamina,1.574959005
+COL4A2,Lamina,-0.376315369
+DZIP1,Lamina,0.812030558
+UBAC2,Lamina,-0.460058381
+ARGLU1,Lamina,1.77834926
+BIVM,Lamina,1.101044234
+ARHGAP32,Lamina,0.912455788
+TMED7,Lamina,-0.583047974
+APC,Lamina,0.753495415
+WDR36,Lamina,0.540028823
+NAA35,Lamina,0.06143568
+TMEM2,Lamina,-0.447432907
+GOLM1,Lamina,-0.25326435
+TAOK3,Lamina,0.494149874
+DMTF1,Lamina,1.8044219
+TMEM243,Lamina,0.604903063
+PNPLA8,Lamina,-0.364953439
+MDFIC,Lamina,-0.353131838
+ANKRD6,Lamina,0.864930878
+KIAA1009,Lamina,1.173751265
+SNX14,Lamina,0.204104483
+EPHA7,Lamina,0.399587987
+DNAJC14,Lamina,-0.580272863
+GDF11,Lamina,0.749769347
+TROAP,Lamina,0.629729573
+TSPAN31,Lamina,-0.625573828
+TFCP2,Lamina,-0.3719755
+PAN2,Lamina,0.275685624
+HNRNPA1,Lamina,0.85327661
+ACVR1B,Lamina,0.487312025
+OS9,Lamina,-0.683395628
+MAP7,Lamina,0.013709945
+CD164,Lamina,-0.078257104
+NHSL1,Lamina,0.342632535
+AHI1,Lamina,0.580092495
+SEMA4F,Lamina,-0.463758508
+RAB11FIP5,Lamina,1.242727992
+CCDC142,Lamina,0.886521104
+GNS,Lamina,-0.502417313
+MDM2,Lamina,0.06019236
+KLHL36,Lamina,1.038297356
+DYNC1LI2,Lamina,1.341620282
+EGLN1,Lamina,-0.043662709
+ABCB10,Lamina,0.272037641
+TAF5L,Lamina,0.414647446
+STX6,Lamina,0.062805435
+CEP350,Lamina,0.779065331
+LAMC1,Lamina,-0.474499167
+RC3H1,Lamina,1.411710335
+TTLL4,Lamina,0.062044015
+USP37,Lamina,0.683166576
+ITM2C,Lamina,-0.840151991
+SERPINE2,Lamina,-0.038679641
+TMEM127,Lamina,-0.610376839
+GCC2,Lamina,0.16021847
+C2orf49,Lamina,0.598076769
+EPC2,Lamina,0.174520382
+ARHGEF4,Lamina,0.77054409
+ALDH1L2,Lamina,0.917568021
+CKAP4,Lamina,-0.657315007
+NEK3,Lamina,0.812828076
+RCBTB1,Lamina,0.805935146
+COG3,Lamina,0.84549643
+SCRN1,Lamina,-0.042299452
+CHST12,Lamina,0.763582151
+KDELR2,Lamina,-0.631373106
+NUPL2,Lamina,0.895250513
+DBNL,Lamina,0.226879067
+TTYH3,Lamina,-0.049836549
+IREB2,Lamina,0.082848438
+RSAD1,Lamina,0.519542514
+VEZF1,Lamina,0.306122767
+TEX2,Lamina,-0.340342435
+BRIP1,Lamina,0.635730013
+SKIL,Lamina,1.209890927
+RPS6KC1,Lamina,-0.111229871
+BIN1,Lamina,0.811324189
+HS6ST1,Lamina,-0.276677632
+UGGT1,Lamina,-0.050338633
+DNAJC1,Lamina,-0.889110023
+LRRC8A,Lamina,-0.465910236
+CDK9,Lamina,0.128026407
+TOR1B,Lamina,0.312808416
+SMC2,Lamina,-0.035586721
+TOR1A,Lamina,-0.631368166
+RALGPS1,Lamina,1.840536241
+FAM129B,Lamina,0.14497317
+SLC2A8,Lamina,0.360847457
+SLC31A1,Lamina,-0.12320505
+ZNF189,Lamina,0.914023109
+STX17,Lamina,0.754397964
+TSTD2,Lamina,0.998074764
+LMX1B,Lamina,0.746793611
+RANBP6,Lamina,1.760265251
+TLN1,Lamina,-0.447541956
+ALDH1B1,Lamina,-1.037699864
+CNPY3,Lamina,-0.716486612
+TMEM63B,Lamina,-0.018430425
+TJAP1,Lamina,0.462110084
+SLC22A23,Lamina,0.703716778
+FOXF2,Lamina,0.003205882
+RIPK1,Lamina,-0.107262612
+ATAT1,Lamina,1.448451863
+NRM,Lamina,-0.655705369
+VARS2,Lamina,0.419099713
+FAM8A1,Lamina,0.223743675
+PRKRIR,Lamina,0.119049949
+CREBZF,Lamina,1.735429126
+PRCP,Lamina,-0.847933696
+RNF121,Lamina,-0.390720912
+SULF1,Lamina,-0.279387955
+SORL1,Lamina,-0.051129644
+YAP1,Lamina,-0.165397917
+RDX,Lamina,-0.364522117
+MAP2K5,Lamina,-0.398970468
+MAPKBP1,Lamina,0.740265471
+CASC5,Lamina,0.154497592
+HAUS2,Lamina,0.440453881
+PARP6,Lamina,1.114458652
+TUBGCP4,Lamina,1.047842606
+RMDN3,Lamina,0.297854012
+UACA,Lamina,0.699674068
+SMAD6,Lamina,2.610205602
+ADAM10,Lamina,-0.111867012
+TTLL7,Lamina,0.761044039
+FNBP1L,Lamina,0.820212311
+RABGGTB,Lamina,1.098435391
+ARHGAP29,Lamina,0.345967512
+SLC44A5,Lamina,1.979500763
+DBT,Lamina,0.650088861
+EPT1,Lamina,0.666141613
+ADCY3,Lamina,0.345590116
+PNPT1,Lamina,0.008133253
+THUMPD2,Lamina,0.947678469
+PREPL,Lamina,-0.136636947
+ACTR1A,Lamina,-0.613053853
+TMEM180,Lamina,0.70381641
+ATAD1,Lamina,0.28664724
+KIF20B,Lamina,0.274717214
+TET1,Lamina,0.436177561
+DNA2,Lamina,0.499944506
+BARD1,Lamina,1.031770916
+NAB1,Lamina,1.359403179
+PPIG,Lamina,0.643276483
+FASTKD1,Lamina,0.785357281
+SSFA2,Lamina,0.319541537
+ITGAV,Lamina,0.572490793
+SLC35A5,Lamina,-0.38871131
+SECISBP2L,Lamina,0.459155918
+SPPL2A,Lamina,-0.41301598
+GLCE,Lamina,0.166328421
+PPCDC,Lamina,0.956446024
+PCDH10,Lamina,-0.670007046
+AP1AR,Lamina,0.11340417
+FGF2,Lamina,1.708601212
+KIAA1109,Lamina,0.94071757
+LARP1B,Lamina,0.144395412
+BMP2K,Lamina,1.073508408
+FRAS1,Lamina,0.618142953
+SCARB2,Lamina,-0.208340535
+USO1,Lamina,-0.464400541
+CENPE,Lamina,0.803330343
+GSTCD,Lamina,0.062299602
+LEF1,Lamina,0.07797128
+PPP3CA,Lamina,-0.274706982
+FBN2,Lamina,-0.352041391
+MAPK8IP3,Lamina,1.893449224
+B4GALNT3,Lamina,0.297853345
+AEBP2,Lamina,0.116938733
+ETNK1,Lamina,1.104752596
+CLSTN3,Lamina,-0.832650793
+SCAF11,Lamina,0.579567839
+COL2A1,Lamina,-0.59603058
+LRIG3,Lamina,0.279452022
+TMEM19,Lamina,-0.209403088
+POC1B,Lamina,-0.233248269
+TMTC3,Lamina,0.491219416
+GAS2L3,Lamina,0.94908368
+SLC15A4,Lamina,0.665729854
+TDG,Lamina,0.141523636
+NUPL1,Lamina,0.380618976
+MTMR6,Lamina,-0.3841882
+SLC7A1,Lamina,0.246123315
+BRCA2,Lamina,1.504820986
+CERS5,Lamina,-0.302547113
+ESYT1,Lamina,-0.822156564
+TMBIM6,Lamina,-0.729665841
+ANKRD52,Lamina,0.801104029
+ZNF740,Lamina,0.859835276
+HNRNPA1L2,Lamina,0.941192896
+SBNO1,Lamina,0.27313516
+SETD1B,Lamina,0.442948438
+RBM26,Lamina,1.088358285
+ZIC5,Lamina,0.866377204
+TMX1,Lamina,-0.492079703
+NAA30,Lamina,0.352890977
+DCAF5,Lamina,0.298031925
+RAB15,Lamina,-0.014069036
+NIPA2,Lamina,-0.091526426
+ZSCAN29,Lamina,0.873570452
+BNIP2,Lamina,0.227369771
+MAN2C1,Lamina,1.367694934
+MESDC1,Lamina,-0.442112029
+IGF1R,Lamina,0.69854253
+ARRDC4,Lamina,0.285913121
+PML,Lamina,0.107589915
+LINS,Lamina,1.40052382
+PCSK6,Lamina,0.24511908
+SCAMP2,Lamina,-0.512301889
+POLG,Lamina,0.239524323
+ABHD2,Lamina,0.193531274
+TICRR,Lamina,0.662375154
+MFGE8,Lamina,0.683424661
+FURIN,Lamina,-0.370238821
+IQGAP1,Lamina,-0.532962082
+CRTC3,Lamina,0.860027508
+FTO,Lamina,-0.499211181
+MBTPS1,Lamina,-0.017358294
+RHOT2,Lamina,0.320639911
+PDPK1,Lamina,0.488098632
+TCF25,Lamina,0.746523452
+GALNS,Lamina,0.643758697
+GAS8,Lamina,1.40154016
+MED9,Lamina,0.288905472
+GID4,Lamina,-0.09974684
+KSR1,Lamina,0.031328736
+SGSM2,Lamina,0.88537717
+SSH2,Lamina,0.932992323
+PTRH2,Lamina,0.809623714
+SS18,Lamina,0.572695476
+SLC39A6,Lamina,-0.285243242
+GALNT1,Lamina,-0.409460996
+ESCO1,Lamina,0.088699791
+GREB1L,Lamina,0.408374382
+NPC1,Lamina,-0.176416403
+MINK1,Lamina,0.395103292
+TTYH2,Lamina,1.234456708
+CSNK1D,Lamina,0.28555483
+FOXK2,Lamina,-0.001276174
+TRIM65,Lamina,0.77898866
+RNF157,Lamina,-0.004371714
+CBX4,Lamina,0.308775301
+MBD1,Lamina,0.356857064
+ZCCHC2,Lamina,0.449447781
+LEPREL4,Lamina,-0.218114438
+FAM134C,Lamina,-0.473668837
+ERBB2,Lamina,-0.537164959
+FKBP10,Lamina,0.206997771
+PRDM15,Lamina,1.086315186
+DUS3L,Lamina,-0.144189556
+ATHL1,Lamina,1.21205492
+COL6A1,Lamina,-0.03463828
+IFNAR1,Lamina,-0.058703633
+COL6A2,Lamina,-0.089763115
+TMEM50B,Lamina,0.46465483
+APP,Lamina,-0.578134688
+URB1,Lamina,0.01367911
+CAPN10,Lamina,0.94936821
+ERVK3-1,Lamina,3.014518654
+SLC47A1,Lamina,0.062681412
+RERE,Lamina,0.231075989
+EPHA2,Lamina,-0.134197808
+KIAA0319L,Lamina,0.268889054
+PLK4,Lamina,0.551585606
+GPN2,Lamina,1.250562329
+PIGK,Lamina,-0.65065598
+PTPRF,Lamina,-0.014846499
+SYPL2,Lamina,-0.158607392
+IGSF3,Lamina,-0.329897926
+CELSR2,Lamina,-0.043482327
+ATP1B1,Lamina,-0.581525862
+CREG1,Lamina,-0.234981254
+POU2F1,Lamina,1.380479066
+PPOX,Lamina,0.739014031
+USP21,Lamina,0.55203285
+PIGM,Lamina,1.090695437
+ABL2,Lamina,1.413247694
+XPR1,Lamina,-0.087984215
+TOR1AIP1,Lamina,-0.761478226
+TUFT1,Lamina,0.252062128
+TARS2,Lamina,-0.678566824
+CERS2,Lamina,-0.779700385
+SEMA6C,Lamina,0.814932406
+ATP8B2,Lamina,-0.145772745
+ADAM15,Lamina,-0.116712405
+SLC39A1,Lamina,-0.559187706
+GATAD2B,Lamina,0.497496028
+HCN3,Lamina,1.294018606
+GALNT2,Lamina,-0.357446985
+TTC13,Lamina,0.961137812
+MLK4,Lamina,1.559975583
+CEP170,Lamina,0.476481736
+SDE2,Lamina,-0.602151557
+FBXO28,Lamina,0.668253219
+CDC42BPA,Lamina,0.55975721
+MBOAT2,Lamina,0.813304773
+PSEN2,Lamina,-0.492118092
+LBR,Lamina,-0.160353632
+RHOB,Lamina,-0.291967622
+ASXL2,Lamina,1.218997647
+ETAA1,Lamina,1.072666474
+ZNF514,Lamina,2.019808909
+SFXN5,Lamina,1.181534466
+TEX261,Lamina,0.065313899
+RALB,Lamina,0.050974152
+SLC20A1,Lamina,-0.11576475
+ZC3H8,Lamina,1.289018699
+UBXN4,Lamina,0.20955966
+AMMECR1L,Lamina,0.055548525
+GALNT13,Lamina,-0.047718724
+SCRN3,Lamina,-0.034234992
+KIAA1715,Lamina,-0.015005595
+CDCA7,Lamina,0.645861104
+DLX1,Lamina,0.731147214
+GULP1,Lamina,0.905322875
+FAM171B,Lamina,0.315613733
+CCDC150,Lamina,1.662171658
+SUMF1,Lamina,-1.722513421
+RHBDD1,Lamina,-0.102596295
+FAM134A,Lamina,-0.434448264
+CTDSP1,Lamina,-0.035105157
+EAF1,Lamina,0.396692244
+GOLGA4,Lamina,1.125634604
+IQSEC1,Lamina,0.899136427
+PTPRG,Lamina,-0.284676755
+IL17RD,Lamina,0.575204722
+ARL6IP5,Lamina,-0.900410619
+TMF1,Lamina,0.582056116
+LRIG1,Lamina,0.253818028
+LIMD1,Lamina,0.705924727
+NXPE3,Lamina,-0.04339626
+SRPRB,Lamina,-0.861282083
+TCTA,Lamina,-0.259660033
+VPRBP,Lamina,0.089508017
+SLIT2,Lamina,-0.142525783
+DGKQ,Lamina,0.719109083
+ATP10D,Lamina,-0.405234768
+SCD5,Lamina,-0.831512219
+ENOPH1,Lamina,-0.267348967
+TRMT10A,Lamina,-0.165606645
+KLHL8,Lamina,0.009397295
+USP53,Lamina,0.899908683
+MARCH6,Lamina,0.566070127
+FAM105A,Lamina,-0.167350268
+PIK3R1,Lamina,0.529573038
+LHFPL2,Lamina,0.243367129
+IQGAP2,Lamina,-0.091314643
+PPIP5K2,Lamina,0.443077563
+PAM,Lamina,-0.508316274
+BDP1,Lamina,0.897088541
+SLC30A5,Lamina,-0.127431491
+ATG12,Lamina,1.025018378
+YIPF5,Lamina,-0.369045734
+RNF145,Lamina,0.154923429
+FBXO38,Lamina,0.625083777
+PCYOX1L,Lamina,1.036947262
+TNIP1,Lamina,-0.410471223
+ZNF300,Lamina,1.119184454
+GFOD1,Lamina,2.73343456
+TRIM41,Lamina,0.732556218
+FAM193B,Lamina,1.14349123
+RNF44,Lamina,0.935082983
+MUT,Lamina,-0.3514453
+PHIP,Lamina,1.066396279
+MMS22L,Lamina,1.204665554
+PM20D2,Lamina,0.150779076
+RNF217,Lamina,2.342851536
+AIG1,Lamina,-0.381571669
+TMEM181,Lamina,1.107246986
+SDK1,Lamina,-0.32172525
+RBAK,Lamina,2.068196088
+CREB5,Lamina,2.233485901
+PURB,Lamina,2.639903632
+GBAS,Lamina,-0.000817822
+ZNF92,Lamina,0.91583818
+TMEM168,Lamina,0.526256177
+C7orf43,Lamina,0.759698341
+SLC12A9,Lamina,0.110927058
+GIGYF1,Lamina,1.401169736
+TMEM209,Lamina,-0.603977737
+NOM1,Lamina,1.294306363
+SH3KBP1,Lamina,-0.389735482
+CASK,Lamina,-0.186250974
+SLC16A2,Lamina,-0.380796158
+OGT,Lamina,1.504817817
+ZNF711,Lamina,1.043279992
+DIAPH2,Lamina,0.280260428
+CXorf57,Lamina,0.884538877
+GPC3,Lamina,-1.296461116
+BIN3,Lamina,1.341641198
+SLC25A37,Lamina,2.690270597
+CHMP7,Lamina,-0.25377099
+ERLIN2,Lamina,-0.204383615
+TACC1,Lamina,0.580646062
+WHSC1L1,Lamina,1.072477715
+TERF1,Lamina,-0.202504284
+MTDH,Lamina,-0.338716662
+LRP12,Lamina,-0.24104532
+EBAG9,Lamina,-0.473100078
+UTP23,Lamina,0.072269288
+ZNF7,Lamina,0.914166275
+ARHGAP39,Lamina,0.820254694
+NAPRT1,Lamina,0.242783268
+UHRF2,Lamina,1.850668451
+ZCCHC7,Lamina,0.120968787
+SIGMAR1,Lamina,-0.433127053
+CEP78,Lamina,0.775984616
+HIATL1,Lamina,-0.15978134
+INIP,Lamina,-0.049584693
+UGCG,Lamina,0.070201072
+STOM,Lamina,-0.582651406
+MRRF,Lamina,1.340900528
+NR6A1,Lamina,1.307186814
+SURF4,Lamina,-0.489536738
+MED22,Lamina,1.09146299
+SH3GLB2,Lamina,1.14548323
+FAM73B,Lamina,0.849766802
+GPR107,Lamina,0.302211083
+C9orf142,Lamina,-0.022821493
+INPP5E,Lamina,1.057761721
+DPH7,Lamina,0.513618699
+NOTCH1,Lamina,0.199137746
+NACC2,Lamina,1.273747879
+USP6NL,Lamina,-0.010988949
+FAM171A1,Lamina,-0.020707175
+PARD3,Lamina,-0.505800211
+POLR3A,Lamina,-0.182879137
+FRA10AC1,Lamina,0.737284557
+ADD3,Lamina,0.155666832
+DNAJB12,Lamina,0.228727149
+EIF4EBP2,Lamina,0.092961769
+MKI67,Lamina,1.914863382
+MTG1,Lamina,1.489697825
+PPRC1,Lamina,0.734169427
+ITPRIP,Lamina,-0.076460594
+CNNM2,Lamina,0.659711595
+PDCD11,Lamina,-0.310357634
+LIN7C,Lamina,0.56273545
+DGKZ,Lamina,0.390380264
+TNKS1BP1,Lamina,0.689436013
+SLC43A1,Lamina,0.467564527
+PTPRJ,Lamina,0.131381138
+CELF1,Lamina,1.080751357
+SESN3,Lamina,1.526787956
+ENDOD1,Lamina,-0.364005008
+SERPINH1,Lamina,-1.108369345
+NCAM1,Lamina,0.493200185
+NPAT,Lamina,0.371320024
+ATM,Lamina,1.423699667
+GLB1L2,Lamina,0.159403083
+HYOU1,Lamina,-0.5474597
+DAK,Lamina,0.064686162
+TMEM138,Lamina,0.822934295
+FADS1,Lamina,-0.421840938
+EML3,Lamina,0.737473052
+B3GAT3,Lamina,-0.445548816
+SIDT2,Lamina,0.061430181
+SOGA1,Lamina,0.921096445
+LSM14B,Lamina,0.86674528
+ORAOV1,Lamina,2.099955248
+TAOK2,Lamina,0.728053487
+ITGB1,Lamina,-0.514878378
+ARID5B,Lamina,-0.075937215
+TMCO3,Lamina,-0.151553456
+LATS2,Lamina,-0.393533202
+LPHN3,Lamina,-0.509779733
+PRSS23,Lamina,-0.612858472
+PIP4K2A,Lamina,-0.781151845
+FREM2,Lamina,0.207170599
+CRIM1,Lamina,-0.322194838
+IPMK,Lamina,0.900952978
+PLBD2,Lamina,-0.482470257
+GXYLT1,Lamina,0.333773439
+CSNK1G3,Lamina,0.008175501
+MIPOL1,Lamina,0.552286449
+EXT2,Lamina,-0.76155158
+TMEM18,Lamina,0.840591191
+NEK7,Lamina,0.434423389
+FER,Lamina,1.483316855
+VIPAS39,Lamina,-0.167889822
+ANKRD50,Lamina,0.893303858
+UPF2,Lamina,0.117419706
+EPS8,Lamina,0.388071281
+FAM160B1,Lamina,1.516293836
+ADAM17,Lamina,-0.005917582
+WWC2,Lamina,0.132509876
+BICD1,Lamina,0.935736447
+NBAS,Lamina,-0.562562936
+GUF1,Lamina,0.677651292
+SACS,Lamina,2.057134245
+PABPC3,Lamina,-0.288090249
+DST,Lamina,0.525862449
+TIAL1,Lamina,0.780077162
+TMEM56,Lamina,0.381829147
+FAM168B,Lamina,0.240327368
+AC093838.4,Lamina,1.878362113
+MGAT5,Lamina,0.308537226
+GPATCH11,Lamina,0.315033496
+POU4F1,Lamina,1.234587277
+RNF219,Lamina,0.169893265
+EPG5,Lamina,1.18427051
+C18orf25,Lamina,1.074344657
+PDK1,Lamina,0.644417002
+PDE3B,Lamina,0.459818964
+TGOLN2,Lamina,0.515405572
+UHMK1,Lamina,1.725543896
+TADA1,Lamina,0.230751086
+CWF19L2,Lamina,-0.509870551
+JMY,Lamina,1.4073292
+HOMER1,Lamina,-0.19416907
+USP12,Lamina,0.272201521
+CCDC50,Lamina,0.603064926
+PAN3,Lamina,0.80785217
+TMEM123,Lamina,-0.187873765
+GJA1,Lamina,-0.433972902
+SLC30A6,Lamina,-0.340434285
+SAR1B,Lamina,0.214982368
+GPR180,Lamina,0.531380038
+UTRN,Lamina,0.204910141
+PTPRK,Lamina,-0.361982157
+PLOD2,Lamina,-0.514785327
+GPR125,Lamina,0.198836988
+SREK1IP1,Lamina,1.154529437
+TXNDC11,Lamina,-0.15625039
+BCL2L11,Lamina,1.508020695
+CLGN,Lamina,-0.267655357
+RASSF3,Lamina,-0.638537834
+RANBP2,Lamina,0.279500647
+TMEM87B,Lamina,0.42937312
+RBMS1,Lamina,0.60770008
+LPCAT1,Lamina,-0.247517325
+UBALD1,Lamina,1.745803177
+RMND5A,Lamina,-0.396546885
+ZDHHC7,Lamina,-0.253961506
+TRIP12,Lamina,-0.159265042
+CEBPG,Lamina,0.521144016
+SREK1,Lamina,1.559340716
+CHD1,Lamina,0.515596295
+DGKE,Lamina,2.084826894
+HS2ST1,Lamina,-0.073511587
+MSI2,Lamina,0.546985647
+CACNA2D1,Lamina,0.536200711
+NUS1,Lamina,-0.012246163
+IMPACT,Lamina,0.242156523
+TBCEL,Lamina,1.076437631
+FAM105B,Lamina,0.818828127
+TBRG1,Lamina,1.605455193
+CC2D1B,Lamina,0.085343902
+MIA3,Lamina,0.307586309
+TRIM11,Lamina,1.180391131
+CCSAP,Lamina,0.359842493
+CXADR,Lamina,-0.278872964
+GABPA,Lamina,0.919396449
+ADAMTS1,Lamina,0.128478139
+TSEN2,Lamina,1.482777085
+FLCN,Lamina,0.279064643
+SKA1,Lamina,-0.534145584
+RAB6B,Lamina,1.038599824
+ACSS1,Lamina,0.385763459
+ANKRD40,Lamina,-0.074739073
+VOPP1,Lamina,-0.541217424
+APOOL,Lamina,0.311684492
+CYP2U1,Lamina,-0.134076908
+AGPAT5,Lamina,0.152492141
+MARVELD1,Lamina,0.194955415
+ZFYVE27,Lamina,0.871495766
+SLC25A28,Lamina,1.851423609
+HSPA13,Lamina,-0.370837019
+USP25,Lamina,0.352734932
+RHOC,Lamina,-1.170690569
+SLC16A1,Lamina,-0.719622361
+LARP1,Lamina,-0.236828769
+MIER3,Lamina,0.641806331
+ZKSCAN2,Lamina,0.893845396
+PDIA4,Lamina,-0.722077078
+FAM126B,Lamina,1.869768961
+FZD7,Lamina,-0.016236999
+FMN2,Lamina,0.054678509
+PPARGC1B,Lamina,2.103910485
+SLC26A2,Lamina,1.188162853
+LSM11,Lamina,1.598897607
+PSD3,Lamina,1.291331333
+DCK,Lamina,-0.340120168
+ADAMTS3,Lamina,0.905149861
+DPY19L4,Lamina,0.310728315
+NDUFAF6,Lamina,1.02576966
+N6AMT1,Lamina,1.39116332
+CDK20,Lamina,0.829249192
+PCGF6,Lamina,-0.051802604
+ANKRD9,Lamina,0.914201663
+SFXN2,Lamina,0.551178984
+PTDSS1,Lamina,-0.592946577
+SUPV3L1,Lamina,0.267719845
+TYSND1,Lamina,1.856374535
+CD109,Lamina,0.270676965
+ZDHHC5,Lamina,-0.488621116
+ZFAND3,Lamina,-0.582278932
+NPTN,Lamina,-0.23531057
+KAT6B,Lamina,0.101017854
+SAMD8,Lamina,1.224960808
+BAG4,Lamina,-0.458986756
+ATAD2,Lamina,-0.006570042
+PHKG2,Lamina,0.366089388
+SASS6,Lamina,0.483019717
+ZIC3,Lamina,0.943610099
+EXOG,Lamina,1.297208861
+SMG1,Lamina,1.142505071
+FCHO2,Lamina,0.315025821
+C1orf27,Lamina,0.807133585
+LRP8,Lamina,1.355919307
+PAXIP1,Lamina,0.599936035
+SSBP3,Lamina,-0.126062425
+CLDN12,Lamina,0.511888906
+GATAD1,Lamina,0.754369105
+ST3GAL2,Lamina,-0.139824565
+FUK,Lamina,1.240273405
+KIT,Lamina,0.227415137
+AASDH,Lamina,0.753355953
+DYRK1A,Lamina,-0.234220194
+TSPAN18,Lamina,0.261703479
+SLC35B2,Lamina,-0.813113101
+TMEM164,Lamina,0.299733199
+TAB3,Lamina,1.309030823
+SLC38A10,Lamina,-0.487929267
+ZNF618,Lamina,0.856377457
+C9orf91,Lamina,0.231630809
+UBN2,Lamina,2.520469626
+BRAF,Lamina,1.562468106
+SLC37A3,Lamina,0.160089329
+DPYSL5,Lamina,-0.12331495
+FAM213B,Lamina,0.009704585
+C12orf43,Lamina,-0.594166136
+RER1,Lamina,0.368129131
+UBXN11,Lamina,0.914349229
+RHPN1,Lamina,1.262172226
+CNNM4,Lamina,-0.444124923
+EYA3,Lamina,0.82222248
+MRAS,Lamina,-0.126156155
+COLEC12,Lamina,-0.328705922
+CUL4B,Lamina,-0.212588837
+MITD1,Lamina,0.97490685
+EIF5B,Lamina,-0.282542312
+TSPAN33,Lamina,-0.938859498
+AHCYL2,Lamina,0.039497917
+B4GALT5,Lamina,-0.345318757
+TSR2,Lamina,-0.048596452
+ZC3H18,Lamina,-0.09017251
+TMED4,Lamina,-0.004190247
+PPP1R15B,Lamina,0.423439408
+AGPAT6,Lamina,-0.076175236
+ZSCAN12,Lamina,1.524670083
+ELK4,Lamina,2.681891015
+F11R,Lamina,-0.480905965
+ZNF276,Lamina,1.128806967
+PINK1,Lamina,-0.525786988
+B4GALT3,Lamina,-0.417931409
+FAM160B2,Lamina,0.542663572
+CACHD1,Lamina,0.13841885
+PAXBP1,Lamina,2.185396913
+IFNAR2,Lamina,0.430278447
+SON,Lamina,0.434178846
+SV2A,Lamina,-0.180271461
+HLCS,Lamina,0.220355126
+ADPGK,Lamina,1.123997019
+ALDH4A1,Lamina,0.190043149
+STARD9,Lamina,1.33480153
+UBR1,Lamina,0.102211305
+AMFR,Lamina,-0.50760465
+RSPRY1,Lamina,-0.416062006
+ARHGAP35,Lamina,0.342789796
+CALM3,Lamina,-0.847711308
+IQCC,Lamina,0.204616348
+BSDC1,Lamina,1.171020634
+ATAD3B,Lamina,0.361538678
+VMA21,Lamina,0.207990808
+WDR4,Lamina,0.424515591
+CBS,Lamina,0.982053626
+PDXK,Lamina,0.917875237
+G6PD,Lamina,-0.541168814
+AGPAT3,Lamina,0.651537377
+C21orf2,Lamina,1.410379007
+LRRC3,Lamina,0.682759864
+LSS,Lamina,0.89156582
+VAV2,Lamina,0.254359219
+MCM3AP,Lamina,0.332323764
+C21orf58,Lamina,1.582233232
+PCNT,Lamina,0.486479698
+DIP2A,Lamina,0.876871754
+ZNF714,Lamina,1.097703349
+PKN3,Lamina,0.802857797
+TAOK1,Lamina,1.327900344
+SIK3,Lamina,0.561899248
+PCSK7,Lamina,0.88603993
+CHTOP,Lamina,1.114614983
+ZBTB7B,Lamina,1.029446085
+NLRX1,Lamina,0.392429887
+ANO10,Lamina,-1.006261628
+SLC25A44,Lamina,0.349962493
+NBEAL2,Lamina,0.962417273
+IER2,Lamina,0.987302427
+ZNF394,Lamina,0.29119628
+CPSF4,Lamina,0.717456413
+TONSL,Lamina,0.742902004
+MUM1,Lamina,0.657919916
+RECQL4,Lamina,0.809834418
+LRRC14,Lamina,0.913681525
+PPP1R16A,Lamina,0.409223446
+C5orf45,Lamina,2.102726377
+MFSD12,Lamina,0.313198764
+FDXR,Lamina,-0.804660359
+ALDH16A1,Lamina,0.187885622
+ITGA5,Lamina,-0.518693318
+ZNF385A,Lamina,-0.707948104
+MPP3,Lamina,1.374721746
+EMC10,Lamina,-0.479152526
+FAM171A2,Lamina,-0.025565088
+DBF4B,Lamina,1.301260657
+LARP4,Lamina,0.211259376
+LEMD2,Lamina,0.993103515
+WDR90,Lamina,1.514784313
+C16orf59,Lamina,-0.009298391
+AMDHD2,Lamina,1.496324265
+PAQR4,Lamina,0.008547274
+ADCY9,Lamina,0.039219666
+CLPB,Lamina,-0.589894825
+NEU3,Lamina,1.533686435
+CYB561A3,Lamina,-0.024724825
+TAF6L,Lamina,0.430150275
+LRP5,Lamina,0.081472834
+ZYG11B,Lamina,0.793796349
+PPAP2B,Lamina,-0.209917852
+PRKAA2,Lamina,1.650880603
+KLHL21,Lamina,0.770069039
+GMEB1,Lamina,0.020060497
+SEPN1,Lamina,-0.246669665
+AK4,Lamina,0.430148545
+RAVER2,Lamina,1.005531065
+PDPN,Lamina,-0.700449152
+SDC3,Lamina,0.530553131
+KIAA1522,Lamina,0.448372202
+C1orf86,Lamina,0.756465898
+NFIA,Lamina,0.735209704
+OMA1,Lamina,0.156177423
+MYSM1,Lamina,2.364769696
+FUBP1,Lamina,0.949376273
+DNAJB4,Lamina,0.478298575
+FAM102B,Lamina,0.128789897
+ATXN7L2,Lamina,1.007217948
+ZNF326,Lamina,0.617231751
+EXTL2,Lamina,0.43436535
+SLC30A7,Lamina,0.898991913
+PEA15,Lamina,-0.241509171
+NCSTN,Lamina,-0.398760721
+VANGL2,Lamina,0.285114261
+FLVCR1,Lamina,1.247201717
+RBM15,Lamina,1.440623761
+BPNT1,Lamina,-0.332291307
+BROX,Lamina,-0.052019984
+ACP6,Lamina,1.797657406
+PPP1R21,Lamina,0.553491468
+B3GALNT2,Lamina,0.671851893
+C2orf47,Lamina,-0.652490203
+ARL5A,Lamina,0.12710054
+SGCB,Lamina,-0.476498048
+SMARCAD1,Lamina,0.109237887
+RNF149,Lamina,1.113691777
+FZD5,Lamina,0.443986559
+DCAF16,Lamina,1.341172708
+PAQR3,Lamina,0.454505956
+ANTXR2,Lamina,-1.042548267
+PBXIP1,Lamina,-0.03214118
+PYGO2,Lamina,-0.152846036
+HIPK1,Lamina,0.285217924
+KBTBD8,Lamina,0.552605808
+EOGT,Lamina,0.918541237
+POGLUT1,Lamina,0.86106652
+ATP1A1,Lamina,-0.954524861
+EIF4E3,Lamina,0.148811515
+LRRC58,Lamina,1.133231464
+FSTL1,Lamina,-0.504514596
+KRTCAP2,Lamina,0.391717976
+KIAA1524,Lamina,0.188474657
+TGFBR2,Lamina,-0.173826084
+ANKZF1,Lamina,1.025517084
+STT3B,Lamina,-0.355900594
+PPM1L,Lamina,1.824357026
+RYBP,Lamina,0.350703677
+PPP4R2,Lamina,2.15e-05
+C3orf17,Lamina,0.49512622
+SPICE1,Lamina,0.895324588
+WDFY3,Lamina,0.65841874
+ATXN7,Lamina,1.416047037
+PPM1K,Lamina,1.326079885
+CCNL1,Lamina,1.810098003
+RPP14,Lamina,1.058498247
+ABHD6,Lamina,-0.189049234
+CRELD1,Lamina,-0.432643272
+U2SURP,Lamina,0.697376498
+TTC14,Lamina,1.267096904
+SNRK,Lamina,0.600777226
+SLC4A1AP,Lamina,-0.245020082
+ZDHHC3,Lamina,0.644310389
+FYCO1,Lamina,1.094310552
+YEATS2,Lamina,0.52875846
+SNIP1,Lamina,-0.067062283
+TMEM41A,Lamina,0.229469508
+RPN1,Lamina,-1.007073837
+SFMBT1,Lamina,0.724871321
+PBRM1,Lamina,-0.346903725
+FAM208A,Lamina,0.242397839
+ARHGEF3,Lamina,-0.250973548
+UBXN7,Lamina,1.229344799
+ZNF691,Lamina,1.017044789
+SGMS2,Lamina,0.112243677
+DNAJB14,Lamina,1.471177209
+ZNF589,Lamina,1.778482152
+SHISA5,Lamina,-0.509273007
+INTU,Lamina,1.052680034
+RNF123,Lamina,0.161421858
+MFSD8,Lamina,0.771151724
+C4orf29,Lamina,1.269324725
+RAD54L2,Lamina,0.363100408
+MAP9,Lamina,0.576473035
+CEP44,Lamina,1.258207272
+ABCE1,Lamina,-0.170311563
+TMEM184C,Lamina,-0.122297962
+TMEM161B,Lamina,1.645493965
+ELOVL7,Lamina,0.044594641
+LMBRD2,Lamina,0.808236737
+NIPBL,Lamina,-0.089492095
+SLC25A46,Lamina,0.099075722
+STARD4,Lamina,0.91846062
+PGGT1B,Lamina,1.485782488
+NDUFS4,Lamina,-0.776043758
+ARSK,Lamina,0.359636812
+GPX8,Lamina,-0.115994676
+SERINC5,Lamina,0.156003699
+GFM2,Lamina,-0.055474724
+CCDC127,Lamina,1.170287376
+SEPT8,Lamina,0.212515177
+DCBLD1,Lamina,0.311345185
+PDSS2,Lamina,-0.354626782
+STXBP5,Lamina,1.106616103
+DAGLB,Lamina,-0.162336319
+GALNT10,Lamina,0.451164938
+ZNF12,Lamina,1.179974353
+USP49,Lamina,2.807590105
+ZNF704,Lamina,1.935870658
+LMTK2,Lamina,0.144794142
+CTSB,Lamina,-0.818760531
+ADCY1,Lamina,1.31537995
+EN2,Lamina,0.30887973
+SUN1,Lamina,0.450110556
+OXR1,Lamina,0.17858047
+SLC4A2,Lamina,-0.408087705
+FASTK,Lamina,0.769408158
+TMUB1,Lamina,0.064835712
+C7orf55,Lamina,-0.969584271
+FOXK1,Lamina,1.617753305
+FZD6,Lamina,0.6351968
+KIAA1429,Lamina,-0.208519086
+TMEM67,Lamina,0.766456223
+SNAPC3,Lamina,0.632117672
+KIAA1161,Lamina,0.345055341
+METTL2B,Lamina,0.254946801
+HGSNAT,Lamina,0.648507946
+RASEF,Lamina,0.461304677
+ANKS6,Lamina,1.609243331
+TMEM246,Lamina,-0.20183778
+ZHX1,Lamina,-0.321769877
+KIAA1958,Lamina,2.304911154
+PIGA,Lamina,0.760799417
+WNK2,Lamina,1.028309323
+ATP7A,Lamina,-0.100789373
+PIGO,Lamina,0.031330246
+BRWD3,Lamina,0.174965718
+SLITRK5,Lamina,1.266499384
+DDX26B,Lamina,1.261830146
+MARCH8,Lamina,0.223845751
+GTF2A1,Lamina,0.043802964
+ZCCHC24,Lamina,0.351182417
+REEP3,Lamina,0.182065841
+MICU2,Lamina,-0.023524516
+PCF11,Lamina,1.138715051
+PKNOX2,Lamina,0.640308211
+ZNF22,Lamina,0.068020901
+RPUSD4,Lamina,0.978403101
+ARF6,Lamina,-0.092590648
+TTC8,Lamina,0.222990177
+CDX2,Lamina,-0.402068789
+BEND7,Lamina,1.604854428
+TAF3,Lamina,-0.095389322
+PDZD8,Lamina,0.273673169
+ZNF503,Lamina,-0.484876493
+FAM175B,Lamina,0.083008592
+QSOX2,Lamina,0.980757008
+NSD1,Lamina,0.591890274
+SNAPC4,Lamina,0.776124351
+PMPCA,Lamina,-0.510114731
+SDCCAG3,Lamina,0.654826373
+TSC1,Lamina,0.841622166
+FAM69B,Lamina,0.698827342
+KIAA1462,Lamina,0.217840666
+ZNF219,Lamina,-0.136497968
+METTL3,Lamina,0.734738303
+HSPA12A,Lamina,1.556846458
+TC2N,Lamina,0.695578028
+CPSF2,Lamina,-0.148768758
+ARL5B,Lamina,1.072616746
+TAF1D,Lamina,0.99470086
+HTRA1,Lamina,-0.296577737
+CEP57,Lamina,0.849788073
+JAM3,Lamina,0.13491209
+HIF1AN,Lamina,1.182673348
+ZFYVE19,Lamina,0.761141045
+FBN1,Lamina,0.295788519
+BAG5,Lamina,0.97288685
+GABRB3,Lamina,-0.17927995
+SGPL1,Lamina,-0.352425017
+FRS2,Lamina,0.762667139
+ZNF202,Lamina,0.916052853
+STXBP4,Lamina,0.722775335
+CUL5,Lamina,0.600376368
+WBP1L,Lamina,-0.597551732
+TRIM44,Lamina,1.352190526
+TPP1,Lamina,-0.343831422
+C11orf74,Lamina,-0.289589543
+TUB,Lamina,1.82692123
+RNF169,Lamina,2.329557906
+PRTG,Lamina,1.497227157
+TMEM41B,Lamina,0.261510578
+TMX3,Lamina,0.510616706
+WEE1,Lamina,0.614517383
+ZNF3,Lamina,0.814921455
+RIMKLB,Lamina,1.113084738
+TMED3,Lamina,0.160666932
+NDEL1,Lamina,0.633810189
+BLCAP,Lamina,0.264614271
+CASC4,Lamina,-0.354314319
+AP1G1,Lamina,-0.14561883
+KIF7,Lamina,0.751144104
+PEX11A,Lamina,0.73229856
+ZBTB39,Lamina,1.433619243
+TMEM194A,Lamina,0.755968039
+SMAD3,Lamina,0.79241447
+MAP1A,Lamina,-0.201206588
+MBD6,Lamina,0.664393534
+PDIA3,Lamina,-0.706335639
+ACSF2,Lamina,0.279998396
+COQ4,Lamina,1.518351058
+SLC27A4,Lamina,-0.572341321
+CERCAM,Lamina,0.468548509
+DOLPP1,Lamina,-0.690954825
+GPRC5B,Lamina,-0.227169438
+CRK,Lamina,-0.184713368
+FBXO22,Lamina,1.498317206
+TBC1D2B,Lamina,1.133243347
+CDK12,Lamina,-0.037147053
+ENGASE,Lamina,0.839272919
+TBC1D16,Lamina,0.644213191
+ENTHD2,Lamina,0.440799871
+STIM1,Lamina,-0.34631041
+IRGQ,Lamina,1.147901611
+PPP2R3B,Lamina,0.410710417
+ZNF646,Lamina,1.566024999
+MIDN,Lamina,0.498657274
+MVD,Lamina,-0.149255073
+ANKRD11,Lamina,1.176468956
+SPATA33,Lamina,1.145080137
+ZNF641,Lamina,1.480881627
+DHRS13,Lamina,-0.949421564
+TP53I13,Lamina,-0.535793777
+KMT2D,Lamina,1.281883835
+C19orf55,Lamina,0.704570767
+LENG8,Lamina,2.054756241
+ZNF146,Lamina,0.210388746
+ZNF444,Lamina,1.647813358
+FAM57A,Lamina,-0.221726987
+SLC43A2,Lamina,0.030902622
+SRR,Lamina,-0.509256359
+GHDC,Lamina,-0.546422705
+ITFG3,Lamina,-0.466707218
+ZNF598,Lamina,0.399569714
+E4F1,Lamina,0.610214387
+ABCA3,Lamina,-0.109083338
+SRRM2,Lamina,0.791112276
+LTBP3,Lamina,0.681571762
+SAC3D1,Lamina,-1.110620763
+SF1,Lamina,0.243046673
+PAFAH1B2,Lamina,-0.598501474
+ANKS3,Lamina,1.213548477
+SETD5,Lamina,0.902251764
+HOOK3,Lamina,1.089448552
+RBPJ,Lamina,0.369561745
+TTC39C,Lamina,0.609341343
+KIF5C,Lamina,-0.264433262
+MGAT2,Lamina,-0.360277267
+BMI1,Lamina,-0.011947033
+KCTD6,Lamina,0.321880415
+TAP1,Lamina,-0.910995466
+ING5,Lamina,0.487673228
+ATG4B,Lamina,0.324234567
+SOGA2,Lamina,1.081517589
+SNRNP48,Lamina,0.812745629
+SLC20A2,Lamina,0.164773431
+TMUB2,Lamina,-0.123999837
+STAT3,Lamina,-0.652339623
+ADAM9,Lamina,-0.441066672
+PKIG,Lamina,-0.291253283
+SEMA4C,Lamina,1.027324628
+CNNM3,Lamina,-0.09342364
+TET2,Lamina,0.994179973
+TCTN2,Lamina,0.106366977
+TSPAN5,Lamina,-0.128775088
+ZBTB5,Lamina,1.798452043
+SNTB2,Lamina,1.573311697
+ZNF507,Lamina,1.566341348
+STX18,Lamina,0.891329477
+GFM1,Lamina,-0.37739249
+ANKRD49,Lamina,1.29544816
+MAT2A,Lamina,1.736752302
+ZNF608,Lamina,0.008378009
+LETM1,Lamina,-0.034772692
+TMEM129,Lamina,0.399098589
+FEM1B,Lamina,0.970935941
+HNRNPH1,Lamina,0.827290518
+MECP2,Lamina,2.043410062
+UPF3A,Lamina,1.470711035
+CHST14,Lamina,-0.411275888
+PARM1,Lamina,0.057958241
+CSNK1G1,Lamina,0.979025899
+ZBTB43,Lamina,1.831447904
+GPRIN1,Lamina,-0.260558736
+MRPL1,Lamina,-0.473960485
+SLC33A1,Lamina,0.254390232
+SDC2,Lamina,-0.772705008
+MMGT1,Lamina,-0.390771529
+CLIC4,Lamina,-0.525531406
+CCDC8,Lamina,0.243881202
+INO80E,Lamina,1.194062183
+DFFB,Lamina,0.725663728
+ANTXR1,Lamina,-0.517626755
+CKAP2L,Lamina,-0.205265134
+C15orf40,Lamina,1.645660272
+HIC2,Lamina,1.180202897
+LUZP1,Lamina,1.33510379
+HEXDC,Lamina,0.801412423
+LRRC45,Lamina,0.32420168
+ASPSCR1,Lamina,0.704974972
+TAPT1,Lamina,0.217369673
+CSGALNACT2,Lamina,0.844956041
+PCDH7,Lamina,-0.189029466
+ROBO1,Lamina,-0.194022546
+P2RY1,Lamina,2.250640475
+TPST1,Lamina,-0.309981865
+TOR1AIP2,Lamina,0.276882109
+OTUD3,Lamina,2.211378598
+GUSB,Lamina,0.342766351
+BRD3,Lamina,0.453148937
+MAP3K2,Lamina,1.810344371
+NLGN2,Lamina,0.546658235
+ALCAM,Lamina,0.232827663
+YWHAG,Lamina,-0.369103754
+TMEM192,Lamina,0.232945647
+ZNF778,Lamina,0.367837848
+NIPA1,Lamina,1.178816666
+SIK2,Lamina,0.650595893
+RNF150,Lamina,1.893749769
+ZNF212,Lamina,0.651541115
+FAM161A,Lamina,0.926850822
+CRTAP,Lamina,-0.36363685
+PRDM10,Lamina,1.141871611
+FOS,Lamina,-0.976947625
+TMED10,Lamina,-0.691295339
+SLC30A1,Lamina,0.197696951
+DNAJC18,Lamina,0.349542329
+RALGAPB,Lamina,0.753349125
+LONRF2,Lamina,2.068173551
+ELOVL6,Lamina,0.558609051
+ARL6IP1,Lamina,-0.466494151
+CDH2,Lamina,-0.588831364
+EMB,Lamina,0.374679564
+STAT2,Lamina,0.739961305
+TRABD,Lamina,0.393675004
+POLH,Lamina,0.076963305
+KIF5B,Lamina,-0.049269928
+AKAP13,Lamina,0.769771962
+CHCHD7,Lamina,0.558051238
+GPR27,Lamina,-0.057595367
+KBTBD2,Lamina,0.523286484
+KIAA0232,Lamina,0.876857214
+TMEM43,Lamina,-0.031003155
+RNF139,Lamina,-0.373850769
+PAQR8,Lamina,-0.009828607
+TANC2,Lamina,1.229077521
+DNAJC24,Lamina,1.379295894
+HS6ST2,Lamina,-0.457694462
+INSR,Lamina,-0.126316179
+ATP6V0E2,Lamina,-0.841983779
+ZNF692,Lamina,1.637070486
+NETO2,Lamina,0.24312916
+NPTX1,Lamina,0.701057942
+FAM98B,Lamina,-0.025048192
+GAA,Lamina,0.326819286
+CANT1,Lamina,-0.715154653
+CHST11,Lamina,-0.133324511
+CLCN5,Lamina,0.395644443
+ZBTB26,Lamina,1.828415027
+ZNF562,Lamina,1.646405405
+ZNF318,Lamina,0.198041442
+WIPF2,Lamina,0.242055025
+LRRC8C,Lamina,0.006345624
+LRRC8D,Lamina,-0.012182478
+ETFDH,Lamina,0.467577714
+LPAR3,Lamina,0.395952338
+CLSTN1,Lamina,-0.163014185
+BPTF,Lamina,0.095556618
+ATF7IP,Lamina,0.112634693
+TCEA2,Lamina,0.481824135
+ANO5,Lamina,0.288771651
+MLLT3,Lamina,-0.377159374
+PRNP,Lamina,-0.233997252
+ZNF217,Lamina,0.287292324
+JMJD1C,Lamina,0.656137281
+THOP1,Lamina,0.033308196
+ORMDL3,Lamina,-0.212272474
+KLF11,Lamina,0.208575898
+MTBP,Lamina,1.533711227
+ZNF131,Lamina,0.503090515
+BSG,Lamina,-0.720806273
+CERS6,Lamina,0.059112727
+TP53RK,Lamina,0.01522695
+FAM195A,Lamina,0.795106309
+GTPBP2,Lamina,0.67566679
+ZNF24,Lamina,1.21527637
+MANEA,Lamina,0.166183825
+RAD9A,Lamina,0.25974256
+FAM21C,Lamina,0.462055786
+CORO1B,Lamina,-0.634318961
+LRRC20,Lamina,-0.123530146
+NAA16,Lamina,1.504097772
+DCP2,Lamina,0.392192011
+CES3,Lamina,1.268763402
+CES2,Lamina,0.194725812
+PDP2,Lamina,1.667398003
+SP3,Lamina,-0.23835075
+METAP1D,Lamina,0.954633382
+ZNF621,Lamina,2.770145204
+NADSYN1,Lamina,0.643550164
+DHCR7,Lamina,-0.523436994
+NBEA,Lamina,0.24086085
+ANKRD13D,Lamina,0.966705491
+LCLAT1,Lamina,-0.032848622
+TADA2B,Lamina,0.116797204
+HECTD4,Lamina,0.613734013
+ESRRA,Lamina,0.51104343
+AHSA2,Lamina,2.189357287
+VANGL1,Lamina,0.093182368
+IQCB1,Lamina,1.197735819
+GOLGB1,Lamina,1.406747129
+TNKS,Lamina,0.781107435
+ZBTB21,Lamina,1.444346375
+STOX2,Lamina,2.028640935
+DAG1,Lamina,-0.857504709
+RNF26,Lamina,-0.447127596
+PEAK1,Lamina,1.425126199
+TNFRSF10D,Lamina,-1.351860471
+MOB1B,Lamina,0.960282954
+SNX33,Lamina,1.248292944
+CHD2,Lamina,1.594985853
+CCDC41,Lamina,1.288257303
+PC,Lamina,-0.155088129
+SUSD5,Lamina,-0.506713371
+HEG1,Lamina,0.445708871
+TOMM20,Lamina,-0.1523298
+CNP,Lamina,-1.009518016
+DPY19L1,Lamina,-0.094725144
+ZNF791,Lamina,1.51417258
+PHC3,Lamina,2.327746031
+GOLIM4,Lamina,-0.342636864
+XXYLT1,Lamina,-0.891291576
+UBXN2A,Lamina,0.022545852
+CCS,Lamina,0.547337153
+FAM3C2,Lamina,-0.102214996
+CTSF,Lamina,-0.886977737
+MSRB3,Lamina,0.297495828
+LEMD3,Lamina,-0.153091547
+RGMB,Lamina,0.8418004
+ZDHHC24,Lamina,-0.436108878
+MGA,Lamina,0.689543681
+PIGG,Lamina,1.044907137
+ADCY6,Lamina,0.514539526
+ZBTB4,Lamina,1.079401004
+ZHX3,Lamina,2.282757341
+RALGAPA1,Lamina,0.87901313
+ATP2A2,Lamina,0.098815539
+CNTNAP2,Lamina,0.143434303
+DENND4A,Lamina,0.444680274
+MSL2,Lamina,1.595555421
+UGT8,Lamina,-0.892093858
+ZNF266,Lamina,1.393425064
+SLC29A2,Lamina,0.781246179
+BRSK2,Lamina,0.680754906
+B3GNT1,Lamina,-0.578456586
+TMEM167A,Lamina,0.625838726
+CEP135,Lamina,1.124274818
+FZD4,Lamina,1.779977413
+PDE12,Lamina,-0.298534421
+GLMN,Lamina,0.955508389
+SEZ6L2,Lamina,-0.744821591
+KLC2,Lamina,0.212876943
+GK5,Lamina,1.506207212
+VCPIP1,Lamina,0.867326123
+PCCA,Lamina,-0.245876215
+GOLGA8A,Lamina,1.790082851
+TP53I11,Lamina,0.301255149
+PHYKPL,Lamina,1.91075947
+ARL10,Lamina,1.683607572
+CCDC14,Lamina,1.713316282
+ALG10B,Lamina,1.539780001
+RAB6A,Lamina,-0.326282908
+ERCC4,Lamina,0.676663048
+RMI2,Lamina,0.34100507
+TOM1L2,Lamina,0.626938105
+MLXIP,Lamina,0.73794516
+SLC35E3,Lamina,1.134289506
+ARL4D,Lamina,-1.029070689
+LYSMD3,Lamina,0.921793383
+B3GALT6,Lamina,-0.707247885
+MBLAC2,Lamina,1.188452445
+TPRN,Lamina,0.93852308
+YES1,Lamina,-0.069343819
+TMEM39A,Lamina,0.3008569
+CCDC57,Lamina,1.998228442
+FOXG1,Lamina,0.704736343
+ATAD5,Lamina,0.521463616
+ANAPC2,Lamina,1.117497334
+SPRYD4,Lamina,1.090864995
+CLK2,Lamina,0.96550304
+LPCAT4,Lamina,1.00383628
+B3GNT5,Lamina,0.627323612
+LMNB2,Lamina,-0.31449213
+RMDN1,Lamina,0.671758461
+ACSF3,Lamina,1.039769502
+ANKLE2,Lamina,0.607071889
+NFATC2IP,Lamina,1.17631435
+SMCR8,Lamina,1.599058639
+MTX3,Lamina,1.736445888
+FBXO46,Lamina,0.956317822
+WDR73,Lamina,0.567112062
+ANO6,Lamina,0.165295228
+ZBTB34,Lamina,1.707483602
+FAM210A,Lamina,-0.046548744
+ULK1,Lamina,0.762065159
+RPS6KA3,Lamina,0.322262995
+PUS1,Lamina,0.6731626
+CHD9,Lamina,0.923624184
+PDDC1,Lamina,1.65577553
+TOP3A,Lamina,0.359909072
+NR2C2,Lamina,1.870574164
+ZBTB33,Lamina,1.080866676
+SLC25A22,Lamina,-0.1881044
+PIDD,Lamina,0.759891796
+GBA,Lamina,-1.088734961
+IL17RA,Lamina,0.269727233
+THAP5,Lamina,0.428640418
+PVRL3,Lamina,-0.711542089
+KIAA0195,Lamina,0.093869821
+SOX12,Lamina,0.773641773
+CHID1,Lamina,-0.879037985
+ZNF518A,Lamina,1.111535137
+ZBTB41,Lamina,1.358792793
+DMAP1,Lamina,1.599104896
+C2orf69,Lamina,1.351526032
+SH2B1,Lamina,1.085268934
+KDELC2,Lamina,0.066183894
+GALNT11,Lamina,-0.015116938
+WDR6,Lamina,0.543463067
+GEN1,Lamina,2.52920086
+GLDC,Lamina,-0.669248775
+CTNNBIP1,Lamina,-0.071568521
+ERN1,Lamina,0.341118019
+KCTD12,Lamina,0.171608696
+DHFRL1,Lamina,0.016670191
+FAM132B,Lamina,1.249280443
+FAM219B,Lamina,1.481547841
+DPY19L3,Lamina,1.320189664
+PFAS,Lamina,-0.233884847
+C17orf62,Lamina,0.719993173
+ZBTB7A,Lamina,1.884296427
+SPTY2D1,Lamina,0.096442085
+FUCA1,Lamina,-0.757332775
+CALR,Lamina,-1.368476954
+LDLRAD3,Lamina,0.275041453
+CLK3,Lamina,0.876749864
+PACS2,Lamina,0.979411297
+ELMOD2,Lamina,0.376653495
+FJX1,Lamina,-0.181668267
+ZBTB18,Lamina,1.337094569
+GCC1,Lamina,-0.447506364
+PLD6,Lamina,0.549265953
+CDC42EP4,Lamina,-0.649887434
+PCBP1-AS1,Lamina,1.878389987
+MYADM,Lamina,-0.375166128
+SERTAD2,Lamina,1.2553666
+BBS10,Lamina,1.180946715
+SOCS4,Lamina,2.545230871
+ZADH2,Lamina,0.885541987
+EXOC3,Lamina,0.418790081
+C7orf41,Lamina,0.850523763
+ZNF609,Lamina,0.530926497
+CCDC66,Lamina,1.044447823
+MCFD2,Lamina,-0.056534744
+GAS1,Lamina,-0.661399601
+FAM73A,Lamina,0.632447213
+NRIP1,Lamina,0.929014616
+PCGF5,Lamina,0.527706712
+YOD1,Lamina,1.563908584
+SLC36A4,Lamina,0.935790517
+ZDHHC20,Lamina,0.023821978
+PSMG4,Lamina,1.899856956
+PDIA3P,Lamina,-0.712107961
+CUEDC1,Lamina,0.80151541
+KCTD2,Lamina,-0.370016283
+D2HGDH,Lamina,1.293775236
+FKRP,Lamina,0.624776518
+SLC26A11,Lamina,0.042966217
+F2R,Lamina,0.355702349
+DHTKD1,Lamina,0.038281907
+ZNF746,Lamina,0.050525954
+TMEM136,Lamina,1.160311778
+ZNF322,Lamina,0.280477421
+OGFOD3,Lamina,-0.086946146
+ZNF678,Lamina,1.280052922
+ZBTB2,Lamina,-0.25277847
+SGSH,Lamina,0.565184805
+SETD2,Lamina,0.2102918
+YIPF6,Lamina,0.354081065
+IBA57,Lamina,1.587555947
+C5orf24,Lamina,0.768307364
+ADO,Lamina,0.649160175
+CREB3L2,Lamina,0.122331528
+UNC5C,Lamina,0.991020077
+RGMA,Lamina,0.241020592
+EXT1,Lamina,-0.664116128
+ATP6AP2,Lamina,-0.549591065
+BACE2,Lamina,-0.370710189
+FIGN,Lamina,2.273272359
+B4GALNT4,Lamina,0.475847925
+AP1S2,Lamina,0.96746657
+FBXL6,Lamina,1.112444913
+YBEY,Lamina,0.184257133
+CLN8,Lamina,1.971547983
+PLCXD1,Lamina,1.568007614
+EXOC7,Lamina,-0.051538705
+CEP97,Lamina,1.283459924
+MXRA7,Lamina,-0.853570808
+SATB1,Lamina,0.38617664
+PLCB1,Lamina,0.408633548
+TTC3,Lamina,0.298792045
+COL18A1,Lamina,-0.307420412
+ZNF721,Lamina,1.441289001
+SRPR,Lamina,-0.29241603
+EWSR1,Lamina,1.137851373
+GJC1,Lamina,1.210995525
+MTA1,Lamina,1.176528138
+CADM1,Lamina,-0.465268236
+LYSMD4,Lamina,1.491920755
+NKX2-5,Lamina,-0.232539738
+GPC6,Lamina,-0.311384079
+PTTG1IP,Lamina,-0.349943403
+ZNF623,Lamina,1.572083323
+BCOR,Lamina,0.003133611
+ASB7,Lamina,0.485208552
+EP400,Lamina,0.51589474
+COA5,Lamina,1.812511298
+PRR14L,Lamina,1.104483661
+ZNRF3,Lamina,0.274541947
+TRMT12,Lamina,0.205067302
+FAM101B,Lamina,-0.311412017
+TRIM52,Lamina,2.631958588
+CMTM4,Lamina,1.447430219
+TMEM50A,Lamina,-0.578602058
+CBX6,Lamina,1.378387512
+KREMEN1,Lamina,0.308277049
+TRAIP,Lamina,0.339734251
+EMILIN3,Lamina,-0.001053499
+RBM12B,Lamina,1.604798525
+BTBD9,Lamina,-0.443426463
+KIRREL,Lamina,0.963323251
+IQGAP3,Lamina,0.471285726
+PRKX,Lamina,1.170129219
+SMTN,Lamina,0.094372318
+TBX1,Lamina,0.67742865
+TSPYL2,Lamina,0.613272706
+C22orf46,Lamina,1.671549157
+PCDH9,Lamina,1.257459482
+OAF,Lamina,0.871811448
+ZDHHC23,Lamina,1.512064006
+EFNA5,Lamina,0.204974627
+SS18L1,Lamina,1.23532891
+KNTC1,Lamina,0.686786293
+WDR27,Lamina,2.269600459
+FOXO4,Lamina,-0.033383755
+POU3F2,Lamina,1.108243085
+PROS1,Lamina,0.032145422
+ZFP1,Lamina,-0.208736995
+XPOT,Lamina,0.169484702
+SNN,Lamina,-0.041445166
+AMER1,Lamina,1.533350908
+ZBTB40,Lamina,1.016307585
+ATL3,Lamina,0.043502665
+UBE2G2,Lamina,0.895014777
+TMED9,Lamina,-0.802463495
+RBM33,Lamina,2.258584214
+JAG2,Lamina,0.85931196
+ZFP90,Lamina,1.219397952
+SIVA1,Lamina,0.129645913
+BRI3BP,Lamina,0.093930179
+ROBO2,Lamina,0.029089289
+BRF1,Lamina,0.237147318
+MANEAL,Lamina,-0.593793583
+PURA,Lamina,1.197777665
+DDX51,Lamina,0.83911488
+NOMO2,Lamina,-1.093644025
+NRBP2,Lamina,1.24501724
+ZNF445,Lamina,1.928387961
+PRPF39,Lamina,1.175537263
+CDK10,Lamina,1.539957648
+ATP6V0A2,Lamina,0.78740731
+C14orf80,Lamina,1.208949996
+HGS,Lamina,0.285067173
+MRPL30,Lamina,-0.216000751
+METTL7A,Lamina,-1.282760467
+NR2F2,Lamina,0.135356197
+SP1,Lamina,1.161188998
+PCGF3,Lamina,0.460564143
+P4HB,Lamina,-0.897679347
+PBX1,Lamina,0.06032014
+BRWD1,Lamina,1.665726494
+EP400NL,Lamina,2.031158614
+MYBL1,Lamina,1.521836856
+DMWD,Lamina,0.42150786
+SLC52A2,Lamina,-1.01256106
+NAT8L,Lamina,1.049879321
+GNB1L,Lamina,0.280864981
+LAMP1,Lamina,-0.458507678
+KLHDC8B,Lamina,-0.262734835
+SETD4,Lamina,1.433689147
+RNPC3,Lamina,1.838555204
+BICD2,Lamina,0.404549766
+LRCH3,Lamina,0.898856189
+ZNF529,Lamina,1.723733725
+AIDA,Lamina,-0.438412141
+ZBTB6,Lamina,1.443827962
+BCL9L,Lamina,0.560906153
+KIF18B,Lamina,0.594403444
+MKL2,Lamina,1.086102209
+CA5BP1,Lamina,1.313401179
+BACE1,Lamina,-0.288837821
+KPNA4,Lamina,-0.537984572
+ZNF197,Lamina,0.687677841
+BTN3A2,Lamina,0.324109703
+INSIG1,Lamina,-0.172947593
+TMEM222,Lamina,0.1857639
+SMYD4,Lamina,1.426165777
+GPATCH8,Lamina,0.738293162
+LYRM7,Lamina,0.537052665
+ZNF397,Lamina,2.252214105
+ZSCAN30,Lamina,1.805306307
+TPCN1,Lamina,0.759504615
+POFUT2,Lamina,0.691224209
+ZDHHC17,Lamina,1.513815973
+PPARA,Lamina,1.376555363
+TEAD1,Lamina,1.143125207
+ENTPD5,Lamina,0.839084707
+KIAA1598,Lamina,1.367336097
+TSPYL4,Lamina,1.664328958
+FNBP1,Lamina,-0.026640635
+BCAM,Lamina,0.103441813
+COL4A1,Lamina,0.142026186
+SIRT7,Lamina,1.145753242
+TET3,Lamina,1.23394738
+ZNF286A,Lamina,0.747991519
+SAMD11,Lamina,0.313079021
+B3GALTL,Lamina,-0.299844781
+TCEA1,Lamina,-0.204336912
+FANCA,Lamina,0.468690781
+SEMA4D,Lamina,0.821203414
+LIN28B,Lamina,0.075476129
+FANCM,Lamina,0.744218283
+FAM122A,Lamina,1.173157079
+ARHGAP11B,Lamina,2.748187847
+CYHR1,Lamina,2.139422778
+KLHL17,Lamina,1.036171079
+ANKRD19P,Lamina,1.662010336
+ARL4C,Lamina,0.123164314
+C11orf95,Lamina,0.74131687
+MAPK12,Lamina,0.510534174
+COL4A5,Lamina,0.369723411
+NCR3LG1,Lamina,2.159012136
+HES4,Lamina,1.001346888
+CHM,Lamina,0.56376147
+H2AFX,Lamina,-0.631929672
+SRSF10,Lamina,1.085857455
+NDOR1,Lamina,0.796836003
+FAM72B,Lamina,0.222143039
+LDOC1L,Lamina,-0.178657386
+PTAR1,Lamina,2.877439855
+ZDHHC9,Lamina,-0.477526725
+ZBED6CL,Lamina,1.06900491
+TMEM120B,Lamina,1.430733754
+MTF1,Lamina,1.672498179
+TMEM201,Lamina,-0.121511764
+NHLRC3,Lamina,2.191525893
+BEND4,Lamina,1.431408707
+MSL1,Lamina,0.664046729
+DHFRP1,Lamina,-0.663355919
+ZNF292,Lamina,1.669495761
+ADAT2,Lamina,2.231037771
+H1F0,Lamina,-0.739608689
+LITAF,Lamina,-0.755417534
+ARID2,Lamina,0.184869884
+S100A13,Lamina,-0.472504657
+ZNF33A,Lamina,1.819992202
+LIN54,Lamina,0.11369182
+KAZN,Lamina,0.98455641
+SLC35E2B,Lamina,0.28622704
+KIAA0895L,Lamina,0.997731564
+PLEKHG4,Lamina,0.456740355
+ACADSB,Lamina,0.642949218
+TMEM63A,Lamina,1.219142826
+MPHOSPH8,Lamina,-0.061516717
+FAM217B,Lamina,-0.052662269
+LCOR,Lamina,1.791362803
+POM121,Lamina,-0.03519196
+ZBTB44,Lamina,1.244502505
+SLC35F1,Lamina,-0.090478817
+PTPN1,Lamina,-0.281466796
+EVL,Lamina,1.557164403
+EPHB4,Lamina,-0.161995333
+PPP1R26,Lamina,0.545705042
+TSC22D2,Lamina,0.916047503
+PIK3R4,Lamina,-0.234987117
+GDAP2,Lamina,-0.029783521
+AFAP1,Lamina,0.801348843
+MAN2A2,Lamina,0.646883428
+CACNA1H,Lamina,0.73325821
+SULF2,Lamina,-0.620314702
+PLXNB2,Lamina,-0.380937539
+XRCC2,Lamina,3.215327992
+MYO6,Lamina,0.093336793
+TCF4,Lamina,0.797094527
+RABL6,Lamina,1.155697768
+ZKSCAN5,Lamina,-0.001417816
+ZFP62,Lamina,1.811678754
+ERI2,Lamina,0.117467621
+ZNF33B,Lamina,1.607448766
+ZNF512B,Lamina,0.689151706
+ZNF431,Lamina,2.19130267
+NF1,Lamina,0.189746059
+VKORC1L1,Lamina,0.323682847
+COL27A1,Lamina,1.952325545
+GM2A,Lamina,-0.986131319
+SNHG17,Lamina,1.502671577
+CD47,Lamina,0.40179738
+CTBP1-AS2,Lamina,0.614465967
+C6orf106,Lamina,-0.154067829
+NHLRC2,Lamina,1.845227008
+KPNA5,Lamina,0.447404327
+ZNF252P,Lamina,0.506615273
+PDLIM7,Lamina,0.221988727
+SLC39A10,Lamina,-0.549093799
+ZNF100,Lamina,0.435704423
+ZNF398,Lamina,1.401646833
+GMFB,Lamina,-0.266573373
+ZMYM1,Lamina,0.882596836
+MAFG,Lamina,1.999363371
+ARRDC1,Lamina,0.411238936
+KIAA1671,Lamina,0.74208063
+IGF2R,Lamina,-0.242644196
+SLC25A29,Lamina,1.934229287
+PGAP1,Lamina,1.318910274
+SRC,Lamina,-0.506090786
+PCNXL3,Lamina,0.054271844
+LRRC8B,Lamina,0.238869559
+ABCB8,Lamina,0.523183972
+SND1,Lamina,-0.850999658
+ENTPD4,Lamina,1.061627248
+KANK2,Lamina,-0.796264704
+FITM2,Lamina,0.53632525
+DDI2,Lamina,0.868395351
+TRIM33,Lamina,0.758548745
+LRP10,Lamina,-0.305483067
+ZNF655,Lamina,1.292336944
+SLC22A5,Lamina,1.527938124
+ADARB1,Lamina,1.565214415
+OGDHL,Lamina,1.326820202
+STMN3,Lamina,-0.510194211
+SIPA1L1,Lamina,0.021999323
+PIGN,Lamina,-0.534554207
+COL4A6,Lamina,0.344783792
+GPX1P1,Lamina,-1.158284681
+ENTPD6,Lamina,0.517780031
+ENPP1,Lamina,0.090029806
+PHF2,Lamina,0.171317744
+RPS26,Lamina,0.392228593
+PSAP,Lamina,-0.634036189
+HOXC6,Lamina,1.286950169
+EME2,Lamina,1.688837701
+ZNF780A,Lamina,1.541157026
+SLC9A8,Lamina,1.442193137
+OCLN,Lamina,-0.263127821
+GPAA1,Lamina,-0.313020602
+SPG7,Lamina,1.075298904
+ERO1L,Lamina,-0.22375096
+ZNF121,Lamina,2.610348984
+MPZL1,Lamina,-0.300960606
+VPS13A,Lamina,1.555562567
+AKAP17A,Lamina,1.017464518
+ELOVL2,Lamina,0.480073224
+SNHG12,Lamina,0.031176558
+NOL8,Lamina,0.476880783
+MRPL42,Lamina,0.419961597
+ENTPD7,Lamina,-0.808160552
+ZNF84,Lamina,1.471062367
+SIRPA,Lamina,0.30019523
+CD2AP,Lamina,0.140160132
+NUP62CL,Lamina,0.510642778
+SFI1,Lamina,1.171981504
+ZNF248,Lamina,1.498573999
+ZNF770,Lamina,0.61293277
+HMGN5,Lamina,0.266276862
+MIER1,Lamina,0.480574711
+MAN1A2,Lamina,0.351012576
+RPS6KL1,Lamina,1.677528034
+DDX42,Lamina,0.720937072
+STYX,Lamina,-0.304202799
+UCKL1,Lamina,0.849167886
+ZKSCAN8,Lamina,2.271450436
+HOXC4,Lamina,0.971176633
+ASPH,Lamina,0.085695434
+WWP2,Lamina,-0.687162052
+GFPT1,Lamina,0.061995802
+ITSN2,Lamina,0.015971021
+MGEA5,Lamina,0.81984077
+FAM115A,Lamina,-0.288169372
+ZNF587,Lamina,2.709871331
+MAFK,Lamina,1.317680056
+DDX39B,Lamina,1.985292383
+NUDT16,Lamina,0.927346992
+TLK1,Lamina,-0.069181981
+MDM4,Lamina,2.663602707
+KLHL9,Lamina,1.144231212
+C6orf89,Lamina,0.422190002
+SLC9A6,Lamina,0.136234548
+FAN1,Lamina,1.140412395
+CEP290,Lamina,1.05731731
+C1orf85,Lamina,-0.451388115
+SMOC1,Lamina,-0.327201045
+ZNF652,Lamina,1.80942346
+SMURF1,Lamina,0.816661323
+SLC5A3,Lamina,1.183790682
+COLGALT2,Lamina,0.852792099
+TMEM184B,Lamina,0.496986485
+LRIG2,Lamina,1.399102441
+FOXJ3,Lamina,1.192064981
+ARHGAP11A,Lamina,0.252264948
+DENND4B,Lamina,0.572614778
+R3HDM4,Lamina,-0.120974983
+RUNDC1,Lamina,0.53082496
+TYW1,Lamina,-0.434095315
+SFMBT2,Lamina,1.35891288
+ITPRIPL1,Lamina,-0.420840369
+SMC5,Lamina,0.787183034
+SREBF2,Lamina,-0.179558164
+DZIP3,Lamina,0.387044806
+KIAA0753,Lamina,1.034659863
+ATG9A,Lamina,-0.379339192
+TBKBP1,Lamina,1.663369622
+NAGA,Lamina,-1.033232005
+SGMS1,Lamina,0.341791646
+COX20,Lamina,1.212785394
+CHML,Lamina,1.479070525
+TATDN3,Lamina,0.060483321
+RBM20,Lamina,1.847363119
+PCMTD2,Lamina,1.356633636
+EFCAB7,Lamina,0.717719159
+ZYG11A,Lamina,1.643414569
+SYS1,Lamina,-0.512837967
+TRAF3IP1,Lamina,0.094016955
+PHACTR4,Lamina,0.311798515
+ZDHHC18,Lamina,-0.26110179
+TMEM57,Lamina,-0.407870156
+ZDBF2,Lamina,2.118918981
+BMPR2,Lamina,0.262058341
+OXLD1,Lamina,1.010751862
+MRPL38,Lamina,0.400455594
+SDHD,Lamina,-0.465227658
+NEU1,Lamina,-1.077363752
+HSPA1B,Lamina,-1.047031655
+HLA-C,Lamina,-0.945384416
+DDR1,Lamina,-0.528036119
+HLA-E,Lamina,-0.38149245
+ZNF616,Lamina,0.08190782
+CRHR1-IT1,Lamina,1.435998752
+GABBR1,Lamina,1.713891256
+RANBP17,Lamina,1.110832256
+ZNF204P,Lamina,1.319983176
+TCTN1,Lamina,0.537040835
+ZBTB48,Lamina,1.209563588
+C11orf83,Lamina,-0.769907867
+ZNF783,Lamina,0.849124965
+SLC35B4,Lamina,0.467868974
+TRIQK,Lamina,-0.195305349
+PSENEN,Lamina,-1.290895623
+LGR4,Lamina,-0.072052009
+PDE7A,Lamina,0.719881445
+TMEM170B,Lamina,1.001986696
+TECPR1,Lamina,0.28457686
+ITPRIPL2,Lamina,2.21065697
+CRYZL1,Lamina,0.378779816
+C5orf51,Lamina,0.361442414
+ZNF316,Lamina,0.615124621
+NYNRIN,Lamina,0.360196093
+AP000525.9,Lamina,1.275477442
+PTPLB,Lamina,-0.3404731
+WDR52,Lamina,1.724638349
+SETD5-AS1,Lamina,2.249681618
+SACM1L,Lamina,0.30245884
+C17orf51,Lamina,2.483383715
+ZNF580,Lamina,1.03187531
+DENND1B,Lamina,1.578149032
+SFT2D2,Lamina,0.495939435
+FGFR1OP,Lamina,1.626689265
+KLHL23,Lamina,0.15727285
+TRIM59,Lamina,0.567258585
+NRAS,Lamina,-0.437085477
+QTRT1,Lamina,0.671017588
+CHUK,Lamina,0.548326724
+COG8,Lamina,1.396219405
+RBMXL1,Lamina,0.071369995
+LEPROT,Lamina,1.067576253
+ATF6B,Lamina,-0.18073264
+SLC35F6,Lamina,-0.701430043
+ZNF134,Lamina,0.837687685
+EMP2,Lamina,-0.217490316
+DNASE1,Lamina,1.269219266
+CSNK1E,Lamina,0.432144908
+GALT,Lamina,0.672343639
+ITGA1,Lamina,0.144939113
+AP1G2,Lamina,1.001433907
+CARKD,Lamina,1.173736344
+TTLL3,Lamina,2.507982179
+CPNE1,Lamina,-0.020780335
+RCN1P2,Lamina,-0.905464317
+ZBED1,Lamina,-0.337812361
+DDX12P,Lamina,1.40248848
+C22orf29,Lamina,2.196262709
+NEURL4,Lamina,0.470528463
+TTC3P1,Lamina,0.447090463
+PEX26,Lamina,0.973016751
+GOLGA8B,Lamina,1.650383922
+MIR17HG,Lamina,2.424974793
+TMEM242,Lamina,0.624466472
+TMEM167B,Lamina,0.558159017
+CYB5RL,Lamina,1.25822175
+CROCCP2,Lamina,3.111988578
+FNIP1,Lamina,1.227883583
+RP11-204C16.4,Lamina,-1.041850357
+TENM3,Lamina,-0.436668015
+AC007390.5,Lamina,0.191867283
+NBPF1,Lamina,1.088143966
+CCNL2,Lamina,1.760716478
+PPT2,Lamina,-0.007326128
+CKMT1A,Lamina,0.038236916
+NSUN5P1,Lamina,1.83095078
+RP11-54O7.3,Lamina,2.060449602
+AFG3L1P,Lamina,2.095314806
+EPB41L4A-AS1,Lamina,0.734930451
+ATXN1L,Lamina,2.456098954
+SMIM13,Lamina,0.324421463
+CTD-2228K2.7,Lamina,2.758483677
+PLEKHM1,Lamina,0.752419087
+FAM195B,Lamina,-0.614643487
+IPO7P2,Lamina,0.016580154
+SLC26A6,Lamina,0.412542794
+FGD5-AS1,Lamina,0.785458922
+TLK2P1,Lamina,-0.501567423
+BAIAP2-AS1,Lamina,-0.06876116
+RP11-31F15.1,Lamina,2.74436021
+TMEM185B,Lamina,0.420408043
+C14orf132,Lamina,2.162503668
+PARG,Lamina,-0.003774541
+TP73-AS1,Lamina,1.024467853
+SCAMP4,Lamina,0.053313749
+SEC63P1,Lamina,-0.154712916
+HCG11,Lamina,1.311782949
+DHFR,Lamina,-0.292728403
+RP11-206L10.11,Lamina,0.627221979
+XIST,Lamina,3.184490832
+RP5-1180C10.2,Lamina,0.846925984
+HCG18,Lamina,1.405218072
+CTD-2619J13.14,Lamina,1.390794379
+SNHG15,Lamina,0.845503626
+SNHG7,Lamina,0.609839904
+HOTAIRM1,Lamina,1.389770039
+RP4-775C13.1,Lamina,-0.506847949
+ZNF37BP,Lamina,2.043331689
+ZNF736,Lamina,2.354762109
+MAGI2-AS3,Lamina,1.38519827
+JRK,Lamina,3.250200823
+GAS5,Lamina,0.767193323
+LINC00338,Lamina,1.373161293
+PPP1R3E,Lamina,2.533821273
+RP5-827C21.1,Lamina,-1.217055944
+NUS1P1,Lamina,-0.477893246
+ZBTB22,Lamina,0.128480135
+AD000090.2,Lamina,1.754272585
+ZBED5,Lamina,1.216304821
+RNF103,Lamina,1.272742928
+TMEM189,Lamina,0.206584568
+RPL36A,Lamina,2.080568423
+CD302,Lamina,0.217291984
+SNHG3,Lamina,0.923578679
+C15orf38,Lamina,0.769349972
+AP5Z1,Lamina,0.953118309
+MICAL3,Lamina,0.373814239
+KCTD7,Lamina,1.518994437
+SCARF2,Lamina,0.432421724
+APOBEC3C,Lamina,-0.405935812
+N4BP2L2,Lamina,1.06073589
+NEAT1,Lamina,2.586249077
+CRNDE,Lamina,1.573005221
+OIP5-AS1,Lamina,1.455743272
+MARS2,Lamina,0.440141741
+HAUS5,Lamina,0.447858041
+PDCD6,Lamina,0.628737694
+THAP9-AS1,Lamina,1.815326801
+SHANK3,Lamina,1.172601001
+RTN3P1,Lamina,-0.910539477
+MALAT1,Lamina,2.063992057
+TUG1,Lamina,1.668908264
+GS1-251I9.4,Lamina,0.286799153
+UTP14C,Lamina,0.476626895
+ZNF260,Lamina,0.98050821
+PABPC4L,Lamina,1.799694788
+FPGT,Lamina,0.290266468
+MEX3A,Lamina,1.643592969
+POLR2M,Lamina,-0.158271589
+SNHG1,Lamina,0.497532501
+POLG2,Lamina,1.60853927
+KIAA1147,Lamina,1.319331191
+RP11-349A22.5,Lamina,2.6146345
+LINC00641,Lamina,1.534259432
+RP4-773N10.4,Lamina,2.643489352
+CEP95,Lamina,1.251564473
+LINC00657,Lamina,1.04964291
+RP1-239B22.5,Lamina,2.500710642
+RP6-24A23.6,Lamina,0.545388688
+ERVK13-1,Lamina,2.984664118
+GS1-358P8.4,Lamina,0.970644905
+VPS9D1-AS1,Lamina,0.47593584
+RP6-24A23.7,Lamina,2.726679221
+GAN,Lamina,1.910077854
+SPON1,Lamina,-0.292147212
+RP11-159D12.2,Lamina,1.793449389
+OTUD7B,Lamina,0.321728852
+RNF115,Lamina,0.813467139
+BAHCC1,Lamina,0.733345581
+NBPF15,Lamina,2.084425577
+RP11-242D8.1,Lamina,3.4016264
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+NBPF9,Lamina,1.379694078
+EGLN2,Lamina,1.205736343
+NUDT3,Lamina,1.705287389
+KMT2B,Lamina,0.941916422
+POM121C,Lamina,0.033465365
+RP11-504P24.8,Lamina,2.089126999
+DCP1A,Lamina,0.82885772
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+NOL12,Lamina,1.389118468
+SOCS7,Lamina,1.429133296
+PI4KAP1,Lamina,1.852786961
+ZNF280B,Lamina,1.475394193
+MLLT6,Lamina,0.864407568
+SYNRG,Lamina,-0.116061966
+TADA2A,Lamina,0.239198836
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+AL133325.3,Lamina,3.024265838
+DDX52,Lamina,0.030889412
+MYO19,Lamina,0.498590028
+DHRS11,Lamina,0.995883852
+ACACA,Lamina,0.021794832
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+EBLN3P,Lamina,0.927134917
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+NFYA,Nuclear Pore,-0.327345831
+NIPAL3,Nuclear Pore,-0.087370787
+ENPP4,Nuclear Pore,-0.631704421
+SEMA3F,Nuclear Pore,0.275560444
+CD99,Nuclear Pore,0.098430837
+LASP1,Nuclear Pore,0.759169841
+M6PR,Nuclear Pore,-0.113955472
+CFLAR,Nuclear Pore,-0.134199676
+NDUFAF7,Nuclear Pore,0.121622375
+RBM5,Nuclear Pore,0.357172672
+SLC7A2,Nuclear Pore,-0.683437149
+SARM1,Nuclear Pore,0.701814032
+CAMKK1,Nuclear Pore,0.795417828
+RECQL,Nuclear Pore,-0.924135243
+ARHGAP33,Nuclear Pore,1.624740306
+CDC27,Nuclear Pore,0.08424114
+SPPL2B,Nuclear Pore,1.225629887
+CREBBP,Nuclear Pore,0.199014935
+GCFC2,Nuclear Pore,-0.100251456
+RHBDD2,Nuclear Pore,0.459995936
+IBTK,Nuclear Pore,0.533140218
+ZNF195,Nuclear Pore,0.059979761
+MYCBP2,Nuclear Pore,-0.12594951
+ZFX,Nuclear Pore,0.097931683
+LAMP2,Nuclear Pore,-0.407188704
+GDE1,Nuclear Pore,0.075715082
+TMEM98,Nuclear Pore,0.498032562
+TMEM132A,Nuclear Pore,1.03605955
+ZNF263,Nuclear Pore,0.81731504
+MAP3K9,Nuclear Pore,0.076057946
+JHDM1D,Nuclear Pore,-0.210773832
+PHTF2,Nuclear Pore,-0.116088592
+FARP2,Nuclear Pore,0.122797958
+IFRD1,Nuclear Pore,-0.223469331
+ARHGAP44,Nuclear Pore,-0.156091841
+ELAC2,Nuclear Pore,0.182814287
+ADIPOR2,Nuclear Pore,-0.438220138
+PAFAH1B1,Nuclear Pore,0.089640393
+KIAA0100,Nuclear Pore,-0.068196145
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+LUC7L,Nuclear Pore,0.388215977
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+JARID2,Nuclear Pore,0.469567213
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+CYB561,Nuclear Pore,0.163413871
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+IFFO1,Nuclear Pore,0.612101074
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+MRI1,Nuclear Pore,0.457108074
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+TRIO,Nuclear Pore,0.887164296
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+POLR1A,Nuclear Pore,0.058037549
+IP6K2,Nuclear Pore,0.36134509
+KIF2A,Nuclear Pore,-0.272047716
+TGFBR3,Nuclear Pore,0.06678458
+NEDD4,Nuclear Pore,-0.523786619
+MAPK6,Nuclear Pore,-0.296651463
+UFD1L,Nuclear Pore,-0.034850058
+LRP6,Nuclear Pore,-0.574180991
+NUCB2,Nuclear Pore,-0.658853602
+SLC44A1,Nuclear Pore,-0.206898701
+TMEM260,Nuclear Pore,-0.140064299
+SMG6,Nuclear Pore,0.251429615
+CLTCL1,Nuclear Pore,0.743133795
+DGCR2,Nuclear Pore,0.431050164
+MNT,Nuclear Pore,0.89276002
+ZXDC,Nuclear Pore,0.593940798
+NDST1,Nuclear Pore,-0.072362555
+AP3M2,Nuclear Pore,0.055670472
+RAD18,Nuclear Pore,-0.036958357
+ATP2B1,Nuclear Pore,-0.202970314
+MGAT4A,Nuclear Pore,-0.082480621
+SNX13,Nuclear Pore,-0.321787196
+VASH1,Nuclear Pore,0.005434639
+SEL1L,Nuclear Pore,-0.74771705
+ATP6AP1,Nuclear Pore,0.114051107
+DAZAP1,Nuclear Pore,0.65920313
+CPSF1,Nuclear Pore,0.431861381
+SLC6A15,Nuclear Pore,-0.34197052
+RDH11,Nuclear Pore,-0.343882383
+PRKACA,Nuclear Pore,0.357933731
+LPHN1,Nuclear Pore,0.683184262
+RPS6KA6,Nuclear Pore,0.168703942
+EPN2,Nuclear Pore,0.070574004
+PTPN18,Nuclear Pore,0.845485457
+TFRC,Nuclear Pore,-0.484085528
+AFF4,Nuclear Pore,0.418892425
+MPP5,Nuclear Pore,0.084253282
+HMMR,Nuclear Pore,-0.666991871
+P4HA2,Nuclear Pore,0.083284891
+TRNT1,Nuclear Pore,0.110655825
+ACADVL,Nuclear Pore,0.913231857
+EVC,Nuclear Pore,0.004136021
+DERL2,Nuclear Pore,-0.322130101
+NDE1,Nuclear Pore,0.249285667
+PVR,Nuclear Pore,0.52604473
+SCARB1,Nuclear Pore,0.196108894
+SELO,Nuclear Pore,1.055771851
+LLGL2,Nuclear Pore,1.246885317
+PDE8A,Nuclear Pore,-0.240646634
+SDHA,Nuclear Pore,-0.062268467
+KDM5A,Nuclear Pore,-0.309428188
+ADAM11,Nuclear Pore,0.959248336
+ST6GAL1,Nuclear Pore,-0.830742648
+GLI2,Nuclear Pore,1.080506182
+NOTCH3,Nuclear Pore,1.174381185
+BCS1L,Nuclear Pore,0.245526287
+DPP8,Nuclear Pore,0.16312351
+SLC24A1,Nuclear Pore,-0.737511339
+LMAN1,Nuclear Pore,-0.101321177
+PTPLAD1,Nuclear Pore,-0.614137367
+TUBE1,Nuclear Pore,0.785402066
+SEMA3C,Nuclear Pore,-0.433536555
+TTC38,Nuclear Pore,0.028922882
+CELSR1,Nuclear Pore,-0.445630581
+ZNF638,Nuclear Pore,-0.262506369
+SLC25A40,Nuclear Pore,0.52056426
+RASAL2,Nuclear Pore,-0.373882327
+ZNF37A,Nuclear Pore,-0.258842409
+FNDC3B,Nuclear Pore,0.831690371
+FRYL,Nuclear Pore,0.227297264
+TMEM131,Nuclear Pore,-0.071804804
+WDR62,Nuclear Pore,0.272851756
+BCAP29,Nuclear Pore,0.076232857
+SEC31B,Nuclear Pore,0.966597014
+RBMS2,Nuclear Pore,0.466582228
+SLC46A1,Nuclear Pore,-0.106294048
+PLXNA2,Nuclear Pore,0.38903582
+ANKRD13A,Nuclear Pore,-0.424452697
+PAG1,Nuclear Pore,-0.457463853
+MCAM,Nuclear Pore,0.666701878
+GPC4,Nuclear Pore,-0.266698673
+MBNL3,Nuclear Pore,-1.197588729
+DGKD,Nuclear Pore,0.090186056
+TM9SF3,Nuclear Pore,-0.214427148
+PPP1R12B,Nuclear Pore,-0.385339404
+DNAJC10,Nuclear Pore,-0.497062603
+GTF3C1,Nuclear Pore,0.026921629
+IL4R,Nuclear Pore,0.348226111
+LRCH4,Nuclear Pore,1.355716051
+FAM76B,Nuclear Pore,0.094421115
+SIRT6,Nuclear Pore,0.998867462
+POLD3,Nuclear Pore,0.116072775
+PHF17,Nuclear Pore,-0.156377618
+FBLN1,Nuclear Pore,-0.705177396
+ITGA8,Nuclear Pore,-0.170446129
+MCCC1,Nuclear Pore,-1.466984745
+ACER3,Nuclear Pore,-0.24156015
+N4BP2,Nuclear Pore,0.102244151
+HOXA9,Nuclear Pore,0.485188789
+PCM1,Nuclear Pore,-0.720784619
+TNRC6C,Nuclear Pore,0.814775603
+ITCH,Nuclear Pore,-0.115589471
+SDF4,Nuclear Pore,0.579561842
+FKBP7,Nuclear Pore,-0.067966482
+SLC1A3,Nuclear Pore,-0.485796012
+SAR1A,Nuclear Pore,-0.338109728
+PAFAH1B3,Nuclear Pore,0.82205348
+MOXD1,Nuclear Pore,0.43684318
+STX7,Nuclear Pore,-0.224529891
+RABL2B,Nuclear Pore,0.181431796
+SLC35C2,Nuclear Pore,0.106584492
+CRYBG3,Nuclear Pore,-0.283823486
+RIF1,Nuclear Pore,-0.410347431
+PSEN1,Nuclear Pore,-0.40818434
+RBL1,Nuclear Pore,-0.054051347
+RSBN1,Nuclear Pore,0.228804547
+MAGI3,Nuclear Pore,-0.246752506
+OSTM1,Nuclear Pore,-0.781306977
+EXD2,Nuclear Pore,-1.084411359
+MEF2C,Nuclear Pore,-0.457909987
+UBA5,Nuclear Pore,-0.250676243
+STK17B,Nuclear Pore,-0.404846272
+ZNF510,Nuclear Pore,-2.241082618
+LRP2,Nuclear Pore,0.498009786
+DUSP12,Nuclear Pore,0.170392848
+KIAA0141,Nuclear Pore,0.452214372
+PHLPP1,Nuclear Pore,0.392936319
+SMARCD3,Nuclear Pore,0.794220971
+C5orf22,Nuclear Pore,-0.413518037
+CCNT2,Nuclear Pore,0.307225224
+NFE2L1,Nuclear Pore,0.145236181
+GSK3B,Nuclear Pore,-0.09922387
+ITGB5,Nuclear Pore,0.07554486
+ERC1,Nuclear Pore,0.007723403
+RNF13,Nuclear Pore,-0.890929432
+LYRM2,Nuclear Pore,-0.294290612
+KAT6A,Nuclear Pore,0.000552289
+PLOD1,Nuclear Pore,-0.091055704
+TDRD3,Nuclear Pore,0.948248469
+PDS5B,Nuclear Pore,-0.374241169
+OXCT1,Nuclear Pore,0.017586821
+RRAGB,Nuclear Pore,0.560975263
+FAT1,Nuclear Pore,0.067776415
+YTHDC1,Nuclear Pore,0.018811957
+ZMPSTE24,Nuclear Pore,-0.536189482
+REST,Nuclear Pore,-0.005356187
+APLP2,Nuclear Pore,-0.132456935
+KIAA1467,Nuclear Pore,-0.044332964
+TXLNA,Nuclear Pore,0.454819305
+NCOA1,Nuclear Pore,0.046477588
+AGBL5,Nuclear Pore,0.004827616
+CD59,Nuclear Pore,0.125318341
+ATRX,Nuclear Pore,-0.594712173
+SCAMP1,Nuclear Pore,-0.265797777
+HACE1,Nuclear Pore,0.635170629
+WDFY1,Nuclear Pore,-0.57588595
+MTIF2,Nuclear Pore,-0.187246639
+ATG16L1,Nuclear Pore,0.527911547
+POMGNT1,Nuclear Pore,-0.070197866
+B4GALT1,Nuclear Pore,0.021798938
+IPO11,Nuclear Pore,-0.741055715
+TMED2,Nuclear Pore,-0.128793785
+ERO1LB,Nuclear Pore,0.011974365
+PPP1R15A,Nuclear Pore,0.617568046
+NLK,Nuclear Pore,0.112847583
+PIGS,Nuclear Pore,0.182017089
+ATXN7L3,Nuclear Pore,0.434918351
+PGS1,Nuclear Pore,0.538743424
+SH3BP2,Nuclear Pore,0.721003477
+ADD1,Nuclear Pore,0.26004183
+TXNDC16,Nuclear Pore,-0.824704142
+NID2,Nuclear Pore,0.895786147
+KLHL42,Nuclear Pore,0.112791676
+ERGIC2,Nuclear Pore,-0.495635663
+METTL2A,Nuclear Pore,-0.26196319
+PTPN4,Nuclear Pore,-0.496478855
+KHSRP,Nuclear Pore,0.283865331
+EPB41L1,Nuclear Pore,0.106267948
+ANKRD10,Nuclear Pore,0.753998365
+DOCK3,Nuclear Pore,1.013314757
+PPP1R13B,Nuclear Pore,0.457767527
+ATRN,Nuclear Pore,0.148040973
+ZNF343,Nuclear Pore,0.274847889
+MAVS,Nuclear Pore,0.380452576
+LZTS3,Nuclear Pore,1.328927702
+SNX5,Nuclear Pore,0.132899951
+MAPKAPK5,Nuclear Pore,0.604294988
+ESF1,Nuclear Pore,-0.919940842
+SLC23A2,Nuclear Pore,0.262354636
+KIF16B,Nuclear Pore,0.214073299
+ERP29,Nuclear Pore,0.334389598
+FUS,Nuclear Pore,0.22890242
+ZNF302,Nuclear Pore,0.208740425
+GRAMD1A,Nuclear Pore,1.02127492
+GANAB,Nuclear Pore,-0.040643196
+RBM41,Nuclear Pore,-0.370048658
+GPATCH2L,Nuclear Pore,0.377989539
+SLC9A1,Nuclear Pore,-0.662889222
+SPTLC1,Nuclear Pore,-0.355780752
+PAPOLA,Nuclear Pore,-0.485427297
+MUL1,Nuclear Pore,-0.101681725
+RAB11FIP3,Nuclear Pore,0.427793021
+GOLGA3,Nuclear Pore,0.682979245
+EFNB1,Nuclear Pore,0.697076976
+PDPR,Nuclear Pore,0.243114819
+GLG1,Nuclear Pore,-0.642312508
+TNRC6A,Nuclear Pore,0.085164729
+PLEKHG2,Nuclear Pore,1.117957406
+NAT14,Nuclear Pore,-0.040151665
+RBM27,Nuclear Pore,-0.092224231
+OSBPL8,Nuclear Pore,-0.14756041
+NRCAM,Nuclear Pore,-0.051676647
+LAMB1,Nuclear Pore,-0.054118719
+CMTM6,Nuclear Pore,-0.231092322
+ITGA6,Nuclear Pore,-0.185059576
+SEL1L3,Nuclear Pore,-1.539626561
+ORC6,Nuclear Pore,0.486617811
+TMEM101,Nuclear Pore,0.642471846
+OSGEP,Nuclear Pore,0.685061658
+G2E3,Nuclear Pore,-0.145349141
+HECTD1,Nuclear Pore,-0.078923664
+SEMA6A,Nuclear Pore,0.520764444
+TRPM7,Nuclear Pore,-0.855039463
+TYRO3,Nuclear Pore,-0.222915226
+AGO1,Nuclear Pore,0.271503439
+MFSD11,Nuclear Pore,-0.175226116
+GPATCH2,Nuclear Pore,-1.219885981
+NUP50,Nuclear Pore,0.09144715
+LRRFIP2,Nuclear Pore,0.295216302
+SEC22C,Nuclear Pore,0.070474924
+XYLB,Nuclear Pore,0.145036784
+HDAC6,Nuclear Pore,1.240522222
+CBX5,Nuclear Pore,0.02942204
+SUCO,Nuclear Pore,0.119641253
+HOOK2,Nuclear Pore,0.92708025
+ARCN1,Nuclear Pore,-0.3337645
+TMEM38B,Nuclear Pore,-0.510003854
+BTAF1,Nuclear Pore,-0.245047908
+IKZF5,Nuclear Pore,0.142118312
+WAC,Nuclear Pore,-0.022336175
+CREM,Nuclear Pore,0.037541328
+BRPF3,Nuclear Pore,0.511124079
+EFHC1,Nuclear Pore,1.121703992
+ABL1,Nuclear Pore,0.219400859
+SH3GLB1,Nuclear Pore,-0.766607127
+SCD,Nuclear Pore,-0.305689094
+ABLIM1,Nuclear Pore,-0.221046982
+ERMP1,Nuclear Pore,-0.108200869
+NRP1,Nuclear Pore,-0.496625279
+MZF1,Nuclear Pore,1.554791434
+FBXL19,Nuclear Pore,0.671484167
+MTAP,Nuclear Pore,-0.164944143
+CEP170B,Nuclear Pore,0.943594048
+POLRMT,Nuclear Pore,0.315766172
+ARVCF,Nuclear Pore,0.984220539
+TRMT2A,Nuclear Pore,0.93589034
+ZDHHC8,Nuclear Pore,0.865386529
+KLHL22,Nuclear Pore,0.512223395
+CRKL,Nuclear Pore,-0.026079315
+LZTR1,Nuclear Pore,0.458593536
+CECR2,Nuclear Pore,0.320394157
+DERL3,Nuclear Pore,-0.779714632
+PPM1F,Nuclear Pore,0.235162988
+TOP3B,Nuclear Pore,0.851281228
+CRYBB2P1,Nuclear Pore,0.401391548
+ADRBK2,Nuclear Pore,0.115808033
+GGA1,Nuclear Pore,0.856971359
+HPS4,Nuclear Pore,0.096716217
+TTC28,Nuclear Pore,0.267784943
+SEPT3,Nuclear Pore,0.665763849
+KDELR3,Nuclear Pore,-0.885546507
+DDX17,Nuclear Pore,0.535455639
+TCF20,Nuclear Pore,0.453091451
+TIMP3,Nuclear Pore,-0.658716791
+PPP6R2,Nuclear Pore,0.932996898
+SUN2,Nuclear Pore,0.934118925
+ARSA,Nuclear Pore,1.073649628
+MYH9,Nuclear Pore,0.348045512
+FOXRED2,Nuclear Pore,0.338004895
+TNRC6B,Nuclear Pore,-0.121256301
+SGSM3,Nuclear Pore,0.351641442
+IFT27,Nuclear Pore,0.875058809
+KIAA0930,Nuclear Pore,-0.106129447
+EP300,Nuclear Pore,0.24643131
+ZC3H7B,Nuclear Pore,0.274815506
+ACO2,Nuclear Pore,-0.136738693
+TRMU,Nuclear Pore,0.027570177
+ZBED4,Nuclear Pore,0.174799383
+ABHD4,Nuclear Pore,0.137197025
+KHNYN,Nuclear Pore,0.668004344
+NIN,Nuclear Pore,-0.215070097
+GNPNAT1,Nuclear Pore,-0.378575726
+DDHD1,Nuclear Pore,0.191425085
+CNIH1,Nuclear Pore,-0.475649177
+TMED8,Nuclear Pore,-0.371399151
+SPTLC2,Nuclear Pore,-0.898630019
+PPM1A,Nuclear Pore,-0.767856512
+SIX4,Nuclear Pore,-0.0477053
+GALNT16,Nuclear Pore,0.215669684
+KIAA0247,Nuclear Pore,-0.013911063
+SRSF5,Nuclear Pore,0.182380319
+DICER1,Nuclear Pore,-0.021642932
+ZFYVE21,Nuclear Pore,0.726817393
+TELO2,Nuclear Pore,1.064856645
+PCNX,Nuclear Pore,-0.141571015
+GSKIP,Nuclear Pore,-1.252262523
+SMEK1,Nuclear Pore,-0.433166574
+TRIP11,Nuclear Pore,-1.038360785
+PABPN1,Nuclear Pore,0.923797483
+ARHGAP5,Nuclear Pore,-0.395293238
+CHD8,Nuclear Pore,0.141158152
+PCK2,Nuclear Pore,0.543088344
+PNN,Nuclear Pore,0.149756394
+PLTP,Nuclear Pore,0.496345846
+ABHD12,Nuclear Pore,0.188088059
+GINS1,Nuclear Pore,-0.238780211
+RIMS4,Nuclear Pore,-0.470342559
+PABPC1L,Nuclear Pore,0.907615327
+STK4,Nuclear Pore,-0.276082799
+BMP7,Nuclear Pore,-0.432483594
+DNAJC5,Nuclear Pore,0.379980728
+SLCO4A1,Nuclear Pore,0.84712376
+DIDO1,Nuclear Pore,0.609788095
+ARFGAP1,Nuclear Pore,0.675848857
+ARFRP1,Nuclear Pore,0.894264888
+CDS2,Nuclear Pore,0.036312309
+TM9SF4,Nuclear Pore,0.158779892
+POFUT1,Nuclear Pore,-0.33194062
+SAMHD1,Nuclear Pore,-0.482185592
+KIF3B,Nuclear Pore,-0.153503702
+E2F1,Nuclear Pore,0.578154814
+APMAP,Nuclear Pore,-0.155784621
+ZNF516,Nuclear Pore,0.273432243
+LPIN2,Nuclear Pore,-0.446498474
+SMCHD1,Nuclear Pore,-0.026016986
+LAMA1,Nuclear Pore,0.156583563
+RNF125,Nuclear Pore,-0.280416103
+ANKRD12,Nuclear Pore,-1.097712283
+MIB1,Nuclear Pore,-0.164037186
+MID1,Nuclear Pore,0.410978774
+WDR13,Nuclear Pore,0.76775122
+XIAP,Nuclear Pore,0.053961794
+ATP11C,Nuclear Pore,-0.220282714
+SYP,Nuclear Pore,-0.254936124
+FMR1,Nuclear Pore,0.010661466
+SLC35A2,Nuclear Pore,-0.515353185
+TAZ,Nuclear Pore,0.903381425
+MAGT1,Nuclear Pore,-0.469037782
+CD99L2,Nuclear Pore,-0.054162718
+EEA1,Nuclear Pore,-0.319533998
+NDFIP2,Nuclear Pore,-0.12683896
+DNAJC3,Nuclear Pore,0.072762642
+UGGT2,Nuclear Pore,-0.427171494
+ARHGEF7,Nuclear Pore,0.108649137
+PARP4,Nuclear Pore,0.361788822
+FLT1,Nuclear Pore,0.279217968
+VWA8,Nuclear Pore,-0.415389243
+DGKH,Nuclear Pore,0.044220158
+INTS6,Nuclear Pore,0.070541157
+CLN5,Nuclear Pore,-0.770804241
+MGRN1,Nuclear Pore,0.657002374
+ZNF629,Nuclear Pore,0.37003129
+CENPT,Nuclear Pore,0.52403707
+NFAT5,Nuclear Pore,0.11883054
+SETD6,Nuclear Pore,0.419779128
+SLC38A7,Nuclear Pore,0.343910624
+SLC7A6OS,Nuclear Pore,-0.411230436
+SLC7A6,Nuclear Pore,-0.168320057
+WDR59,Nuclear Pore,0.662505036
+TAF1C,Nuclear Pore,0.797189867
+TSC2,Nuclear Pore,0.573326354
+ZNF500,Nuclear Pore,1.27630849
+ABCC1,Nuclear Pore,-0.093887707
+NOMO3,Nuclear Pore,-0.445716669
+NARFL,Nuclear Pore,-0.025696511
+MTHFSD,Nuclear Pore,0.041815549
+CLCN7,Nuclear Pore,0.759040494
+SLC7A5,Nuclear Pore,-0.04393538
+FBXO31,Nuclear Pore,0.126284379
+EEF2K,Nuclear Pore,0.196989536
+CAPN15,Nuclear Pore,0.558989633
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+BFAR,Nuclear Pore,-0.022485192
+NOMO1,Nuclear Pore,-0.345068198
+CCP110,Nuclear Pore,-0.442574985
+RNF40,Nuclear Pore,0.231318746
+LACTB,Nuclear Pore,-0.40194266
+CD276,Nuclear Pore,0.166710043
+HOMER2,Nuclear Pore,0.336695101
+TMEM87A,Nuclear Pore,-0.911062648
+ZNF106,Nuclear Pore,-0.056796054
+CEP152,Nuclear Pore,0.109398578
+TJP1,Nuclear Pore,0.444642206
+VPS18,Nuclear Pore,-0.106348898
+MYEF2,Nuclear Pore,0.039561154
+CSPP1,Nuclear Pore,0.019702528
+ZFAND1,Nuclear Pore,0.109452531
+FZD3,Nuclear Pore,0.108040394
+EYA1,Nuclear Pore,0.224619972
+NBN,Nuclear Pore,-0.352492406
+IMPAD1,Nuclear Pore,-0.110855481
+UBE2W,Nuclear Pore,0.551211556
+IKBKB,Nuclear Pore,0.709001893
+PLAT,Nuclear Pore,0.690855772
+JPH1,Nuclear Pore,0.227178769
+TRPS1,Nuclear Pore,-0.061512785
+PYCRL,Nuclear Pore,0.8197007
+EEF1D,Nuclear Pore,0.141360792
+SQLE,Nuclear Pore,-0.602997872
+SLC39A14,Nuclear Pore,-0.324490819
+MTMR9,Nuclear Pore,0.36278644
+LEPROTL1,Nuclear Pore,-0.673656987
+PPP2CB,Nuclear Pore,-0.661884799
+KLHDC4,Nuclear Pore,0.691699104
+KCTD9,Nuclear Pore,-1.443419441
+MAN2B1,Nuclear Pore,0.215285872
+NUCB1,Nuclear Pore,0.289325494
+SARS2,Nuclear Pore,0.429632991
+SNRNP70,Nuclear Pore,0.92368226
+CLPTM1,Nuclear Pore,0.471076351
+CLASRP,Nuclear Pore,0.748994709
+FCGRT,Nuclear Pore,0.57863799
+ERCC2,Nuclear Pore,0.616330839
+DOT1L,Nuclear Pore,0.799064787
+SF3A2,Nuclear Pore,0.485338067
+AMH,Nuclear Pore,1.285623523
+DMPK,Nuclear Pore,1.091053526
+TIMM44,Nuclear Pore,0.128803781
+AKAP8,Nuclear Pore,0.189392058
+AKT2,Nuclear Pore,0.454217919
+PLD3,Nuclear Pore,0.312313926
+FSD1,Nuclear Pore,0.990557489
+APLP1,Nuclear Pore,0.474930268
+CACTIN,Nuclear Pore,0.355465224
+TYK2,Nuclear Pore,0.604781026
+PTPRS,Nuclear Pore,0.408156282
+MEGF8,Nuclear Pore,0.447186485
+KDELR1,Nuclear Pore,0.157520377
+CYTH2,Nuclear Pore,1.282920948
+LIG1,Nuclear Pore,0.335863155
+BCAT2,Nuclear Pore,0.751178389
+TNPO2,Nuclear Pore,0.235934308
+DNASE2,Nuclear Pore,0.650741339
+ISYNA1,Nuclear Pore,0.604649228
+CRTC1,Nuclear Pore,0.360036341
+SUGP1,Nuclear Pore,0.71852894
+SIPA1L3,Nuclear Pore,0.503236126
+CADM4,Nuclear Pore,0.854451623
+SMG9,Nuclear Pore,0.427534838
+AVL9,Nuclear Pore,-0.043140154
+CDK6,Nuclear Pore,-0.402529271
+DNAJC2,Nuclear Pore,-0.296496498
+WDR91,Nuclear Pore,0.524345742
+CBLL1,Nuclear Pore,-0.707739579
+MTPN,Nuclear Pore,-0.456876388
+ZC3HAV1,Nuclear Pore,-0.571956844
+OGDH,Nuclear Pore,0.068848028
+MET,Nuclear Pore,-0.521727695
+LMBR1,Nuclear Pore,-0.4939003
+HOXA3,Nuclear Pore,1.195302304
+HOXA6,Nuclear Pore,1.47649646
+BRAT1,Nuclear Pore,0.591747195
+FKBP14,Nuclear Pore,-1.011071054
+NSUN5P2,Nuclear Pore,1.05547859
+CASP2,Nuclear Pore,0.111045859
+HSPB1,Nuclear Pore,1.069581575
+ZKSCAN1,Nuclear Pore,-0.258824734
+WASL,Nuclear Pore,-0.734227498
+RBM28,Nuclear Pore,0.11527769
+C1GALT1,Nuclear Pore,-0.944512529
+PLOD3,Nuclear Pore,0.261435936
+CLDN15,Nuclear Pore,1.089206758
+TMEM106B,Nuclear Pore,-0.247249023
+CEP41,Nuclear Pore,-0.705903495
+GLI3,Nuclear Pore,0.818594053
+TMEM248,Nuclear Pore,0.273237602
+TBL2,Nuclear Pore,-0.031981954
+FKTN,Nuclear Pore,-0.100058102
+TMEM245,Nuclear Pore,-0.490369608
+MEGF9,Nuclear Pore,-0.54457372
+TGFBR1,Nuclear Pore,-0.275639102
+DNM1,Nuclear Pore,1.065830337
+KANK1,Nuclear Pore,0.315173334
+RAPGEF1,Nuclear Pore,0.225750858
+NPDC1,Nuclear Pore,1.483713139
+SETX,Nuclear Pore,-0.049514162
+CCNJ,Nuclear Pore,-0.563377016
+RAB11FIP2,Nuclear Pore,-0.206915091
+ERLIN1,Nuclear Pore,-0.629949306
+MAPK8,Nuclear Pore,0.190077739
+ATE1,Nuclear Pore,-0.135781237
+PLEKHA1,Nuclear Pore,-0.040524825
+UNC5B,Nuclear Pore,0.551782723
+BMPR1A,Nuclear Pore,0.063863041
+ACTA2,Nuclear Pore,0.33079752
+LIPA,Nuclear Pore,-0.644710899
+LZTS2,Nuclear Pore,1.434297088
+ARHGAP21,Nuclear Pore,-0.299683452
+ANKRD26,Nuclear Pore,-0.447021929
+LARP4B,Nuclear Pore,0.418754384
+C10orf137,Nuclear Pore,0.435346302
+MTPAP,Nuclear Pore,0.211256337
+SH3PXD2A,Nuclear Pore,0.152244965
+PITRM1,Nuclear Pore,-0.433033516
+FAM208B,Nuclear Pore,-0.192512709
+TSPAN14,Nuclear Pore,-0.205114546
+NUFIP2,Nuclear Pore,0.250094617
+DHX40,Nuclear Pore,-0.178053655
+CDK5RAP3,Nuclear Pore,0.317522566
+RECQL5,Nuclear Pore,0.785556402
+INTS2,Nuclear Pore,-0.3884808
+CAMTA2,Nuclear Pore,0.281067447
+MED13,Nuclear Pore,-0.222229817
+HOXB6,Nuclear Pore,0.782230073
+CPD,Nuclear Pore,-0.332837757
+GOSR1,Nuclear Pore,-0.381609757
+CCDC47,Nuclear Pore,-0.164028091
+AKAP10,Nuclear Pore,0.074952699
+CYTH1,Nuclear Pore,0.124009209
+LGALS3BP,Nuclear Pore,0.46605079
+EZH1,Nuclear Pore,0.802619209
+PPP1R9B,Nuclear Pore,0.483062375
+LUC7L3,Nuclear Pore,0.469170179
+DUSP3,Nuclear Pore,0.204852083
+EFNB3,Nuclear Pore,0.646771606
+DPH1,Nuclear Pore,0.826891387
+NAT9,Nuclear Pore,0.696281905
+TMEM104,Nuclear Pore,0.73636096
+TMEM97,Nuclear Pore,-0.69691539
+UNC119,Nuclear Pore,0.972705489
+TMEM33,Nuclear Pore,-0.357407045
+DCUN1D4,Nuclear Pore,-0.532543505
+MANBA,Nuclear Pore,0.162651597
+ELF2,Nuclear Pore,-0.013650751
+WFS1,Nuclear Pore,0.705150535
+FRG1,Nuclear Pore,-0.342468378
+CLCN3,Nuclear Pore,-0.109350666
+GALNT7,Nuclear Pore,0.009621398
+TRIM2,Nuclear Pore,-0.070833788
+NEIL3,Nuclear Pore,-0.157431605
+SH3D19,Nuclear Pore,0.132612346
+STIM2,Nuclear Pore,-0.101518519
+RAPGEF2,Nuclear Pore,0.108378174
+UGDH,Nuclear Pore,-0.586108124
+CCDC34,Nuclear Pore,0.116632228
+FNBP4,Nuclear Pore,0.655417439
+SC5D,Nuclear Pore,-0.392450829
+SIAE,Nuclear Pore,-0.978711545
+EHD1,Nuclear Pore,0.10202279
+FOXRED1,Nuclear Pore,-0.363244985
+ST3GAL4,Nuclear Pore,-0.070875641
+CPT1A,Nuclear Pore,0.38747471
+TMEM109,Nuclear Pore,0.246990074
+PANX1,Nuclear Pore,0.027066206
+UBE4A,Nuclear Pore,-0.273975191
+DDX6,Nuclear Pore,-0.037668466
+PVRL1,Nuclear Pore,0.467960492
+HIPK3,Nuclear Pore,-0.306206586
+MDK,Nuclear Pore,0.898139429
+AMBRA1,Nuclear Pore,-0.020737978
+NAA40,Nuclear Pore,0.476059989
+SLC35F2,Nuclear Pore,-0.249616295
+LEPREL2,Nuclear Pore,0.872490437
+CORO1C,Nuclear Pore,-0.051534336
+ASIC1,Nuclear Pore,0.184462296
+CAPRIN2,Nuclear Pore,1.268314423
+SLC11A2,Nuclear Pore,-0.62613627
+MLEC,Nuclear Pore,-0.027609368
+BCL7A,Nuclear Pore,0.098845569
+RSRC2,Nuclear Pore,0.168497607
+PPM1H,Nuclear Pore,-0.118403482
+ELK3,Nuclear Pore,0.188176378
+MAGOHB,Nuclear Pore,-0.608971486
+ITFG2,Nuclear Pore,0.943467363
+PARP11,Nuclear Pore,0.007820318
+DUSP16,Nuclear Pore,0.401134435
+ACAD10,Nuclear Pore,0.490151687
+NAA25,Nuclear Pore,-0.148931105
+DDX55,Nuclear Pore,-0.076356878
+SLC38A1,Nuclear Pore,-0.249803593
+C12orf49,Nuclear Pore,-0.211942054
+MDM1,Nuclear Pore,-0.165700009
+CPSF6,Nuclear Pore,0.111314894
+GNPTAB,Nuclear Pore,-0.141734291
+ATN1,Nuclear Pore,0.973539465
+C12orf57,Nuclear Pore,0.270213324
+LPCAT3,Nuclear Pore,0.626427145
+SUDS3,Nuclear Pore,-0.020403059
+GOLT1B,Nuclear Pore,-0.430396101
+C2CD5,Nuclear Pore,0.052009689
+RAB35,Nuclear Pore,0.062235123
+RIC8B,Nuclear Pore,0.374065913
+RP11-22B23.1,Nuclear Pore,0.809309383
+DSE,Nuclear Pore,-0.131042883
+MAN1A1,Nuclear Pore,-0.059624579
+SERINC1,Nuclear Pore,-0.456446555
+UST,Nuclear Pore,-0.933279785
+KCTD20,Nuclear Pore,-0.702988577
+RNF8,Nuclear Pore,0.252409335
+ICK,Nuclear Pore,1.031537424
+RAB23,Nuclear Pore,-0.948148428
+FBXL4,Nuclear Pore,0.079690313
+CCNC,Nuclear Pore,-0.338820433
+ALDH5A1,Nuclear Pore,-0.019804311
+EYA4,Nuclear Pore,0.357835923
+PERP,Nuclear Pore,-0.860386455
+SLC16A10,Nuclear Pore,-0.33072804
+PHACTR2,Nuclear Pore,0.302027143
+SLC39A7,Nuclear Pore,0.077071632
+PPP2R5D,Nuclear Pore,0.212742959
+PTK7,Nuclear Pore,0.594908831
+CUL9,Nuclear Pore,1.40678985
+TMEM30A,Nuclear Pore,0.149498444
+SENP6,Nuclear Pore,-0.198689348
+VEGFA,Nuclear Pore,0.458872291
+PRPF4B,Nuclear Pore,-0.205259595
+BTN2A1,Nuclear Pore,0.199522673
+LAMA4,Nuclear Pore,-0.361026268
+ERBB2IP,Nuclear Pore,-0.079001777
+HARS2,Nuclear Pore,-1.318190579
+MAN2A1,Nuclear Pore,0.245057186
+PAPD7,Nuclear Pore,0.209106454
+NNT,Nuclear Pore,-0.035508006
+APBB3,Nuclear Pore,0.545923911
+SPARC,Nuclear Pore,-0.285157535
+HMGCR,Nuclear Pore,-0.432246041
+FAF2,Nuclear Pore,-0.347641599
+CLK4,Nuclear Pore,0.575910503
+ARSB,Nuclear Pore,0.144637339
+CNOT6,Nuclear Pore,-0.002203049
+DROSHA,Nuclear Pore,-0.039570664
+FAM172A,Nuclear Pore,-0.688637625
+LNPEP,Nuclear Pore,-0.000116916
+SLC12A7,Nuclear Pore,0.924659787
+NR3C1,Nuclear Pore,-0.407788952
+C5orf15,Nuclear Pore,-0.072843979
+LIFR,Nuclear Pore,-0.079424311
+TRAPPC13,Nuclear Pore,-0.376591933
+TXNDC15,Nuclear Pore,0.187166382
+H2AFY,Nuclear Pore,-0.000721856
+TCERG1,Nuclear Pore,0.016870621
+SMAD5,Nuclear Pore,-0.154518599
+ERGIC1,Nuclear Pore,-0.481935143
+STC2,Nuclear Pore,0.634305571
+ARL6,Nuclear Pore,-0.241422976
+NIT2,Nuclear Pore,-0.146954085
+UBE3A,Nuclear Pore,-0.597760181
+SLC25A36,Nuclear Pore,0.52613671
+TFDP2,Nuclear Pore,0.154648429
+XRN1,Nuclear Pore,0.332387903
+WNT5A,Nuclear Pore,-0.380275987
+PFKFB4,Nuclear Pore,0.39036358
+PRKAR2A,Nuclear Pore,-0.140600447
+ACAP2,Nuclear Pore,0.707085045
+CBLB,Nuclear Pore,-0.758561291
+BBX,Nuclear Pore,0.265886013
+GNB4,Nuclear Pore,-0.187496767
+C3orf52,Nuclear Pore,-0.519159983
+PLXNA1,Nuclear Pore,0.732340717
+CSPG5,Nuclear Pore,-0.454074784
+SCAP,Nuclear Pore,0.197176779
+HEMK1,Nuclear Pore,1.026780217
+ACVR2B,Nuclear Pore,-0.069402199
+ABCC5,Nuclear Pore,0.530334752
+SSR3,Nuclear Pore,-0.695934807
+NKTR,Nuclear Pore,0.893687062
+FOXP1,Nuclear Pore,0.284096127
+INO80D,Nuclear Pore,0.763248049
+ADAM23,Nuclear Pore,-0.671250391
+MOB1A,Nuclear Pore,-0.298277678
+LMAN2L,Nuclear Pore,0.050306561
+RTKN,Nuclear Pore,0.366112857
+PIKFYVE,Nuclear Pore,-0.421332693
+FAHD2A,Nuclear Pore,0.065395356
+SLC35F5,Nuclear Pore,-0.296729035
+STEAP3,Nuclear Pore,-0.043753952
+EPB41L5,Nuclear Pore,0.130775795
+GPD2,Nuclear Pore,-0.431162014
+ACVR1,Nuclear Pore,0.281438073
+MPV17,Nuclear Pore,-0.004023738
+TTC31,Nuclear Pore,-0.63911701
+NDUFS7,Nuclear Pore,0.385021209
+SPTBN1,Nuclear Pore,0.13404817
+CCDC88A,Nuclear Pore,-0.284904671
+FN1,Nuclear Pore,0.048723977
+ELMOD3,Nuclear Pore,1.484826491
+IGFBP5,Nuclear Pore,0.28819452
+USP34,Nuclear Pore,-0.674267208
+GGCX,Nuclear Pore,0.550476498
+CHST10,Nuclear Pore,0.08544797
+MOB4,Nuclear Pore,-1.948043357
+UXS1,Nuclear Pore,0.045933796
+PASK,Nuclear Pore,0.289157188
+TAF1B,Nuclear Pore,-0.405526246
+DCAF17,Nuclear Pore,-0.612677681
+SDC1,Nuclear Pore,0.918542359
+SLC1A4,Nuclear Pore,-0.733233799
+SOS1,Nuclear Pore,-0.0637606
+WIPF1,Nuclear Pore,0.433264582
+THADA,Nuclear Pore,-0.103703006
+TRAK2,Nuclear Pore,0.083026094
+TIA1,Nuclear Pore,0.215655454
+PCYOX1,Nuclear Pore,-0.489879123
+ARID3A,Nuclear Pore,0.947483041
+EPHA4,Nuclear Pore,-0.763097061
+ALMS1,Nuclear Pore,-0.421294526
+BCL9,Nuclear Pore,0.318539587
+DHCR24,Nuclear Pore,-0.090975489
+DNAJC16,Nuclear Pore,-0.34739524
+RALGPS2,Nuclear Pore,-0.775967676
+CEP104,Nuclear Pore,0.536060366
+FAM20B,Nuclear Pore,-0.201406859
+TCEANC2,Nuclear Pore,0.1394358
+WRAP73,Nuclear Pore,0.511460935
+ICMT,Nuclear Pore,-0.408985494
+QSOX1,Nuclear Pore,0.17610679
+AMPD2,Nuclear Pore,0.162212939
+EDEM3,Nuclear Pore,-0.044629278
+RAP1A,Nuclear Pore,-0.733123602
+S100PBP,Nuclear Pore,-0.29716598
+ASH1L,Nuclear Pore,0.106174654
+SFPQ,Nuclear Pore,-0.000222037
+MEF2D,Nuclear Pore,0.465993523
+C1orf21,Nuclear Pore,-0.084828567
+LEPR,Nuclear Pore,-0.775534778
+IVNS1ABP,Nuclear Pore,0.013419385
+KIAA2013,Nuclear Pore,-0.036035834
+MIIP,Nuclear Pore,0.946129084
+SLC35D1,Nuclear Pore,0.442555369
+WLS,Nuclear Pore,-0.184671609
+PRDM2,Nuclear Pore,0.288685003
+TROVE2,Nuclear Pore,0.075853813
+SRSF11,Nuclear Pore,0.540874569
+PHTF1,Nuclear Pore,0.286282836
+TMEM9,Nuclear Pore,0.480048129
+EXOC8,Nuclear Pore,0.06030365
+NID1,Nuclear Pore,-0.202310445
+MTR,Nuclear Pore,-0.170800269
+BMP8B,Nuclear Pore,0.494568779
+RIMS3,Nuclear Pore,0.692451025
+AKT3,Nuclear Pore,-0.276274028
+ETV3,Nuclear Pore,-0.589422493
+LPHN2,Nuclear Pore,-0.321747258
+RBBP5,Nuclear Pore,-0.136720322
+ECE1,Nuclear Pore,0.289437734
+CD46,Nuclear Pore,0.327155646
+APH1A,Nuclear Pore,0.285472974
+LEPRE1,Nuclear Pore,-0.010076544
+SLC2A1,Nuclear Pore,0.037971454
+SLC19A2,Nuclear Pore,-0.584512049
+NSUN4,Nuclear Pore,0.048230337
+TMED5,Nuclear Pore,-0.26839594
+DR1,Nuclear Pore,-0.2819683
+PTBP2,Nuclear Pore,0.62057577
+DARS2,Nuclear Pore,-0.302020181
+DIEXF,Nuclear Pore,-0.799569296
+RCAN3,Nuclear Pore,-0.085373129
+C1orf63,Nuclear Pore,0.710360233
+SLC35A3,Nuclear Pore,-0.074680725
+RCOR3,Nuclear Pore,0.713027032
+ARID1A,Nuclear Pore,0.592963371
+CENPF,Nuclear Pore,0.03208416
+ESYT2,Nuclear Pore,-0.012360644
+CD3EAP,Nuclear Pore,-0.300472491
+MESDC2,Nuclear Pore,-0.229801212
+CTSD,Nuclear Pore,-0.000822878
+STK11,Nuclear Pore,0.600090104
+KMT2A,Nuclear Pore,0.075858587
+KPTN,Nuclear Pore,0.369694542
+KIF14,Nuclear Pore,0.086961663
+ATF6,Nuclear Pore,0.030848722
+FASTKD2,Nuclear Pore,-0.034036051
+NRP2,Nuclear Pore,0.094976514
+CREB1,Nuclear Pore,0.039589878
+B4GALT6,Nuclear Pore,-0.899226417
+ELOVL4,Nuclear Pore,0.187137704
+CASP8AP2,Nuclear Pore,0.014891456
+PHF3,Nuclear Pore,-0.026213212
+PLAGL1,Nuclear Pore,0.536979433
+FBXO30,Nuclear Pore,0.430471482
+TMEM5,Nuclear Pore,-0.690514549
+ZNF430,Nuclear Pore,0.097127735
+DCLRE1B,Nuclear Pore,-0.429542367
+PKD2,Nuclear Pore,0.250140482
+UBN1,Nuclear Pore,0.083736085
+KLF12,Nuclear Pore,0.076977798
+WDR35,Nuclear Pore,0.10986832
+CCND2,Nuclear Pore,0.167922655
+SATB2,Nuclear Pore,0.472833409
+SENP5,Nuclear Pore,-0.16432357
+C1orf198,Nuclear Pore,0.417776896
+HEATR1,Nuclear Pore,-0.095119209
+PTBP3,Nuclear Pore,-0.231554818
+FAM206A,Nuclear Pore,-1.392155123
+RBM18,Nuclear Pore,-0.443302725
+MAPKAP1,Nuclear Pore,0.013810621
+KDSR,Nuclear Pore,-0.266759028
+ONECUT2,Nuclear Pore,0.380254905
+IRF2BPL,Nuclear Pore,1.078268207
+AREL1,Nuclear Pore,-0.870014659
+ABCD4,Nuclear Pore,0.96647303
+RBM25,Nuclear Pore,0.187484925
+NRDE2,Nuclear Pore,-0.034605938
+KLHL29,Nuclear Pore,-0.904959759
+DNMT3A,Nuclear Pore,0.432562972
+ATAD2B,Nuclear Pore,-0.341242604
+ATL2,Nuclear Pore,0.060556328
+YIPF4,Nuclear Pore,-0.871729672
+AFTPH,Nuclear Pore,0.145190563
+BCL11A,Nuclear Pore,0.921871148
+SLC17A5,Nuclear Pore,-0.465642939
+FAM178A,Nuclear Pore,0.140035034
+GPAM,Nuclear Pore,-0.283549225
+HELLS,Nuclear Pore,0.335565418
+TCTN3,Nuclear Pore,-0.232116413
+C10orf76,Nuclear Pore,-0.11174188
+HOXB8,Nuclear Pore,0.710987358
+HOXB3,Nuclear Pore,0.855540237
+PANK3,Nuclear Pore,0.278309916
+NUP43,Nuclear Pore,-0.031172156
+LRP11,Nuclear Pore,-0.478730222
+MASTL,Nuclear Pore,-0.06557683
+ELF1,Nuclear Pore,-0.42710268
+EGR1,Nuclear Pore,1.102467061
+NR2C1,Nuclear Pore,0.043869304
+MTERFD3,Nuclear Pore,0.545679327
+CLU,Nuclear Pore,0.71755762
+TNFRSF10B,Nuclear Pore,0.31360771
+TARDBP,Nuclear Pore,-0.28312697
+CRISPLD1,Nuclear Pore,-1.28035482
+AKAP1,Nuclear Pore,0.10751747
+TRIM25,Nuclear Pore,-0.121407077
+KIAA0922,Nuclear Pore,-0.508585155
+PAPD5,Nuclear Pore,-0.392200496
+CEP89,Nuclear Pore,0.28135569
+B4GALT4,Nuclear Pore,0.086347982
+KIF18A,Nuclear Pore,-0.059502764
+CRY2,Nuclear Pore,0.37953335
+ZNF639,Nuclear Pore,-0.517570648
+PDS5A,Nuclear Pore,0.039929346
+CLCC1,Nuclear Pore,0.067060441
+ACVR2A,Nuclear Pore,0.446024108
+RPL21,Nuclear Pore,0.772286564
+MTERFD2,Nuclear Pore,0.266309213
+KIAA1191,Nuclear Pore,-0.370950302
+RBBP6,Nuclear Pore,-0.151243394
+ZC3H7A,Nuclear Pore,-0.37383762
+FAM35A,Nuclear Pore,-1.243207092
+FAM213A,Nuclear Pore,0.343611312
+ODF2L,Nuclear Pore,0.229184464
+TRMT13,Nuclear Pore,0.018567318
+RPAP2,Nuclear Pore,-0.139894006
+FAM126A,Nuclear Pore,-0.092689804
+FKBP9,Nuclear Pore,0.185798978
+POLM,Nuclear Pore,1.490608844
+SLC25A51,Nuclear Pore,-0.324368097
+DCAF10,Nuclear Pore,-0.426566524
+KIAA1549,Nuclear Pore,0.447291213
+CALD1,Nuclear Pore,-0.295689688
+CHST3,Nuclear Pore,0.377988225
+P4HA1,Nuclear Pore,-0.538575785
+RBM19,Nuclear Pore,0.075719503
+GIPC1,Nuclear Pore,0.914070152
+ATP7B,Nuclear Pore,0.489463396
+ZC3H13,Nuclear Pore,-0.33773751
+NLN,Nuclear Pore,-0.191244639
+CENPK,Nuclear Pore,-0.812308258
+OPTN,Nuclear Pore,0.187644483
+SPATS2,Nuclear Pore,-0.186408275
+LRP1,Nuclear Pore,0.546178656
+HJURP,Nuclear Pore,-0.009849547
+USP45,Nuclear Pore,0.094196396
+SLC36A1,Nuclear Pore,-0.158276312
+LPGAT1,Nuclear Pore,-0.551242277
+EXOSC9,Nuclear Pore,-0.14749532
+PLA2G12A,Nuclear Pore,-0.472968013
+ADCK4,Nuclear Pore,1.01351584
+PFKFB2,Nuclear Pore,-0.023107903
+AGO2,Nuclear Pore,-0.483527098
+MXD4,Nuclear Pore,1.077466508
+ACSL3,Nuclear Pore,-0.451871607
+SLC12A4,Nuclear Pore,1.106501027
+FAM210B,Nuclear Pore,-0.363668687
+SDC4,Nuclear Pore,0.44357775
+NCOA3,Nuclear Pore,0.408560877
+PIGT,Nuclear Pore,0.06096939
+VAPB,Nuclear Pore,-0.161385663
+CHD6,Nuclear Pore,-0.225839578
+SRSF6,Nuclear Pore,0.206838392
+RAB22A,Nuclear Pore,-0.928937808
+STX16,Nuclear Pore,0.484976512
+STAMBP,Nuclear Pore,-0.664927265
+NAGK,Nuclear Pore,1.059164289
+PAIP2B,Nuclear Pore,0.921165153
+ATP8A1,Nuclear Pore,0.26750004
+BTN2A2,Nuclear Pore,0.289402187
+ABCC10,Nuclear Pore,0.762864284
+AARS2,Nuclear Pore,0.291703603
+ZNF391,Nuclear Pore,0.316391223
+CDKN1A,Nuclear Pore,1.114413723
+SSR1,Nuclear Pore,-0.477385647
+NRN1,Nuclear Pore,-0.132497058
+ATXN1,Nuclear Pore,0.822802194
+EEF1E1,Nuclear Pore,-0.540903458
+LRRFIP1,Nuclear Pore,-0.255778379
+AHNAK,Nuclear Pore,0.816624688
+ABCC4,Nuclear Pore,-0.099470478
+EFNB2,Nuclear Pore,-0.086534941
+ATP5S,Nuclear Pore,0.733869449
+FAM193A,Nuclear Pore,0.261250858
+GGA3,Nuclear Pore,0.867885707
+GTF3C4,Nuclear Pore,-0.674850318
+PPP1R12C,Nuclear Pore,0.762985214
+MBOAT7,Nuclear Pore,0.096348847
+CCDC93,Nuclear Pore,-0.242827888
+THOC2,Nuclear Pore,-0.011878709
+MED1,Nuclear Pore,0.136628544
+GPR108,Nuclear Pore,0.584578989
+GPCPD1,Nuclear Pore,-0.839653495
+PANK2,Nuclear Pore,0.311408271
+NAPB,Nuclear Pore,-0.06805347
+TMX4,Nuclear Pore,-0.651080281
+RRBP1,Nuclear Pore,0.027884663
+ZNF133,Nuclear Pore,0.82310656
+MCM8,Nuclear Pore,0.137875319
+NCLN,Nuclear Pore,0.558409818
+ZNF436,Nuclear Pore,0.180735392
+AMOT,Nuclear Pore,0.154085879
+TMEM115,Nuclear Pore,0.489587623
+AGO3,Nuclear Pore,1.080880805
+HECTD3,Nuclear Pore,0.621544239
+KLC1,Nuclear Pore,1.592232391
+XRCC3,Nuclear Pore,1.080829271
+TUBGCP3,Nuclear Pore,-0.857738514
+PCID2,Nuclear Pore,0.011210687
+FRMD8,Nuclear Pore,0.624338445
+PCNXL4,Nuclear Pore,-0.675980796
+ATG14,Nuclear Pore,-0.515677294
+KTN1,Nuclear Pore,-0.297240901
+PLEKHG3,Nuclear Pore,0.088571386
+WDR60,Nuclear Pore,-0.068725622
+AIF1L,Nuclear Pore,-0.266386957
+SLC10A3,Nuclear Pore,0.58748531
+CANX,Nuclear Pore,-0.611585362
+CPSF3L,Nuclear Pore,0.434239865
+TRAF2,Nuclear Pore,0.928240858
+HELB,Nuclear Pore,-0.462024175
+DYRK2,Nuclear Pore,-0.173741312
+LRRC61,Nuclear Pore,0.777705458
+FGFRL1,Nuclear Pore,0.263054353
+EMC1,Nuclear Pore,-0.191578099
+HP1BP3,Nuclear Pore,0.389894848
+SIN3B,Nuclear Pore,0.532046768
+SLC35E1,Nuclear Pore,0.290208443
+GFER,Nuclear Pore,0.480883142
+PKMYT1,Nuclear Pore,1.010742324
+CHTF18,Nuclear Pore,1.208869404
+MACF1,Nuclear Pore,0.109089124
+RNF6,Nuclear Pore,-0.508245472
+AKAP9,Nuclear Pore,0.18317041
+HIP1,Nuclear Pore,-0.262974214
+POR,Nuclear Pore,-0.191478869
+PEX1,Nuclear Pore,0.090604614
+LRFN1,Nuclear Pore,0.86895577
+SRD5A3,Nuclear Pore,-0.45635793
+PPAT,Nuclear Pore,-0.457685311
+TUBGCP6,Nuclear Pore,0.83979427
+DGCR8,Nuclear Pore,0.526939962
+TPST2,Nuclear Pore,-0.098242445
+MPST,Nuclear Pore,0.794080605
+SPECC1,Nuclear Pore,-0.02798408
+NAA38,Nuclear Pore,-0.201433317
+PRKRIP1,Nuclear Pore,0.305059217
+PODXL,Nuclear Pore,-0.09967885
+STRIP2,Nuclear Pore,-0.290897966
+MKLN1,Nuclear Pore,-0.738078119
+CALU,Nuclear Pore,-0.326497864
+CCDC136,Nuclear Pore,0.15707605
+SMO,Nuclear Pore,0.28552811
+KLHDC10,Nuclear Pore,-0.080423204
+OSGEPL1,Nuclear Pore,-0.144314193
+HOXD10,Nuclear Pore,0.944091544
+HOXD11,Nuclear Pore,0.869458897
+HERC2,Nuclear Pore,0.343099688
+TWSG1,Nuclear Pore,0.024215185
+MYO5C,Nuclear Pore,-0.145648473
+TMOD2,Nuclear Pore,-0.996753844
+TTBK2,Nuclear Pore,0.837798945
+IVD,Nuclear Pore,0.021044339
+CLN6,Nuclear Pore,0.047132493
+ARPP19,Nuclear Pore,-0.299420552
+VPS13C,Nuclear Pore,0.388395855
+SUMF2,Nuclear Pore,-0.047712606
+SPCS3,Nuclear Pore,-0.578489667
+RPAIN,Nuclear Pore,0.26197483
+PLD2,Nuclear Pore,1.10263322
+MPDU1,Nuclear Pore,0.144225643
+CCNT1,Nuclear Pore,0.097240931
+PUS7L,Nuclear Pore,-0.504853506
+KRI1,Nuclear Pore,0.788025147
+SLC44A2,Nuclear Pore,0.286181705
+BCL2L2,Nuclear Pore,0.671937813
+PARP2,Nuclear Pore,0.041012366
+TEP1,Nuclear Pore,0.283976079
+MAP7D3,Nuclear Pore,-0.225570184
+ABHD17A,Nuclear Pore,1.355954791
+ERMARD,Nuclear Pore,-0.051265922
+SAT1,Nuclear Pore,0.744898052
+GNL3L,Nuclear Pore,-0.6140622
+SH3BP4,Nuclear Pore,0.786077748
+LDLR,Nuclear Pore,-0.073499272
+PRKCSH,Nuclear Pore,0.227040074
+THEM6,Nuclear Pore,0.319679904
+PVRL2,Nuclear Pore,0.079745251
+SAFB2,Nuclear Pore,0.417496037
+KIF1A,Nuclear Pore,0.34095173
+COLGALT1,Nuclear Pore,0.233276336
+MLLT1,Nuclear Pore,0.695519563
+MLLT4,Nuclear Pore,0.455983995
+ACTN4,Nuclear Pore,0.196337484
+NDUFA10,Nuclear Pore,0.143123999
+ZSWIM6,Nuclear Pore,0.446104146
+PXDN,Nuclear Pore,-0.223131372
+COL5A1,Nuclear Pore,0.992062699
+ZNF337,Nuclear Pore,0.760453799
+TAF4,Nuclear Pore,-0.66459458
+LAMA5,Nuclear Pore,1.29481248
+EXOSC2,Nuclear Pore,-0.184730124
+POMT1,Nuclear Pore,0.186594648
+PRRC2B,Nuclear Pore,0.304439178
+YIPF2,Nuclear Pore,0.054921817
+ZC3H4,Nuclear Pore,0.926948567
+CLIP1,Nuclear Pore,-0.643947521
+HIP1R,Nuclear Pore,0.935019192
+PPAN,Nuclear Pore,1.082630814
+SLC6A8,Nuclear Pore,0.332398366
+PLXNA3,Nuclear Pore,1.090152599
+PRRG1,Nuclear Pore,-0.100690534
+AKAP12,Nuclear Pore,-0.137640796
+RBM39,Nuclear Pore,0.076976644
+GGT7,Nuclear Pore,0.348112779
+PPT1,Nuclear Pore,-0.41753025
+RLIM,Nuclear Pore,-0.372440683
+ABCB7,Nuclear Pore,-0.970405901
+MRPS25,Nuclear Pore,0.107934195
+CAPN7,Nuclear Pore,-0.473848725
+ZFYVE20,Nuclear Pore,0.11611961
+SLC6A6,Nuclear Pore,-0.388713017
+MGAT1,Nuclear Pore,0.515727265
+PSMC3IP,Nuclear Pore,-0.28752971
+DIAPH1,Nuclear Pore,-0.569402359
+NDFIP1,Nuclear Pore,-0.343517099
+ACAP3,Nuclear Pore,1.166142986
+C1orf159,Nuclear Pore,0.875993706
+MAP1B,Nuclear Pore,0.500139587
+IL13RA1,Nuclear Pore,-0.721421728
+WDR44,Nuclear Pore,-0.134755556
+PRKAB2,Nuclear Pore,0.041644174
+CLUHP3,Nuclear Pore,0.779406096
+CHSY1,Nuclear Pore,-0.456349675
+SNRPA1,Nuclear Pore,-0.149934115
+FBXW9,Nuclear Pore,-0.072788682
+RFX1,Nuclear Pore,1.259978826
+CC2D1A,Nuclear Pore,1.252876117
+NUP210,Nuclear Pore,0.14075492
+ENOSF1,Nuclear Pore,-1.046654557
+EMILIN2,Nuclear Pore,0.05691295
+PRKAA1,Nuclear Pore,0.250430041
+PNISR,Nuclear Pore,0.679049177
+ZRANB2,Nuclear Pore,0.23163142
+KDM6B,Nuclear Pore,1.364330125
+GPS2,Nuclear Pore,0.714355895
+VPS13B,Nuclear Pore,-0.070536247
+REEP2,Nuclear Pore,0.910118402
+PRMT7,Nuclear Pore,0.876860274
+PCED1A,Nuclear Pore,0.691523928
+PTPRA,Nuclear Pore,-0.493307746
+KIAA0907,Nuclear Pore,1.019287175
+DCAF8,Nuclear Pore,0.396648667
+IGHMBP2,Nuclear Pore,0.41020695
+LPIN3,Nuclear Pore,0.604871992
+SERINC3,Nuclear Pore,-0.243416355
+FBXO44,Nuclear Pore,1.209216597
+USPL1,Nuclear Pore,0.46540786
+XPO4,Nuclear Pore,-0.12278049
+SCO1,Nuclear Pore,0.075978296
+MPRIP,Nuclear Pore,0.467405156
+DSTYK,Nuclear Pore,-0.275542536
+SLC41A1,Nuclear Pore,0.222770827
+GPALPP1,Nuclear Pore,-0.718434207
+IRS4,Nuclear Pore,0.327896486
+FAM104A,Nuclear Pore,0.10286924
+SLC39A11,Nuclear Pore,-0.34481718
+EPHB2,Nuclear Pore,0.640399683
+SRRM1,Nuclear Pore,0.062132137
+SUV420H2,Nuclear Pore,1.003869045
+WDR74,Nuclear Pore,-0.113389893
+RTN3,Nuclear Pore,-0.681203106
+MORC2,Nuclear Pore,0.093293662
+LARGE,Nuclear Pore,-0.952814155
+ADCK2,Nuclear Pore,0.143817766
+AGAP3,Nuclear Pore,0.082128463
+KRBA1,Nuclear Pore,0.986390597
+ZNF767,Nuclear Pore,1.059993059
+ATP13A3,Nuclear Pore,-0.412238796
+TMEM254,Nuclear Pore,-0.123999858
+TMTC1,Nuclear Pore,-0.648299102
+KRAS,Nuclear Pore,-0.582009354
+SWAP70,Nuclear Pore,-0.360743023
+ZFC3H1,Nuclear Pore,0.688890327
+TEX15,Nuclear Pore,0.091245313
+CTIF,Nuclear Pore,0.499910714
+VHL,Nuclear Pore,0.156149784
+ARL8B,Nuclear Pore,-0.157922105
+EDEM1,Nuclear Pore,0.095372231
+PRPF38B,Nuclear Pore,0.41477794
+SORT1,Nuclear Pore,-0.188206298
+PTGFRN,Nuclear Pore,0.50019361
+NOTCH2,Nuclear Pore,0.042884506
+CEPT1,Nuclear Pore,-0.343247488
+AP4B1,Nuclear Pore,0.117731615
+SPIRE1,Nuclear Pore,-0.115450273
+SLC38A2,Nuclear Pore,-0.127627341
+KIDINS220,Nuclear Pore,-0.065437508
+ROCK2,Nuclear Pore,0.055434737
+LPIN1,Nuclear Pore,0.748198038
+IL6ST,Nuclear Pore,-0.065212255
+TMEM241,Nuclear Pore,0.502325992
+LRP4,Nuclear Pore,1.249395652
+DDB2,Nuclear Pore,0.933751455
+ACP2,Nuclear Pore,-0.413127673
+AGO4,Nuclear Pore,0.478266395
+HOOK1,Nuclear Pore,-0.178056902
+DSC2,Nuclear Pore,-0.224681587
+DSC3,Nuclear Pore,-0.590885855
+DTNA,Nuclear Pore,-0.368551302
+FHOD3,Nuclear Pore,0.13004875
+FADS2,Nuclear Pore,0.20481173
+CLOCK,Nuclear Pore,0.040553006
+COL4A2,Nuclear Pore,0.145261202
+DZIP1,Nuclear Pore,-0.012859897
+UBAC2,Nuclear Pore,0.346424306
+ARGLU1,Nuclear Pore,0.457707576
+BIVM,Nuclear Pore,0.564509603
+ARHGAP32,Nuclear Pore,-0.626926954
+TMED7,Nuclear Pore,-1.005280853
+APC,Nuclear Pore,-0.208045029
+WDR36,Nuclear Pore,-0.372134504
+NAA35,Nuclear Pore,-0.343215281
+TMEM2,Nuclear Pore,-1.058387672
+GOLM1,Nuclear Pore,-0.520229949
+TAOK3,Nuclear Pore,0.862721574
+DMTF1,Nuclear Pore,0.731841384
+TMEM243,Nuclear Pore,0.315090349
+PNPLA8,Nuclear Pore,-1.496276764
+MDFIC,Nuclear Pore,0.419207072
+ANKRD6,Nuclear Pore,-0.405732211
+KIAA1009,Nuclear Pore,0.597437271
+SNX14,Nuclear Pore,-0.403506517
+EPHA7,Nuclear Pore,-0.23333684
+DNAJC14,Nuclear Pore,0.284324168
+GDF11,Nuclear Pore,0.436922083
+TROAP,Nuclear Pore,0.888133375
+TSPAN31,Nuclear Pore,-0.363398207
+TFCP2,Nuclear Pore,-0.196629509
+PAN2,Nuclear Pore,0.323326526
+HNRNPA1,Nuclear Pore,-0.009734974
+ACVR1B,Nuclear Pore,-0.447051416
+OS9,Nuclear Pore,0.197439884
+MAP7,Nuclear Pore,-0.436851591
+CD164,Nuclear Pore,-0.196815547
+NHSL1,Nuclear Pore,1.151060006
+AHI1,Nuclear Pore,-0.755049349
+SEMA4F,Nuclear Pore,-0.172524982
+RAB11FIP5,Nuclear Pore,0.687030886
+CCDC142,Nuclear Pore,1.310373858
+GNS,Nuclear Pore,-0.036626718
+MDM2,Nuclear Pore,0.051436535
+KLHL36,Nuclear Pore,0.418795423
+DYNC1LI2,Nuclear Pore,0.063990765
+EGLN1,Nuclear Pore,-0.383089578
+ABCB10,Nuclear Pore,-0.602547884
+TAF5L,Nuclear Pore,-0.287718821
+STX6,Nuclear Pore,-0.638866871
+CEP350,Nuclear Pore,0.093841837
+LAMC1,Nuclear Pore,-0.137285562
+RC3H1,Nuclear Pore,0.069584937
+TTLL4,Nuclear Pore,0.118777471
+USP37,Nuclear Pore,-0.390246347
+ITM2C,Nuclear Pore,0.431297417
+SERPINE2,Nuclear Pore,-0.464806785
+TMEM127,Nuclear Pore,-0.228276874
+GCC2,Nuclear Pore,-0.330303537
+C2orf49,Nuclear Pore,0.241480965
+EPC2,Nuclear Pore,-0.126819867
+ARHGEF4,Nuclear Pore,0.379895936
+ALDH1L2,Nuclear Pore,-0.278510436
+CKAP4,Nuclear Pore,0.027946949
+NEK3,Nuclear Pore,-0.090406545
+RCBTB1,Nuclear Pore,0.316866644
+COG3,Nuclear Pore,0.678440489
+SCRN1,Nuclear Pore,-0.245488969
+CHST12,Nuclear Pore,1.179461789
+KDELR2,Nuclear Pore,-0.285522838
+NUPL2,Nuclear Pore,-0.164291602
+DBNL,Nuclear Pore,0.50973008
+TTYH3,Nuclear Pore,0.654232672
+IREB2,Nuclear Pore,-0.526973586
+RSAD1,Nuclear Pore,0.256704785
+VEZF1,Nuclear Pore,-0.233768496
+TEX2,Nuclear Pore,-0.084997129
+BRIP1,Nuclear Pore,0.368746251
+SKIL,Nuclear Pore,0.079961249
+RPS6KC1,Nuclear Pore,0.243644914
+BIN1,Nuclear Pore,0.525184324
+HS6ST1,Nuclear Pore,0.167524909
+UGGT1,Nuclear Pore,0.065123637
+DNAJC1,Nuclear Pore,-0.316635571
+LRRC8A,Nuclear Pore,0.511199658
+CDK9,Nuclear Pore,0.82295781
+TOR1B,Nuclear Pore,0.402506337
+SMC2,Nuclear Pore,-0.558717573
+TOR1A,Nuclear Pore,-0.432095271
+RALGPS1,Nuclear Pore,-0.008295207
+FAM129B,Nuclear Pore,0.44859804
+SLC2A8,Nuclear Pore,0.06227557
+SLC31A1,Nuclear Pore,-0.218281251
+ZNF189,Nuclear Pore,-0.04217383
+STX17,Nuclear Pore,0.81431502
+TSTD2,Nuclear Pore,-0.017140319
+LMX1B,Nuclear Pore,0.817275655
+RANBP6,Nuclear Pore,0.287965153
+TLN1,Nuclear Pore,0.136813776
+ALDH1B1,Nuclear Pore,0.124392377
+CNPY3,Nuclear Pore,0.389790685
+TMEM63B,Nuclear Pore,0.467845032
+TJAP1,Nuclear Pore,0.790972129
+SLC22A23,Nuclear Pore,0.713789532
+FOXF2,Nuclear Pore,-0.416844533
+RIPK1,Nuclear Pore,-0.467496031
+ATAT1,Nuclear Pore,0.818791096
+NRM,Nuclear Pore,0.086625894
+VARS2,Nuclear Pore,0.968430934
+FAM8A1,Nuclear Pore,-0.39865386
+PRKRIR,Nuclear Pore,-0.493544008
+CREBZF,Nuclear Pore,0.516523581
+PRCP,Nuclear Pore,-0.232959978
+RNF121,Nuclear Pore,0.21140885
+SULF1,Nuclear Pore,-0.57687343
+SORL1,Nuclear Pore,-0.014772713
+YAP1,Nuclear Pore,-0.359425786
+RDX,Nuclear Pore,-0.496837112
+MAP2K5,Nuclear Pore,-0.374680529
+MAPKBP1,Nuclear Pore,-0.145850625
+CASC5,Nuclear Pore,-0.580417588
+HAUS2,Nuclear Pore,0.493666236
+PARP6,Nuclear Pore,0.381173887
+TUBGCP4,Nuclear Pore,0.334606997
+RMDN3,Nuclear Pore,0.307429013
+UACA,Nuclear Pore,-0.266273082
+SMAD6,Nuclear Pore,0.314412054
+ADAM10,Nuclear Pore,-0.339425816
+TTLL7,Nuclear Pore,0.361153863
+FNBP1L,Nuclear Pore,0.164383531
+RABGGTB,Nuclear Pore,0.238202277
+ARHGAP29,Nuclear Pore,-0.028562326
+SLC44A5,Nuclear Pore,0.908563604
+DBT,Nuclear Pore,-0.260657227
+EPT1,Nuclear Pore,-1.019309842
+ADCY3,Nuclear Pore,0.219639013
+PNPT1,Nuclear Pore,-0.255149091
+THUMPD2,Nuclear Pore,0.263870355
+PREPL,Nuclear Pore,-0.477169707
+ACTR1A,Nuclear Pore,-0.116032924
+TMEM180,Nuclear Pore,-0.186765418
+ATAD1,Nuclear Pore,-0.731927973
+KIF20B,Nuclear Pore,-0.293966711
+TET1,Nuclear Pore,0.108848274
+DNA2,Nuclear Pore,-0.002921387
+BARD1,Nuclear Pore,-0.776911312
+NAB1,Nuclear Pore,0.023951651
+PPIG,Nuclear Pore,-0.085569597
+FASTKD1,Nuclear Pore,-0.553552863
+SSFA2,Nuclear Pore,-0.099307337
+ITGAV,Nuclear Pore,-0.102284742
+SLC35A5,Nuclear Pore,-0.411710386
+SECISBP2L,Nuclear Pore,0.007707645
+SPPL2A,Nuclear Pore,0.05172453
+GLCE,Nuclear Pore,-0.675888819
+PPCDC,Nuclear Pore,0.274149162
+PCDH10,Nuclear Pore,0.309878414
+AP1AR,Nuclear Pore,-0.54265856
+FGF2,Nuclear Pore,-0.402952921
+KIAA1109,Nuclear Pore,0.658826581
+LARP1B,Nuclear Pore,0.184582672
+BMP2K,Nuclear Pore,0.698523921
+FRAS1,Nuclear Pore,0.180808589
+SCARB2,Nuclear Pore,-0.457668108
+USO1,Nuclear Pore,-0.422313548
+CENPE,Nuclear Pore,-0.053918531
+GSTCD,Nuclear Pore,-0.333203559
+LEF1,Nuclear Pore,1.191187392
+PPP3CA,Nuclear Pore,0.224517184
+FBN2,Nuclear Pore,-0.347105332
+MAPK8IP3,Nuclear Pore,1.487063023
+B4GALNT3,Nuclear Pore,-0.261703885
+AEBP2,Nuclear Pore,-0.802778821
+ETNK1,Nuclear Pore,0.018178602
+CLSTN3,Nuclear Pore,0.428261343
+SCAF11,Nuclear Pore,-0.396319419
+COL2A1,Nuclear Pore,0.358039185
+LRIG3,Nuclear Pore,0.069118194
+TMEM19,Nuclear Pore,-0.070019799
+POC1B,Nuclear Pore,-1.032722984
+TMTC3,Nuclear Pore,0.025618256
+GAS2L3,Nuclear Pore,0.19494783
+SLC15A4,Nuclear Pore,0.154439544
+TDG,Nuclear Pore,-0.576703224
+NUPL1,Nuclear Pore,-0.392028131
+MTMR6,Nuclear Pore,-0.426245435
+SLC7A1,Nuclear Pore,0.273370747
+BRCA2,Nuclear Pore,0.156221213
+CERS5,Nuclear Pore,-0.378441962
+ESYT1,Nuclear Pore,0.333913294
+TMBIM6,Nuclear Pore,-0.156385151
+ANKRD52,Nuclear Pore,0.614294138
+ZNF740,Nuclear Pore,-0.078801203
+HNRNPA1L2,Nuclear Pore,-0.454896121
+SBNO1,Nuclear Pore,-0.355490795
+SETD1B,Nuclear Pore,1.167555704
+RBM26,Nuclear Pore,0.384133884
+ZIC5,Nuclear Pore,0.310806966
+TMX1,Nuclear Pore,-0.47663505
+NAA30,Nuclear Pore,-0.256656871
+DCAF5,Nuclear Pore,0.390769882
+RAB15,Nuclear Pore,0.100664944
+NIPA2,Nuclear Pore,-0.797834986
+ZSCAN29,Nuclear Pore,0.385917646
+BNIP2,Nuclear Pore,0.063501961
+MAN2C1,Nuclear Pore,0.949846701
+MESDC1,Nuclear Pore,0.290346095
+IGF1R,Nuclear Pore,0.260680887
+ARRDC4,Nuclear Pore,-0.689685797
+PML,Nuclear Pore,0.964043639
+LINS,Nuclear Pore,0.379946776
+PCSK6,Nuclear Pore,-0.252114869
+SCAMP2,Nuclear Pore,-0.223056417
+POLG,Nuclear Pore,0.63275121
+ABHD2,Nuclear Pore,-0.206606716
+TICRR,Nuclear Pore,0.34365122
+MFGE8,Nuclear Pore,0.310152183
+FURIN,Nuclear Pore,0.58497037
+IQGAP1,Nuclear Pore,-0.34310767
+CRTC3,Nuclear Pore,0.22885482
+FTO,Nuclear Pore,0.101851446
+MBTPS1,Nuclear Pore,0.09301757
+RHOT2,Nuclear Pore,0.916774244
+PDPK1,Nuclear Pore,0.220575171
+TCF25,Nuclear Pore,0.455652373
+GALNS,Nuclear Pore,0.628688377
+GAS8,Nuclear Pore,0.711192162
+MED9,Nuclear Pore,0.457360991
+GID4,Nuclear Pore,0.227088058
+KSR1,Nuclear Pore,0.150027886
+SGSM2,Nuclear Pore,0.97584621
+SSH2,Nuclear Pore,0.647251454
+PTRH2,Nuclear Pore,-0.724800906
+SS18,Nuclear Pore,-0.038693551
+SLC39A6,Nuclear Pore,-0.068982984
+GALNT1,Nuclear Pore,-0.780364462
+ESCO1,Nuclear Pore,-1.061826574
+GREB1L,Nuclear Pore,-0.287842007
+NPC1,Nuclear Pore,0.076156344
+MINK1,Nuclear Pore,0.77871514
+TTYH2,Nuclear Pore,0.270934068
+CSNK1D,Nuclear Pore,0.856857438
+FOXK2,Nuclear Pore,0.593382175
+TRIM65,Nuclear Pore,0.214459168
+RNF157,Nuclear Pore,-0.018695624
+CBX4,Nuclear Pore,1.065235151
+MBD1,Nuclear Pore,0.380736976
+ZCCHC2,Nuclear Pore,0.548942594
+LEPREL4,Nuclear Pore,0.244117413
+FAM134C,Nuclear Pore,0.423548213
+ERBB2,Nuclear Pore,0.004317885
+FKBP10,Nuclear Pore,0.344749847
+PRDM15,Nuclear Pore,0.240652372
+DUS3L,Nuclear Pore,0.179122675
+ATHL1,Nuclear Pore,1.196592457
+COL6A1,Nuclear Pore,0.574664809
+IFNAR1,Nuclear Pore,-0.051322409
+COL6A2,Nuclear Pore,1.199679413
+TMEM50B,Nuclear Pore,0.26312767
+APP,Nuclear Pore,-0.080528365
+URB1,Nuclear Pore,-0.159627755
+CAPN10,Nuclear Pore,1.163017856
+ERVK3-1,Nuclear Pore,0.555851086
+SLC47A1,Nuclear Pore,-1.742791963
+RERE,Nuclear Pore,0.125811774
+EPHA2,Nuclear Pore,0.673392001
+KIAA0319L,Nuclear Pore,-0.042618038
+PLK4,Nuclear Pore,0.158484708
+GPN2,Nuclear Pore,-0.008689842
+PIGK,Nuclear Pore,-0.614085659
+PTPRF,Nuclear Pore,0.387633899
+SYPL2,Nuclear Pore,-0.169085415
+IGSF3,Nuclear Pore,0.056119187
+CELSR2,Nuclear Pore,0.337706763
+ATP1B1,Nuclear Pore,0.061949187
+CREG1,Nuclear Pore,-0.31771114
+POU2F1,Nuclear Pore,0.010801557
+PPOX,Nuclear Pore,0.524458132
+USP21,Nuclear Pore,0.971681751
+PIGM,Nuclear Pore,0.575955913
+ABL2,Nuclear Pore,-0.224622009
+XPR1,Nuclear Pore,-0.458088279
+TOR1AIP1,Nuclear Pore,-0.445688201
+TUFT1,Nuclear Pore,0.128359673
+TARS2,Nuclear Pore,0.191420779
+CERS2,Nuclear Pore,-0.157889439
+SEMA6C,Nuclear Pore,0.953033681
+ATP8B2,Nuclear Pore,0.063475724
+ADAM15,Nuclear Pore,-0.011948338
+SLC39A1,Nuclear Pore,0.279638346
+GATAD2B,Nuclear Pore,0.608423777
+HCN3,Nuclear Pore,0.991325683
+GALNT2,Nuclear Pore,0.145463843
+TTC13,Nuclear Pore,-0.206820527
+MLK4,Nuclear Pore,0.354143792
+CEP170,Nuclear Pore,0.010700097
+SDE2,Nuclear Pore,-0.171895429
+FBXO28,Nuclear Pore,-0.460674912
+CDC42BPA,Nuclear Pore,0.036818513
+MBOAT2,Nuclear Pore,-0.102341914
+PSEN2,Nuclear Pore,0.362399647
+LBR,Nuclear Pore,-0.43889835
+RHOB,Nuclear Pore,0.846793853
+ASXL2,Nuclear Pore,0.062642022
+ETAA1,Nuclear Pore,-0.739124845
+ZNF514,Nuclear Pore,0.892735664
+SFXN5,Nuclear Pore,-0.040720753
+TEX261,Nuclear Pore,-0.257289118
+RALB,Nuclear Pore,-0.163609275
+SLC20A1,Nuclear Pore,-0.29159594
+ZC3H8,Nuclear Pore,-0.745103713
+UBXN4,Nuclear Pore,-0.517932052
+AMMECR1L,Nuclear Pore,0.081652329
+GALNT13,Nuclear Pore,-1.072101747
+SCRN3,Nuclear Pore,-0.247618124
+KIAA1715,Nuclear Pore,-0.24873972
+CDCA7,Nuclear Pore,-0.351103379
+DLX1,Nuclear Pore,0.470651157
+GULP1,Nuclear Pore,-1.446926217
+FAM171B,Nuclear Pore,1.184440792
+CCDC150,Nuclear Pore,0.832651626
+SUMF1,Nuclear Pore,-0.96919468
+RHBDD1,Nuclear Pore,-0.129366497
+FAM134A,Nuclear Pore,0.622450043
+CTDSP1,Nuclear Pore,0.760481713
+EAF1,Nuclear Pore,-0.022551069
+GOLGA4,Nuclear Pore,-0.322007955
+IQSEC1,Nuclear Pore,0.52646297
+PTPRG,Nuclear Pore,-0.283601415
+IL17RD,Nuclear Pore,0.467277296
+ARL6IP5,Nuclear Pore,-0.65005079
+TMF1,Nuclear Pore,-0.396867512
+LRIG1,Nuclear Pore,0.062510041
+LIMD1,Nuclear Pore,-0.19149678
+NXPE3,Nuclear Pore,0.07023398
+SRPRB,Nuclear Pore,-0.505416677
+TCTA,Nuclear Pore,-0.09313324
+VPRBP,Nuclear Pore,-0.400307697
+SLIT2,Nuclear Pore,0.151794748
+DGKQ,Nuclear Pore,1.103055436
+ATP10D,Nuclear Pore,-0.815356317
+SCD5,Nuclear Pore,0.1948291
+ENOPH1,Nuclear Pore,-0.086880263
+TRMT10A,Nuclear Pore,-0.975599327
+KLHL8,Nuclear Pore,-0.755378806
+USP53,Nuclear Pore,-0.055201912
+MARCH6,Nuclear Pore,0.027528636
+FAM105A,Nuclear Pore,-0.470067283
+PIK3R1,Nuclear Pore,0.206846515
+LHFPL2,Nuclear Pore,-0.302059222
+IQGAP2,Nuclear Pore,0.137682972
+PPIP5K2,Nuclear Pore,-0.785947394
+PAM,Nuclear Pore,-0.413283474
+BDP1,Nuclear Pore,0.152494747
+SLC30A5,Nuclear Pore,0.104650403
+ATG12,Nuclear Pore,-0.438724348
+YIPF5,Nuclear Pore,-0.716100181
+RNF145,Nuclear Pore,-0.226310441
+FBXO38,Nuclear Pore,0.006366921
+PCYOX1L,Nuclear Pore,0.566559025
+TNIP1,Nuclear Pore,0.979422569
+ZNF300,Nuclear Pore,1.016850981
+GFOD1,Nuclear Pore,0.454506336
+TRIM41,Nuclear Pore,0.78741324
+FAM193B,Nuclear Pore,0.527072235
+RNF44,Nuclear Pore,0.776091963
+MUT,Nuclear Pore,-0.415105069
+PHIP,Nuclear Pore,-0.094211274
+MMS22L,Nuclear Pore,0.22256147
+PM20D2,Nuclear Pore,-0.067354494
+RNF217,Nuclear Pore,0.586662478
+AIG1,Nuclear Pore,-0.602669555
+TMEM181,Nuclear Pore,-0.61556736
+SDK1,Nuclear Pore,0.679321894
+RBAK,Nuclear Pore,-0.315246505
+CREB5,Nuclear Pore,-0.001516554
+PURB,Nuclear Pore,-0.171717276
+GBAS,Nuclear Pore,-0.096878081
+ZNF92,Nuclear Pore,-0.283051417
+TMEM168,Nuclear Pore,0.465616097
+C7orf43,Nuclear Pore,0.336315323
+SLC12A9,Nuclear Pore,0.946306633
+GIGYF1,Nuclear Pore,1.421040172
+TMEM209,Nuclear Pore,-0.362061333
+NOM1,Nuclear Pore,-0.266691022
+SH3KBP1,Nuclear Pore,-0.445222763
+CASK,Nuclear Pore,-0.081889191
+SLC16A2,Nuclear Pore,-0.431464267
+OGT,Nuclear Pore,0.66536086
+ZNF711,Nuclear Pore,0.118763912
+DIAPH2,Nuclear Pore,-0.306213431
+CXorf57,Nuclear Pore,-0.180361123
+GPC3,Nuclear Pore,-0.172908354
+BIN3,Nuclear Pore,0.591427941
+SLC25A37,Nuclear Pore,0.778186622
+CHMP7,Nuclear Pore,-0.744756296
+ERLIN2,Nuclear Pore,-0.472572875
+TACC1,Nuclear Pore,-0.077898378
+WHSC1L1,Nuclear Pore,0.314638925
+TERF1,Nuclear Pore,-0.463034403
+MTDH,Nuclear Pore,-0.254904975
+LRP12,Nuclear Pore,-1.511533106
+EBAG9,Nuclear Pore,-0.656556697
+UTP23,Nuclear Pore,-0.213679732
+ZNF7,Nuclear Pore,-0.240775638
+ARHGAP39,Nuclear Pore,1.046432741
+NAPRT1,Nuclear Pore,0.081349461
+UHRF2,Nuclear Pore,0.403420224
+ZCCHC7,Nuclear Pore,-0.881337605
+SIGMAR1,Nuclear Pore,-0.043038254
+CEP78,Nuclear Pore,0.211422282
+HIATL1,Nuclear Pore,-0.022658516
+INIP,Nuclear Pore,-0.682871495
+UGCG,Nuclear Pore,-0.443320005
+STOM,Nuclear Pore,0.165336915
+MRRF,Nuclear Pore,-0.18885724
+NR6A1,Nuclear Pore,0.4112267
+SURF4,Nuclear Pore,-0.076433428
+MED22,Nuclear Pore,0.583075693
+SH3GLB2,Nuclear Pore,1.301373962
+FAM73B,Nuclear Pore,1.062344605
+GPR107,Nuclear Pore,0.09201829
+C9orf142,Nuclear Pore,0.697089055
+INPP5E,Nuclear Pore,0.843813125
+DPH7,Nuclear Pore,0.408684096
+NOTCH1,Nuclear Pore,0.82700872
+NACC2,Nuclear Pore,0.423866499
+USP6NL,Nuclear Pore,-0.555951823
+FAM171A1,Nuclear Pore,-0.31758012
+PARD3,Nuclear Pore,0.149772386
+POLR3A,Nuclear Pore,0.393029548
+FRA10AC1,Nuclear Pore,0.452167747
+ADD3,Nuclear Pore,0.273615952
+DNAJB12,Nuclear Pore,0.423243008
+EIF4EBP2,Nuclear Pore,-0.631509346
+MKI67,Nuclear Pore,-0.033950837
+MTG1,Nuclear Pore,0.738387757
+PPRC1,Nuclear Pore,0.365290396
+ITPRIP,Nuclear Pore,0.250342899
+CNNM2,Nuclear Pore,-0.257363099
+PDCD11,Nuclear Pore,-0.381086798
+LIN7C,Nuclear Pore,-0.055888138
+DGKZ,Nuclear Pore,0.85803663
+TNKS1BP1,Nuclear Pore,0.549053733
+SLC43A1,Nuclear Pore,0.33596079
+PTPRJ,Nuclear Pore,0.51121753
+CELF1,Nuclear Pore,0.267066509
+SESN3,Nuclear Pore,0.676743187
+ENDOD1,Nuclear Pore,-0.667345244
+SERPINH1,Nuclear Pore,0.543209757
+NCAM1,Nuclear Pore,0.321336543
+NPAT,Nuclear Pore,-1.053214024
+ATM,Nuclear Pore,0.807453939
+GLB1L2,Nuclear Pore,-0.297729927
+HYOU1,Nuclear Pore,0.17990195
+DAK,Nuclear Pore,0.992098661
+TMEM138,Nuclear Pore,0.494586546
+FADS1,Nuclear Pore,-0.391314917
+EML3,Nuclear Pore,0.727563717
+B3GAT3,Nuclear Pore,0.441834914
+SIDT2,Nuclear Pore,0.601665389
+SOGA1,Nuclear Pore,0.457315184
+LSM14B,Nuclear Pore,0.718185594
+ORAOV1,Nuclear Pore,1.350663602
+TAOK2,Nuclear Pore,0.671580975
+ITGB1,Nuclear Pore,-0.846871241
+ARID5B,Nuclear Pore,0.468288972
+TMCO3,Nuclear Pore,-0.016299388
+LATS2,Nuclear Pore,0.244689353
+LPHN3,Nuclear Pore,-0.036722365
+PRSS23,Nuclear Pore,-0.360618199
+PIP4K2A,Nuclear Pore,-0.555749373
+FREM2,Nuclear Pore,-0.339859735
+CRIM1,Nuclear Pore,-0.101204666
+IPMK,Nuclear Pore,0.260286661
+PLBD2,Nuclear Pore,-0.653569995
+GXYLT1,Nuclear Pore,-0.673488173
+CSNK1G3,Nuclear Pore,-0.068560691
+MIPOL1,Nuclear Pore,0.048820081
+EXT2,Nuclear Pore,-0.032634662
+TMEM18,Nuclear Pore,-0.111535031
+NEK7,Nuclear Pore,-0.330831092
+FER,Nuclear Pore,-0.616944427
+VIPAS39,Nuclear Pore,-0.707691107
+ANKRD50,Nuclear Pore,0.7165741
+UPF2,Nuclear Pore,-0.937165407
+EPS8,Nuclear Pore,-0.070915939
+FAM160B1,Nuclear Pore,0.37625374
+ADAM17,Nuclear Pore,-0.695647366
+WWC2,Nuclear Pore,-0.532892058
+BICD1,Nuclear Pore,0.065137319
+NBAS,Nuclear Pore,-0.926787057
+GUF1,Nuclear Pore,-0.358574539
+SACS,Nuclear Pore,0.049017186
+PABPC3,Nuclear Pore,0.101637269
+DST,Nuclear Pore,-0.056956383
+TIAL1,Nuclear Pore,0.135768295
+TMEM56,Nuclear Pore,-0.650710848
+FAM168B,Nuclear Pore,-0.181957346
+AC093838.4,Nuclear Pore,0.755458124
+MGAT5,Nuclear Pore,-0.264349789
+GPATCH11,Nuclear Pore,-0.779511654
+POU4F1,Nuclear Pore,0.31138597
+RNF219,Nuclear Pore,0.025780263
+EPG5,Nuclear Pore,0.632228417
+C18orf25,Nuclear Pore,0.189607946
+PDK1,Nuclear Pore,0.351974811
+PDE3B,Nuclear Pore,-0.125871881
+TGOLN2,Nuclear Pore,-0.01614354
+UHMK1,Nuclear Pore,0.108763038
+TADA1,Nuclear Pore,0.187319303
+CWF19L2,Nuclear Pore,-1.494481611
+JMY,Nuclear Pore,0.361814471
+HOMER1,Nuclear Pore,0.214986443
+USP12,Nuclear Pore,0.025400462
+CCDC50,Nuclear Pore,-1.453704545
+PAN3,Nuclear Pore,0.182418108
+TMEM123,Nuclear Pore,-0.288544099
+GJA1,Nuclear Pore,-0.417401898
+SLC30A6,Nuclear Pore,-0.241011145
+SAR1B,Nuclear Pore,-0.949522578
+GPR180,Nuclear Pore,-0.16024434
+UTRN,Nuclear Pore,0.031895412
+PTPRK,Nuclear Pore,0.046136586
+PLOD2,Nuclear Pore,-0.124434947
+GPR125,Nuclear Pore,-0.464256684
+SREK1IP1,Nuclear Pore,0.088878594
+TXNDC11,Nuclear Pore,0.173425053
+BCL2L11,Nuclear Pore,0.114995971
+CLGN,Nuclear Pore,-0.402938565
+RASSF3,Nuclear Pore,-0.014607311
+RANBP2,Nuclear Pore,0.000514209
+TMEM87B,Nuclear Pore,-0.192267497
+RBMS1,Nuclear Pore,0.270834198
+LPCAT1,Nuclear Pore,0.016805845
+UBALD1,Nuclear Pore,0.702767438
+RMND5A,Nuclear Pore,-0.186021699
+ZDHHC7,Nuclear Pore,0.838992776
+TRIP12,Nuclear Pore,-0.110592568
+CEBPG,Nuclear Pore,0.085524384
+SREK1,Nuclear Pore,0.477593163
+CHD1,Nuclear Pore,0.049205406
+DGKE,Nuclear Pore,0.503047252
+HS2ST1,Nuclear Pore,-0.596815689
+MSI2,Nuclear Pore,0.350267562
+CACNA2D1,Nuclear Pore,0.290808634
+NUS1,Nuclear Pore,-0.038072859
+IMPACT,Nuclear Pore,-0.783332043
+TBCEL,Nuclear Pore,-0.14510136
+FAM105B,Nuclear Pore,0.197592138
+TBRG1,Nuclear Pore,0.175174337
+CC2D1B,Nuclear Pore,0.357219344
+MIA3,Nuclear Pore,-0.729980705
+TRIM11,Nuclear Pore,0.226747627
+CCSAP,Nuclear Pore,0.029614717
+CXADR,Nuclear Pore,-0.19955815
+GABPA,Nuclear Pore,-0.325489098
+ADAMTS1,Nuclear Pore,0.278226355
+TSEN2,Nuclear Pore,-0.573933079
+FLCN,Nuclear Pore,0.520603929
+SKA1,Nuclear Pore,-0.368015768
+RAB6B,Nuclear Pore,-0.003338099
+ACSS1,Nuclear Pore,0.292750532
+ANKRD40,Nuclear Pore,0.323146884
+VOPP1,Nuclear Pore,-0.273147791
+APOOL,Nuclear Pore,-0.172225284
+CYP2U1,Nuclear Pore,-0.549454387
+AGPAT5,Nuclear Pore,-0.1673626
+MARVELD1,Nuclear Pore,-0.224959944
+ZFYVE27,Nuclear Pore,0.160728249
+SLC25A28,Nuclear Pore,1.352679866
+HSPA13,Nuclear Pore,-0.530880544
+USP25,Nuclear Pore,-0.314808973
+RHOC,Nuclear Pore,0.861834986
+SLC16A1,Nuclear Pore,-0.54458165
+LARP1,Nuclear Pore,0.215125509
+MIER3,Nuclear Pore,-0.056279556
+ZKSCAN2,Nuclear Pore,0.648270256
+PDIA4,Nuclear Pore,-0.080442229
+FAM126B,Nuclear Pore,0.052887196
+FZD7,Nuclear Pore,-0.32917027
+FMN2,Nuclear Pore,0.668681377
+PPARGC1B,Nuclear Pore,0.533522805
+SLC26A2,Nuclear Pore,0.058957956
+LSM11,Nuclear Pore,0.086030604
+PSD3,Nuclear Pore,-0.65629096
+DCK,Nuclear Pore,0.004185601
+ADAMTS3,Nuclear Pore,0.488339952
+DPY19L4,Nuclear Pore,-0.2929643
+NDUFAF6,Nuclear Pore,-0.193701725
+N6AMT1,Nuclear Pore,0.813845747
+CDK20,Nuclear Pore,1.214137064
+PCGF6,Nuclear Pore,-0.43130084
+ANKRD9,Nuclear Pore,0.786071791
+SFXN2,Nuclear Pore,0.300034821
+PTDSS1,Nuclear Pore,-0.044411458
+SUPV3L1,Nuclear Pore,-0.286643656
+TYSND1,Nuclear Pore,0.569094342
+CD109,Nuclear Pore,-0.65325165
+ZDHHC5,Nuclear Pore,0.34103001
+ZFAND3,Nuclear Pore,-0.018722647
+NPTN,Nuclear Pore,0.137520097
+KAT6B,Nuclear Pore,-0.580228039
+SAMD8,Nuclear Pore,-0.218996641
+BAG4,Nuclear Pore,-0.067589862
+ATAD2,Nuclear Pore,-0.509256077
+PHKG2,Nuclear Pore,0.783282808
+SASS6,Nuclear Pore,-0.079330994
+ZIC3,Nuclear Pore,0.87814575
+EXOG,Nuclear Pore,-0.108230038
+SMG1,Nuclear Pore,0.446868231
+FCHO2,Nuclear Pore,-0.309065941
+C1orf27,Nuclear Pore,0.454084049
+LRP8,Nuclear Pore,0.729542117
+PAXIP1,Nuclear Pore,-0.150563615
+SSBP3,Nuclear Pore,-0.073036377
+CLDN12,Nuclear Pore,0.301055486
+GATAD1,Nuclear Pore,-0.16930693
+ST3GAL2,Nuclear Pore,-0.383848905
+FUK,Nuclear Pore,0.064159002
+KIT,Nuclear Pore,-0.256296063
+AASDH,Nuclear Pore,-0.602656913
+DYRK1A,Nuclear Pore,0.172397348
+TSPAN18,Nuclear Pore,0.183186126
+SLC35B2,Nuclear Pore,0.319171163
+TMEM164,Nuclear Pore,0.28023617
+TAB3,Nuclear Pore,0.098458914
+SLC38A10,Nuclear Pore,0.391276216
+ZNF618,Nuclear Pore,0.472306546
+C9orf91,Nuclear Pore,-0.073325492
+UBN2,Nuclear Pore,0.198223547
+BRAF,Nuclear Pore,0.264878952
+SLC37A3,Nuclear Pore,0.386294491
+DPYSL5,Nuclear Pore,0.158069288
+FAM213B,Nuclear Pore,0.17327958
+C12orf43,Nuclear Pore,0.212862394
+RER1,Nuclear Pore,0.048127207
+UBXN11,Nuclear Pore,0.776461018
+RHPN1,Nuclear Pore,1.086149774
+CNNM4,Nuclear Pore,0.513621897
+EYA3,Nuclear Pore,0.223287284
+MRAS,Nuclear Pore,0.592945237
+COLEC12,Nuclear Pore,-0.221361255
+CUL4B,Nuclear Pore,-0.543281636
+MITD1,Nuclear Pore,-0.351084693
+EIF5B,Nuclear Pore,-0.710756426
+TSPAN33,Nuclear Pore,-0.010482281
+AHCYL2,Nuclear Pore,-0.086874354
+B4GALT5,Nuclear Pore,0.183263151
+TSR2,Nuclear Pore,-0.069830451
+ZC3H18,Nuclear Pore,0.630967934
+TMED4,Nuclear Pore,0.324537446
+PPP1R15B,Nuclear Pore,0.0240105
+AGPAT6,Nuclear Pore,0.168575706
+ZSCAN12,Nuclear Pore,0.046749865
+ELK4,Nuclear Pore,0.768168546
+F11R,Nuclear Pore,-0.170057497
+ZNF276,Nuclear Pore,0.228330438
+PINK1,Nuclear Pore,0.111532669
+B4GALT3,Nuclear Pore,0.620476533
+FAM160B2,Nuclear Pore,0.646800149
+CACHD1,Nuclear Pore,-0.11360329
+PAXBP1,Nuclear Pore,0.688746893
+IFNAR2,Nuclear Pore,-0.331843017
+SON,Nuclear Pore,0.108421112
+SV2A,Nuclear Pore,0.481425441
+HLCS,Nuclear Pore,-0.751392647
+ADPGK,Nuclear Pore,-0.060900475
+ALDH4A1,Nuclear Pore,0.618624257
+STARD9,Nuclear Pore,0.457591458
+UBR1,Nuclear Pore,0.17810938
+AMFR,Nuclear Pore,-0.053898196
+RSPRY1,Nuclear Pore,-0.171518401
+ARHGAP35,Nuclear Pore,-0.067838872
+CALM3,Nuclear Pore,0.279078716
+IQCC,Nuclear Pore,0.415830636
+BSDC1,Nuclear Pore,0.590459583
+ATAD3B,Nuclear Pore,1.025698605
+VMA21,Nuclear Pore,-0.055999249
+WDR4,Nuclear Pore,-0.011612982
+CBS,Nuclear Pore,0.755750407
+PDXK,Nuclear Pore,0.25895769
+G6PD,Nuclear Pore,0.898442635
+AGPAT3,Nuclear Pore,0.509598501
+C21orf2,Nuclear Pore,0.426864564
+LRRC3,Nuclear Pore,0.164864914
+LSS,Nuclear Pore,0.559641425
+VAV2,Nuclear Pore,0.413744563
+MCM3AP,Nuclear Pore,0.178814954
+C21orf58,Nuclear Pore,1.234973563
+PCNT,Nuclear Pore,0.125327567
+DIP2A,Nuclear Pore,0.856264556
+ZNF714,Nuclear Pore,-0.30885324
+PKN3,Nuclear Pore,0.944878252
+TAOK1,Nuclear Pore,0.275517231
+SIK3,Nuclear Pore,-0.310840197
+PCSK7,Nuclear Pore,0.597458996
+CHTOP,Nuclear Pore,0.128832546
+ZBTB7B,Nuclear Pore,1.284004567
+NLRX1,Nuclear Pore,0.03845734
+ANO10,Nuclear Pore,-0.212349334
+SLC25A44,Nuclear Pore,0.003267616
+NBEAL2,Nuclear Pore,0.774886317
+IER2,Nuclear Pore,0.524414286
+ZNF394,Nuclear Pore,-0.358925704
+CPSF4,Nuclear Pore,0.459032747
+TONSL,Nuclear Pore,1.096624618
+MUM1,Nuclear Pore,1.057016526
+RECQL4,Nuclear Pore,0.940583244
+LRRC14,Nuclear Pore,1.047973627
+PPP1R16A,Nuclear Pore,1.291891662
+C5orf45,Nuclear Pore,1.089502186
+MFSD12,Nuclear Pore,0.924422945
+FDXR,Nuclear Pore,0.837472328
+ALDH16A1,Nuclear Pore,0.074370416
+ITGA5,Nuclear Pore,0.496854676
+ZNF385A,Nuclear Pore,1.416262082
+MPP3,Nuclear Pore,-0.758657199
+EMC10,Nuclear Pore,0.260599833
+FAM171A2,Nuclear Pore,0.426422052
+DBF4B,Nuclear Pore,0.871232155
+LARP4,Nuclear Pore,-0.852314028
+LEMD2,Nuclear Pore,0.33732429
+WDR90,Nuclear Pore,1.402581674
+C16orf59,Nuclear Pore,0.767275989
+AMDHD2,Nuclear Pore,0.344985256
+PAQR4,Nuclear Pore,0.556015319
+ADCY9,Nuclear Pore,0.224526137
+CLPB,Nuclear Pore,0.15450573
+NEU3,Nuclear Pore,0.383477033
+CYB561A3,Nuclear Pore,0.220395008
+TAF6L,Nuclear Pore,0.164606576
+LRP5,Nuclear Pore,0.389653455
+ZYG11B,Nuclear Pore,-0.024568923
+PPAP2B,Nuclear Pore,0.219900453
+PRKAA2,Nuclear Pore,-0.490492048
+KLHL21,Nuclear Pore,0.493532744
+GMEB1,Nuclear Pore,0.338528047
+SEPN1,Nuclear Pore,0.60111468
+AK4,Nuclear Pore,-0.543908794
+RAVER2,Nuclear Pore,-0.4568267
+PDPN,Nuclear Pore,0.026555659
+SDC3,Nuclear Pore,0.041529495
+KIAA1522,Nuclear Pore,0.926203646
+C1orf86,Nuclear Pore,1.061771582
+NFIA,Nuclear Pore,0.190303821
+OMA1,Nuclear Pore,-0.378098033
+MYSM1,Nuclear Pore,0.799507449
+FUBP1,Nuclear Pore,-0.053821047
+DNAJB4,Nuclear Pore,0.17701883
+FAM102B,Nuclear Pore,-0.471355755
+ATXN7L2,Nuclear Pore,0.564364981
+ZNF326,Nuclear Pore,-0.072346007
+EXTL2,Nuclear Pore,-0.427976429
+SLC30A7,Nuclear Pore,-0.276255295
+PEA15,Nuclear Pore,-0.017168441
+NCSTN,Nuclear Pore,0.282706844
+VANGL2,Nuclear Pore,0.459199173
+FLVCR1,Nuclear Pore,0.017494072
+RBM15,Nuclear Pore,0.870296004
+BPNT1,Nuclear Pore,-0.031937845
+BROX,Nuclear Pore,-0.342465777
+ACP6,Nuclear Pore,0.125908323
+PPP1R21,Nuclear Pore,0.209964427
+B3GALNT2,Nuclear Pore,-0.368618695
+C2orf47,Nuclear Pore,-0.632282388
+ARL5A,Nuclear Pore,-0.355489882
+SGCB,Nuclear Pore,-0.983346125
+SMARCAD1,Nuclear Pore,-0.03042448
+RNF149,Nuclear Pore,-0.344986155
+FZD5,Nuclear Pore,-0.881926342
+DCAF16,Nuclear Pore,0.024783477
+PAQR3,Nuclear Pore,-0.493282224
+ANTXR2,Nuclear Pore,-1.615433253
+PBXIP1,Nuclear Pore,0.661434787
+PYGO2,Nuclear Pore,0.298835925
+HIPK1,Nuclear Pore,-0.039390486
+KBTBD8,Nuclear Pore,-0.138606604
+EOGT,Nuclear Pore,0.461415444
+POGLUT1,Nuclear Pore,-0.245767344
+ATP1A1,Nuclear Pore,-0.271223039
+EIF4E3,Nuclear Pore,-0.161875987
+LRRC58,Nuclear Pore,-0.757520194
+FSTL1,Nuclear Pore,-0.580959628
+KRTCAP2,Nuclear Pore,0.520935328
+KIAA1524,Nuclear Pore,-0.029920628
+TGFBR2,Nuclear Pore,-1.118284525
+ANKZF1,Nuclear Pore,0.664656296
+STT3B,Nuclear Pore,-0.486043493
+PPM1L,Nuclear Pore,0.850638402
+RYBP,Nuclear Pore,-0.221494601
+PPP4R2,Nuclear Pore,-0.505829826
+C3orf17,Nuclear Pore,-0.31336404
+SPICE1,Nuclear Pore,-0.041165138
+WDFY3,Nuclear Pore,0.466611434
+ATXN7,Nuclear Pore,0.096220138
+PPM1K,Nuclear Pore,0.17111066
+CCNL1,Nuclear Pore,0.724497682
+RPP14,Nuclear Pore,0.26215317
+ABHD6,Nuclear Pore,-0.158234558
+CRELD1,Nuclear Pore,0.604045342
+U2SURP,Nuclear Pore,-0.242426603
+TTC14,Nuclear Pore,-0.230559642
+SNRK,Nuclear Pore,-0.034969733
+SLC4A1AP,Nuclear Pore,-0.662679797
+ZDHHC3,Nuclear Pore,-0.140376774
+FYCO1,Nuclear Pore,0.074779923
+YEATS2,Nuclear Pore,0.343179697
+SNIP1,Nuclear Pore,0.476593868
+TMEM41A,Nuclear Pore,-0.31405219
+RPN1,Nuclear Pore,-0.160793629
+SFMBT1,Nuclear Pore,0.046711318
+PBRM1,Nuclear Pore,-0.205970178
+FAM208A,Nuclear Pore,-0.409407817
+ARHGEF3,Nuclear Pore,0.821403959
+UBXN7,Nuclear Pore,-0.122635267
+ZNF691,Nuclear Pore,-0.227170957
+SGMS2,Nuclear Pore,0.329658621
+DNAJB14,Nuclear Pore,-0.067583472
+ZNF589,Nuclear Pore,0.181746055
+SHISA5,Nuclear Pore,0.331637947
+INTU,Nuclear Pore,0.114903021
+RNF123,Nuclear Pore,0.389589365
+MFSD8,Nuclear Pore,0.277739304
+C4orf29,Nuclear Pore,0.330641552
+RAD54L2,Nuclear Pore,0.069309599
+MAP9,Nuclear Pore,-0.167434135
+CEP44,Nuclear Pore,0.430467508
+ABCE1,Nuclear Pore,-0.53976441
+TMEM184C,Nuclear Pore,0.104950429
+TMEM161B,Nuclear Pore,0.711679733
+ELOVL7,Nuclear Pore,-0.317330816
+LMBRD2,Nuclear Pore,-0.173290591
+NIPBL,Nuclear Pore,-0.236315091
+SLC25A46,Nuclear Pore,-0.372386969
+STARD4,Nuclear Pore,0.081680581
+PGGT1B,Nuclear Pore,-0.050859221
+NDUFS4,Nuclear Pore,-0.696954861
+ARSK,Nuclear Pore,-0.157922186
+GPX8,Nuclear Pore,-0.814421005
+SERINC5,Nuclear Pore,0.395505686
+GFM2,Nuclear Pore,-0.337316087
+CCDC127,Nuclear Pore,-0.048432783
+SEPT8,Nuclear Pore,-0.055600715
+DCBLD1,Nuclear Pore,0.028069599
+PDSS2,Nuclear Pore,-0.401975064
+STXBP5,Nuclear Pore,-0.553131148
+DAGLB,Nuclear Pore,0.490930349
+GALNT10,Nuclear Pore,0.408802603
+ZNF12,Nuclear Pore,-0.546238432
+USP49,Nuclear Pore,1.206506695
+ZNF704,Nuclear Pore,-0.211130916
+LMTK2,Nuclear Pore,0.041292509
+CTSB,Nuclear Pore,-0.154217556
+ADCY1,Nuclear Pore,0.61478059
+EN2,Nuclear Pore,0.47260687
+SUN1,Nuclear Pore,0.417964459
+OXR1,Nuclear Pore,0.554552245
+SLC4A2,Nuclear Pore,0.234924893
+FASTK,Nuclear Pore,1.153338058
+TMUB1,Nuclear Pore,0.952023178
+C7orf55,Nuclear Pore,0.915975326
+FOXK1,Nuclear Pore,0.605443982
+FZD6,Nuclear Pore,0.154258251
+KIAA1429,Nuclear Pore,0.22327127
+TMEM67,Nuclear Pore,0.384105163
+SNAPC3,Nuclear Pore,-0.389435195
+KIAA1161,Nuclear Pore,-0.16810544
+METTL2B,Nuclear Pore,-0.344471323
+HGSNAT,Nuclear Pore,0.223094168
+RASEF,Nuclear Pore,-0.089079672
+ANKS6,Nuclear Pore,0.365468154
+TMEM246,Nuclear Pore,-0.088068378
+ZHX1,Nuclear Pore,0.069764924
+KIAA1958,Nuclear Pore,0.531855893
+PIGA,Nuclear Pore,-0.305743814
+WNK2,Nuclear Pore,-0.111498907
+ATP7A,Nuclear Pore,-0.587207284
+PIGO,Nuclear Pore,-0.073982304
+BRWD3,Nuclear Pore,0.650720157
+SLITRK5,Nuclear Pore,0.185570951
+DDX26B,Nuclear Pore,-0.733979797
+MARCH8,Nuclear Pore,-0.501058523
+GTF2A1,Nuclear Pore,-0.479622643
+ZCCHC24,Nuclear Pore,-0.12797941
+REEP3,Nuclear Pore,-0.085248547
+MICU2,Nuclear Pore,-0.695120611
+PCF11,Nuclear Pore,0.240417608
+PKNOX2,Nuclear Pore,-0.020376305
+ZNF22,Nuclear Pore,-1.162026354
+RPUSD4,Nuclear Pore,0.026501207
+ARF6,Nuclear Pore,-0.081614603
+TTC8,Nuclear Pore,-0.019606929
+CDX2,Nuclear Pore,-0.490185498
+BEND7,Nuclear Pore,-0.01339955
+TAF3,Nuclear Pore,0.11494844
+PDZD8,Nuclear Pore,-0.80634129
+ZNF503,Nuclear Pore,0.811379754
+FAM175B,Nuclear Pore,0.055010754
+QSOX2,Nuclear Pore,0.503188806
+NSD1,Nuclear Pore,0.108120887
+SNAPC4,Nuclear Pore,0.469997034
+PMPCA,Nuclear Pore,0.053252429
+SDCCAG3,Nuclear Pore,0.806259515
+TSC1,Nuclear Pore,0.410155832
+FAM69B,Nuclear Pore,0.315689496
+KIAA1462,Nuclear Pore,-1.002464687
+ZNF219,Nuclear Pore,-0.527370769
+METTL3,Nuclear Pore,0.347061377
+HSPA12A,Nuclear Pore,-0.080002069
+TC2N,Nuclear Pore,-0.350658549
+CPSF2,Nuclear Pore,-0.028850438
+ARL5B,Nuclear Pore,-0.403967379
+TAF1D,Nuclear Pore,0.380896305
+HTRA1,Nuclear Pore,-1.049109335
+CEP57,Nuclear Pore,-0.199128627
+JAM3,Nuclear Pore,0.021023478
+HIF1AN,Nuclear Pore,-0.12574423
+ZFYVE19,Nuclear Pore,-0.721448762
+FBN1,Nuclear Pore,-0.453122295
+BAG5,Nuclear Pore,0.081091189
+GABRB3,Nuclear Pore,-0.39236826
+SGPL1,Nuclear Pore,0.184571373
+FRS2,Nuclear Pore,0.375495252
+ZNF202,Nuclear Pore,0.574642805
+STXBP4,Nuclear Pore,-0.213131202
+CUL5,Nuclear Pore,-0.678190804
+WBP1L,Nuclear Pore,0.153294522
+TRIM44,Nuclear Pore,-0.400333046
+TPP1,Nuclear Pore,0.078395005
+C11orf74,Nuclear Pore,-0.380751801
+TUB,Nuclear Pore,0.190629184
+RNF169,Nuclear Pore,0.016789496
+PRTG,Nuclear Pore,-0.15247993
+TMEM41B,Nuclear Pore,-0.047911257
+TMX3,Nuclear Pore,-0.234397786
+WEE1,Nuclear Pore,0.195084508
+ZNF3,Nuclear Pore,0.002190408
+RIMKLB,Nuclear Pore,0.313408877
+TMED3,Nuclear Pore,-0.253129641
+NDEL1,Nuclear Pore,0.429062928
+BLCAP,Nuclear Pore,0.029932733
+CASC4,Nuclear Pore,0.006173138
+AP1G1,Nuclear Pore,-0.6465209
+KIF7,Nuclear Pore,0.558521314
+PEX11A,Nuclear Pore,0.060515086
+ZBTB39,Nuclear Pore,-0.191932716
+TMEM194A,Nuclear Pore,-0.141213616
+SMAD3,Nuclear Pore,0.156735913
+MAP1A,Nuclear Pore,-0.197062023
+MBD6,Nuclear Pore,0.202185467
+PDIA3,Nuclear Pore,-0.544139574
+ACSF2,Nuclear Pore,0.549229453
+COQ4,Nuclear Pore,0.431113639
+SLC27A4,Nuclear Pore,0.02124236
+CERCAM,Nuclear Pore,0.52304245
+DOLPP1,Nuclear Pore,-0.127016924
+GPRC5B,Nuclear Pore,-0.15654347
+CRK,Nuclear Pore,0.034559291
+FBXO22,Nuclear Pore,0.189871475
+TBC1D2B,Nuclear Pore,0.260801456
+CDK12,Nuclear Pore,-0.084014019
+ENGASE,Nuclear Pore,0.802201267
+TBC1D16,Nuclear Pore,0.113636996
+ENTHD2,Nuclear Pore,0.914305228
+STIM1,Nuclear Pore,-0.131223706
+IRGQ,Nuclear Pore,0.631215078
+PPP2R3B,Nuclear Pore,0.712924359
+ZNF646,Nuclear Pore,0.532906276
+MIDN,Nuclear Pore,0.681336711
+MVD,Nuclear Pore,0.686974991
+ANKRD11,Nuclear Pore,0.490645725
+SPATA33,Nuclear Pore,-0.477897959
+ZNF641,Nuclear Pore,0.701401456
+DHRS13,Nuclear Pore,0.616211208
+TP53I13,Nuclear Pore,0.256755301
+KMT2D,Nuclear Pore,0.977223948
+C19orf55,Nuclear Pore,0.793147264
+LENG8,Nuclear Pore,1.321870859
+ZNF146,Nuclear Pore,-0.42699762
+ZNF444,Nuclear Pore,1.376188142
+FAM57A,Nuclear Pore,0.325374117
+SLC43A2,Nuclear Pore,-0.435781347
+SRR,Nuclear Pore,-0.974625229
+GHDC,Nuclear Pore,0.026136507
+ITFG3,Nuclear Pore,0.265821369
+ZNF598,Nuclear Pore,0.78489705
+E4F1,Nuclear Pore,0.8809765
+ABCA3,Nuclear Pore,0.119326698
+SRRM2,Nuclear Pore,0.427257079
+LTBP3,Nuclear Pore,1.213286674
+SAC3D1,Nuclear Pore,0.836335606
+SF1,Nuclear Pore,0.534676569
+PAFAH1B2,Nuclear Pore,-0.633851904
+ANKS3,Nuclear Pore,-0.019394451
+SETD5,Nuclear Pore,0.37731364
+HOOK3,Nuclear Pore,0.045350927
+RBPJ,Nuclear Pore,-0.082465799
+TTC39C,Nuclear Pore,0.433260668
+KIF5C,Nuclear Pore,-0.257875728
+MGAT2,Nuclear Pore,-0.509678494
+BMI1,Nuclear Pore,-0.318504958
+KCTD6,Nuclear Pore,0.286238101
+TAP1,Nuclear Pore,0.059828555
+ING5,Nuclear Pore,0.451438319
+ATG4B,Nuclear Pore,0.797464539
+SOGA2,Nuclear Pore,0.393828208
+SNRNP48,Nuclear Pore,-0.14848555
+SLC20A2,Nuclear Pore,-0.372405777
+TMUB2,Nuclear Pore,0.65460393
+STAT3,Nuclear Pore,0.171522622
+ADAM9,Nuclear Pore,-0.319514227
+PKIG,Nuclear Pore,0.779136759
+SEMA4C,Nuclear Pore,0.985395623
+CNNM3,Nuclear Pore,0.275138026
+TET2,Nuclear Pore,0.142350515
+TCTN2,Nuclear Pore,0.353840503
+TSPAN5,Nuclear Pore,-0.581736931
+ZBTB5,Nuclear Pore,0.104897707
+SNTB2,Nuclear Pore,-0.378446213
+ZNF507,Nuclear Pore,0.200378944
+STX18,Nuclear Pore,-0.053206107
+GFM1,Nuclear Pore,-0.596362384
+ANKRD49,Nuclear Pore,0.057701337
+MAT2A,Nuclear Pore,0.795518213
+ZNF608,Nuclear Pore,0.686238653
+LETM1,Nuclear Pore,0.19768585
+TMEM129,Nuclear Pore,-0.138446008
+FEM1B,Nuclear Pore,0.098712363
+HNRNPH1,Nuclear Pore,0.59849495
+MECP2,Nuclear Pore,0.439482967
+UPF3A,Nuclear Pore,1.323384447
+CHST14,Nuclear Pore,0.107927837
+PARM1,Nuclear Pore,0.115192608
+CSNK1G1,Nuclear Pore,-0.174481415
+ZBTB43,Nuclear Pore,0.487869138
+GPRIN1,Nuclear Pore,0.067567815
+MRPL1,Nuclear Pore,-0.10053285
+SLC33A1,Nuclear Pore,0.447997805
+SDC2,Nuclear Pore,-0.569672154
+MMGT1,Nuclear Pore,-0.556200648
+CLIC4,Nuclear Pore,-0.580510534
+CCDC8,Nuclear Pore,0.183316875
+INO80E,Nuclear Pore,0.586151554
+DFFB,Nuclear Pore,0.73980031
+ANTXR1,Nuclear Pore,-0.15697321
+CKAP2L,Nuclear Pore,-0.596336559
+C15orf40,Nuclear Pore,-0.698140341
+HIC2,Nuclear Pore,0.858468971
+LUZP1,Nuclear Pore,-0.522274572
+HEXDC,Nuclear Pore,1.745231685
+LRRC45,Nuclear Pore,0.478051415
+ASPSCR1,Nuclear Pore,0.551430707
+TAPT1,Nuclear Pore,-1.220876579
+CSGALNACT2,Nuclear Pore,-0.161969518
+PCDH7,Nuclear Pore,-0.026824022
+ROBO1,Nuclear Pore,-0.34897611
+P2RY1,Nuclear Pore,1.145300205
+TPST1,Nuclear Pore,0.05893386
+TOR1AIP2,Nuclear Pore,-0.225127647
+OTUD3,Nuclear Pore,0.288893072
+GUSB,Nuclear Pore,0.10086538
+BRD3,Nuclear Pore,0.139069525
+MAP3K2,Nuclear Pore,0.312451512
+NLGN2,Nuclear Pore,0.99456124
+ALCAM,Nuclear Pore,0.069430195
+YWHAG,Nuclear Pore,-0.031251522
+TMEM192,Nuclear Pore,0.256439909
+ZNF778,Nuclear Pore,-0.18606449
+NIPA1,Nuclear Pore,-0.303917684
+SIK2,Nuclear Pore,0.328916098
+RNF150,Nuclear Pore,-0.616649581
+ZNF212,Nuclear Pore,0.263899919
+FAM161A,Nuclear Pore,-0.231663497
+CRTAP,Nuclear Pore,-0.311168305
+PRDM10,Nuclear Pore,-0.29749997
+FOS,Nuclear Pore,0.092333571
+TMED10,Nuclear Pore,-0.302040559
+SLC30A1,Nuclear Pore,-0.502163684
+DNAJC18,Nuclear Pore,0.03327778
+RALGAPB,Nuclear Pore,0.014429831
+LONRF2,Nuclear Pore,-0.489917032
+ELOVL6,Nuclear Pore,0.308641078
+ARL6IP1,Nuclear Pore,-0.348574081
+CDH2,Nuclear Pore,-0.113012274
+EMB,Nuclear Pore,-0.713191717
+STAT2,Nuclear Pore,0.974815759
+TRABD,Nuclear Pore,0.944800006
+POLH,Nuclear Pore,0.278896601
+KIF5B,Nuclear Pore,-0.415470984
+AKAP13,Nuclear Pore,0.224776304
+CHCHD7,Nuclear Pore,0.130287172
+GPR27,Nuclear Pore,-0.23552724
+KBTBD2,Nuclear Pore,-0.137177067
+KIAA0232,Nuclear Pore,-0.077217968
+TMEM43,Nuclear Pore,-0.006109376
+RNF139,Nuclear Pore,-0.28282054
+PAQR8,Nuclear Pore,0.190402012
+TANC2,Nuclear Pore,-0.320035932
+DNAJC24,Nuclear Pore,0.210844346
+HS6ST2,Nuclear Pore,-0.451320997
+INSR,Nuclear Pore,0.457793133
+ATP6V0E2,Nuclear Pore,0.180217001
+ZNF692,Nuclear Pore,1.223799204
+NETO2,Nuclear Pore,-0.437897966
+NPTX1,Nuclear Pore,0.648319457
+FAM98B,Nuclear Pore,-0.756081771
+GAA,Nuclear Pore,0.330627605
+CANT1,Nuclear Pore,0.460325678
+CHST11,Nuclear Pore,-0.251768458
+CLCN5,Nuclear Pore,0.075248389
+ZBTB26,Nuclear Pore,-0.249346487
+ZNF562,Nuclear Pore,-0.257706316
+ZNF318,Nuclear Pore,-0.024934673
+WIPF2,Nuclear Pore,0.393306352
+LRRC8C,Nuclear Pore,-0.459641207
+LRRC8D,Nuclear Pore,-0.303183354
+ETFDH,Nuclear Pore,-0.917959422
+LPAR3,Nuclear Pore,0.222221555
+CLSTN1,Nuclear Pore,0.351722493
+BPTF,Nuclear Pore,0.064918215
+ATF7IP,Nuclear Pore,-0.123699365
+TCEA2,Nuclear Pore,0.444414259
+ANO5,Nuclear Pore,-0.324973118
+MLLT3,Nuclear Pore,-0.249748495
+PRNP,Nuclear Pore,0.153139659
+ZNF217,Nuclear Pore,-0.380598445
+JMJD1C,Nuclear Pore,0.400739042
+THOP1,Nuclear Pore,0.633645233
+ORMDL3,Nuclear Pore,0.470687761
+KLF11,Nuclear Pore,0.858726347
+MTBP,Nuclear Pore,0.513555013
+ZNF131,Nuclear Pore,0.17279011
+BSG,Nuclear Pore,0.271287349
+CERS6,Nuclear Pore,-0.102754226
+TP53RK,Nuclear Pore,-0.137576602
+FAM195A,Nuclear Pore,0.818043728
+GTPBP2,Nuclear Pore,0.505636043
+ZNF24,Nuclear Pore,-0.283447797
+MANEA,Nuclear Pore,-0.19324722
+RAD9A,Nuclear Pore,0.768573586
+FAM21C,Nuclear Pore,-0.092347263
+CORO1B,Nuclear Pore,0.842415089
+LRRC20,Nuclear Pore,0.685976222
+NAA16,Nuclear Pore,0.625711704
+DCP2,Nuclear Pore,-0.050898659
+CES3,Nuclear Pore,1.22294188
+CES2,Nuclear Pore,0.206610383
+PDP2,Nuclear Pore,0.009156733
+SP3,Nuclear Pore,-0.070694877
+METAP1D,Nuclear Pore,0.422457178
+ZNF621,Nuclear Pore,0.185903595
+NADSYN1,Nuclear Pore,0.604364408
+DHCR7,Nuclear Pore,0.23811947
+NBEA,Nuclear Pore,0.228428647
+ANKRD13D,Nuclear Pore,1.335835355
+LCLAT1,Nuclear Pore,-0.113434147
+TADA2B,Nuclear Pore,0.707567785
+HECTD4,Nuclear Pore,1.064577096
+ESRRA,Nuclear Pore,0.267513738
+AHSA2,Nuclear Pore,1.219031507
+VANGL1,Nuclear Pore,-0.022919494
+IQCB1,Nuclear Pore,-0.162498249
+GOLGB1,Nuclear Pore,-0.211199477
+TNKS,Nuclear Pore,-0.212169376
+ZBTB21,Nuclear Pore,-0.04288631
+STOX2,Nuclear Pore,0.023986043
+DAG1,Nuclear Pore,0.282144608
+RNF26,Nuclear Pore,0.042097416
+PEAK1,Nuclear Pore,-0.403156876
+TNFRSF10D,Nuclear Pore,-0.0511877
+MOB1B,Nuclear Pore,0.010863413
+SNX33,Nuclear Pore,0.292464596
+CHD2,Nuclear Pore,0.242312614
+CCDC41,Nuclear Pore,0.139132098
+PC,Nuclear Pore,0.850204613
+SUSD5,Nuclear Pore,-0.33051193
+HEG1,Nuclear Pore,-0.804794374
+TOMM20,Nuclear Pore,-0.295913263
+CNP,Nuclear Pore,0.123029762
+DPY19L1,Nuclear Pore,-0.7330561
+ZNF791,Nuclear Pore,0.162510703
+PHC3,Nuclear Pore,0.217833951
+GOLIM4,Nuclear Pore,-0.391679239
+XXYLT1,Nuclear Pore,0.145905715
+UBXN2A,Nuclear Pore,-0.571912895
+CCS,Nuclear Pore,1.038892288
+FAM3C2,Nuclear Pore,-0.777229522
+CTSF,Nuclear Pore,-0.204818274
+MSRB3,Nuclear Pore,-0.58160372
+LEMD3,Nuclear Pore,-0.609433493
+RGMB,Nuclear Pore,-0.822759393
+ZDHHC24,Nuclear Pore,0.778961908
+MGA,Nuclear Pore,-0.318138633
+PIGG,Nuclear Pore,0.808999338
+ADCY6,Nuclear Pore,0.503786148
+ZBTB4,Nuclear Pore,0.537521776
+ZHX3,Nuclear Pore,-0.348601155
+RALGAPA1,Nuclear Pore,0.385453996
+ATP2A2,Nuclear Pore,0.062955102
+CNTNAP2,Nuclear Pore,-1.185631517
+DENND4A,Nuclear Pore,-0.813285276
+MSL2,Nuclear Pore,0.003195732
+UGT8,Nuclear Pore,-0.465884135
+ZNF266,Nuclear Pore,0.327611819
+SLC29A2,Nuclear Pore,1.04803302
+BRSK2,Nuclear Pore,1.1674779
+B3GNT1,Nuclear Pore,-0.238323083
+TMEM167A,Nuclear Pore,-0.190395754
+CEP135,Nuclear Pore,0.134516173
+FZD4,Nuclear Pore,1.080835522
+PDE12,Nuclear Pore,-0.20867467
+GLMN,Nuclear Pore,-0.215277939
+SEZ6L2,Nuclear Pore,0.431941147
+KLC2,Nuclear Pore,0.813398336
+GK5,Nuclear Pore,0.223786598
+VCPIP1,Nuclear Pore,0.658601658
+PCCA,Nuclear Pore,-0.89277713
+GOLGA8A,Nuclear Pore,0.646809851
+TP53I11,Nuclear Pore,-0.134874693
+PHYKPL,Nuclear Pore,0.75744493
+ARL10,Nuclear Pore,0.058802952
+CCDC14,Nuclear Pore,-0.050977464
+ALG10B,Nuclear Pore,-0.143546533
+RAB6A,Nuclear Pore,-0.045958511
+ERCC4,Nuclear Pore,-0.165271164
+RMI2,Nuclear Pore,-0.568758159
+TOM1L2,Nuclear Pore,0.680217299
+MLXIP,Nuclear Pore,0.729534923
+SLC35E3,Nuclear Pore,-0.406883292
+ARL4D,Nuclear Pore,0.761754473
+LYSMD3,Nuclear Pore,0.220433813
+B3GALT6,Nuclear Pore,-0.255009063
+MBLAC2,Nuclear Pore,-1.311959413
+TPRN,Nuclear Pore,0.251322082
+YES1,Nuclear Pore,-0.412533337
+TMEM39A,Nuclear Pore,-0.107848271
+CCDC57,Nuclear Pore,1.05374454
+FOXG1,Nuclear Pore,1.330531107
+ATAD5,Nuclear Pore,0.149485288
+ANAPC2,Nuclear Pore,1.211436306
+SPRYD4,Nuclear Pore,-0.103661928
+CLK2,Nuclear Pore,0.703062084
+LPCAT4,Nuclear Pore,1.268988045
+B3GNT5,Nuclear Pore,0.8008311
+LMNB2,Nuclear Pore,0.569067578
+RMDN1,Nuclear Pore,0.055770351
+ACSF3,Nuclear Pore,0.033696369
+ANKLE2,Nuclear Pore,0.508379054
+NFATC2IP,Nuclear Pore,0.426218039
+SMCR8,Nuclear Pore,0.184910597
+MTX3,Nuclear Pore,0.161929454
+FBXO46,Nuclear Pore,1.163745334
+WDR73,Nuclear Pore,0.360809478
+ANO6,Nuclear Pore,-0.530439561
+ZBTB34,Nuclear Pore,-0.029318853
+FAM210A,Nuclear Pore,-0.140982437
+ULK1,Nuclear Pore,1.184695937
+RPS6KA3,Nuclear Pore,-0.545796525
+PUS1,Nuclear Pore,0.28511149
+CHD9,Nuclear Pore,0.192780177
+PDDC1,Nuclear Pore,0.86338571
+TOP3A,Nuclear Pore,0.141011145
+NR2C2,Nuclear Pore,1.030677393
+ZBTB33,Nuclear Pore,0.139048717
+SLC25A22,Nuclear Pore,0.530747145
+PIDD,Nuclear Pore,1.684907468
+GBA,Nuclear Pore,-0.438939466
+IL17RA,Nuclear Pore,0.572033075
+THAP5,Nuclear Pore,-0.903436684
+PVRL3,Nuclear Pore,0.082951123
+KIAA0195,Nuclear Pore,0.809080573
+SOX12,Nuclear Pore,0.353628271
+CHID1,Nuclear Pore,0.021806088
+ZNF518A,Nuclear Pore,-0.586118118
+ZBTB41,Nuclear Pore,-0.137932607
+DMAP1,Nuclear Pore,1.327884395
+C2orf69,Nuclear Pore,-1.441864369
+SH2B1,Nuclear Pore,1.052867523
+KDELC2,Nuclear Pore,-0.204224954
+GALNT11,Nuclear Pore,-0.019419575
+WDR6,Nuclear Pore,0.65999245
+GEN1,Nuclear Pore,0.213894436
+GLDC,Nuclear Pore,-0.923730552
+CTNNBIP1,Nuclear Pore,0.207943878
+ERN1,Nuclear Pore,0.239928887
+KCTD12,Nuclear Pore,-0.043193095
+DHFRL1,Nuclear Pore,-0.895996365
+FAM132B,Nuclear Pore,1.154234304
+FAM219B,Nuclear Pore,0.689039827
+DPY19L3,Nuclear Pore,-0.251135077
+PFAS,Nuclear Pore,0.781130902
+C17orf62,Nuclear Pore,1.010589998
+ZBTB7A,Nuclear Pore,1.229709393
+SPTY2D1,Nuclear Pore,-0.371507613
+FUCA1,Nuclear Pore,-0.055232055
+CALR,Nuclear Pore,-0.156797316
+LDLRAD3,Nuclear Pore,-0.51682083
+CLK3,Nuclear Pore,0.738942733
+PACS2,Nuclear Pore,0.697118627
+ELMOD2,Nuclear Pore,-0.169848659
+FJX1,Nuclear Pore,0.646453286
+ZBTB18,Nuclear Pore,0.730414085
+GCC1,Nuclear Pore,-0.091971032
+PLD6,Nuclear Pore,-0.110693588
+CDC42EP4,Nuclear Pore,0.775311691
+PCBP1-AS1,Nuclear Pore,0.445781721
+MYADM,Nuclear Pore,0.629783104
+SERTAD2,Nuclear Pore,-0.356884748
+BBS10,Nuclear Pore,0.401998502
+SOCS4,Nuclear Pore,0.599544878
+ZADH2,Nuclear Pore,0.055699609
+EXOC3,Nuclear Pore,-0.041430905
+C7orf41,Nuclear Pore,-0.557337009
+ZNF609,Nuclear Pore,0.58709869
+CCDC66,Nuclear Pore,0.311043149
+MCFD2,Nuclear Pore,-0.239282939
+GAS1,Nuclear Pore,0.015530462
+FAM73A,Nuclear Pore,0.169792722
+NRIP1,Nuclear Pore,-0.165492197
+PCGF5,Nuclear Pore,-0.134047478
+YOD1,Nuclear Pore,-0.039159251
+SLC36A4,Nuclear Pore,-0.709649301
+ZDHHC20,Nuclear Pore,-0.447225126
+PSMG4,Nuclear Pore,-0.053846717
+PDIA3P,Nuclear Pore,-0.508389677
+CUEDC1,Nuclear Pore,-0.24292825
+KCTD2,Nuclear Pore,-0.498451162
+D2HGDH,Nuclear Pore,0.264168085
+FKRP,Nuclear Pore,-0.304684151
+SLC26A11,Nuclear Pore,0.572768228
+F2R,Nuclear Pore,-0.813764834
+DHTKD1,Nuclear Pore,0.001960993
+ZNF746,Nuclear Pore,0.914349637
+TMEM136,Nuclear Pore,-1.467462451
+ZNF322,Nuclear Pore,-0.325638569
+OGFOD3,Nuclear Pore,0.850547923
+ZNF678,Nuclear Pore,0.160836024
+ZBTB2,Nuclear Pore,-0.35087373
+SGSH,Nuclear Pore,0.690227445
+SETD2,Nuclear Pore,0.220853794
+YIPF6,Nuclear Pore,-0.441525385
+IBA57,Nuclear Pore,1.153860537
+C5orf24,Nuclear Pore,0.155447106
+ADO,Nuclear Pore,0.052262737
+CREB3L2,Nuclear Pore,0.036064426
+UNC5C,Nuclear Pore,-1.651138679
+RGMA,Nuclear Pore,0.500162564
+EXT1,Nuclear Pore,-0.758088176
+ATP6AP2,Nuclear Pore,0.149619614
+BACE2,Nuclear Pore,-0.459587268
+FIGN,Nuclear Pore,0.710210265
+B4GALNT4,Nuclear Pore,0.453491315
+AP1S2,Nuclear Pore,-0.299142717
+FBXL6,Nuclear Pore,0.821122004
+YBEY,Nuclear Pore,0.922543124
+CLN8,Nuclear Pore,0.066220232
+PLCXD1,Nuclear Pore,1.093297287
+EXOC7,Nuclear Pore,0.584495273
+CEP97,Nuclear Pore,0.449295433
+MXRA7,Nuclear Pore,-0.299223316
+SATB1,Nuclear Pore,0.757381594
+PLCB1,Nuclear Pore,0.621371721
+TTC3,Nuclear Pore,0.009956393
+COL18A1,Nuclear Pore,0.386800345
+ZNF721,Nuclear Pore,-1.032660375
+SRPR,Nuclear Pore,-0.46277931
+EWSR1,Nuclear Pore,0.351888763
+GJC1,Nuclear Pore,0.404016557
+MTA1,Nuclear Pore,0.83008056
+CADM1,Nuclear Pore,-0.300341125
+LYSMD4,Nuclear Pore,0.462481633
+NKX2-5,Nuclear Pore,0.688248349
+GPC6,Nuclear Pore,-0.138578566
+PTTG1IP,Nuclear Pore,0.15127223
+ZNF623,Nuclear Pore,0.333535235
+BCOR,Nuclear Pore,-0.121418031
+ASB7,Nuclear Pore,0.378151817
+EP400,Nuclear Pore,0.100799758
+COA5,Nuclear Pore,0.063266902
+PRR14L,Nuclear Pore,0.194338804
+ZNRF3,Nuclear Pore,0.273261399
+TRMT12,Nuclear Pore,-1.287440276
+FAM101B,Nuclear Pore,0.068728785
+TRIM52,Nuclear Pore,0.40810903
+CMTM4,Nuclear Pore,0.30100456
+TMEM50A,Nuclear Pore,-0.940186002
+CBX6,Nuclear Pore,0.830552392
+KREMEN1,Nuclear Pore,0.307765393
+TRAIP,Nuclear Pore,0.249193145
+EMILIN3,Nuclear Pore,0.147561418
+RBM12B,Nuclear Pore,-0.031728844
+BTBD9,Nuclear Pore,-0.036274041
+KIRREL,Nuclear Pore,0.141927866
+IQGAP3,Nuclear Pore,0.881696099
+PRKX,Nuclear Pore,0.372586772
+SMTN,Nuclear Pore,0.898186271
+TBX1,Nuclear Pore,0.683849053
+TSPYL2,Nuclear Pore,1.214820619
+C22orf46,Nuclear Pore,0.240751872
+PCDH9,Nuclear Pore,-0.04590763
+OAF,Nuclear Pore,0.750746785
+ZDHHC23,Nuclear Pore,0.882140133
+EFNA5,Nuclear Pore,0.250862449
+SS18L1,Nuclear Pore,0.477210916
+KNTC1,Nuclear Pore,0.341774433
+WDR27,Nuclear Pore,1.374835027
+FOXO4,Nuclear Pore,0.081346012
+POU3F2,Nuclear Pore,-0.354725502
+PROS1,Nuclear Pore,-1.055761552
+ZFP1,Nuclear Pore,-0.502133679
+XPOT,Nuclear Pore,-0.534082234
+SNN,Nuclear Pore,-0.257390067
+AMER1,Nuclear Pore,0.317119235
+ZBTB40,Nuclear Pore,0.1868014
+ATL3,Nuclear Pore,-0.452832414
+UBE2G2,Nuclear Pore,-0.068486263
+TMED9,Nuclear Pore,-0.049113266
+RBM33,Nuclear Pore,0.587387141
+JAG2,Nuclear Pore,1.279136712
+ZFP90,Nuclear Pore,0.721491169
+SIVA1,Nuclear Pore,0.757638744
+BRI3BP,Nuclear Pore,-0.321881028
+ROBO2,Nuclear Pore,-0.743106244
+BRF1,Nuclear Pore,0.136602082
+MANEAL,Nuclear Pore,-0.59216755
+PURA,Nuclear Pore,0.089435658
+DDX51,Nuclear Pore,0.413609137
+NOMO2,Nuclear Pore,0.06853979
+NRBP2,Nuclear Pore,0.706124953
+ZNF445,Nuclear Pore,0.168388256
+PRPF39,Nuclear Pore,0.519081447
+CDK10,Nuclear Pore,0.997922558
+ATP6V0A2,Nuclear Pore,0.156701617
+C14orf80,Nuclear Pore,1.426819323
+HGS,Nuclear Pore,0.700916663
+MRPL30,Nuclear Pore,-0.452952894
+METTL7A,Nuclear Pore,0.207437416
+NR2F2,Nuclear Pore,0.548156933
+SP1,Nuclear Pore,0.145875486
+PCGF3,Nuclear Pore,0.550837183
+P4HB,Nuclear Pore,0.069006227
+PBX1,Nuclear Pore,-0.202235836
+BRWD1,Nuclear Pore,0.277019469
+EP400NL,Nuclear Pore,0.7067796
+MYBL1,Nuclear Pore,0.660674309
+DMWD,Nuclear Pore,0.828999838
+SLC52A2,Nuclear Pore,0.078598519
+NAT8L,Nuclear Pore,0.634938873
+GNB1L,Nuclear Pore,1.451667004
+LAMP1,Nuclear Pore,-0.271139767
+KLHDC8B,Nuclear Pore,1.437882082
+SETD4,Nuclear Pore,0.510721773
+RNPC3,Nuclear Pore,-0.379459153
+BICD2,Nuclear Pore,0.323030444
+LRCH3,Nuclear Pore,-0.254047411
+ZNF529,Nuclear Pore,0.762293571
+AIDA,Nuclear Pore,-0.039447916
+ZBTB6,Nuclear Pore,-0.110296913
+BCL9L,Nuclear Pore,1.123627012
+KIF18B,Nuclear Pore,0.958779487
+MKL2,Nuclear Pore,0.420467028
+CA5BP1,Nuclear Pore,0.787375164
+BACE1,Nuclear Pore,-0.311927037
+KPNA4,Nuclear Pore,-0.485526697
+ZNF197,Nuclear Pore,0.254366187
+BTN3A2,Nuclear Pore,0.91308102
+INSIG1,Nuclear Pore,-0.013638035
+TMEM222,Nuclear Pore,0.664913229
+SMYD4,Nuclear Pore,0.968094419
+GPATCH8,Nuclear Pore,0.059903311
+LYRM7,Nuclear Pore,0.035407536
+ZNF397,Nuclear Pore,0.251667398
+ZSCAN30,Nuclear Pore,0.295460548
+TPCN1,Nuclear Pore,0.439699482
+POFUT2,Nuclear Pore,0.772703034
+ZDHHC17,Nuclear Pore,-0.419626253
+PPARA,Nuclear Pore,-0.069978607
+TEAD1,Nuclear Pore,-0.474384537
+ENTPD5,Nuclear Pore,0.164472322
+KIAA1598,Nuclear Pore,-0.166226011
+TSPYL4,Nuclear Pore,0.669489685
+FNBP1,Nuclear Pore,0.193484212
+BCAM,Nuclear Pore,0.881992525
+COL4A1,Nuclear Pore,-0.254759362
+SIRT7,Nuclear Pore,1.126348698
+TET3,Nuclear Pore,0.468212499
+ZNF286A,Nuclear Pore,-0.319765385
+SAMD11,Nuclear Pore,1.212856212
+B3GALTL,Nuclear Pore,-0.492242461
+TCEA1,Nuclear Pore,-0.823593225
+FANCA,Nuclear Pore,0.730809131
+SEMA4D,Nuclear Pore,0.987061126
+LIN28B,Nuclear Pore,-0.38552468
+FANCM,Nuclear Pore,0.202236704
+FAM122A,Nuclear Pore,0.04542935
+ARHGAP11B,Nuclear Pore,0.51585394
+CYHR1,Nuclear Pore,1.191424699
+KLHL17,Nuclear Pore,1.448698422
+ANKRD19P,Nuclear Pore,0.549297311
+ARL4C,Nuclear Pore,0.644016235
+C11orf95,Nuclear Pore,0.46482674
+MAPK12,Nuclear Pore,0.490782881
+COL4A5,Nuclear Pore,0.156329699
+NCR3LG1,Nuclear Pore,-0.029478174
+HES4,Nuclear Pore,1.221558386
+CHM,Nuclear Pore,-0.010099236
+H2AFX,Nuclear Pore,0.422896045
+SRSF10,Nuclear Pore,0.187850513
+NDOR1,Nuclear Pore,1.461091848
+FAM72B,Nuclear Pore,0.040463264
+LDOC1L,Nuclear Pore,-0.485254445
+PTAR1,Nuclear Pore,0.266441287
+ZDHHC9,Nuclear Pore,-0.057121016
+ZBED6CL,Nuclear Pore,-0.376562874
+TMEM120B,Nuclear Pore,0.715831895
+MTF1,Nuclear Pore,-0.212631229
+TMEM201,Nuclear Pore,0.552296623
+NHLRC3,Nuclear Pore,0.513450064
+BEND4,Nuclear Pore,0.281458582
+MSL1,Nuclear Pore,0.182118792
+DHFRP1,Nuclear Pore,-0.326591944
+ZNF292,Nuclear Pore,-0.102594279
+ADAT2,Nuclear Pore,0.329546001
+H1F0,Nuclear Pore,0.766525081
+LITAF,Nuclear Pore,-0.454695621
+ARID2,Nuclear Pore,-0.166561235
+S100A13,Nuclear Pore,-0.316040844
+ZNF33A,Nuclear Pore,0.351857008
+LIN54,Nuclear Pore,-0.665107741
+KAZN,Nuclear Pore,0.138111115
+SLC35E2B,Nuclear Pore,0.200817201
+KIAA0895L,Nuclear Pore,1.065914083
+PLEKHG4,Nuclear Pore,1.546277052
+ACADSB,Nuclear Pore,0.051197589
+TMEM63A,Nuclear Pore,0.562237553
+MPHOSPH8,Nuclear Pore,-0.41573675
+FAM217B,Nuclear Pore,-0.504613637
+LCOR,Nuclear Pore,-0.262638952
+POM121,Nuclear Pore,0.12030513
+ZBTB44,Nuclear Pore,-0.656338896
+SLC35F1,Nuclear Pore,-0.802582812
+PTPN1,Nuclear Pore,-0.079350666
+EVL,Nuclear Pore,0.698477309
+EPHB4,Nuclear Pore,0.295130007
+PPP1R26,Nuclear Pore,0.723701851
+TSC22D2,Nuclear Pore,-0.10069874
+PIK3R4,Nuclear Pore,-0.533213216
+GDAP2,Nuclear Pore,-0.50014388
+AFAP1,Nuclear Pore,0.245048804
+MAN2A2,Nuclear Pore,0.2228478
+CACNA1H,Nuclear Pore,1.151628939
+SULF2,Nuclear Pore,-0.151190981
+PLXNB2,Nuclear Pore,0.50999671
+XRCC2,Nuclear Pore,0.309442486
+MYO6,Nuclear Pore,-0.515192533
+TCF4,Nuclear Pore,-0.169634403
+RABL6,Nuclear Pore,0.880362458
+ZKSCAN5,Nuclear Pore,0.127250312
+ZFP62,Nuclear Pore,-0.1639492
+ERI2,Nuclear Pore,-0.221938788
+ZNF33B,Nuclear Pore,-0.051994865
+ZNF512B,Nuclear Pore,1.074497343
+ZNF431,Nuclear Pore,0.421211629
+NF1,Nuclear Pore,-0.047906738
+VKORC1L1,Nuclear Pore,-0.045719403
+COL27A1,Nuclear Pore,0.763289174
+GM2A,Nuclear Pore,0.063493258
+SNHG17,Nuclear Pore,0.47724877
+CD47,Nuclear Pore,-0.239615988
+CTBP1-AS2,Nuclear Pore,-0.330576634
+C6orf106,Nuclear Pore,-0.269175649
+NHLRC2,Nuclear Pore,-0.347087558
+KPNA5,Nuclear Pore,-0.264307724
+ZNF252P,Nuclear Pore,-0.185682631
+PDLIM7,Nuclear Pore,0.078734138
+SLC39A10,Nuclear Pore,-0.308445037
+ZNF100,Nuclear Pore,0.428834391
+ZNF398,Nuclear Pore,-0.389970532
+GMFB,Nuclear Pore,-0.38888303
+ZMYM1,Nuclear Pore,0.708397626
+MAFG,Nuclear Pore,1.064905652
+ARRDC1,Nuclear Pore,0.737048098
+KIAA1671,Nuclear Pore,0.31922782
+IGF2R,Nuclear Pore,-0.071289324
+SLC25A29,Nuclear Pore,1.0793268
+PGAP1,Nuclear Pore,-1.208621893
+SRC,Nuclear Pore,0.546281397
+PCNXL3,Nuclear Pore,0.663289391
+LRRC8B,Nuclear Pore,-0.516240786
+ABCB8,Nuclear Pore,0.995815507
+SND1,Nuclear Pore,0.046456389
+ENTPD4,Nuclear Pore,-0.111203651
+KANK2,Nuclear Pore,0.385840452
+FITM2,Nuclear Pore,-0.417365151
+DDI2,Nuclear Pore,0.021024914
+TRIM33,Nuclear Pore,-0.371986655
+LRP10,Nuclear Pore,0.352459713
+ZNF655,Nuclear Pore,0.110580071
+SLC22A5,Nuclear Pore,0.225995497
+ADARB1,Nuclear Pore,0.723421234
+OGDHL,Nuclear Pore,0.601621074
+STMN3,Nuclear Pore,0.494751818
+SIPA1L1,Nuclear Pore,0.704761584
+PIGN,Nuclear Pore,-0.679320818
+COL4A6,Nuclear Pore,0.355867862
+GPX1P1,Nuclear Pore,0.900744736
+ENTPD6,Nuclear Pore,0.503552479
+ENPP1,Nuclear Pore,-0.007447701
+PHF2,Nuclear Pore,0.514648361
+RPS26,Nuclear Pore,0.183502874
+PSAP,Nuclear Pore,-0.021513499
+HOXC6,Nuclear Pore,0.749221642
+EME2,Nuclear Pore,1.124313214
+ZNF780A,Nuclear Pore,0.280411241
+SLC9A8,Nuclear Pore,1.117304023
+OCLN,Nuclear Pore,-0.500587296
+GPAA1,Nuclear Pore,0.765851433
+SPG7,Nuclear Pore,0.599275216
+ERO1L,Nuclear Pore,-0.059803943
+ZNF121,Nuclear Pore,0.837654406
+MPZL1,Nuclear Pore,-0.195770005
+VPS13A,Nuclear Pore,0.511801244
+AKAP17A,Nuclear Pore,0.625180831
+ELOVL2,Nuclear Pore,-0.662768321
+SNHG12,Nuclear Pore,0.958164728
+NOL8,Nuclear Pore,0.514445915
+MRPL42,Nuclear Pore,-0.204588655
+ENTPD7,Nuclear Pore,-0.354041992
+ZNF84,Nuclear Pore,-0.144250795
+SIRPA,Nuclear Pore,-1.220553214
+CD2AP,Nuclear Pore,-0.431407926
+NUP62CL,Nuclear Pore,-0.604498471
+SFI1,Nuclear Pore,0.80007572
+ZNF248,Nuclear Pore,0.166716275
+ZNF770,Nuclear Pore,-0.241838687
+HMGN5,Nuclear Pore,-0.536796234
+MIER1,Nuclear Pore,0.268585689
+MAN1A2,Nuclear Pore,-0.326860286
+RPS6KL1,Nuclear Pore,-0.258014345
+DDX42,Nuclear Pore,0.022244732
+STYX,Nuclear Pore,-0.520898837
+UCKL1,Nuclear Pore,0.218236036
+ZKSCAN8,Nuclear Pore,0.304206511
+HOXC4,Nuclear Pore,0.826692398
+ASPH,Nuclear Pore,-0.078761485
+WWP2,Nuclear Pore,-0.471887594
+GFPT1,Nuclear Pore,0.175047253
+ITSN2,Nuclear Pore,-0.49742863
+MGEA5,Nuclear Pore,0.077557398
+FAM115A,Nuclear Pore,-0.090550089
+ZNF587,Nuclear Pore,1.031470627
+MAFK,Nuclear Pore,0.559537208
+DDX39B,Nuclear Pore,0.781602449
+NUDT16,Nuclear Pore,-0.761197475
+TLK1,Nuclear Pore,-0.684174459
+MDM4,Nuclear Pore,0.598531089
+KLHL9,Nuclear Pore,-0.072183398
+C6orf89,Nuclear Pore,0.111357308
+SLC9A6,Nuclear Pore,-0.057959993
+FAN1,Nuclear Pore,0.625128772
+CEP290,Nuclear Pore,-0.481424931
+C1orf85,Nuclear Pore,0.094665027
+SMOC1,Nuclear Pore,0.059371226
+ZNF652,Nuclear Pore,0.326826691
+SMURF1,Nuclear Pore,0.377650355
+SLC5A3,Nuclear Pore,0.095583461
+COLGALT2,Nuclear Pore,0.079405457
+TMEM184B,Nuclear Pore,0.203435481
+LRIG2,Nuclear Pore,-0.677353381
+FOXJ3,Nuclear Pore,0.398483423
+ARHGAP11A,Nuclear Pore,0.025773241
+DENND4B,Nuclear Pore,0.473799722
+R3HDM4,Nuclear Pore,0.504279703
+RUNDC1,Nuclear Pore,0.472523559
+TYW1,Nuclear Pore,0.016735338
+SFMBT2,Nuclear Pore,-0.335265397
+ITPRIPL1,Nuclear Pore,0.110392354
+SMC5,Nuclear Pore,-0.454729405
+SREBF2,Nuclear Pore,0.397786323
+DZIP3,Nuclear Pore,0.227595457
+KIAA0753,Nuclear Pore,-0.545997828
+ATG9A,Nuclear Pore,0.14931536
+TBKBP1,Nuclear Pore,1.604477803
+NAGA,Nuclear Pore,0.1311724
+SGMS1,Nuclear Pore,-0.13360578
+COX20,Nuclear Pore,0.683060173
+CHML,Nuclear Pore,-0.211457644
+TATDN3,Nuclear Pore,-1.369937605
+RBM20,Nuclear Pore,0.291420046
+PCMTD2,Nuclear Pore,-0.486559674
+EFCAB7,Nuclear Pore,-0.306760163
+ZYG11A,Nuclear Pore,0.053494682
+SYS1,Nuclear Pore,-0.80742875
+TRAF3IP1,Nuclear Pore,-0.797621286
+PHACTR4,Nuclear Pore,0.177825771
+ZDHHC18,Nuclear Pore,0.552707098
+TMEM57,Nuclear Pore,-0.337110734
+ZDBF2,Nuclear Pore,0.573160408
+BMPR2,Nuclear Pore,-0.534092425
+OXLD1,Nuclear Pore,0.1300292
+MRPL38,Nuclear Pore,0.848804643
+SDHD,Nuclear Pore,-0.918376664
+NEU1,Nuclear Pore,0.40496688
+HSPA1B,Nuclear Pore,0.444425496
+HLA-C,Nuclear Pore,0.090830086
+DDR1,Nuclear Pore,1.066482522
+HLA-E,Nuclear Pore,0.405988266
+ZNF616,Nuclear Pore,-0.466170197
+CRHR1-IT1,Nuclear Pore,0.061212774
+GABBR1,Nuclear Pore,0.587429836
+RANBP17,Nuclear Pore,-0.840971205
+ZNF204P,Nuclear Pore,0.433089855
+TCTN1,Nuclear Pore,0.573427202
+ZBTB48,Nuclear Pore,0.293309814
+C11orf83,Nuclear Pore,0.82592019
+ZNF783,Nuclear Pore,0.234585232
+SLC35B4,Nuclear Pore,-0.18600367
+TRIQK,Nuclear Pore,-0.407030477
+PSENEN,Nuclear Pore,0.751009499
+LGR4,Nuclear Pore,0.03586871
+PDE7A,Nuclear Pore,-0.426311456
+TMEM170B,Nuclear Pore,0.018648969
+TECPR1,Nuclear Pore,0.888345839
+ITPRIPL2,Nuclear Pore,0.6671037
+CRYZL1,Nuclear Pore,-0.1875732
+C5orf51,Nuclear Pore,-0.123600401
+ZNF316,Nuclear Pore,0.857051572
+NYNRIN,Nuclear Pore,1.040046692
+AP000525.9,Nuclear Pore,0.05124926
+PTPLB,Nuclear Pore,-0.422300483
+WDR52,Nuclear Pore,0.526480918
+SETD5-AS1,Nuclear Pore,0.705378462
+SACM1L,Nuclear Pore,-0.480200967
+C17orf51,Nuclear Pore,0.293538303
+ZNF580,Nuclear Pore,0.921278123
+DENND1B,Nuclear Pore,-0.26336434
+SFT2D2,Nuclear Pore,-0.040724321
+FGFR1OP,Nuclear Pore,0.300919495
+KLHL23,Nuclear Pore,0.001698288
+TRIM59,Nuclear Pore,0.075495581
+NRAS,Nuclear Pore,-0.077968288
+QTRT1,Nuclear Pore,-0.00026842
+CHUK,Nuclear Pore,-0.091747903
+COG8,Nuclear Pore,0.47204971
+RBMXL1,Nuclear Pore,-0.563982387
+LEPROT,Nuclear Pore,-0.039428751
+ATF6B,Nuclear Pore,0.712778216
+SLC35F6,Nuclear Pore,-0.030224368
+ZNF134,Nuclear Pore,-0.024220886
+EMP2,Nuclear Pore,-0.209766902
+DNASE1,Nuclear Pore,1.300438388
+CSNK1E,Nuclear Pore,0.735602455
+GALT,Nuclear Pore,0.774023799
+ITGA1,Nuclear Pore,0.106175651
+AP1G2,Nuclear Pore,0.671216809
+CARKD,Nuclear Pore,0.660876842
+TTLL3,Nuclear Pore,0.991507428
+CPNE1,Nuclear Pore,0.002405881
+RCN1P2,Nuclear Pore,-0.799439241
+ZBED1,Nuclear Pore,-0.265841181
+DDX12P,Nuclear Pore,0.804971336
+C22orf29,Nuclear Pore,0.734203524
+NEURL4,Nuclear Pore,1.011420697
+TTC3P1,Nuclear Pore,-0.18373762
+PEX26,Nuclear Pore,0.529778826
+GOLGA8B,Nuclear Pore,0.226804394
+MIR17HG,Nuclear Pore,1.03361201
+TMEM242,Nuclear Pore,-0.550102977
+TMEM167B,Nuclear Pore,-0.303826768
+CYB5RL,Nuclear Pore,-0.189787004
+CROCCP2,Nuclear Pore,1.192752455
+FNIP1,Nuclear Pore,0.489411701
+RP11-204C16.4,Nuclear Pore,-0.392656485
+TENM3,Nuclear Pore,-0.502907298
+AC007390.5,Nuclear Pore,0.072410721
+NBPF1,Nuclear Pore,0.301227491
+CCNL2,Nuclear Pore,1.08909866
+PPT2,Nuclear Pore,-0.313129649
+CKMT1A,Nuclear Pore,0.27751381
+NSUN5P1,Nuclear Pore,0.811293915
+RP11-54O7.3,Nuclear Pore,1.162211304
+AFG3L1P,Nuclear Pore,0.80422441
+EPB41L4A-AS1,Nuclear Pore,-0.527595839
+ATXN1L,Nuclear Pore,0.63718833
+SMIM13,Nuclear Pore,-0.099020581
+CTD-2228K2.7,Nuclear Pore,1.842283833
+PLEKHM1,Nuclear Pore,0.270835345
+FAM195B,Nuclear Pore,0.932183199
+IPO7P2,Nuclear Pore,-0.610505235
+SLC26A6,Nuclear Pore,1.291800631
+FGD5-AS1,Nuclear Pore,0.238779689
+TLK2P1,Nuclear Pore,-0.093374489
+BAIAP2-AS1,Nuclear Pore,0.208331519
+RP11-31F15.1,Nuclear Pore,0.522740574
+TMEM185B,Nuclear Pore,-0.819304226
+C14orf132,Nuclear Pore,0.716826509
+PARG,Nuclear Pore,0.291829284
+TP73-AS1,Nuclear Pore,0.611889221
+SCAMP4,Nuclear Pore,0.531693351
+SEC63P1,Nuclear Pore,-0.00053184
+HCG11,Nuclear Pore,0.021550982
+DHFR,Nuclear Pore,-0.225787336
+RP11-206L10.11,Nuclear Pore,-1.3679462
+XIST,Nuclear Pore,0.63348449
+RP5-1180C10.2,Nuclear Pore,0.110862939
+HCG18,Nuclear Pore,0.052981847
+CTD-2619J13.14,Nuclear Pore,1.655960247
+SNHG15,Nuclear Pore,0.59860563
+SNHG7,Nuclear Pore,-0.609081406
+HOTAIRM1,Nuclear Pore,1.084976638
+RP4-775C13.1,Nuclear Pore,-0.301705478
+ZNF37BP,Nuclear Pore,-0.149275919
+ZNF736,Nuclear Pore,0.951506425
+MAGI2-AS3,Nuclear Pore,-0.062535951
+JRK,Nuclear Pore,0.725553926
+GAS5,Nuclear Pore,0.787000906
+LINC00338,Nuclear Pore,0.244160392
+PPP1R3E,Nuclear Pore,0.496282185
+RP5-827C21.1,Nuclear Pore,-0.205484981
+NUS1P1,Nuclear Pore,-0.186597405
+ZBTB22,Nuclear Pore,1.127429777
+AD000090.2,Nuclear Pore,0.302339466
+ZBED5,Nuclear Pore,0.555382576
+RNF103,Nuclear Pore,0.892651202
+TMEM189,Nuclear Pore,-0.000352293
+RPL36A,Nuclear Pore,0.749744273
+CD302,Nuclear Pore,-0.672988134
+SNHG3,Nuclear Pore,0.567445889
+C15orf38,Nuclear Pore,0.366828559
+AP5Z1,Nuclear Pore,1.204601377
+MICAL3,Nuclear Pore,0.547305307
+KCTD7,Nuclear Pore,-0.343538189
+SCARF2,Nuclear Pore,0.231090043
+APOBEC3C,Nuclear Pore,0.01458905
+N4BP2L2,Nuclear Pore,-0.230447239
+NEAT1,Nuclear Pore,1.33320342
+CRNDE,Nuclear Pore,0.315396039
+OIP5-AS1,Nuclear Pore,-0.365862366
+MARS2,Nuclear Pore,-0.954054898
+HAUS5,Nuclear Pore,0.476292588
+PDCD6,Nuclear Pore,-0.247297501
+THAP9-AS1,Nuclear Pore,0.674853838
+SHANK3,Nuclear Pore,0.702202494
+RTN3P1,Nuclear Pore,-1.192256145
+MALAT1,Nuclear Pore,1.721283123
+TUG1,Nuclear Pore,0.033839498
+GS1-251I9.4,Nuclear Pore,-0.321689311
+UTP14C,Nuclear Pore,-0.080017732
+ZNF260,Nuclear Pore,-0.787439512
+PABPC4L,Nuclear Pore,1.093392265
+FPGT,Nuclear Pore,-0.177902187
+MEX3A,Nuclear Pore,0.44463729
+POLR2M,Nuclear Pore,-0.109420987
+SNHG1,Nuclear Pore,0.409815332
+POLG2,Nuclear Pore,0.302936961
+KIAA1147,Nuclear Pore,0.291100462
+RP11-349A22.5,Nuclear Pore,-0.113088483
+LINC00641,Nuclear Pore,-0.008086232
+RP4-773N10.4,Nuclear Pore,1.071476654
+CEP95,Nuclear Pore,0.229065308
+LINC00657,Nuclear Pore,0.050764105
+RP1-239B22.5,Nuclear Pore,-0.418635717
+RP6-24A23.6,Nuclear Pore,-0.829997053
+ERVK13-1,Nuclear Pore,0.183719182
+GS1-358P8.4,Nuclear Pore,-0.033257091
+VPS9D1-AS1,Nuclear Pore,0.170802859
+RP6-24A23.7,Nuclear Pore,0.426872315
+GAN,Nuclear Pore,0.320544785
+SPON1,Nuclear Pore,-2.758137889
+RP11-159D12.2,Nuclear Pore,1.02438651
+OTUD7B,Nuclear Pore,-0.974011023
+RNF115,Nuclear Pore,-0.63571119
+BAHCC1,Nuclear Pore,0.536711294
+NBPF15,Nuclear Pore,1.065089222
+RP11-242D8.1,Nuclear Pore,0.146898202
+RP11-18I14.10,Nuclear Pore,-0.612656074
+NBPF9,Nuclear Pore,0.999328049
+EGLN2,Nuclear Pore,0.753596441
+NUDT3,Nuclear Pore,-0.267338023
+KMT2B,Nuclear Pore,1.043114039
+POM121C,Nuclear Pore,0.071350381
+RP11-504P24.8,Nuclear Pore,1.170109395
+DCP1A,Nuclear Pore,0.148772041
+EPOP,Nuclear Pore,-0.192136485
+NOL12,Nuclear Pore,1.057915341
+SOCS7,Nuclear Pore,0.711682569
+PI4KAP1,Nuclear Pore,1.103892383
+ZNF280B,Nuclear Pore,1.078270524
+MLLT6,Nuclear Pore,0.996283365
+SYNRG,Nuclear Pore,0.238036728
+TADA2A,Nuclear Pore,0.59814958
+PIP4K2B,Nuclear Pore,0.127486548
+AL133325.3,Nuclear Pore,0.30735742
+DDX52,Nuclear Pore,-0.287923575
+MYO19,Nuclear Pore,0.463458379
+DHRS11,Nuclear Pore,1.041839015
+ACACA,Nuclear Pore,-0.100114593
+AC005332.6,Nuclear Pore,0.124210071
+RP11-574K11,Nuclear Pore,0.512784649
+AL035425.4,Nuclear Pore,1.309426528
+EBLN3P,Nuclear Pore,-0.1392595
+GCLC,Cytosol,0.01540649
+NFYA,Cytosol,0.366742127
+NIPAL3,Cytosol,0.458413746
+ENPP4,Cytosol,-0.7962733
+SEMA3F,Cytosol,0.037640617
+CD99,Cytosol,-0.399164293
+LASP1,Cytosol,1.346663757
+M6PR,Cytosol,-0.397579323
+CFLAR,Cytosol,0.442018322
+NDUFAF7,Cytosol,-0.553627564
+RBM5,Cytosol,-0.365167793
+SLC7A2,Cytosol,0.067409511
+SARM1,Cytosol,0.535253399
+CAMKK1,Cytosol,0.543916548
+RECQL,Cytosol,-0.343332638
+ARHGAP33,Cytosol,0.049455192
+CDC27,Cytosol,0.409569105
+SPPL2B,Cytosol,-0.613135069
+CREBBP,Cytosol,0.447492716
+GCFC2,Cytosol,-0.404505369
+RHBDD2,Cytosol,-0.557634134
+IBTK,Cytosol,0.350302754
+ZNF195,Cytosol,-0.591389069
+MYCBP2,Cytosol,0.275336515
+ZFX,Cytosol,0.02432748
+LAMP2,Cytosol,-0.514378505
+GDE1,Cytosol,-0.192398086
+TMEM98,Cytosol,-0.286951761
+TMEM132A,Cytosol,-0.337702368
+ZNF263,Cytosol,0.510820786
+MAP3K9,Cytosol,0.477431351
+JHDM1D,Cytosol,0.434655945
+PHTF2,Cytosol,-0.270217045
+FARP2,Cytosol,0.67918839
+IFRD1,Cytosol,-0.731476524
+ARHGAP44,Cytosol,0.755742128
+ELAC2,Cytosol,-0.267293231
+ADIPOR2,Cytosol,0.447198948
+PAFAH1B1,Cytosol,0.460586471
+KIAA0100,Cytosol,0.187472738
+PAX6,Cytosol,-0.195482298
+LUC7L,Cytosol,-0.578038055
+CACNA2D2,Cytosol,-0.446894241
+PIGQ,Cytosol,-0.280373522
+CRAMP1L,Cytosol,0.141985781
+JARID2,Cytosol,0.771816041
+ADAM22,Cytosol,-0.171628247
+CYB561,Cytosol,0.044820207
+SPAG9,Cytosol,0.020656106
+CELSR3,Cytosol,0.019128591
+AASS,Cytosol,0.016639157
+PKD1,Cytosol,-0.28652617
+SEC62,Cytosol,-0.331533987
+REV3L,Cytosol,0.377191991
+POMT2,Cytosol,-0.07939883
+BAZ1B,Cytosol,0.894280837
+ZNF207,Cytosol,-0.471188682
+IFFO1,Cytosol,-0.20044896
+NISCH,Cytosol,0.038181916
+IDS,Cytosol,0.271949989
+CLCN6,Cytosol,-0.315033309
+MRC2,Cytosol,0.047460318
+TSPAN9,Cytosol,0.602045896
+BTBD7,Cytosol,0.492575015
+MBTD1,Cytosol,0.095273751
+LARS2,Cytosol,0.186324159
+PIK3C2A,Cytosol,0.164583836
+ANLN,Cytosol,0.527534166
+QPCTL,Cytosol,-0.314748443
+MAP4K3,Cytosol,-0.125668903
+BRCA1,Cytosol,0.562991418
+MBTPS2,Cytosol,-0.589184536
+EXTL3,Cytosol,0.331824897
+ELOVL5,Cytosol,-0.725340186
+MAP4K5,Cytosol,0.36524139
+MAN2B2,Cytosol,-0.271921249
+CLK1,Cytosol,-1.413994799
+ANGEL1,Cytosol,-0.595763102
+DDX11,Cytosol,0.071019629
+UFL1,Cytosol,-0.64802489
+SLC30A9,Cytosol,0.035744105
+COX15,Cytosol,0.460405072
+ZMYND11,Cytosol,0.426446042
+XYLT2,Cytosol,-0.267869212
+NUDCD3,Cytosol,0.967920708
+CHDH,Cytosol,0.631515815
+GLT8D1,Cytosol,-0.258295133
+ATP2C1,Cytosol,-0.597059646
+RALBP1,Cytosol,0.807253599
+CNTN1,Cytosol,-0.438790102
+PHLDB1,Cytosol,0.664803991
+MRE11A,Cytosol,0.452439292
+SPAST,Cytosol,-0.087906606
+NRXN3,Cytosol,0.27709724
+CPS1,Cytosol,0.224934093
+SLC45A4,Cytosol,0.437567242
+ZNF839,Cytosol,0.247049638
+ZDHHC6,Cytosol,-0.12274632
+RNH1,Cytosol,-0.258379499
+RB1CC1,Cytosol,0.26748416
+ERP44,Cytosol,-0.873905782
+AKAP11,Cytosol,0.460558485
+GCLM,Cytosol,0.057793839
+DEPDC1,Cytosol,0.110045587
+SEC63,Cytosol,-0.588715304
+FAS,Cytosol,-0.306419459
+RNASET2,Cytosol,-0.644163634
+AGPAT4,Cytosol,-0.16339469
+MIPEP,Cytosol,-0.156738236
+VEZT,Cytosol,-0.168573681
+BRD9,Cytosol,0.429251241
+SNX1,Cytosol,0.09751064
+BCLAF1,Cytosol,0.106805512
+SLC39A9,Cytosol,0.297524921
+RABEP1,Cytosol,0.234732473
+FAM13B,Cytosol,0.639865897
+PNPLA6,Cytosol,-0.330566334
+ZCCHC8,Cytosol,0.002546493
+CHPF2,Cytosol,0.46523451
+FUT8,Cytosol,-0.21335441
+UBA6,Cytosol,0.389903419
+ATP6V0A1,Cytosol,-0.289388716
+SLC4A7,Cytosol,-0.40311773
+VCL,Cytosol,0.155314612
+ADSS,Cytosol,-0.28970413
+TIMP2,Cytosol,0.298429624
+RFC1,Cytosol,0.299540546
+ZZZ3,Cytosol,0.252267586
+MFAP3,Cytosol,-0.491829519
+MRI1,Cytosol,0.30202133
+AGA,Cytosol,-1.1996691
+BOD1L1,Cytosol,0.495450052
+TRIO,Cytosol,0.410578489
+VCAN,Cytosol,-0.226972091
+CLEC16A,Cytosol,0.789135465
+ZFYVE16,Cytosol,0.110030536
+RAI14,Cytosol,0.826861568
+PNKP,Cytosol,-0.564004625
+PQLC2,Cytosol,-0.140022619
+CTNS,Cytosol,-0.380123887
+INPP4A,Cytosol,0.508081167
+RETSAT,Cytosol,-0.561626895
+CUL7,Cytosol,0.174162026
+PHKA2,Cytosol,0.170098435
+DSG2,Cytosol,-0.470853144
+OFD1,Cytosol,-0.135450832
+GPM6B,Cytosol,-0.489521975
+YTHDC2,Cytosol,0.24623936
+TPR,Cytosol,0.543516062
+SCML1,Cytosol,-0.335162447
+MAP4,Cytosol,0.941594314
+GOPC,Cytosol,0.076008743
+ZNF800,Cytosol,-0.219750637
+SNX29,Cytosol,0.77470219
+KITLG,Cytosol,-0.010131777
+H6PD,Cytosol,0.317382759
+LTBP1,Cytosol,-0.626461159
+RCN1,Cytosol,-0.632982323
+PTCD2,Cytosol,-0.283321338
+LIMA1,Cytosol,0.46663056
+LETMD1,Cytosol,-0.651707351
+POLQ,Cytosol,0.528167139
+MPHOSPH9,Cytosol,0.148656908
+PLEKHA5,Cytosol,0.096543705
+SIKE1,Cytosol,0.786967027
+MSMO1,Cytosol,-1.250260502
+TTC17,Cytosol,-0.23754864
+LAMA3,Cytosol,-0.337777721
+AP5M1,Cytosol,0.001956441
+ANAPC4,Cytosol,-0.434449419
+ARID4B,Cytosol,0.505509463
+SDCCAG8,Cytosol,0.014880318
+FOXC1,Cytosol,0.520808962
+PLEKHH1,Cytosol,0.109066495
+ATP9A,Cytosol,0.428995875
+FAM168A,Cytosol,1.05605424
+RELT,Cytosol,-0.086921112
+NOP58,Cytosol,-0.685351238
+TAB2,Cytosol,0.509367914
+USP36,Cytosol,0.418191335
+KMT2C,Cytosol,0.382751551
+MCOLN3,Cytosol,-1.146204578
+PUM2,Cytosol,0.18491274
+RC3H2,Cytosol,0.284331715
+DCBLD2,Cytosol,-0.123097901
+SOAT1,Cytosol,-0.151574635
+ATP11B,Cytosol,0.085527159
+SEC61A1,Cytosol,-0.266876822
+PPP1R12A,Cytosol,0.411209245
+POLR3E,Cytosol,-0.200751283
+ATP2B4,Cytosol,-0.059886112
+ZC3H11A,Cytosol,0.435216201
+NDC1,Cytosol,-0.705840273
+UNKL,Cytosol,0.481390738
+ALDH18A1,Cytosol,-0.418548981
+TARBP1,Cytosol,-0.135602564
+WNK1,Cytosol,0.538446364
+CCAR1,Cytosol,0.289825136
+PTPRU,Cytosol,-0.269821064
+QSER1,Cytosol,0.335676647
+BCAT1,Cytosol,-0.024832301
+PRDM6,Cytosol,0.363956434
+TNK2,Cytosol,0.489962496
+MON2,Cytosol,0.223272945
+GPBP1,Cytosol,0.027412622
+WAPAL,Cytosol,0.376299473
+VMP1,Cytosol,-0.764286227
+APPBP2,Cytosol,0.482476705
+AHRR,Cytosol,0.542683715
+ZNF275,Cytosol,0.621526543
+MTMR1,Cytosol,0.782461002
+GPC1,Cytosol,0.216848966
+TM7SF3,Cytosol,-0.412309112
+CDON,Cytosol,0.07238098
+HIPK2,Cytosol,0.521237906
+SUGP2,Cytosol,0.257154518
+SLC12A2,Cytosol,-0.667925998
+HMG20B,Cytosol,-0.13600863
+UHRF1BP1,Cytosol,0.534077027
+PKN2,Cytosol,-0.232990115
+TRAM2,Cytosol,0.141829185
+NTN1,Cytosol,0.308133347
+ADAT1,Cytosol,0.573947013
+SPEN,Cytosol,0.363810006
+MAP2K4,Cytosol,0.341603495
+SLK,Cytosol,0.379472345
+CYB5R4,Cytosol,-0.646167847
+ASB1,Cytosol,0.616332192
+FAM107B,Cytosol,0.222067954
+SLC9A7,Cytosol,0.03996969
+FOXJ2,Cytosol,0.593198748
+PPP2R5A,Cytosol,-0.159778315
+ASPM,Cytosol,0.11046812
+ZBTB11,Cytosol,-0.035089506
+ATXN3,Cytosol,0.444902725
+FGFR2,Cytosol,0.133786689
+LRRC40,Cytosol,-0.901674336
+ATG2B,Cytosol,0.334610756
+ARFGEF1,Cytosol,0.293127846
+KLF6,Cytosol,-0.057376201
+NEO1,Cytosol,-0.308461923
+TRAM1,Cytosol,-1.12628226
+TP53BP1,Cytosol,0.818128767
+IARS2,Cytosol,-0.597433181
+ROCK1,Cytosol,0.684368782
+HYAL2,Cytosol,-0.44737766
+FGFR3,Cytosol,-0.227700204
+MEF2A,Cytosol,0.436629498
+ACSL4,Cytosol,0.091735131
+PRR11,Cytosol,0.989549218
+REEP1,Cytosol,-0.112764263
+ATP11A,Cytosol,0.025053483
+POLR1A,Cytosol,0.727475351
+IP6K2,Cytosol,-0.539972426
+KIF2A,Cytosol,0.248202003
+TGFBR3,Cytosol,-0.205046344
+NEDD4,Cytosol,0.604812092
+MAPK6,Cytosol,-0.097552078
+UFD1L,Cytosol,-0.782213254
+LRP6,Cytosol,-0.074710213
+NUCB2,Cytosol,-0.51868005
+SLC44A1,Cytosol,-0.216505559
+TMEM260,Cytosol,-0.373094071
+SMG6,Cytosol,1.064778075
+CLTCL1,Cytosol,0.175762746
+DGCR2,Cytosol,0.531635607
+MNT,Cytosol,0.135138343
+ZXDC,Cytosol,0.090538336
+NDST1,Cytosol,0.483600497
+AP3M2,Cytosol,0.168427283
+RAD18,Cytosol,0.014665053
+ATP2B1,Cytosol,-0.394219127
+MGAT4A,Cytosol,0.25169003
+SNX13,Cytosol,-0.22318057
+VASH1,Cytosol,0.805294999
+SEL1L,Cytosol,-0.418171322
+ATP6AP1,Cytosol,-0.585532192
+DAZAP1,Cytosol,0.208330252
+CPSF1,Cytosol,-0.131516074
+SLC6A15,Cytosol,-0.670739612
+RDH11,Cytosol,-0.518438974
+PRKACA,Cytosol,0.82982581
+LPHN1,Cytosol,0.21203075
+RPS6KA6,Cytosol,0.211281514
+EPN2,Cytosol,0.350997124
+PTPN18,Cytosol,0.749991192
+TFRC,Cytosol,-0.83339607
+AFF4,Cytosol,0.680889271
+MPP5,Cytosol,0.422706598
+HMMR,Cytosol,-0.022751228
+P4HA2,Cytosol,-0.702850026
+TRNT1,Cytosol,-0.493853526
+ACADVL,Cytosol,-0.304025541
+EVC,Cytosol,0.61797877
+DERL2,Cytosol,-0.523963908
+NDE1,Cytosol,0.886483676
+PVR,Cytosol,0.243379669
+SCARB1,Cytosol,-0.642891257
+SELO,Cytosol,-0.219543161
+LLGL2,Cytosol,0.023639327
+PDE8A,Cytosol,-0.008282091
+SDHA,Cytosol,-0.27818134
+KDM5A,Cytosol,0.539636196
+ADAM11,Cytosol,-0.074986518
+ST6GAL1,Cytosol,0.407786489
+GLI2,Cytosol,0.489837008
+NOTCH3,Cytosol,-0.001345016
+BCS1L,Cytosol,-0.536061002
+DPP8,Cytosol,0.626662769
+SLC24A1,Cytosol,0.65175652
+LMAN1,Cytosol,-0.522027907
+PTPLAD1,Cytosol,-0.289406458
+TUBE1,Cytosol,-0.823973519
+SEMA3C,Cytosol,-0.631580058
+TTC38,Cytosol,0.204748882
+CELSR1,Cytosol,0.033828141
+ZNF638,Cytosol,0.277001109
+SLC25A40,Cytosol,-0.533205594
+RASAL2,Cytosol,0.296616202
+ZNF37A,Cytosol,0.364325566
+FNDC3B,Cytosol,0.538664849
+FRYL,Cytosol,0.633654995
+TMEM131,Cytosol,-0.215277767
+WDR62,Cytosol,-0.096578316
+BCAP29,Cytosol,-0.25126725
+SEC31B,Cytosol,-0.788301369
+RBMS2,Cytosol,0.567882479
+SLC46A1,Cytosol,0.030871074
+PLXNA2,Cytosol,-0.108348676
+ANKRD13A,Cytosol,0.312920147
+PAG1,Cytosol,0.231019441
+MCAM,Cytosol,-0.291760162
+GPC4,Cytosol,-0.440466491
+MBNL3,Cytosol,0.316881138
+DGKD,Cytosol,0.377131686
+TM9SF3,Cytosol,-0.668059239
+PPP1R12B,Cytosol,0.204052443
+DNAJC10,Cytosol,-0.354749365
+GTF3C1,Cytosol,0.216850499
+IL4R,Cytosol,0.562756988
+LRCH4,Cytosol,-0.050203948
+FAM76B,Cytosol,0.249322343
+SIRT6,Cytosol,-0.203828267
+POLD3,Cytosol,0.383401219
+PHF17,Cytosol,0.415102377
+FBLN1,Cytosol,-0.789986769
+ITGA8,Cytosol,-0.131105689
+MCCC1,Cytosol,-0.373974717
+ACER3,Cytosol,0.091789411
+N4BP2,Cytosol,0.516587353
+HOXA9,Cytosol,-0.234105278
+PCM1,Cytosol,0.584072797
+TNRC6C,Cytosol,0.63426393
+ITCH,Cytosol,0.441440772
+SDF4,Cytosol,-0.084736864
+FKBP7,Cytosol,-0.595123587
+SLC1A3,Cytosol,-0.568806127
+SAR1A,Cytosol,0.125641709
+PAFAH1B3,Cytosol,-0.437132581
+MOXD1,Cytosol,-0.428981972
+STX7,Cytosol,0.136265349
+RABL2B,Cytosol,0.081546668
+SLC35C2,Cytosol,0.040071796
+CRYBG3,Cytosol,0.235990501
+RIF1,Cytosol,0.340763013
+PSEN1,Cytosol,0.065475403
+RBL1,Cytosol,0.401117742
+RSBN1,Cytosol,-0.037388955
+MAGI3,Cytosol,0.397570603
+OSTM1,Cytosol,-0.485514224
+EXD2,Cytosol,-0.156781334
+MEF2C,Cytosol,0.526467817
+UBA5,Cytosol,-0.464637286
+STK17B,Cytosol,0.349916346
+ZNF510,Cytosol,0.007887824
+LRP2,Cytosol,-0.42771502
+DUSP12,Cytosol,-1.045236404
+KIAA0141,Cytosol,-0.013124076
+PHLPP1,Cytosol,0.30415083
+SMARCD3,Cytosol,-0.360975623
+C5orf22,Cytosol,0.583676399
+CCNT2,Cytosol,-0.005847091
+NFE2L1,Cytosol,0.343697517
+GSK3B,Cytosol,0.641252646
+ITGB5,Cytosol,0.09193412
+ERC1,Cytosol,0.877785587
+RNF13,Cytosol,-0.974177643
+LYRM2,Cytosol,0.281570072
+KAT6A,Cytosol,0.720703923
+PLOD1,Cytosol,-0.318966712
+TDRD3,Cytosol,-0.282924677
+PDS5B,Cytosol,0.201262602
+OXCT1,Cytosol,0.18283102
+RRAGB,Cytosol,-0.377919174
+FAT1,Cytosol,-0.316770448
+YTHDC1,Cytosol,0.159485634
+ZMPSTE24,Cytosol,-0.903754543
+REST,Cytosol,0.388986251
+APLP2,Cytosol,-0.190350886
+KIAA1467,Cytosol,-0.140640578
+TXLNA,Cytosol,0.709110496
+NCOA1,Cytosol,0.702078855
+AGBL5,Cytosol,0.256041564
+CD59,Cytosol,-0.34076645
+ATRX,Cytosol,0.402569366
+SCAMP1,Cytosol,-0.571142055
+HACE1,Cytosol,-0.056743096
+WDFY1,Cytosol,0.606072481
+MTIF2,Cytosol,-0.074556662
+ATG16L1,Cytosol,0.015535594
+POMGNT1,Cytosol,-0.117530117
+B4GALT1,Cytosol,0.173907059
+IPO11,Cytosol,0.215763576
+TMED2,Cytosol,-0.652841798
+ERO1LB,Cytosol,-0.272305973
+PPP1R15A,Cytosol,0.427384644
+NLK,Cytosol,0.321386443
+PIGS,Cytosol,-0.133061411
+ATXN7L3,Cytosol,0.795086242
+PGS1,Cytosol,-0.295552686
+SH3BP2,Cytosol,0.115143885
+ADD1,Cytosol,0.924288378
+TXNDC16,Cytosol,-0.333317644
+NID2,Cytosol,-0.769030353
+KLHL42,Cytosol,0.563732248
+ERGIC2,Cytosol,-0.609481679
+METTL2A,Cytosol,-0.34691578
+PTPN4,Cytosol,-0.047252231
+KHSRP,Cytosol,0.817775993
+EPB41L1,Cytosol,0.782476249
+ANKRD10,Cytosol,-0.450621769
+DOCK3,Cytosol,0.014296449
+PPP1R13B,Cytosol,0.139284784
+ATRN,Cytosol,-0.235784386
+ZNF343,Cytosol,0.137929713
+MAVS,Cytosol,0.671903664
+LZTS3,Cytosol,0.747063772
+SNX5,Cytosol,-0.7095118
+MAPKAPK5,Cytosol,-0.275140085
+ESF1,Cytosol,-0.150661096
+SLC23A2,Cytosol,-0.024958967
+KIF16B,Cytosol,0.44434053
+ERP29,Cytosol,-0.523571093
+FUS,Cytosol,-0.411057617
+ZNF302,Cytosol,-0.814515903
+GRAMD1A,Cytosol,-0.316847934
+GANAB,Cytosol,-0.526081257
+RBM41,Cytosol,-0.086893486
+GPATCH2L,Cytosol,0.407264607
+SLC9A1,Cytosol,0.128086493
+SPTLC1,Cytosol,-0.510698092
+PAPOLA,Cytosol,0.300607419
+MUL1,Cytosol,0.047347951
+RAB11FIP3,Cytosol,0.573784339
+GOLGA3,Cytosol,0.686031323
+EFNB1,Cytosol,0.362337406
+PDPR,Cytosol,-0.253917681
+GLG1,Cytosol,-0.445653351
+TNRC6A,Cytosol,0.52628732
+PLEKHG2,Cytosol,0.391021525
+NAT14,Cytosol,0.219706049
+RBM27,Cytosol,0.450547146
+OSBPL8,Cytosol,0.472320349
+NRCAM,Cytosol,-0.506567838
+LAMB1,Cytosol,-0.559340907
+CMTM6,Cytosol,0.014121484
+ITGA6,Cytosol,-0.464633427
+SEL1L3,Cytosol,-0.401867964
+ORC6,Cytosol,-0.502780418
+TMEM101,Cytosol,-0.542469058
+OSGEP,Cytosol,-0.739504378
+G2E3,Cytosol,-0.216682065
+HECTD1,Cytosol,0.399050452
+SEMA6A,Cytosol,0.371785068
+TRPM7,Cytosol,0.097646876
+TYRO3,Cytosol,-0.185093297
+AGO1,Cytosol,0.399004506
+MFSD11,Cytosol,-0.653878464
+GPATCH2,Cytosol,0.172740728
+NUP50,Cytosol,0.63649965
+LRRFIP2,Cytosol,0.168760077
+SEC22C,Cytosol,0.335960844
+XYLB,Cytosol,0.513375797
+HDAC6,Cytosol,-0.025276978
+CBX5,Cytosol,0.435035163
+SUCO,Cytosol,-0.109586465
+HOOK2,Cytosol,-0.366973426
+ARCN1,Cytosol,0.895508815
+TMEM38B,Cytosol,-0.870106198
+BTAF1,Cytosol,-0.010464561
+IKZF5,Cytosol,-0.230106553
+WAC,Cytosol,0.380241245
+CREM,Cytosol,-0.913809712
+BRPF3,Cytosol,0.358189181
+EFHC1,Cytosol,-0.686629978
+ABL1,Cytosol,0.893051745
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+SCD,Cytosol,0.028343769
+ABLIM1,Cytosol,0.588750527
+ERMP1,Cytosol,-0.170013005
+NRP1,Cytosol,-0.027241158
+MZF1,Cytosol,-0.379860541
+FBXL19,Cytosol,0.918987892
+MTAP,Cytosol,0.113078286
+CEP170B,Cytosol,0.407644387
+POLRMT,Cytosol,-0.206806354
+ARVCF,Cytosol,0.161036691
+TRMT2A,Cytosol,-0.194514111
+ZDHHC8,Cytosol,-0.210167533
+KLHL22,Cytosol,0.397916004
+CRKL,Cytosol,0.71341594
+LZTR1,Cytosol,0.261133948
+CECR2,Cytosol,0.486349581
+DERL3,Cytosol,-0.77405701
+PPM1F,Cytosol,0.77809568
+TOP3B,Cytosol,-0.461748215
+CRYBB2P1,Cytosol,-0.295455714
+ADRBK2,Cytosol,0.739383785
+GGA1,Cytosol,0.204914472
+HPS4,Cytosol,0.39300486
+TTC28,Cytosol,0.644705432
+SEPT3,Cytosol,0.734819111
+KDELR3,Cytosol,-0.39303927
+DDX17,Cytosol,0.152104792
+TCF20,Cytosol,0.901058122
+TIMP3,Cytosol,-0.471978098
+PPP6R2,Cytosol,0.086612323
+SUN2,Cytosol,-0.035408289
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+MYH9,Cytosol,0.795429524
+FOXRED2,Cytosol,0.389793375
+TNRC6B,Cytosol,0.534258808
+SGSM3,Cytosol,-0.102065809
+IFT27,Cytosol,-0.634079781
+KIAA0930,Cytosol,0.778687936
+EP300,Cytosol,0.597558102
+ZC3H7B,Cytosol,0.752556539
+ACO2,Cytosol,-0.516126167
+TRMU,Cytosol,-0.145260019
+ZBED4,Cytosol,0.514938385
+ABHD4,Cytosol,1.056281021
+KHNYN,Cytosol,0.667921596
+NIN,Cytosol,0.167171382
+GNPNAT1,Cytosol,0.384810527
+DDHD1,Cytosol,0.313076399
+CNIH1,Cytosol,-0.676736663
+TMED8,Cytosol,0.709531393
+SPTLC2,Cytosol,0.087246234
+PPM1A,Cytosol,0.136608109
+SIX4,Cytosol,0.057312896
+GALNT16,Cytosol,0.03258943
+KIAA0247,Cytosol,0.161718686
+SRSF5,Cytosol,-0.598098341
+DICER1,Cytosol,0.480734686
+ZFYVE21,Cytosol,-0.520828808
+TELO2,Cytosol,-0.075663568
+PCNX,Cytosol,-0.021572003
+GSKIP,Cytosol,-0.923843116
+SMEK1,Cytosol,0.275696756
+TRIP11,Cytosol,0.089808472
+PABPN1,Cytosol,0.254651043
+ARHGAP5,Cytosol,0.23016856
+CHD8,Cytosol,0.510555335
+PCK2,Cytosol,-1.034686521
+PNN,Cytosol,-0.391940302
+PLTP,Cytosol,-0.509068379
+ABHD12,Cytosol,-0.208096692
+GINS1,Cytosol,0.578389842
+RIMS4,Cytosol,0.94554575
+PABPC1L,Cytosol,-0.906137356
+STK4,Cytosol,0.618045026
+BMP7,Cytosol,0.198315994
+DNAJC5,Cytosol,0.88393685
+SLCO4A1,Cytosol,-0.633201501
+DIDO1,Cytosol,0.485256888
+ARFGAP1,Cytosol,0.463216314
+ARFRP1,Cytosol,-0.202832619
+CDS2,Cytosol,-0.181123924
+TM9SF4,Cytosol,-0.27185838
+POFUT1,Cytosol,0.113476004
+SAMHD1,Cytosol,0.979774224
+KIF3B,Cytosol,0.618894233
+E2F1,Cytosol,0.04329577
+APMAP,Cytosol,-0.607242877
+ZNF516,Cytosol,0.592297044
+LPIN2,Cytosol,0.840696954
+SMCHD1,Cytosol,0.431157545
+LAMA1,Cytosol,-0.536794103
+RNF125,Cytosol,0.608285843
+ANKRD12,Cytosol,0.229794397
+MIB1,Cytosol,0.051720715
+MID1,Cytosol,0.436083702
+WDR13,Cytosol,-0.214425757
+XIAP,Cytosol,0.385697707
+ATP11C,Cytosol,-0.449608337
+SYP,Cytosol,-0.3718833
+FMR1,Cytosol,-0.028743613
+SLC35A2,Cytosol,-0.158935236
+TAZ,Cytosol,-0.605497635
+MAGT1,Cytosol,-0.294629535
+CD99L2,Cytosol,0.219579527
+EEA1,Cytosol,0.139685621
+NDFIP2,Cytosol,-0.722815758
+DNAJC3,Cytosol,0.138861289
+UGGT2,Cytosol,-0.79097789
+ARHGEF7,Cytosol,0.40813327
+PARP4,Cytosol,0.767541645
+FLT1,Cytosol,0.101171273
+VWA8,Cytosol,0.122146535
+DGKH,Cytosol,0.202697939
+INTS6,Cytosol,-0.254947925
+CLN5,Cytosol,-0.858467848
+MGRN1,Cytosol,0.432501175
+ZNF629,Cytosol,0.83879455
+CENPT,Cytosol,-0.230848426
+NFAT5,Cytosol,0.169548276
+SETD6,Cytosol,-0.526192169
+SLC38A7,Cytosol,0.148125562
+SLC7A6OS,Cytosol,0.190770382
+SLC7A6,Cytosol,-0.145216252
+WDR59,Cytosol,0.195319269
+TAF1C,Cytosol,0.05181973
+TSC2,Cytosol,0.090206158
+ZNF500,Cytosol,0.35075151
+ABCC1,Cytosol,-0.56755403
+NOMO3,Cytosol,-0.795828931
+NARFL,Cytosol,0.123449827
+MTHFSD,Cytosol,0.284272592
+CLCN7,Cytosol,-0.042715671
+SLC7A5,Cytosol,-0.316928988
+FBXO31,Cytosol,0.704405582
+EEF2K,Cytosol,0.166829282
+CAPN15,Cytosol,0.043592396
+PIEZO1,Cytosol,-0.572859947
+BFAR,Cytosol,-0.190726468
+NOMO1,Cytosol,-0.756267425
+CCP110,Cytosol,0.200974365
+RNF40,Cytosol,0.760022815
+LACTB,Cytosol,-0.386171336
+CD276,Cytosol,-0.098265573
+HOMER2,Cytosol,0.250318766
+TMEM87A,Cytosol,-0.865152841
+ZNF106,Cytosol,0.610881676
+CEP152,Cytosol,0.28932731
+TJP1,Cytosol,0.717392449
+VPS18,Cytosol,0.853852243
+MYEF2,Cytosol,0.582842401
+CSPP1,Cytosol,0.221461571
+ZFAND1,Cytosol,-1.038063252
+FZD3,Cytosol,-0.062474069
+EYA1,Cytosol,0.417464977
+NBN,Cytosol,0.42638145
+IMPAD1,Cytosol,0.254910523
+UBE2W,Cytosol,0.027899013
+IKBKB,Cytosol,0.504135475
+PLAT,Cytosol,-0.117752093
+JPH1,Cytosol,0.314018914
+TRPS1,Cytosol,0.511596556
+PYCRL,Cytosol,0.137645476
+EEF1D,Cytosol,-0.477076666
+SQLE,Cytosol,-0.892166652
+SLC39A14,Cytosol,-0.443809689
+MTMR9,Cytosol,0.380969379
+LEPROTL1,Cytosol,0.001526275
+PPP2CB,Cytosol,-0.301938757
+KLHDC4,Cytosol,0.092227806
+KCTD9,Cytosol,-0.03972275
+MAN2B1,Cytosol,-0.532410478
+NUCB1,Cytosol,0.034169709
+SARS2,Cytosol,-0.755817604
+SNRNP70,Cytosol,-0.574869275
+CLPTM1,Cytosol,-0.531095632
+CLASRP,Cytosol,-0.306629982
+FCGRT,Cytosol,-0.248410094
+ERCC2,Cytosol,0.538368061
+DOT1L,Cytosol,0.284616082
+SF3A2,Cytosol,-0.784725308
+AMH,Cytosol,-0.771933678
+DMPK,Cytosol,0.125522056
+TIMM44,Cytosol,-0.287043876
+AKAP8,Cytosol,-0.038990788
+AKT2,Cytosol,0.388512627
+PLD3,Cytosol,-0.308601962
+FSD1,Cytosol,-0.088714984
+APLP1,Cytosol,0.344685922
+CACTIN,Cytosol,0.442150894
+TYK2,Cytosol,0.23505439
+PTPRS,Cytosol,-0.229271291
+MEGF8,Cytosol,0.05662073
+KDELR1,Cytosol,-0.335468082
+CYTH2,Cytosol,0.221188841
+LIG1,Cytosol,-0.011335382
+BCAT2,Cytosol,-0.444304398
+TNPO2,Cytosol,0.40869725
+DNASE2,Cytosol,-0.290310989
+ISYNA1,Cytosol,-0.564914533
+CRTC1,Cytosol,0.361927651
+SUGP1,Cytosol,-0.060483379
+SIPA1L3,Cytosol,0.732771734
+CADM4,Cytosol,0.286317395
+SMG9,Cytosol,-0.175410557
+AVL9,Cytosol,0.413204089
+CDK6,Cytosol,0.388667393
+DNAJC2,Cytosol,-0.442987863
+WDR91,Cytosol,0.158089347
+CBLL1,Cytosol,0.298168519
+MTPN,Cytosol,0.507029791
+ZC3HAV1,Cytosol,0.877974911
+OGDH,Cytosol,0.722728794
+MET,Cytosol,-0.104447131
+LMBR1,Cytosol,-0.34114597
+HOXA3,Cytosol,-0.017178478
+HOXA6,Cytosol,0.368703276
+BRAT1,Cytosol,-0.406313873
+FKBP14,Cytosol,-0.344947959
+NSUN5P2,Cytosol,-0.705071505
+CASP2,Cytosol,0.639184702
+HSPB1,Cytosol,-0.497747447
+ZKSCAN1,Cytosol,0.628990865
+WASL,Cytosol,0.138798734
+RBM28,Cytosol,0.188865966
+C1GALT1,Cytosol,-0.542566233
+PLOD3,Cytosol,-0.496871759
+CLDN15,Cytosol,-0.312884223
+TMEM106B,Cytosol,-0.39680359
+CEP41,Cytosol,0.521337264
+GLI3,Cytosol,0.259525555
+TMEM248,Cytosol,0.169851816
+TBL2,Cytosol,-0.607940168
+FKTN,Cytosol,-0.167229009
+TMEM245,Cytosol,0.094551958
+MEGF9,Cytosol,0.426707236
+TGFBR1,Cytosol,-0.082082831
+DNM1,Cytosol,0.152345226
+KANK1,Cytosol,0.393865671
+RAPGEF1,Cytosol,0.76347974
+NPDC1,Cytosol,-0.680782545
+SETX,Cytosol,0.547599239
+CCNJ,Cytosol,0.437335889
+RAB11FIP2,Cytosol,0.286002931
+ERLIN1,Cytosol,-0.298011531
+MAPK8,Cytosol,0.195174721
+ATE1,Cytosol,0.278101748
+PLEKHA1,Cytosol,0.162288457
+UNC5B,Cytosol,-0.044218741
+BMPR1A,Cytosol,-0.344770263
+ACTA2,Cytosol,0.384220286
+LIPA,Cytosol,-0.833833119
+LZTS2,Cytosol,0.276973759
+ARHGAP21,Cytosol,0.082172174
+ANKRD26,Cytosol,0.649531469
+LARP4B,Cytosol,0.542579136
+C10orf137,Cytosol,0.389002753
+MTPAP,Cytosol,-0.169641139
+SH3PXD2A,Cytosol,0.744288925
+PITRM1,Cytosol,-0.082293581
+FAM208B,Cytosol,0.238650664
+TSPAN14,Cytosol,0.347355524
+NUFIP2,Cytosol,0.176380636
+DHX40,Cytosol,0.272653828
+CDK5RAP3,Cytosol,-0.8710997
+RECQL5,Cytosol,-0.288174653
+INTS2,Cytosol,0.060469238
+CAMTA2,Cytosol,0.155458545
+MED13,Cytosol,0.067762776
+HOXB6,Cytosol,0.047114242
+CPD,Cytosol,-0.518941726
+GOSR1,Cytosol,0.480694883
+CCDC47,Cytosol,-0.704975869
+AKAP10,Cytosol,0.292165399
+CYTH1,Cytosol,0.134611804
+LGALS3BP,Cytosol,-1.163354965
+EZH1,Cytosol,-0.35467882
+PPP1R9B,Cytosol,0.940482053
+LUC7L3,Cytosol,-0.528271093
+DUSP3,Cytosol,0.615879286
+EFNB3,Cytosol,0.570678422
+DPH1,Cytosol,-0.045882814
+NAT9,Cytosol,-0.096816714
+TMEM104,Cytosol,0.284795857
+TMEM97,Cytosol,-0.27087874
+UNC119,Cytosol,-0.206484525
+TMEM33,Cytosol,-0.319594034
+DCUN1D4,Cytosol,0.053985649
+MANBA,Cytosol,-0.687923257
+ELF2,Cytosol,0.262253114
+WFS1,Cytosol,-0.321265707
+FRG1,Cytosol,-1.173298541
+CLCN3,Cytosol,0.152909807
+GALNT7,Cytosol,-0.669594469
+TRIM2,Cytosol,0.347068537
+NEIL3,Cytosol,-0.11754333
+SH3D19,Cytosol,0.301278107
+STIM2,Cytosol,-0.188083941
+RAPGEF2,Cytosol,0.236419293
+UGDH,Cytosol,0.089880293
+CCDC34,Cytosol,-0.290805914
+FNBP4,Cytosol,0.222749193
+SC5D,Cytosol,-1.217894476
+SIAE,Cytosol,-0.408989087
+EHD1,Cytosol,0.807912253
+FOXRED1,Cytosol,-0.425646013
+ST3GAL4,Cytosol,-0.149070734
+CPT1A,Cytosol,0.114392182
+TMEM109,Cytosol,-0.426436592
+PANX1,Cytosol,0.082601894
+UBE4A,Cytosol,-0.362837814
+DDX6,Cytosol,0.602663927
+PVRL1,Cytosol,0.16185882
+HIPK3,Cytosol,0.280436792
+MDK,Cytosol,-0.421100047
+AMBRA1,Cytosol,0.766533562
+NAA40,Cytosol,0.507338617
+SLC35F2,Cytosol,-0.369453391
+LEPREL2,Cytosol,-0.077098929
+CORO1C,Cytosol,0.81677614
+ASIC1,Cytosol,-0.257771728
+CAPRIN2,Cytosol,-0.45720112
+SLC11A2,Cytosol,-0.313761688
+MLEC,Cytosol,0.479243454
+BCL7A,Cytosol,0.850861662
+RSRC2,Cytosol,-0.465138436
+PPM1H,Cytosol,0.588192902
+ELK3,Cytosol,0.631643937
+MAGOHB,Cytosol,-0.613018473
+ITFG2,Cytosol,0.209503561
+PARP11,Cytosol,0.717860922
+DUSP16,Cytosol,0.679374734
+ACAD10,Cytosol,-0.298193736
+NAA25,Cytosol,0.185343543
+DDX55,Cytosol,-0.345086459
+SLC38A1,Cytosol,-0.15536281
+C12orf49,Cytosol,0.379047123
+MDM1,Cytosol,-0.300176309
+CPSF6,Cytosol,-0.178320997
+GNPTAB,Cytosol,-0.117590036
+ATN1,Cytosol,0.49031423
+C12orf57,Cytosol,-1.210561646
+LPCAT3,Cytosol,-0.504652032
+SUDS3,Cytosol,1.080309532
+GOLT1B,Cytosol,-0.987572843
+C2CD5,Cytosol,0.311244788
+RAB35,Cytosol,0.79920689
+RIC8B,Cytosol,0.335281841
+RP11-22B23.1,Cytosol,-0.234090199
+DSE,Cytosol,-0.168914161
+MAN1A1,Cytosol,-0.189518411
+SERINC1,Cytosol,-0.542274449
+UST,Cytosol,0.058743908
+KCTD20,Cytosol,0.675363661
+RNF8,Cytosol,0.381406952
+ICK,Cytosol,0.653176989
+RAB23,Cytosol,0.427644743
+FBXL4,Cytosol,-0.064803579
+CCNC,Cytosol,-0.879654997
+ALDH5A1,Cytosol,0.491768622
+EYA4,Cytosol,0.498464455
+PERP,Cytosol,-0.227666573
+SLC16A10,Cytosol,-0.527752674
+PHACTR2,Cytosol,0.411111348
+SLC39A7,Cytosol,-0.236975781
+PPP2R5D,Cytosol,0.777331799
+PTK7,Cytosol,-0.448594064
+CUL9,Cytosol,0.085033091
+TMEM30A,Cytosol,-0.148388156
+SENP6,Cytosol,0.085165613
+VEGFA,Cytosol,-0.032826233
+PRPF4B,Cytosol,0.036847943
+BTN2A1,Cytosol,-0.071790875
+LAMA4,Cytosol,-0.27923443
+ERBB2IP,Cytosol,0.582564313
+HARS2,Cytosol,-0.036225106
+MAN2A1,Cytosol,-0.523632074
+PAPD7,Cytosol,0.214588305
+NNT,Cytosol,0.047976257
+APBB3,Cytosol,-0.582955987
+SPARC,Cytosol,-0.227855484
+HMGCR,Cytosol,-0.701005553
+FAF2,Cytosol,-0.204609159
+CLK4,Cytosol,-1.078826075
+ARSB,Cytosol,0.048823105
+CNOT6,Cytosol,0.301324989
+DROSHA,Cytosol,0.372519796
+FAM172A,Cytosol,0.21828741
+LNPEP,Cytosol,-0.107767388
+SLC12A7,Cytosol,-0.033781321
+NR3C1,Cytosol,0.517391364
+C5orf15,Cytosol,-0.439860749
+LIFR,Cytosol,-0.174302943
+TRAPPC13,Cytosol,-0.27888834
+TXNDC15,Cytosol,-0.142696792
+H2AFY,Cytosol,-0.180665796
+TCERG1,Cytosol,0.27003033
+SMAD5,Cytosol,0.100253902
+ERGIC1,Cytosol,0.371237124
+STC2,Cytosol,-0.371204826
+ARL6,Cytosol,0.062785995
+NIT2,Cytosol,-1.044869827
+UBE3A,Cytosol,0.285894588
+SLC25A36,Cytosol,-0.0240708
+TFDP2,Cytosol,0.446832699
+XRN1,Cytosol,0.430554771
+WNT5A,Cytosol,0.377430445
+PFKFB4,Cytosol,0.056482425
+PRKAR2A,Cytosol,0.656001308
+ACAP2,Cytosol,0.505614185
+CBLB,Cytosol,-0.07508551
+BBX,Cytosol,0.747964015
+GNB4,Cytosol,0.592521094
+C3orf52,Cytosol,-0.403149442
+PLXNA1,Cytosol,0.067620908
+CSPG5,Cytosol,-0.368090656
+SCAP,Cytosol,-0.439635039
+HEMK1,Cytosol,-0.451304545
+ACVR2B,Cytosol,0.153008906
+ABCC5,Cytosol,-0.008994089
+SSR3,Cytosol,-0.275356893
+NKTR,Cytosol,-0.121786186
+FOXP1,Cytosol,0.754537724
+INO80D,Cytosol,-0.022434438
+ADAM23,Cytosol,0.113460584
+MOB1A,Cytosol,0.506445344
+LMAN2L,Cytosol,-0.306805852
+RTKN,Cytosol,0.196442777
+PIKFYVE,Cytosol,0.460973036
+FAHD2A,Cytosol,-0.551517739
+SLC35F5,Cytosol,-0.633922233
+STEAP3,Cytosol,-0.164860959
+EPB41L5,Cytosol,0.862138807
+GPD2,Cytosol,0.11582255
+ACVR1,Cytosol,-0.234483553
+MPV17,Cytosol,-0.605361433
+TTC31,Cytosol,0.093351717
+NDUFS7,Cytosol,-0.985503643
+SPTBN1,Cytosol,0.658041717
+CCDC88A,Cytosol,0.38526059
+FN1,Cytosol,-0.116118334
+ELMOD3,Cytosol,0.460570435
+IGFBP5,Cytosol,0.656121242
+USP34,Cytosol,0.285605873
+GGCX,Cytosol,-0.250033567
+CHST10,Cytosol,0.321432505
+MOB4,Cytosol,-0.461298519
+UXS1,Cytosol,-0.38372038
+PASK,Cytosol,0.090647
+TAF1B,Cytosol,-0.176553395
+DCAF17,Cytosol,0.329544714
+SDC1,Cytosol,0.531439919
+SLC1A4,Cytosol,-0.470198683
+SOS1,Cytosol,0.233737787
+WIPF1,Cytosol,0.668944572
+THADA,Cytosol,0.089964798
+TRAK2,Cytosol,0.752822082
+TIA1,Cytosol,-0.07655114
+PCYOX1,Cytosol,-0.28358398
+ARID3A,Cytosol,0.991476944
+EPHA4,Cytosol,-0.384637674
+ALMS1,Cytosol,0.557752985
+BCL9,Cytosol,0.725405935
+DHCR24,Cytosol,-0.04242983
+DNAJC16,Cytosol,-0.47345261
+RALGPS2,Cytosol,0.56374555
+CEP104,Cytosol,0.187692563
+FAM20B,Cytosol,0.442698583
+TCEANC2,Cytosol,0.564077575
+WRAP73,Cytosol,-0.438777936
+ICMT,Cytosol,0.31784728
+QSOX1,Cytosol,-0.343964464
+AMPD2,Cytosol,-0.165142671
+EDEM3,Cytosol,-0.071938588
+RAP1A,Cytosol,-0.314340815
+S100PBP,Cytosol,0.333961848
+ASH1L,Cytosol,0.503784194
+SFPQ,Cytosol,-0.161327526
+MEF2D,Cytosol,0.532159933
+C1orf21,Cytosol,0.306928313
+LEPR,Cytosol,-0.399836866
+IVNS1ABP,Cytosol,0.109881755
+KIAA2013,Cytosol,0.081447439
+MIIP,Cytosol,-0.575883773
+SLC35D1,Cytosol,0.386256192
+WLS,Cytosol,-0.925725512
+PRDM2,Cytosol,0.414262622
+TROVE2,Cytosol,0.283433772
+SRSF11,Cytosol,-0.592018005
+PHTF1,Cytosol,0.095601555
+TMEM9,Cytosol,-0.393836118
+EXOC8,Cytosol,0.003149989
+NID1,Cytosol,-0.414532687
+MTR,Cytosol,0.559780621
+BMP8B,Cytosol,0.226819371
+RIMS3,Cytosol,0.360271507
+AKT3,Cytosol,0.519669373
+ETV3,Cytosol,0.579684142
+LPHN2,Cytosol,-0.138587876
+RBBP5,Cytosol,0.652242218
+ECE1,Cytosol,0.209322252
+CD46,Cytosol,-0.872291871
+APH1A,Cytosol,0.197143686
+LEPRE1,Cytosol,-1.03420288
+SLC2A1,Cytosol,-0.844286527
+SLC19A2,Cytosol,-0.484921682
+NSUN4,Cytosol,0.115732543
+TMED5,Cytosol,-0.491083662
+DR1,Cytosol,-0.236661609
+PTBP2,Cytosol,-0.623336465
+DARS2,Cytosol,0.120232023
+DIEXF,Cytosol,-0.109582226
+RCAN3,Cytosol,0.745231813
+C1orf63,Cytosol,-1.325485914
+SLC35A3,Cytosol,-0.431122
+RCOR3,Cytosol,-0.167818566
+ARID1A,Cytosol,0.686332147
+CENPF,Cytosol,0.561576679
+ESYT2,Cytosol,-0.176110812
+CD3EAP,Cytosol,1.01197511
+MESDC2,Cytosol,-0.380826534
+CTSD,Cytosol,-0.465079827
+STK11,Cytosol,0.389564453
+KMT2A,Cytosol,0.245247188
+KPTN,Cytosol,-0.446808077
+KIF14,Cytosol,0.72587486
+ATF6,Cytosol,-0.231308898
+FASTKD2,Cytosol,-0.618375741
+NRP2,Cytosol,-0.009186997
+CREB1,Cytosol,0.477913389
+B4GALT6,Cytosol,-0.176432812
+ELOVL4,Cytosol,-0.450924968
+CASP8AP2,Cytosol,0.309563037
+PHF3,Cytosol,0.563815241
+PLAGL1,Cytosol,-0.286672402
+FBXO30,Cytosol,0.209359035
+TMEM5,Cytosol,-0.646648529
+ZNF430,Cytosol,0.203658324
+DCLRE1B,Cytosol,0.580905693
+PKD2,Cytosol,-0.002598217
+UBN1,Cytosol,0.771414755
+KLF12,Cytosol,0.459280084
+WDR35,Cytosol,0.20322548
+CCND2,Cytosol,-0.223429028
+SATB2,Cytosol,0.333120533
+SENP5,Cytosol,0.45461916
+C1orf198,Cytosol,0.931916162
+HEATR1,Cytosol,0.28740301
+PTBP3,Cytosol,0.661564024
+FAM206A,Cytosol,-0.613816674
+RBM18,Cytosol,0.23115548
+MAPKAP1,Cytosol,0.313737722
+KDSR,Cytosol,-0.223842373
+ONECUT2,Cytosol,0.132915107
+IRF2BPL,Cytosol,0.502147474
+AREL1,Cytosol,0.012483153
+ABCD4,Cytosol,-0.385729413
+RBM25,Cytosol,0.167665491
+NRDE2,Cytosol,0.206211332
+KLHL29,Cytosol,-0.031181105
+DNMT3A,Cytosol,0.261728247
+ATAD2B,Cytosol,0.637040072
+ATL2,Cytosol,0.010472932
+YIPF4,Cytosol,-0.147368349
+AFTPH,Cytosol,0.618481098
+BCL11A,Cytosol,0.033100415
+SLC17A5,Cytosol,-0.096368138
+FAM178A,Cytosol,0.509394899
+GPAM,Cytosol,0.041429073
+HELLS,Cytosol,0.108011768
+TCTN3,Cytosol,-0.591807159
+C10orf76,Cytosol,0.946710868
+HOXB8,Cytosol,0.473543362
+HOXB3,Cytosol,-0.628895345
+PANK3,Cytosol,0.05869723
+NUP43,Cytosol,0.584521102
+LRP11,Cytosol,-0.325838117
+MASTL,Cytosol,0.193129896
+ELF1,Cytosol,0.538252251
+EGR1,Cytosol,1.050309042
+NR2C1,Cytosol,-0.70431831
+MTERFD3,Cytosol,-0.536000441
+CLU,Cytosol,-0.988804093
+TNFRSF10B,Cytosol,0.218198703
+TARDBP,Cytosol,0.234757042
+CRISPLD1,Cytosol,-0.688476323
+AKAP1,Cytosol,-0.270665798
+TRIM25,Cytosol,0.723500632
+KIAA0922,Cytosol,-0.468288761
+PAPD5,Cytosol,0.194484854
+CEP89,Cytosol,0.643053673
+B4GALT4,Cytosol,-0.116756568
+KIF18A,Cytosol,0.10909086
+CRY2,Cytosol,0.222035569
+ZNF639,Cytosol,-0.643358077
+PDS5A,Cytosol,0.376632769
+CLCC1,Cytosol,-0.120747613
+ACVR2A,Cytosol,-0.449014915
+RPL21,Cytosol,-1.231637487
+MTERFD2,Cytosol,-0.345424444
+KIAA1191,Cytosol,0.515690625
+RBBP6,Cytosol,0.246380372
+ZC3H7A,Cytosol,0.207476825
+FAM35A,Cytosol,0.093196788
+FAM213A,Cytosol,0.007709792
+ODF2L,Cytosol,-0.33428929
+TRMT13,Cytosol,-0.138185867
+RPAP2,Cytosol,-0.045141926
+FAM126A,Cytosol,0.609110081
+FKBP9,Cytosol,0.13670567
+POLM,Cytosol,-0.316572968
+SLC25A51,Cytosol,0.234173997
+DCAF10,Cytosol,0.182995483
+KIAA1549,Cytosol,-0.036391351
+CALD1,Cytosol,0.801024243
+CHST3,Cytosol,0.712961192
+P4HA1,Cytosol,-0.570545741
+RBM19,Cytosol,0.529734941
+GIPC1,Cytosol,0.236857773
+ATP7B,Cytosol,-0.01501278
+ZC3H13,Cytosol,0.293572596
+NLN,Cytosol,0.707510091
+CENPK,Cytosol,-0.721224033
+OPTN,Cytosol,0.726340612
+SPATS2,Cytosol,0.42621354
+LRP1,Cytosol,-0.124211801
+HJURP,Cytosol,0.186222453
+USP45,Cytosol,-0.2460131
+SLC36A1,Cytosol,0.080638245
+LPGAT1,Cytosol,0.283845865
+EXOSC9,Cytosol,-0.810713068
+PLA2G12A,Cytosol,0.149920658
+ADCK4,Cytosol,-0.059077082
+PFKFB2,Cytosol,0.414318494
+AGO2,Cytosol,0.197400808
+MXD4,Cytosol,0.40187191
+ACSL3,Cytosol,-0.917488225
+SLC12A4,Cytosol,-0.031106922
+FAM210B,Cytosol,0.373001114
+SDC4,Cytosol,0.543367084
+NCOA3,Cytosol,0.542452947
+PIGT,Cytosol,-0.488173549
+VAPB,Cytosol,0.681062776
+CHD6,Cytosol,0.133777721
+SRSF6,Cytosol,0.075808719
+RAB22A,Cytosol,0.297483284
+STX16,Cytosol,0.414717532
+STAMBP,Cytosol,-0.049169385
+NAGK,Cytosol,-0.338390272
+PAIP2B,Cytosol,0.557332671
+ATP8A1,Cytosol,-0.209092406
+BTN2A2,Cytosol,-0.137458746
+ABCC10,Cytosol,-0.290083448
+AARS2,Cytosol,-0.280999321
+ZNF391,Cytosol,0.227896181
+CDKN1A,Cytosol,0.618401632
+SSR1,Cytosol,-0.450185356
+NRN1,Cytosol,-0.041909544
+ATXN1,Cytosol,0.176694479
+EEF1E1,Cytosol,-0.976843951
+LRRFIP1,Cytosol,0.112334489
+AHNAK,Cytosol,0.776476827
+ABCC4,Cytosol,-0.511154834
+EFNB2,Cytosol,0.291130599
+ATP5S,Cytosol,0.107772341
+FAM193A,Cytosol,0.487036622
+GGA3,Cytosol,0.104483481
+GTF3C4,Cytosol,0.466887238
+PPP1R12C,Cytosol,0.157584117
+MBOAT7,Cytosol,-0.175254705
+CCDC93,Cytosol,0.536910492
+THOC2,Cytosol,0.478566713
+MED1,Cytosol,0.712002633
+GPR108,Cytosol,0.064056443
+GPCPD1,Cytosol,0.306264371
+PANK2,Cytosol,-0.255409883
+NAPB,Cytosol,0.243444001
+TMX4,Cytosol,0.148093167
+RRBP1,Cytosol,-0.045550354
+ZNF133,Cytosol,-0.330143922
+MCM8,Cytosol,0.275990989
+NCLN,Cytosol,-0.052743864
+ZNF436,Cytosol,0.438073565
+AMOT,Cytosol,0.418992253
+TMEM115,Cytosol,0.069613135
+AGO3,Cytosol,0.247816009
+HECTD3,Cytosol,0.752880771
+KLC1,Cytosol,0.402799774
+XRCC3,Cytosol,0.24982427
+TUBGCP3,Cytosol,0.064163805
+PCID2,Cytosol,-0.783750412
+FRMD8,Cytosol,0.751005561
+PCNXL4,Cytosol,0.054083439
+ATG14,Cytosol,-0.329368062
+KTN1,Cytosol,0.01195932
+PLEKHG3,Cytosol,0.522855259
+WDR60,Cytosol,0.2996545
+AIF1L,Cytosol,0.794099638
+SLC10A3,Cytosol,0.337075663
+CANX,Cytosol,-0.393348856
+CPSF3L,Cytosol,-0.299454112
+TRAF2,Cytosol,0.42510114
+HELB,Cytosol,-0.017374357
+DYRK2,Cytosol,0.342116561
+LRRC61,Cytosol,0.436893147
+FGFRL1,Cytosol,-0.052695772
+EMC1,Cytosol,-0.577546973
+HP1BP3,Cytosol,0.170255312
+SIN3B,Cytosol,0.39754778
+SLC35E1,Cytosol,0.581343121
+GFER,Cytosol,0.604801567
+PKMYT1,Cytosol,-0.513332472
+CHTF18,Cytosol,-0.537917371
+MACF1,Cytosol,0.387308173
+RNF6,Cytosol,-0.004308116
+AKAP9,Cytosol,-0.072745473
+HIP1,Cytosol,0.841170623
+POR,Cytosol,-0.730513774
+PEX1,Cytosol,0.192908074
+LRFN1,Cytosol,0.123335516
+SRD5A3,Cytosol,-0.653921952
+PPAT,Cytosol,0.194996929
+TUBGCP6,Cytosol,-0.352125292
+DGCR8,Cytosol,0.250510935
+TPST2,Cytosol,-0.192869894
+MPST,Cytosol,-0.336550226
+SPECC1,Cytosol,0.295035577
+NAA38,Cytosol,-0.981912278
+PRKRIP1,Cytosol,0.370288834
+PODXL,Cytosol,-0.062949034
+STRIP2,Cytosol,0.243850627
+MKLN1,Cytosol,0.13581035
+CALU,Cytosol,-0.146870927
+CCDC136,Cytosol,0.244061037
+SMO,Cytosol,0.32702299
+KLHDC10,Cytosol,0.54830242
+OSGEPL1,Cytosol,-0.616752434
+HOXD10,Cytosol,0.510724474
+HOXD11,Cytosol,0.244403534
+HERC2,Cytosol,0.331688993
+TWSG1,Cytosol,-0.357269764
+MYO5C,Cytosol,0.679706204
+TMOD2,Cytosol,0.332742098
+TTBK2,Cytosol,0.38432921
+IVD,Cytosol,0.56587682
+CLN6,Cytosol,-0.26508111
+ARPP19,Cytosol,0.552047974
+VPS13C,Cytosol,0.24849473
+SUMF2,Cytosol,-0.236034642
+SPCS3,Cytosol,-0.17473408
+RPAIN,Cytosol,-1.08259032
+PLD2,Cytosol,0.278259448
+MPDU1,Cytosol,-0.080763382
+CCNT1,Cytosol,0.277479162
+PUS7L,Cytosol,0.350413282
+KRI1,Cytosol,0.290565142
+SLC44A2,Cytosol,-0.045307654
+BCL2L2,Cytosol,0.300727147
+PARP2,Cytosol,-0.140093591
+TEP1,Cytosol,0.161679563
+MAP7D3,Cytosol,-0.243691166
+ABHD17A,Cytosol,-0.367422186
+ERMARD,Cytosol,-0.933249166
+SAT1,Cytosol,-0.930194229
+GNL3L,Cytosol,0.7536638
+SH3BP4,Cytosol,0.467180791
+LDLR,Cytosol,-0.771813459
+PRKCSH,Cytosol,-0.606535559
+THEM6,Cytosol,0.115765242
+PVRL2,Cytosol,-0.229360941
+SAFB2,Cytosol,0.121001745
+KIF1A,Cytosol,0.304787597
+COLGALT1,Cytosol,-0.287074722
+MLLT1,Cytosol,0.937989252
+MLLT4,Cytosol,0.445798652
+ACTN4,Cytosol,0.816293937
+NDUFA10,Cytosol,-0.32581087
+ZSWIM6,Cytosol,0.157594695
+PXDN,Cytosol,0.133445266
+COL5A1,Cytosol,0.249731788
+ZNF337,Cytosol,-0.072321157
+TAF4,Cytosol,0.029954624
+LAMA5,Cytosol,-0.513112145
+EXOSC2,Cytosol,-0.136019089
+POMT1,Cytosol,-0.487470712
+PRRC2B,Cytosol,0.689117115
+YIPF2,Cytosol,0.26079698
+ZC3H4,Cytosol,0.862197733
+CLIP1,Cytosol,0.28347428
+HIP1R,Cytosol,0.443525177
+PPAN,Cytosol,0.214898086
+SLC6A8,Cytosol,-0.067566874
+PLXNA3,Cytosol,-0.090347948
+PRRG1,Cytosol,0.175167332
+AKAP12,Cytosol,0.52607157
+RBM39,Cytosol,-0.628566173
+GGT7,Cytosol,-0.647251374
+PPT1,Cytosol,-0.786207499
+RLIM,Cytosol,0.330910358
+ABCB7,Cytosol,-0.302442713
+MRPS25,Cytosol,-0.51718388
+CAPN7,Cytosol,-0.150743116
+ZFYVE20,Cytosol,0.288266697
+SLC6A6,Cytosol,-0.400562667
+MGAT1,Cytosol,0.418331032
+PSMC3IP,Cytosol,-0.222398562
+DIAPH1,Cytosol,0.526645953
+NDFIP1,Cytosol,-0.481972071
+ACAP3,Cytosol,-0.07908953
+C1orf159,Cytosol,-0.226767811
+MAP1B,Cytosol,0.901931074
+IL13RA1,Cytosol,-0.017743664
+WDR44,Cytosol,0.244595875
+PRKAB2,Cytosol,0.318819336
+CLUHP3,Cytosol,-0.395857965
+CHSY1,Cytosol,0.024799606
+SNRPA1,Cytosol,-0.9736037
+FBXW9,Cytosol,-0.661458838
+RFX1,Cytosol,-0.197453411
+CC2D1A,Cytosol,0.474419043
+NUP210,Cytosol,-0.412297152
+ENOSF1,Cytosol,-0.76029392
+EMILIN2,Cytosol,0.434914394
+PRKAA1,Cytosol,0.216894742
+PNISR,Cytosol,-0.379029313
+ZRANB2,Cytosol,-0.313066794
+KDM6B,Cytosol,0.418553898
+GPS2,Cytosol,-0.351649407
+VPS13B,Cytosol,0.401433102
+REEP2,Cytosol,0.681858823
+PRMT7,Cytosol,-0.21197902
+PCED1A,Cytosol,-0.173288244
+PTPRA,Cytosol,-0.247099865
+KIAA0907,Cytosol,-0.477059812
+DCAF8,Cytosol,-0.016907507
+IGHMBP2,Cytosol,0.35107031
+LPIN3,Cytosol,-0.028227625
+SERINC3,Cytosol,-0.13947721
+FBXO44,Cytosol,0.516128281
+USPL1,Cytosol,0.266579715
+XPO4,Cytosol,0.13397736
+SCO1,Cytosol,-0.047340267
+MPRIP,Cytosol,0.610884129
+DSTYK,Cytosol,0.75773294
+SLC41A1,Cytosol,0.090371806
+GPALPP1,Cytosol,0.043158505
+IRS4,Cytosol,0.316160522
+FAM104A,Cytosol,0.450533375
+SLC39A11,Cytosol,-0.041335474
+EPHB2,Cytosol,0.042283745
+SRRM1,Cytosol,0.278583009
+SUV420H2,Cytosol,0.589388479
+WDR74,Cytosol,-0.65505097
+RTN3,Cytosol,-0.281890327
+MORC2,Cytosol,0.704630021
+LARGE,Cytosol,0.266173279
+ADCK2,Cytosol,0.112236111
+AGAP3,Cytosol,0.1182404
+KRBA1,Cytosol,0.344555925
+ZNF767,Cytosol,-0.5180155
+ATP13A3,Cytosol,-0.128144617
+TMEM254,Cytosol,-0.586712976
+TMTC1,Cytosol,0.133730212
+KRAS,Cytosol,0.348799236
+SWAP70,Cytosol,0.551290171
+ZFC3H1,Cytosol,0.250892898
+TEX15,Cytosol,0.267159026
+CTIF,Cytosol,0.617182561
+VHL,Cytosol,0.725829658
+ARL8B,Cytosol,-0.131603033
+EDEM1,Cytosol,-0.222898308
+PRPF38B,Cytosol,-0.071984485
+SORT1,Cytosol,0.19435539
+PTGFRN,Cytosol,0.279650706
+NOTCH2,Cytosol,-0.28002148
+CEPT1,Cytosol,-1.097946355
+AP4B1,Cytosol,-0.604612474
+SPIRE1,Cytosol,0.36649795
+SLC38A2,Cytosol,-0.909451963
+KIDINS220,Cytosol,0.109523932
+ROCK2,Cytosol,0.7332221
+LPIN1,Cytosol,0.253484174
+IL6ST,Cytosol,0.062130963
+TMEM241,Cytosol,0.197391334
+LRP4,Cytosol,-0.042497846
+DDB2,Cytosol,0.016365891
+ACP2,Cytosol,-0.414515309
+AGO4,Cytosol,0.309508198
+HOOK1,Cytosol,0.007372097
+DSC2,Cytosol,-0.083577342
+DSC3,Cytosol,0.197861265
+DTNA,Cytosol,0.265504534
+FHOD3,Cytosol,0.224221894
+FADS2,Cytosol,0.23960236
+CLOCK,Cytosol,0.340179626
+COL4A2,Cytosol,-0.497169789
+DZIP1,Cytosol,0.241326424
+UBAC2,Cytosol,-0.28218027
+ARGLU1,Cytosol,-0.557944196
+BIVM,Cytosol,0.019014829
+ARHGAP32,Cytosol,0.60934549
+TMED7,Cytosol,-0.791133633
+APC,Cytosol,0.596049663
+WDR36,Cytosol,0.12074036
+NAA35,Cytosol,-0.047701516
+TMEM2,Cytosol,-0.209977761
+GOLM1,Cytosol,-0.052713659
+TAOK3,Cytosol,0.84060824
+DMTF1,Cytosol,-0.198672096
+TMEM243,Cytosol,-1.164246662
+PNPLA8,Cytosol,-0.022176508
+MDFIC,Cytosol,0.172046022
+ANKRD6,Cytosol,0.151766365
+KIAA1009,Cytosol,0.657498289
+SNX14,Cytosol,-0.834771864
+EPHA7,Cytosol,-0.12695159
+DNAJC14,Cytosol,0.278166814
+GDF11,Cytosol,0.466939306
+TROAP,Cytosol,-0.417197346
+TSPAN31,Cytosol,-0.363824773
+TFCP2,Cytosol,0.41184428
+PAN2,Cytosol,-0.277816261
+HNRNPA1,Cytosol,-0.874266016
+ACVR1B,Cytosol,0.399451383
+OS9,Cytosol,-0.410350193
+MAP7,Cytosol,-0.1347163
+CD164,Cytosol,-0.836560223
+NHSL1,Cytosol,0.06771217
+AHI1,Cytosol,0.102521532
+SEMA4F,Cytosol,0.008306818
+RAB11FIP5,Cytosol,0.666105932
+CCDC142,Cytosol,0.372362394
+GNS,Cytosol,-0.293409432
+MDM2,Cytosol,0.757190721
+KLHL36,Cytosol,0.318801102
+DYNC1LI2,Cytosol,0.442786015
+EGLN1,Cytosol,0.067875333
+ABCB10,Cytosol,-0.950673954
+TAF5L,Cytosol,0.574180523
+STX6,Cytosol,0.536698977
+CEP350,Cytosol,0.151535353
+LAMC1,Cytosol,-0.232879353
+RC3H1,Cytosol,0.373810475
+TTLL4,Cytosol,0.262825422
+USP37,Cytosol,0.259406604
+ITM2C,Cytosol,0.288642472
+SERPINE2,Cytosol,-0.490087199
+TMEM127,Cytosol,0.635765749
+GCC2,Cytosol,0.436349436
+C2orf49,Cytosol,0.13128847
+EPC2,Cytosol,0.32719935
+ARHGEF4,Cytosol,-0.203760924
+ALDH1L2,Cytosol,-0.12954531
+CKAP4,Cytosol,-0.193021475
+NEK3,Cytosol,-0.20789238
+RCBTB1,Cytosol,-0.192542026
+COG3,Cytosol,0.170075673
+SCRN1,Cytosol,0.938561721
+CHST12,Cytosol,0.177969723
+KDELR2,Cytosol,-0.318063572
+NUPL2,Cytosol,-0.577425494
+DBNL,Cytosol,0.279611949
+TTYH3,Cytosol,0.327915365
+IREB2,Cytosol,0.302448842
+RSAD1,Cytosol,0.348948125
+VEZF1,Cytosol,0.370105988
+TEX2,Cytosol,0.05378932
+BRIP1,Cytosol,0.281676242
+SKIL,Cytosol,0.427479056
+RPS6KC1,Cytosol,0.288560284
+BIN1,Cytosol,0.069346714
+HS6ST1,Cytosol,1.007161477
+UGGT1,Cytosol,0.019279204
+DNAJC1,Cytosol,-0.641134904
+LRRC8A,Cytosol,0.345501646
+CDK9,Cytosol,0.017588403
+TOR1B,Cytosol,-0.27397135
+SMC2,Cytosol,0.326403397
+TOR1A,Cytosol,-0.406486844
+RALGPS1,Cytosol,0.233894113
+FAM129B,Cytosol,0.893229037
+SLC2A8,Cytosol,-0.687387281
+SLC31A1,Cytosol,0.054741388
+ZNF189,Cytosol,0.087003817
+STX17,Cytosol,0.654405238
+TSTD2,Cytosol,0.04285607
+LMX1B,Cytosol,0.549580106
+RANBP6,Cytosol,-0.028867012
+TLN1,Cytosol,0.788911578
+ALDH1B1,Cytosol,0.121338081
+CNPY3,Cytosol,0.138605034
+TMEM63B,Cytosol,0.128642282
+TJAP1,Cytosol,0.010897986
+SLC22A23,Cytosol,0.473486061
+FOXF2,Cytosol,-0.068209348
+RIPK1,Cytosol,0.809854304
+ATAT1,Cytosol,-0.021068544
+NRM,Cytosol,-0.25580436
+VARS2,Cytosol,-0.245205851
+FAM8A1,Cytosol,-0.083509743
+PRKRIR,Cytosol,0.087767472
+CREBZF,Cytosol,-0.904692315
+PRCP,Cytosol,-0.878437502
+RNF121,Cytosol,-0.133734554
+SULF1,Cytosol,-0.666602447
+SORL1,Cytosol,-0.437009901
+YAP1,Cytosol,0.496982459
+RDX,Cytosol,0.754720375
+MAP2K5,Cytosol,0.086968483
+MAPKBP1,Cytosol,0.211706884
+CASC5,Cytosol,0.445154719
+HAUS2,Cytosol,0.736723381
+PARP6,Cytosol,-0.546039977
+TUBGCP4,Cytosol,0.409539595
+RMDN3,Cytosol,-0.275362005
+UACA,Cytosol,0.516257524
+SMAD6,Cytosol,0.842226012
+ADAM10,Cytosol,-0.325401829
+TTLL7,Cytosol,0.107611774
+FNBP1L,Cytosol,0.746279436
+RABGGTB,Cytosol,-1.218171366
+ARHGAP29,Cytosol,0.193870846
+SLC44A5,Cytosol,-0.228726142
+DBT,Cytosol,0.678715565
+EPT1,Cytosol,-0.162880374
+ADCY3,Cytosol,-0.215489507
+PNPT1,Cytosol,-0.072624091
+THUMPD2,Cytosol,-0.968754658
+PREPL,Cytosol,0.469414395
+ACTR1A,Cytosol,0.783422938
+TMEM180,Cytosol,-0.088710634
+ATAD1,Cytosol,-0.354216586
+KIF20B,Cytosol,0.447641163
+TET1,Cytosol,0.26796224
+DNA2,Cytosol,0.213644715
+BARD1,Cytosol,0.20174626
+NAB1,Cytosol,0.031293349
+PPIG,Cytosol,0.162795664
+FASTKD1,Cytosol,-0.087366656
+SSFA2,Cytosol,0.011402609
+ITGAV,Cytosol,0.056542714
+SLC35A5,Cytosol,-0.599075988
+SECISBP2L,Cytosol,0.719679491
+SPPL2A,Cytosol,-0.068884211
+GLCE,Cytosol,-0.261553439
+PPCDC,Cytosol,-0.320146564
+PCDH10,Cytosol,-0.020374099
+AP1AR,Cytosol,-0.702377568
+FGF2,Cytosol,-0.209799563
+KIAA1109,Cytosol,-0.244771367
+LARP1B,Cytosol,0.069955258
+BMP2K,Cytosol,0.438620609
+FRAS1,Cytosol,0.069730955
+SCARB2,Cytosol,0.071613624
+USO1,Cytosol,0.13674644
+CENPE,Cytosol,0.746627539
+GSTCD,Cytosol,0.73585416
+LEF1,Cytosol,0.367622602
+PPP3CA,Cytosol,0.336969525
+FBN2,Cytosol,-0.286786746
+MAPK8IP3,Cytosol,-0.501430748
+B4GALNT3,Cytosol,0.164441531
+AEBP2,Cytosol,-0.38655954
+ETNK1,Cytosol,0.023843282
+CLSTN3,Cytosol,-0.203476483
+SCAF11,Cytosol,0.425967775
+COL2A1,Cytosol,-0.327898316
+LRIG3,Cytosol,-0.962898849
+TMEM19,Cytosol,-0.425505761
+POC1B,Cytosol,-0.192306746
+TMTC3,Cytosol,-0.033262378
+GAS2L3,Cytosol,0.715783761
+SLC15A4,Cytosol,-0.892296099
+TDG,Cytosol,0.248663594
+NUPL1,Cytosol,0.169693911
+MTMR6,Cytosol,0.329568206
+SLC7A1,Cytosol,-0.019011977
+BRCA2,Cytosol,0.263953131
+CERS5,Cytosol,-0.728117609
+ESYT1,Cytosol,-0.438436254
+TMBIM6,Cytosol,0.086653111
+ANKRD52,Cytosol,0.733506586
+ZNF740,Cytosol,0.451860784
+HNRNPA1L2,Cytosol,-0.077894899
+SBNO1,Cytosol,0.44078494
+SETD1B,Cytosol,0.244307123
+RBM26,Cytosol,0.177043521
+ZIC5,Cytosol,-0.15110035
+TMX1,Cytosol,-0.997170045
+NAA30,Cytosol,-0.039083702
+DCAF5,Cytosol,0.800131958
+RAB15,Cytosol,0.759518448
+NIPA2,Cytosol,-0.138469543
+ZSCAN29,Cytosol,0.52382326
+BNIP2,Cytosol,-0.069120834
+MAN2C1,Cytosol,-0.630308335
+MESDC1,Cytosol,0.616681456
+IGF1R,Cytosol,0.246185369
+ARRDC4,Cytosol,0.49732024
+PML,Cytosol,0.366164658
+LINS,Cytosol,-0.273084507
+PCSK6,Cytosol,-0.082261792
+SCAMP2,Cytosol,-0.095835912
+POLG,Cytosol,0.012143416
+ABHD2,Cytosol,0.27662357
+TICRR,Cytosol,0.341279
+MFGE8,Cytosol,-0.45355814
+FURIN,Cytosol,0.349648415
+IQGAP1,Cytosol,0.760018392
+CRTC3,Cytosol,0.36699882
+FTO,Cytosol,0.855992087
+MBTPS1,Cytosol,-0.088210999
+RHOT2,Cytosol,-0.208775666
+PDPK1,Cytosol,0.851020943
+TCF25,Cytosol,0.244088137
+GALNS,Cytosol,-0.105279828
+GAS8,Cytosol,0.538312138
+MED9,Cytosol,0.829114718
+GID4,Cytosol,0.379200482
+KSR1,Cytosol,0.530959556
+SGSM2,Cytosol,0.133943669
+SSH2,Cytosol,0.364649464
+PTRH2,Cytosol,-1.046542925
+SS18,Cytosol,0.523299312
+SLC39A6,Cytosol,-0.246796101
+GALNT1,Cytosol,-0.661511563
+ESCO1,Cytosol,0.169265349
+GREB1L,Cytosol,0.430824644
+NPC1,Cytosol,-0.872225083
+MINK1,Cytosol,0.214285204
+TTYH2,Cytosol,-0.168433505
+CSNK1D,Cytosol,0.494302735
+FOXK2,Cytosol,0.607227523
+TRIM65,Cytosol,0.52516756
+RNF157,Cytosol,0.532812628
+CBX4,Cytosol,0.069583455
+MBD1,Cytosol,0.163376179
+ZCCHC2,Cytosol,0.412188593
+LEPREL4,Cytosol,-0.124996453
+FAM134C,Cytosol,0.198199415
+ERBB2,Cytosol,0.172782033
+FKBP10,Cytosol,-0.332173812
+PRDM15,Cytosol,0.30885735
+DUS3L,Cytosol,-0.443142088
+ATHL1,Cytosol,-0.624073303
+COL6A1,Cytosol,-0.743099037
+IFNAR1,Cytosol,-0.214280266
+COL6A2,Cytosol,-0.368431767
+TMEM50B,Cytosol,-0.416293536
+APP,Cytosol,-0.477260159
+URB1,Cytosol,0.192321828
+CAPN10,Cytosol,0.1508935
+ERVK3-1,Cytosol,-0.366020807
+SLC47A1,Cytosol,-0.549315241
+RERE,Cytosol,0.321881216
+EPHA2,Cytosol,0.022148091
+KIAA0319L,Cytosol,0.001621497
+PLK4,Cytosol,-0.619922152
+GPN2,Cytosol,0.05940763
+PIGK,Cytosol,-0.667248571
+PTPRF,Cytosol,-0.167069858
+SYPL2,Cytosol,0.208585487
+IGSF3,Cytosol,0.029237432
+CELSR2,Cytosol,0.180440414
+ATP1B1,Cytosol,-0.617865171
+CREG1,Cytosol,-0.405880223
+POU2F1,Cytosol,0.691784027
+PPOX,Cytosol,-0.488453393
+USP21,Cytosol,-0.006249028
+PIGM,Cytosol,0.239115469
+ABL2,Cytosol,0.352509306
+XPR1,Cytosol,-0.09858914
+TOR1AIP1,Cytosol,-0.432837674
+TUFT1,Cytosol,1.272471592
+TARS2,Cytosol,0.012616611
+CERS2,Cytosol,-0.526413555
+SEMA6C,Cytosol,-0.137151064
+ATP8B2,Cytosol,0.275626434
+ADAM15,Cytosol,-0.574218212
+SLC39A1,Cytosol,-0.335022582
+GATAD2B,Cytosol,0.399691961
+HCN3,Cytosol,-0.566021272
+GALNT2,Cytosol,0.17894552
+TTC13,Cytosol,-0.750979336
+MLK4,Cytosol,0.279978167
+CEP170,Cytosol,0.654029107
+SDE2,Cytosol,0.626636932
+FBXO28,Cytosol,0.020832072
+CDC42BPA,Cytosol,0.692290152
+MBOAT2,Cytosol,0.00525518
+PSEN2,Cytosol,-0.030564963
+LBR,Cytosol,-0.440544459
+RHOB,Cytosol,0.234514129
+ASXL2,Cytosol,0.486167913
+ETAA1,Cytosol,-0.020481935
+ZNF514,Cytosol,-0.020601027
+SFXN5,Cytosol,0.857697977
+TEX261,Cytosol,0.313054173
+RALB,Cytosol,0.866080267
+SLC20A1,Cytosol,-0.330592684
+ZC3H8,Cytosol,0.079074602
+UBXN4,Cytosol,0.03059265
+AMMECR1L,Cytosol,0.508427678
+GALNT13,Cytosol,-0.343072981
+SCRN3,Cytosol,0.301175358
+KIAA1715,Cytosol,-0.003858295
+CDCA7,Cytosol,-0.223795046
+DLX1,Cytosol,0.257503695
+GULP1,Cytosol,-0.612006927
+FAM171B,Cytosol,-0.220394997
+CCDC150,Cytosol,-0.890176533
+SUMF1,Cytosol,-0.609943309
+RHBDD1,Cytosol,0.442970218
+FAM134A,Cytosol,0.351761255
+CTDSP1,Cytosol,0.808990515
+EAF1,Cytosol,0.692151325
+GOLGA4,Cytosol,0.08678385
+IQSEC1,Cytosol,0.334712756
+PTPRG,Cytosol,0.306516094
+IL17RD,Cytosol,0.088293901
+ARL6IP5,Cytosol,-0.568412424
+TMF1,Cytosol,0.402144279
+LRIG1,Cytosol,-0.045849278
+LIMD1,Cytosol,0.466462961
+NXPE3,Cytosol,0.394746372
+SRPRB,Cytosol,-0.523612023
+TCTA,Cytosol,0.392290183
+VPRBP,Cytosol,0.470174337
+SLIT2,Cytosol,0.272230171
+DGKQ,Cytosol,-0.336136974
+ATP10D,Cytosol,-0.345564134
+SCD5,Cytosol,-0.030366111
+ENOPH1,Cytosol,-0.127380471
+TRMT10A,Cytosol,0.101968997
+KLHL8,Cytosol,0.511085141
+USP53,Cytosol,0.349638019
+MARCH6,Cytosol,-0.433068314
+FAM105A,Cytosol,-0.294297941
+PIK3R1,Cytosol,0.714759748
+LHFPL2,Cytosol,0.345413276
+IQGAP2,Cytosol,0.656144238
+PPIP5K2,Cytosol,0.301755312
+PAM,Cytosol,-0.512788797
+BDP1,Cytosol,0.422328909
+SLC30A5,Cytosol,-0.631485253
+ATG12,Cytosol,-0.10225956
+YIPF5,Cytosol,-0.418848182
+RNF145,Cytosol,0.158678227
+FBXO38,Cytosol,0.073704497
+PCYOX1L,Cytosol,-0.299685022
+TNIP1,Cytosol,0.512294194
+ZNF300,Cytosol,-0.035266078
+GFOD1,Cytosol,0.029410882
+TRIM41,Cytosol,0.396681096
+FAM193B,Cytosol,0.043133843
+RNF44,Cytosol,0.055331383
+MUT,Cytosol,-0.326703655
+PHIP,Cytosol,0.240415074
+MMS22L,Cytosol,0.048204206
+PM20D2,Cytosol,0.17205624
+RNF217,Cytosol,0.009717433
+AIG1,Cytosol,-0.674894596
+TMEM181,Cytosol,0.025822216
+SDK1,Cytosol,-0.008479419
+RBAK,Cytosol,0.176933668
+CREB5,Cytosol,0.227161803
+PURB,Cytosol,-0.30906354
+GBAS,Cytosol,-0.205613786
+ZNF92,Cytosol,-0.740536371
+TMEM168,Cytosol,-0.608972563
+C7orf43,Cytosol,-0.259099355
+SLC12A9,Cytosol,-0.358826326
+GIGYF1,Cytosol,-0.25118113
+TMEM209,Cytosol,-0.58193202
+NOM1,Cytosol,0.140997822
+SH3KBP1,Cytosol,0.575402876
+CASK,Cytosol,0.057766216
+SLC16A2,Cytosol,-0.248175589
+OGT,Cytosol,-0.306156262
+ZNF711,Cytosol,-0.316373826
+DIAPH2,Cytosol,0.550463262
+CXorf57,Cytosol,-0.280145907
+GPC3,Cytosol,-1.02602161
+BIN3,Cytosol,0.148328298
+SLC25A37,Cytosol,0.229404303
+CHMP7,Cytosol,0.221144187
+ERLIN2,Cytosol,-0.124741426
+TACC1,Cytosol,0.373352833
+WHSC1L1,Cytosol,0.304263161
+TERF1,Cytosol,-0.250984395
+MTDH,Cytosol,-0.386579262
+LRP12,Cytosol,-0.566561622
+EBAG9,Cytosol,-0.957529812
+UTP23,Cytosol,-0.488064788
+ZNF7,Cytosol,-0.158666661
+ARHGAP39,Cytosol,0.398874114
+NAPRT1,Cytosol,-0.543094906
+UHRF2,Cytosol,-0.510375654
+ZCCHC7,Cytosol,-0.160233272
+SIGMAR1,Cytosol,-0.466994241
+CEP78,Cytosol,0.135609446
+HIATL1,Cytosol,-0.287808553
+INIP,Cytosol,0.594008352
+UGCG,Cytosol,0.1042351
+STOM,Cytosol,-0.259945206
+MRRF,Cytosol,-0.501612853
+NR6A1,Cytosol,0.340392811
+SURF4,Cytosol,0.188867902
+MED22,Cytosol,0.886385205
+SH3GLB2,Cytosol,0.052727106
+FAM73B,Cytosol,-0.122026301
+GPR107,Cytosol,0.304638089
+C9orf142,Cytosol,-0.615999214
+INPP5E,Cytosol,-0.005621138
+DPH7,Cytosol,-0.298957289
+NOTCH1,Cytosol,-0.134987023
+NACC2,Cytosol,0.518811154
+USP6NL,Cytosol,0.298892544
+FAM171A1,Cytosol,0.195780371
+PARD3,Cytosol,0.410634334
+POLR3A,Cytosol,0.651887693
+FRA10AC1,Cytosol,-0.331121024
+ADD3,Cytosol,0.419471241
+DNAJB12,Cytosol,0.4849999219999999
+EIF4EBP2,Cytosol,0.424107871
+MKI67,Cytosol,0.67214927
+MTG1,Cytosol,-0.225124468
+PPRC1,Cytosol,0.167887656
+ITPRIP,Cytosol,0.427003769
+CNNM2,Cytosol,0.075354048
+PDCD11,Cytosol,0.216407722
+LIN7C,Cytosol,0.52195457
+DGKZ,Cytosol,0.160261264
+TNKS1BP1,Cytosol,0.73850356
+SLC43A1,Cytosol,-1.018388349
+PTPRJ,Cytosol,-0.264596583
+CELF1,Cytosol,0.483265778
+SESN3,Cytosol,0.403613063
+ENDOD1,Cytosol,0.040057115
+SERPINH1,Cytosol,-0.402439521
+NCAM1,Cytosol,-0.082555278
+NPAT,Cytosol,0.375142281
+ATM,Cytosol,0.284566679
+GLB1L2,Cytosol,-0.342348505
+HYOU1,Cytosol,-0.001722105
+DAK,Cytosol,0.210645618
+TMEM138,Cytosol,-0.301809913
+FADS1,Cytosol,0.102963657
+EML3,Cytosol,0.103194044
+B3GAT3,Cytosol,-0.12993756
+SIDT2,Cytosol,-0.13117898
+SOGA1,Cytosol,0.736436377
+LSM14B,Cytosol,0.534099468
+ORAOV1,Cytosol,0.211872907
+TAOK2,Cytosol,0.408078563
+ITGB1,Cytosol,-0.564491938
+ARID5B,Cytosol,0.672676086
+TMCO3,Cytosol,-0.114019362
+LATS2,Cytosol,0.069111753
+LPHN3,Cytosol,0.07985012
+PRSS23,Cytosol,0.300824167
+PIP4K2A,Cytosol,0.649616592
+FREM2,Cytosol,-0.151651543
+CRIM1,Cytosol,-0.043175179
+IPMK,Cytosol,0.367869374
+PLBD2,Cytosol,-0.131572838
+GXYLT1,Cytosol,0.055605318
+CSNK1G3,Cytosol,0.114498057
+MIPOL1,Cytosol,-0.251891819
+EXT2,Cytosol,-0.458436061
+TMEM18,Cytosol,-0.56899005
+NEK7,Cytosol,0.161807422
+FER,Cytosol,0.629576698
+VIPAS39,Cytosol,0.284519556
+ANKRD50,Cytosol,0.36868904
+UPF2,Cytosol,0.749842894
+EPS8,Cytosol,0.272796757
+FAM160B1,Cytosol,-0.049870063
+ADAM17,Cytosol,-0.263259375
+WWC2,Cytosol,0.504505814
+BICD1,Cytosol,0.105949663
+NBAS,Cytosol,0.376008265
+GUF1,Cytosol,-0.150477254
+SACS,Cytosol,0.081781023
+PABPC3,Cytosol,-0.369724606
+DST,Cytosol,0.541171759
+TIAL1,Cytosol,-0.209569965
+TMEM56,Cytosol,0.228219305
+FAM168B,Cytosol,0.753352681
+AC093838.4,Cytosol,-0.407137477
+MGAT5,Cytosol,0.404568018
+GPATCH11,Cytosol,0.72858671
+POU4F1,Cytosol,0.052174142
+RNF219,Cytosol,0.317773105
+EPG5,Cytosol,0.411116749
+C18orf25,Cytosol,0.491216587
+PDK1,Cytosol,0.049805114
+PDE3B,Cytosol,0.040357236
+TGOLN2,Cytosol,0.320104678
+UHMK1,Cytosol,0.144334275
+TADA1,Cytosol,-0.475746783
+CWF19L2,Cytosol,0.231077797
+JMY,Cytosol,0.09278229
+HOMER1,Cytosol,0.259436507
+USP12,Cytosol,0.417504472
+CCDC50,Cytosol,-0.000452316
+PAN3,Cytosol,0.227044989
+TMEM123,Cytosol,-0.354220316
+GJA1,Cytosol,-0.747724922
+SLC30A6,Cytosol,-0.394803414
+SAR1B,Cytosol,-0.416851442
+GPR180,Cytosol,-0.239799919
+UTRN,Cytosol,0.542860137
+PTPRK,Cytosol,-0.515623351
+PLOD2,Cytosol,-0.742931528
+GPR125,Cytosol,-0.389698452
+SREK1IP1,Cytosol,0.433584254
+TXNDC11,Cytosol,-0.018126987
+BCL2L11,Cytosol,0.313455843
+CLGN,Cytosol,-1.04679384
+RASSF3,Cytosol,0.518577095
+RANBP2,Cytosol,0.369017063
+TMEM87B,Cytosol,0.001608397
+RBMS1,Cytosol,0.645744985
+LPCAT1,Cytosol,0.389993751
+UBALD1,Cytosol,-0.234673936
+RMND5A,Cytosol,0.312420235
+ZDHHC7,Cytosol,0.397417094
+TRIP12,Cytosol,0.579394563
+CEBPG,Cytosol,0.146717314
+SREK1,Cytosol,-0.387828431
+CHD1,Cytosol,0.548492709
+DGKE,Cytosol,-0.207768739
+HS2ST1,Cytosol,0.008865727
+MSI2,Cytosol,0.721142402
+CACNA2D1,Cytosol,-0.740580205
+NUS1,Cytosol,-0.204447463
+IMPACT,Cytosol,-0.223103831
+TBCEL,Cytosol,0.405672257
+FAM105B,Cytosol,0.631175482
+TBRG1,Cytosol,0.113219698
+CC2D1B,Cytosol,0.429265947
+MIA3,Cytosol,-0.47344022
+TRIM11,Cytosol,0.311908887
+CCSAP,Cytosol,0.338184355
+CXADR,Cytosol,-0.371947282
+GABPA,Cytosol,-0.090254904
+ADAMTS1,Cytosol,-0.588573851
+TSEN2,Cytosol,-0.451159343
+FLCN,Cytosol,0.095686021
+SKA1,Cytosol,0.861374601
+RAB6B,Cytosol,0.58405914
+ACSS1,Cytosol,0.596943052
+ANKRD40,Cytosol,0.635948712
+VOPP1,Cytosol,0.295835966
+APOOL,Cytosol,0.347320531
+CYP2U1,Cytosol,-0.153618644
+AGPAT5,Cytosol,-0.379518866
+MARVELD1,Cytosol,0.437782175
+ZFYVE27,Cytosol,0.070725676
+SLC25A28,Cytosol,0.149134419
+HSPA13,Cytosol,-0.833278699
+USP25,Cytosol,0.040330714
+RHOC,Cytosol,0.061471786
+SLC16A1,Cytosol,-0.659171379
+LARP1,Cytosol,0.808708825
+MIER3,Cytosol,0.110177751
+ZKSCAN2,Cytosol,0.496913823
+PDIA4,Cytosol,-0.453530294
+FAM126B,Cytosol,0.350076773
+FZD7,Cytosol,-0.087870751
+FMN2,Cytosol,0.49031914
+PPARGC1B,Cytosol,-0.03006821
+SLC26A2,Cytosol,0.250789727
+LSM11,Cytosol,0.571636094
+PSD3,Cytosol,0.423843536
+DCK,Cytosol,-0.499282332
+ADAMTS3,Cytosol,-0.357732759
+DPY19L4,Cytosol,-0.459367943
+NDUFAF6,Cytosol,-0.440334251
+N6AMT1,Cytosol,-0.044700737
+CDK20,Cytosol,0.075460162
+PCGF6,Cytosol,-0.867308778
+ANKRD9,Cytosol,0.814219772
+SFXN2,Cytosol,0.192988437
+PTDSS1,Cytosol,-0.576958359
+SUPV3L1,Cytosol,-0.066227874
+TYSND1,Cytosol,0.120274583
+CD109,Cytosol,0.086665579
+ZDHHC5,Cytosol,0.632638057
+ZFAND3,Cytosol,0.840785077
+NPTN,Cytosol,-0.681045565
+KAT6B,Cytosol,0.438530105
+SAMD8,Cytosol,0.143112482
+BAG4,Cytosol,0.481747953
+ATAD2,Cytosol,0.658163625
+PHKG2,Cytosol,-0.409027906
+SASS6,Cytosol,-0.131701201
+ZIC3,Cytosol,-0.266125444
+EXOG,Cytosol,-0.238369444
+SMG1,Cytosol,0.361106557
+FCHO2,Cytosol,-0.132490075
+C1orf27,Cytosol,-0.497254621
+LRP8,Cytosol,-0.480050153
+PAXIP1,Cytosol,0.288292853
+SSBP3,Cytosol,0.80845922
+CLDN12,Cytosol,-0.6258789
+GATAD1,Cytosol,0.171426248
+ST3GAL2,Cytosol,1.020200141
+FUK,Cytosol,-0.20524783
+KIT,Cytosol,-0.326892501
+AASDH,Cytosol,-0.290856734
+DYRK1A,Cytosol,0.401378146
+TSPAN18,Cytosol,0.302414498
+SLC35B2,Cytosol,-0.200226598
+TMEM164,Cytosol,0.575428112
+TAB3,Cytosol,0.758023189
+SLC38A10,Cytosol,-0.082083814
+ZNF618,Cytosol,0.466147322
+C9orf91,Cytosol,0.334610345
+UBN2,Cytosol,0.329771557
+BRAF,Cytosol,0.313011152
+SLC37A3,Cytosol,-0.464533846
+DPYSL5,Cytosol,0.921258043
+FAM213B,Cytosol,1.013299991
+C12orf43,Cytosol,0.842455092
+RER1,Cytosol,-0.24646681
+UBXN11,Cytosol,-0.705249572
+RHPN1,Cytosol,-0.245962557
+CNNM4,Cytosol,-0.154401229
+EYA3,Cytosol,0.37749163
+MRAS,Cytosol,1.149654246
+COLEC12,Cytosol,-0.32031629
+CUL4B,Cytosol,0.487394001
+MITD1,Cytosol,-1.299952441
+EIF5B,Cytosol,0.595678512
+TSPAN33,Cytosol,0.013220794
+AHCYL2,Cytosol,0.104151441
+B4GALT5,Cytosol,0.544295837
+TSR2,Cytosol,0.489749592
+ZC3H18,Cytosol,0.890867737
+TMED4,Cytosol,-0.430492658
+PPP1R15B,Cytosol,-0.026153126
+AGPAT6,Cytosol,0.106474913
+ZSCAN12,Cytosol,0.318927282
+ELK4,Cytosol,0.0993811
+F11R,Cytosol,0.566519388
+ZNF276,Cytosol,-0.095068011
+PINK1,Cytosol,0.399845359
+B4GALT3,Cytosol,-0.021579388
+FAM160B2,Cytosol,0.316072198
+CACHD1,Cytosol,-0.175013877
+PAXBP1,Cytosol,-0.429037845
+IFNAR2,Cytosol,-0.038374189
+SON,Cytosol,0.270654344
+SV2A,Cytosol,0.311186564
+HLCS,Cytosol,0.35376772
+ADPGK,Cytosol,-0.361458085
+ALDH4A1,Cytosol,0.612637186
+STARD9,Cytosol,-0.200030344
+UBR1,Cytosol,0.527857514
+AMFR,Cytosol,-0.480223661
+RSPRY1,Cytosol,0.103388527
+ARHGAP35,Cytosol,0.861219613
+CALM3,Cytosol,0.827984241
+IQCC,Cytosol,0.073707862
+BSDC1,Cytosol,0.387549115
+ATAD3B,Cytosol,-0.300637614
+VMA21,Cytosol,0.039961047
+WDR4,Cytosol,-0.029077519
+CBS,Cytosol,0.018470644
+PDXK,Cytosol,0.354222293
+G6PD,Cytosol,0.307018347
+AGPAT3,Cytosol,0.504460632
+C21orf2,Cytosol,-0.4198693
+LRRC3,Cytosol,0.40134753
+LSS,Cytosol,0.161446412
+VAV2,Cytosol,0.999884022
+MCM3AP,Cytosol,0.298958007
+C21orf58,Cytosol,0.441704759
+PCNT,Cytosol,0.164729731
+DIP2A,Cytosol,0.178557766
+ZNF714,Cytosol,0.539341296
+PKN3,Cytosol,0.272046272
+TAOK1,Cytosol,0.311732177
+SIK3,Cytosol,0.316532168
+PCSK7,Cytosol,0.256388344
+CHTOP,Cytosol,-0.45342057
+ZBTB7B,Cytosol,0.812556713
+NLRX1,Cytosol,0.237805965
+ANO10,Cytosol,-0.251128821
+SLC25A44,Cytosol,0.491906951
+NBEAL2,Cytosol,-0.213832896
+IER2,Cytosol,0.36871428
+ZNF394,Cytosol,0.006379421
+CPSF4,Cytosol,-0.065866879
+TONSL,Cytosol,-0.163572538
+MUM1,Cytosol,0.262349716
+RECQL4,Cytosol,-0.017672173
+LRRC14,Cytosol,0.473872752
+PPP1R16A,Cytosol,0.420192349
+C5orf45,Cytosol,-0.255702596
+MFSD12,Cytosol,-0.171127838
+FDXR,Cytosol,0.093780196
+ALDH16A1,Cytosol,-0.597024313
+ITGA5,Cytosol,-0.223188137
+ZNF385A,Cytosol,0.388527317
+MPP3,Cytosol,-1.029362816
+EMC10,Cytosol,-0.27618284
+FAM171A2,Cytosol,0.05012972
+DBF4B,Cytosol,0.453112992
+LARP4,Cytosol,0.353129418
+LEMD2,Cytosol,0.100478662
+WDR90,Cytosol,-0.806991565
+C16orf59,Cytosol,-0.294739464
+AMDHD2,Cytosol,0.025095468
+PAQR4,Cytosol,0.598517011
+ADCY9,Cytosol,0.322215635
+CLPB,Cytosol,0.386220297
+NEU3,Cytosol,0.437775667
+CYB561A3,Cytosol,0.285234703
+TAF6L,Cytosol,-0.275641359
+LRP5,Cytosol,-0.196551099
+ZYG11B,Cytosol,0.350245657
+PPAP2B,Cytosol,-0.175223076
+PRKAA2,Cytosol,0.243747893
+KLHL21,Cytosol,0.236046791
+GMEB1,Cytosol,0.369959381
+SEPN1,Cytosol,-0.073163958
+AK4,Cytosol,0.455020941
+RAVER2,Cytosol,-0.222083493
+PDPN,Cytosol,0.024933447
+SDC3,Cytosol,0.566058985
+KIAA1522,Cytosol,0.730494563
+C1orf86,Cytosol,-0.185388728
+NFIA,Cytosol,0.0791916
+OMA1,Cytosol,-0.652156409
+MYSM1,Cytosol,0.092691871
+FUBP1,Cytosol,-0.090577481
+DNAJB4,Cytosol,-0.051885391
+FAM102B,Cytosol,0.492797171
+ATXN7L2,Cytosol,-0.047442345
+ZNF326,Cytosol,0.015625838
+EXTL2,Cytosol,-0.604742878
+SLC30A7,Cytosol,-0.635148319
+PEA15,Cytosol,1.104477763
+NCSTN,Cytosol,-0.611172618
+VANGL2,Cytosol,0.386138588
+FLVCR1,Cytosol,-0.392132048
+RBM15,Cytosol,0.418551173
+BPNT1,Cytosol,0.340413295
+BROX,Cytosol,0.0519334
+ACP6,Cytosol,-0.093999625
+PPP1R21,Cytosol,0.477586138
+B3GALNT2,Cytosol,0.214567164
+C2orf47,Cytosol,-1.363807635
+ARL5A,Cytosol,-0.222416507
+SGCB,Cytosol,-0.012143945
+SMARCAD1,Cytosol,0.321423745
+RNF149,Cytosol,-0.912764293
+FZD5,Cytosol,0.28797364
+DCAF16,Cytosol,0.343271579
+PAQR3,Cytosol,-0.463018677
+ANTXR2,Cytosol,-0.313196023
+PBXIP1,Cytosol,-0.25937748
+PYGO2,Cytosol,0.759626729
+HIPK1,Cytosol,0.575281499
+KBTBD8,Cytosol,0.290251256
+EOGT,Cytosol,-0.463323013
+POGLUT1,Cytosol,-0.525272971
+ATP1A1,Cytosol,-0.505324347
+EIF4E3,Cytosol,0.208494622
+LRRC58,Cytosol,0.235068463
+FSTL1,Cytosol,0.360052499
+KRTCAP2,Cytosol,-0.888087943
+KIAA1524,Cytosol,0.014629061
+TGFBR2,Cytosol,-0.31514593
+ANKZF1,Cytosol,-0.460563635
+STT3B,Cytosol,-0.648491683
+PPM1L,Cytosol,0.422170908
+RYBP,Cytosol,0.507200444
+PPP4R2,Cytosol,-0.183652113
+C3orf17,Cytosol,-0.200727622
+SPICE1,Cytosol,0.12742872
+WDFY3,Cytosol,0.528024373
+ATXN7,Cytosol,0.171608466
+PPM1K,Cytosol,0.117450544
+CCNL1,Cytosol,-1.033638234
+RPP14,Cytosol,0.556637585
+ABHD6,Cytosol,-0.084583914
+CRELD1,Cytosol,-0.2173184
+U2SURP,Cytosol,0.210447648
+TTC14,Cytosol,-0.676157882
+SNRK,Cytosol,0.310434518
+SLC4A1AP,Cytosol,0.115878519
+ZDHHC3,Cytosol,0.261227819
+FYCO1,Cytosol,0.369203408
+YEATS2,Cytosol,0.537966411
+SNIP1,Cytosol,-0.101451177
+TMEM41A,Cytosol,-0.09294228
+RPN1,Cytosol,-0.890797841
+SFMBT1,Cytosol,0.614695594
+PBRM1,Cytosol,0.488438342
+FAM208A,Cytosol,0.311379178
+ARHGEF3,Cytosol,0.710899268
+UBXN7,Cytosol,0.503430773
+ZNF691,Cytosol,0.055580403
+SGMS2,Cytosol,0.15950163
+DNAJB14,Cytosol,0.103682731
+ZNF589,Cytosol,0.377865316
+SHISA5,Cytosol,0.43879703
+INTU,Cytosol,-0.048216891
+RNF123,Cytosol,-0.007594707
+MFSD8,Cytosol,-0.304716724
+C4orf29,Cytosol,0.361010283
+RAD54L2,Cytosol,0.48256598
+MAP9,Cytosol,-0.010572307
+CEP44,Cytosol,-0.046610455
+ABCE1,Cytosol,0.056395242
+TMEM184C,Cytosol,0.28729027
+TMEM161B,Cytosol,-1.253523112
+ELOVL7,Cytosol,-0.355678015
+LMBRD2,Cytosol,-0.451638713
+NIPBL,Cytosol,0.327899232
+SLC25A46,Cytosol,-0.09679343
+STARD4,Cytosol,0.031907084
+PGGT1B,Cytosol,0.004480659
+NDUFS4,Cytosol,-1.233045683
+ARSK,Cytosol,-0.560931445
+GPX8,Cytosol,0.044384241
+SERINC5,Cytosol,0.310003146
+GFM2,Cytosol,-0.2006284
+CCDC127,Cytosol,0.027364511
+SEPT8,Cytosol,0.864547342
+DCBLD1,Cytosol,-0.065696866
+PDSS2,Cytosol,0.176135196
+STXBP5,Cytosol,0.216228627
+DAGLB,Cytosol,-0.331896668
+GALNT10,Cytosol,0.319926551
+ZNF12,Cytosol,-0.047424765
+USP49,Cytosol,0.090315688
+ZNF704,Cytosol,0.297758876
+LMTK2,Cytosol,0.097739825
+CTSB,Cytosol,-0.221867771
+ADCY1,Cytosol,0.669337387
+EN2,Cytosol,0.573314363
+SUN1,Cytosol,-0.289163818
+OXR1,Cytosol,-0.137971414
+SLC4A2,Cytosol,0.008310063
+FASTK,Cytosol,-0.342268509
+TMUB1,Cytosol,0.261930099
+C7orf55,Cytosol,-1.128199036
+FOXK1,Cytosol,0.609819742
+FZD6,Cytosol,-0.513007817
+KIAA1429,Cytosol,0.534118702
+TMEM67,Cytosol,-0.976659733
+SNAPC3,Cytosol,-0.039022632
+KIAA1161,Cytosol,0.528678279
+METTL2B,Cytosol,0.305864144
+HGSNAT,Cytosol,0.011151157
+RASEF,Cytosol,0.613285713
+ANKS6,Cytosol,0.19556302
+TMEM246,Cytosol,-0.008655473
+ZHX1,Cytosol,0.467365524
+KIAA1958,Cytosol,0.350349945
+PIGA,Cytosol,-0.039124008
+WNK2,Cytosol,0.167022304
+ATP7A,Cytosol,-0.05311781
+PIGO,Cytosol,-0.967223745
+BRWD3,Cytosol,0.824704965
+SLITRK5,Cytosol,0.283365597
+DDX26B,Cytosol,-0.132609237
+MARCH8,Cytosol,0.5716342
+GTF2A1,Cytosol,0.583299347
+ZCCHC24,Cytosol,0.82358451
+REEP3,Cytosol,-0.202305255
+MICU2,Cytosol,-0.913602536
+PCF11,Cytosol,0.362898491
+PKNOX2,Cytosol,1.067554325
+ZNF22,Cytosol,-0.487380154
+RPUSD4,Cytosol,-0.041940492
+ARF6,Cytosol,0.375094377
+TTC8,Cytosol,-0.515484503
+CDX2,Cytosol,0.903711633
+BEND7,Cytosol,0.258183335
+TAF3,Cytosol,0.616651365
+PDZD8,Cytosol,0.25080654
+ZNF503,Cytosol,0.264324058
+FAM175B,Cytosol,0.264003973
+QSOX2,Cytosol,-0.292093688
+NSD1,Cytosol,0.700114484
+SNAPC4,Cytosol,-0.007308809
+PMPCA,Cytosol,-0.221803317
+SDCCAG3,Cytosol,0.283763442
+TSC1,Cytosol,0.426546886
+FAM69B,Cytosol,-0.184049158
+KIAA1462,Cytosol,0.694045681
+ZNF219,Cytosol,0.231055502
+METTL3,Cytosol,-0.476231114
+HSPA12A,Cytosol,0.285344197
+TC2N,Cytosol,0.56998219
+CPSF2,Cytosol,0.487418264
+ARL5B,Cytosol,0.17319183
+TAF1D,Cytosol,-1.15059031
+HTRA1,Cytosol,-0.678725419
+CEP57,Cytosol,-0.341810548
+JAM3,Cytosol,0.187014504
+HIF1AN,Cytosol,0.355277886
+ZFYVE19,Cytosol,0.404402858
+FBN1,Cytosol,-0.444693603
+BAG5,Cytosol,0.276687753
+GABRB3,Cytosol,0.066042645
+SGPL1,Cytosol,0.346896882
+FRS2,Cytosol,0.463054964
+ZNF202,Cytosol,0.056238187
+STXBP4,Cytosol,0.216091151
+CUL5,Cytosol,0.294773656
+WBP1L,Cytosol,0.785549435
+TRIM44,Cytosol,0.917645373
+TPP1,Cytosol,-0.655560199
+C11orf74,Cytosol,-0.731197894
+TUB,Cytosol,0.370781916
+RNF169,Cytosol,0.392186438
+PRTG,Cytosol,-0.019120372
+TMEM41B,Cytosol,-0.252608154
+TMX3,Cytosol,-0.55836233
+WEE1,Cytosol,-0.360773421
+ZNF3,Cytosol,0.032442561
+RIMKLB,Cytosol,-0.172267923
+TMED3,Cytosol,-0.408872724
+NDEL1,Cytosol,0.293353974
+BLCAP,Cytosol,0.051185924
+CASC4,Cytosol,0.059810369
+AP1G1,Cytosol,0.590738299
+KIF7,Cytosol,0.000615861
+PEX11A,Cytosol,0.315799801
+ZBTB39,Cytosol,0.497565511
+TMEM194A,Cytosol,0.13752213
+SMAD3,Cytosol,0.631572392
+MAP1A,Cytosol,1.376187468
+MBD6,Cytosol,0.03136846
+PDIA3,Cytosol,-0.702729242
+ACSF2,Cytosol,-0.348416138
+COQ4,Cytosol,-0.450530924
+SLC27A4,Cytosol,-0.069617749
+CERCAM,Cytosol,-0.178082457
+DOLPP1,Cytosol,0.074390767
+GPRC5B,Cytosol,0.243782657
+CRK,Cytosol,0.478679492
+FBXO22,Cytosol,-0.553798704
+TBC1D2B,Cytosol,0.723164626
+CDK12,Cytosol,0.488107043
+ENGASE,Cytosol,-0.086079541
+TBC1D16,Cytosol,0.456437335
+ENTHD2,Cytosol,-0.504779709
+STIM1,Cytosol,0.173989216
+IRGQ,Cytosol,0.621575831
+PPP2R3B,Cytosol,-0.329078002
+ZNF646,Cytosol,0.514256947
+MIDN,Cytosol,0.598635201
+MVD,Cytosol,0.023367528
+ANKRD11,Cytosol,0.203012775
+SPATA33,Cytosol,0.305133079
+ZNF641,Cytosol,0.76847172
+DHRS13,Cytosol,-0.187527428
+TP53I13,Cytosol,-0.849769208
+KMT2D,Cytosol,0.58872134
+C19orf55,Cytosol,0.236743755
+LENG8,Cytosol,-0.542543549
+ZNF146,Cytosol,0.066865334
+ZNF444,Cytosol,0.444330041
+FAM57A,Cytosol,-0.171007904
+SLC43A2,Cytosol,0.379890188
+SRR,Cytosol,-0.050569569
+GHDC,Cytosol,-0.616415451
+ITFG3,Cytosol,0.272268359
+ZNF598,Cytosol,0.014860419
+E4F1,Cytosol,-0.436811848
+ABCA3,Cytosol,-0.011080647
+SRRM2,Cytosol,0.244399944
+LTBP3,Cytosol,0.019072346
+SAC3D1,Cytosol,-0.156591182
+SF1,Cytosol,0.371737153
+PAFAH1B2,Cytosol,0.696599449
+ANKS3,Cytosol,-0.11406314
+SETD5,Cytosol,0.56478759
+HOOK3,Cytosol,0.279414075
+RBPJ,Cytosol,0.329997438
+TTC39C,Cytosol,0.096674456
+KIF5C,Cytosol,0.183521826
+MGAT2,Cytosol,-0.231418758
+BMI1,Cytosol,-0.677795158
+KCTD6,Cytosol,-0.857666595
+TAP1,Cytosol,-0.256579479
+ING5,Cytosol,0.422927138
+ATG4B,Cytosol,0.040767175
+SOGA2,Cytosol,0.390131148
+SNRNP48,Cytosol,0.512892172
+SLC20A2,Cytosol,-0.308968318
+TMUB2,Cytosol,-0.199344374
+STAT3,Cytosol,0.988672376
+ADAM9,Cytosol,-0.667059155
+PKIG,Cytosol,-0.200260152
+SEMA4C,Cytosol,0.127509204
+CNNM3,Cytosol,-0.210288179
+TET2,Cytosol,0.446925443
+TCTN2,Cytosol,-0.326728299
+TSPAN5,Cytosol,-0.131198341
+ZBTB5,Cytosol,0.496556745
+SNTB2,Cytosol,0.287674642
+ZNF507,Cytosol,0.403722523
+STX18,Cytosol,0.006628685
+GFM1,Cytosol,-0.642271629
+ANKRD49,Cytosol,-0.540937656
+MAT2A,Cytosol,-0.29451621
+ZNF608,Cytosol,0.722079272
+LETM1,Cytosol,0.271527762
+TMEM129,Cytosol,0.555422589
+FEM1B,Cytosol,0.247447635
+HNRNPH1,Cytosol,-0.891750688
+MECP2,Cytosol,0.676850701
+UPF3A,Cytosol,-0.392900253
+CHST14,Cytosol,-0.104590843
+PARM1,Cytosol,0.242938374
+CSNK1G1,Cytosol,0.64213047
+ZBTB43,Cytosol,0.147648158
+GPRIN1,Cytosol,0.751548719
+MRPL1,Cytosol,-0.770597872
+SLC33A1,Cytosol,-0.548703448
+SDC2,Cytosol,-0.470941857
+MMGT1,Cytosol,-0.519563608
+CLIC4,Cytosol,0.03966411
+CCDC8,Cytosol,0.829280478
+INO80E,Cytosol,-0.053235953
+DFFB,Cytosol,-0.016267512
+ANTXR1,Cytosol,-0.169451168
+CKAP2L,Cytosol,0.459288779
+C15orf40,Cytosol,-0.650509935
+HIC2,Cytosol,0.706896122
+LUZP1,Cytosol,0.741915329
+HEXDC,Cytosol,-0.56609041
+LRRC45,Cytosol,-0.164875867
+ASPSCR1,Cytosol,-0.454379652
+TAPT1,Cytosol,-0.444976425
+CSGALNACT2,Cytosol,0.154561858
+PCDH7,Cytosol,0.204914773
+ROBO1,Cytosol,-0.188826309
+P2RY1,Cytosol,0.167977336
+TPST1,Cytosol,0.046303815
+TOR1AIP2,Cytosol,0.308507439
+OTUD3,Cytosol,0.299625814
+GUSB,Cytosol,-1.028482319
+BRD3,Cytosol,0.889411295
+MAP3K2,Cytosol,0.33140818
+NLGN2,Cytosol,0.128491026
+ALCAM,Cytosol,-0.305268377
+YWHAG,Cytosol,0.787295707
+TMEM192,Cytosol,0.40835185
+ZNF778,Cytosol,0.502875056
+NIPA1,Cytosol,0.216386677
+SIK2,Cytosol,0.609789581
+RNF150,Cytosol,0.215613906
+ZNF212,Cytosol,0.223857109
+FAM161A,Cytosol,0.419292751
+CRTAP,Cytosol,-0.297973973
+PRDM10,Cytosol,0.445550692
+FOS,Cytosol,0.841401209
+TMED10,Cytosol,-0.605039462
+SLC30A1,Cytosol,-0.580032875
+DNAJC18,Cytosol,0.434868132
+RALGAPB,Cytosol,0.452819446
+LONRF2,Cytosol,0.67503588
+ELOVL6,Cytosol,0.045511983
+ARL6IP1,Cytosol,-0.380136132
+CDH2,Cytosol,-0.282678637
+EMB,Cytosol,-0.352988321
+STAT2,Cytosol,0.552092979
+TRABD,Cytosol,-0.30573058
+POLH,Cytosol,0.654782368
+KIF5B,Cytosol,0.6908251
+AKAP13,Cytosol,0.739268942
+CHCHD7,Cytosol,-0.351890217
+GPR27,Cytosol,-0.115135544
+KBTBD2,Cytosol,0.537344899
+KIAA0232,Cytosol,0.463199516
+TMEM43,Cytosol,-0.315336221
+RNF139,Cytosol,-0.594652056
+PAQR8,Cytosol,0.176106034
+TANC2,Cytosol,0.359346759
+DNAJC24,Cytosol,0.042591129
+HS6ST2,Cytosol,-0.741540048
+INSR,Cytosol,-0.309717351
+ATP6V0E2,Cytosol,0.359776487
+ZNF692,Cytosol,-0.614049259
+NETO2,Cytosol,-0.202703361
+NPTX1,Cytosol,0.057704774
+FAM98B,Cytosol,-0.541576985
+GAA,Cytosol,-0.776834197
+CANT1,Cytosol,0.349045302
+CHST11,Cytosol,0.17284942
+CLCN5,Cytosol,-0.370756715
+ZBTB26,Cytosol,-0.213143872
+ZNF562,Cytosol,0.086761332
+ZNF318,Cytosol,0.329457274
+WIPF2,Cytosol,1.060220165
+LRRC8C,Cytosol,-0.139318374
+LRRC8D,Cytosol,-0.049840691
+ETFDH,Cytosol,0.037728005
+LPAR3,Cytosol,-0.037068983
+CLSTN1,Cytosol,-0.11413224
+BPTF,Cytosol,0.530728818
+ATF7IP,Cytosol,0.409420993
+TCEA2,Cytosol,-0.269402411
+ANO5,Cytosol,-0.055218376
+MLLT3,Cytosol,0.589929786
+PRNP,Cytosol,-0.873821238
+ZNF217,Cytosol,0.835316428
+JMJD1C,Cytosol,0.27427307
+THOP1,Cytosol,0.028868089
+ORMDL3,Cytosol,0.898564393
+KLF11,Cytosol,0.203072873
+MTBP,Cytosol,-0.124077525
+ZNF131,Cytosol,-0.113151468
+BSG,Cytosol,-0.463144393
+CERS6,Cytosol,0.104788936
+TP53RK,Cytosol,0.055612126
+FAM195A,Cytosol,-0.826384092
+GTPBP2,Cytosol,0.199327792
+ZNF24,Cytosol,0.609254324
+MANEA,Cytosol,-0.73596448
+RAD9A,Cytosol,-0.23413051
+FAM21C,Cytosol,0.329207708
+CORO1B,Cytosol,-0.120891681
+LRRC20,Cytosol,0.778249685
+NAA16,Cytosol,-0.174498804
+DCP2,Cytosol,0.35871889
+CES3,Cytosol,-0.036439706
+CES2,Cytosol,0.928659485
+PDP2,Cytosol,0.586503368
+SP3,Cytosol,0.196115721
+METAP1D,Cytosol,0.197340152
+ZNF621,Cytosol,0.260350763
+NADSYN1,Cytosol,-0.295016389
+DHCR7,Cytosol,-0.811392975
+NBEA,Cytosol,0.547781023
+ANKRD13D,Cytosol,0.169707219
+LCLAT1,Cytosol,0.202516466
+TADA2B,Cytosol,0.68979016
+HECTD4,Cytosol,0.426219707
+ESRRA,Cytosol,0.034442085
+AHSA2,Cytosol,-0.821328315
+VANGL1,Cytosol,-0.069518057
+IQCB1,Cytosol,-0.243304081
+GOLGB1,Cytosol,0.216747967
+TNKS,Cytosol,0.518298218
+ZBTB21,Cytosol,0.326787752
+STOX2,Cytosol,0.155477142
+DAG1,Cytosol,-0.094440926
+RNF26,Cytosol,0.500937204
+PEAK1,Cytosol,0.518445242
+TNFRSF10D,Cytosol,-0.408092299
+MOB1B,Cytosol,0.501130132
+SNX33,Cytosol,1.146117235
+CHD2,Cytosol,0.42738287
+CCDC41,Cytosol,-0.197223713
+PC,Cytosol,0.01495864
+SUSD5,Cytosol,-0.211018332
+HEG1,Cytosol,0.053064735
+TOMM20,Cytosol,-0.213388725
+CNP,Cytosol,0.814053542
+DPY19L1,Cytosol,-0.258701823
+ZNF791,Cytosol,-0.042217537
+PHC3,Cytosol,-0.331520361
+GOLIM4,Cytosol,-0.048616667
+XXYLT1,Cytosol,-0.250799473
+UBXN2A,Cytosol,0.085598035
+CCS,Cytosol,-0.572340598
+FAM3C2,Cytosol,-0.327435271
+CTSF,Cytosol,-0.923289367
+MSRB3,Cytosol,0.343336815
+LEMD3,Cytosol,-0.630550428
+RGMB,Cytosol,-0.088360054
+ZDHHC24,Cytosol,-0.275855128
+MGA,Cytosol,0.547142283
+PIGG,Cytosol,-0.871522307
+ADCY6,Cytosol,0.155930405
+ZBTB4,Cytosol,0.415096656
+ZHX3,Cytosol,0.799918293
+RALGAPA1,Cytosol,0.394272856
+ATP2A2,Cytosol,-0.313046886
+CNTNAP2,Cytosol,0.482753272
+DENND4A,Cytosol,0.389958057
+MSL2,Cytosol,0.271975411
+UGT8,Cytosol,-0.593803623
+ZNF266,Cytosol,-0.075811594
+SLC29A2,Cytosol,-0.376084173
+BRSK2,Cytosol,0.682199906
+B3GNT1,Cytosol,-0.650017563
+TMEM167A,Cytosol,0.1185384
+CEP135,Cytosol,0.337552358
+FZD4,Cytosol,-0.200324447
+PDE12,Cytosol,-0.279472367
+GLMN,Cytosol,-0.96567427
+SEZ6L2,Cytosol,-0.43678946
+KLC2,Cytosol,0.601260949
+GK5,Cytosol,0.449130684
+VCPIP1,Cytosol,0.350391196
+PCCA,Cytosol,-0.637166284
+GOLGA8A,Cytosol,-0.972943499
+TP53I11,Cytosol,0.483650272
+PHYKPL,Cytosol,-0.446697446
+ARL10,Cytosol,0.224491118
+CCDC14,Cytosol,-0.256439784
+ALG10B,Cytosol,-0.197140881
+RAB6A,Cytosol,0.431461253
+ERCC4,Cytosol,0.49039778
+RMI2,Cytosol,-0.611249777
+TOM1L2,Cytosol,0.909838526
+MLXIP,Cytosol,0.734906412
+SLC35E3,Cytosol,0.081525592
+ARL4D,Cytosol,-0.340203925
+LYSMD3,Cytosol,0.277216689
+B3GALT6,Cytosol,0.593737731
+MBLAC2,Cytosol,0.105712055
+TPRN,Cytosol,0.190217505
+YES1,Cytosol,0.33901491
+TMEM39A,Cytosol,-0.373118449
+CCDC57,Cytosol,-0.530461524
+FOXG1,Cytosol,-0.217701149
+ATAD5,Cytosol,0.067961434
+ANAPC2,Cytosol,-0.000321895
+SPRYD4,Cytosol,-0.359947659
+CLK2,Cytosol,-0.245082649
+LPCAT4,Cytosol,-0.16350613
+B3GNT5,Cytosol,0.147195724
+LMNB2,Cytosol,0.937583221
+RMDN1,Cytosol,-0.01242513
+ACSF3,Cytosol,-0.08612737
+ANKLE2,Cytosol,-0.027497318
+NFATC2IP,Cytosol,-0.018160261
+SMCR8,Cytosol,0.601166274
+MTX3,Cytosol,0.3108566
+FBXO46,Cytosol,0.419524951
+WDR73,Cytosol,-0.195022104
+ANO6,Cytosol,-0.001796298
+ZBTB34,Cytosol,0.620977963
+FAM210A,Cytosol,0.144840414
+ULK1,Cytosol,0.201253474
+RPS6KA3,Cytosol,0.243571412
+PUS1,Cytosol,-0.141831048
+CHD9,Cytosol,0.048516163
+PDDC1,Cytosol,0.307636032
+TOP3A,Cytosol,0.549576986
+NR2C2,Cytosol,0.306373761
+ZBTB33,Cytosol,0.63720041
+SLC25A22,Cytosol,0.249238351
+PIDD,Cytosol,-0.139023438
+GBA,Cytosol,-0.178726985
+IL17RA,Cytosol,0.023335946
+THAP5,Cytosol,-0.178757365
+PVRL3,Cytosol,-0.790793424
+KIAA0195,Cytosol,-0.210823462
+SOX12,Cytosol,0.596601432
+CHID1,Cytosol,-0.179204624
+ZNF518A,Cytosol,0.29966232
+ZBTB41,Cytosol,-0.083090156
+DMAP1,Cytosol,-0.536322323
+C2orf69,Cytosol,-0.300503237
+SH2B1,Cytosol,0.552651767
+KDELC2,Cytosol,-0.448103606
+GALNT11,Cytosol,-0.000662104
+WDR6,Cytosol,0.195418426
+GEN1,Cytosol,-0.13380939
+GLDC,Cytosol,-0.20305758
+CTNNBIP1,Cytosol,0.653735835
+ERN1,Cytosol,-0.130204102
+KCTD12,Cytosol,0.561752975
+DHFRL1,Cytosol,0.424446657
+FAM132B,Cytosol,0.057889158
+FAM219B,Cytosol,-0.363887715
+DPY19L3,Cytosol,-0.112007387
+PFAS,Cytosol,-0.051933106
+C17orf62,Cytosol,0.255788565
+ZBTB7A,Cytosol,0.531282735
+SPTY2D1,Cytosol,0.920391862
+FUCA1,Cytosol,-0.90232305
+CALR,Cytosol,-0.312966582
+LDLRAD3,Cytosol,0.013997585
+CLK3,Cytosol,-0.114834562
+PACS2,Cytosol,0.477189746
+ELMOD2,Cytosol,-0.877602513
+FJX1,Cytosol,-0.038904287
+ZBTB18,Cytosol,0.425055387
+GCC1,Cytosol,0.515114433
+PLD6,Cytosol,-0.041519314
+CDC42EP4,Cytosol,0.983092594
+PCBP1-AS1,Cytosol,-0.364638244
+MYADM,Cytosol,-0.138495843
+SERTAD2,Cytosol,0.512495403
+BBS10,Cytosol,-0.388575249
+SOCS4,Cytosol,0.481100844
+ZADH2,Cytosol,0.233605685
+EXOC3,Cytosol,0.506561979
+C7orf41,Cytosol,0.724602176
+ZNF609,Cytosol,0.958027843
+CCDC66,Cytosol,-0.535530354
+MCFD2,Cytosol,0.105768495
+GAS1,Cytosol,-0.177271204
+FAM73A,Cytosol,0.043056726
+NRIP1,Cytosol,0.242398474
+PCGF5,Cytosol,-0.003328929
+YOD1,Cytosol,-0.047491692
+SLC36A4,Cytosol,-0.725986919
+ZDHHC20,Cytosol,0.21672136
+PSMG4,Cytosol,-1.045411314
+PDIA3P,Cytosol,-0.633568351
+CUEDC1,Cytosol,0.509392145
+KCTD2,Cytosol,0.836348285
+D2HGDH,Cytosol,-0.300339611
+FKRP,Cytosol,0.338042245
+SLC26A11,Cytosol,-0.524124975
+F2R,Cytosol,-0.213438233
+DHTKD1,Cytosol,0.544060794
+ZNF746,Cytosol,0.33242653
+TMEM136,Cytosol,-0.249658122
+ZNF322,Cytosol,0.347958479
+OGFOD3,Cytosol,0.005734681
+ZNF678,Cytosol,0.28438356
+ZBTB2,Cytosol,0.2837985
+SGSH,Cytosol,-0.335972016
+SETD2,Cytosol,0.551394136
+YIPF6,Cytosol,-0.000716858
+IBA57,Cytosol,0.488493542
+C5orf24,Cytosol,0.182580876
+ADO,Cytosol,-0.112133122
+CREB3L2,Cytosol,0.484729919
+UNC5C,Cytosol,0.058001335
+RGMA,Cytosol,0.54099386
+EXT1,Cytosol,0.130383968
+ATP6AP2,Cytosol,-0.791700741
+BACE2,Cytosol,-0.256374296
+FIGN,Cytosol,0.507406617
+B4GALNT4,Cytosol,-0.295919845
+AP1S2,Cytosol,-0.524889813
+FBXL6,Cytosol,-0.668586823
+YBEY,Cytosol,-0.886712443
+CLN8,Cytosol,0.251363424
+PLCXD1,Cytosol,0.191474605
+EXOC7,Cytosol,0.828841555
+CEP97,Cytosol,0.342425534
+MXRA7,Cytosol,-0.145181107
+SATB1,Cytosol,0.384442684
+PLCB1,Cytosol,0.596542031
+TTC3,Cytosol,0.583151423
+COL18A1,Cytosol,-0.767199167
+ZNF721,Cytosol,-0.018466796
+SRPR,Cytosol,-0.307028236
+EWSR1,Cytosol,-0.132856298
+GJC1,Cytosol,0.307272792
+MTA1,Cytosol,0.041144507
+CADM1,Cytosol,-0.139468197
+LYSMD4,Cytosol,0.294558828
+NKX2-5,Cytosol,0.209767778
+GPC6,Cytosol,-0.820050433
+PTTG1IP,Cytosol,0.45293103
+ZNF623,Cytosol,0.460905148
+BCOR,Cytosol,0.488805216
+ASB7,Cytosol,0.741907012
+EP400,Cytosol,0.460611104
+COA5,Cytosol,-0.573134404
+PRR14L,Cytosol,0.552738968
+ZNRF3,Cytosol,-0.031301421
+TRMT12,Cytosol,-0.037410112
+FAM101B,Cytosol,0.552285422
+TRIM52,Cytosol,-0.388502455
+CMTM4,Cytosol,0.52600275
+TMEM50A,Cytosol,-0.091303475
+CBX6,Cytosol,0.547526864
+KREMEN1,Cytosol,0.508829757
+TRAIP,Cytosol,-0.303680211
+EMILIN3,Cytosol,0.018839001
+RBM12B,Cytosol,0.140558793
+BTBD9,Cytosol,0.941326433
+KIRREL,Cytosol,-0.239131977
+IQGAP3,Cytosol,0.396206774
+PRKX,Cytosol,0.734230473
+SMTN,Cytosol,0.174848191
+TBX1,Cytosol,-0.367941811
+TSPYL2,Cytosol,-0.061079775
+C22orf46,Cytosol,0.647975192
+PCDH9,Cytosol,0.035129043
+OAF,Cytosol,-0.013488069
+ZDHHC23,Cytosol,0.200082611
+EFNA5,Cytosol,0.073697988
+SS18L1,Cytosol,0.231205634
+KNTC1,Cytosol,0.260445448
+WDR27,Cytosol,-0.416484482
+FOXO4,Cytosol,0.766677681
+POU3F2,Cytosol,0.190429452
+PROS1,Cytosol,-0.3717007
+ZFP1,Cytosol,-0.119788863
+XPOT,Cytosol,0.222749249
+SNN,Cytosol,0.947832529
+AMER1,Cytosol,0.517664405
+ZBTB40,Cytosol,0.542989125
+ATL3,Cytosol,0.157413189
+UBE2G2,Cytosol,0.584383952
+TMED9,Cytosol,-0.351105289
+RBM33,Cytosol,-0.022751964
+JAG2,Cytosol,-0.69025225
+ZFP90,Cytosol,0.564932053
+SIVA1,Cytosol,-1.19378816
+BRI3BP,Cytosol,0.031122427
+ROBO2,Cytosol,-0.225692376
+BRF1,Cytosol,0.772274338
+MANEAL,Cytosol,0.073277317
+PURA,Cytosol,0.019790244
+DDX51,Cytosol,-0.033083349
+NOMO2,Cytosol,-0.753461951
+NRBP2,Cytosol,-0.214375485
+ZNF445,Cytosol,0.49898902
+PRPF39,Cytosol,-0.674573778
+CDK10,Cytosol,-0.441151126
+ATP6V0A2,Cytosol,-0.195663645
+C14orf80,Cytosol,-0.028255708
+HGS,Cytosol,0.052918145
+MRPL30,Cytosol,0.345680412
+METTL7A,Cytosol,0.808734166
+NR2F2,Cytosol,0.335541277
+SP1,Cytosol,0.828829408
+PCGF3,Cytosol,0.318698824
+P4HB,Cytosol,-0.413349086
+PBX1,Cytosol,0.708182458
+BRWD1,Cytosol,0.154132835
+EP400NL,Cytosol,-0.125571698
+MYBL1,Cytosol,0.187309797
+DMWD,Cytosol,0.463290181
+SLC52A2,Cytosol,-0.421672291
+NAT8L,Cytosol,0.327441383
+GNB1L,Cytosol,-0.236588783
+LAMP1,Cytosol,-0.500292487
+KLHDC8B,Cytosol,0.33057069
+SETD4,Cytosol,-0.33630327
+RNPC3,Cytosol,-1.093507444
+BICD2,Cytosol,0.100611489
+LRCH3,Cytosol,0.595878819
+ZNF529,Cytosol,-0.239576403
+AIDA,Cytosol,-0.04449447
+ZBTB6,Cytosol,-0.251835388
+BCL9L,Cytosol,0.84927971
+KIF18B,Cytosol,0.19802425
+MKL2,Cytosol,0.545681326
+CA5BP1,Cytosol,0.401213403
+BACE1,Cytosol,0.331797912
+KPNA4,Cytosol,0.165396064
+ZNF197,Cytosol,0.248768116
+BTN3A2,Cytosol,0.898807157
+INSIG1,Cytosol,-1.108738931
+TMEM222,Cytosol,0.201807078
+SMYD4,Cytosol,0.452545521
+GPATCH8,Cytosol,0.669008244
+LYRM7,Cytosol,0.599129173
+ZNF397,Cytosol,0.12487854
+ZSCAN30,Cytosol,0.584797922
+TPCN1,Cytosol,0.066029611
+POFUT2,Cytosol,-0.19801108
+ZDHHC17,Cytosol,-0.13807449
+PPARA,Cytosol,0.75036028
+TEAD1,Cytosol,0.395475342
+ENTPD5,Cytosol,0.488022149
+KIAA1598,Cytosol,0.492501023
+TSPYL4,Cytosol,0.624406536
+FNBP1,Cytosol,0.909785832
+BCAM,Cytosol,-0.24649625
+COL4A1,Cytosol,-0.423533641
+SIRT7,Cytosol,-0.232973958
+TET3,Cytosol,0.627320166
+ZNF286A,Cytosol,0.300913856
+SAMD11,Cytosol,0.518299938
+B3GALTL,Cytosol,0.003024034
+TCEA1,Cytosol,-0.439292176
+FANCA,Cytosol,-0.107682711
+SEMA4D,Cytosol,0.293376376
+LIN28B,Cytosol,0.380156415
+FANCM,Cytosol,0.118587898
+FAM122A,Cytosol,-0.011710464
+ARHGAP11B,Cytosol,0.259135525
+CYHR1,Cytosol,0.265508329
+KLHL17,Cytosol,-1.033309456
+ANKRD19P,Cytosol,-0.839023334
+ARL4C,Cytosol,0.613550938
+C11orf95,Cytosol,0.684543589
+MAPK12,Cytosol,-0.205473856
+COL4A5,Cytosol,-0.285378423
+NCR3LG1,Cytosol,0.198059248
+HES4,Cytosol,-0.856983692
+CHM,Cytosol,0.329570612
+H2AFX,Cytosol,0.300431193
+SRSF10,Cytosol,-0.461990628
+NDOR1,Cytosol,0.241827059
+FAM72B,Cytosol,0.195867654
+LDOC1L,Cytosol,0.877244371
+PTAR1,Cytosol,0.217281634
+ZDHHC9,Cytosol,0.177896067
+ZBED6CL,Cytosol,0.469574695
+TMEM120B,Cytosol,0.010379803
+MTF1,Cytosol,0.360942357
+TMEM201,Cytosol,0.086155649
+NHLRC3,Cytosol,-0.61954891
+BEND4,Cytosol,0.293122858
+MSL1,Cytosol,0.308629985
+DHFRP1,Cytosol,0.562548142
+ZNF292,Cytosol,0.076700899
+ADAT2,Cytosol,-0.298702656
+H1F0,Cytosol,0.889393105
+LITAF,Cytosol,0.115270811
+ARID2,Cytosol,0.402755719
+S100A13,Cytosol,-0.497685932
+ZNF33A,Cytosol,0.408229985
+LIN54,Cytosol,0.250909482
+KAZN,Cytosol,0.207822038
+SLC35E2B,Cytosol,0.547170191
+KIAA0895L,Cytosol,-0.127505434
+PLEKHG4,Cytosol,0.003399178
+ACADSB,Cytosol,0.558159083
+TMEM63A,Cytosol,-0.123059127
+MPHOSPH8,Cytosol,0.853323256
+FAM217B,Cytosol,0.32616592
+LCOR,Cytosol,0.425437339
+POM121,Cytosol,0.113849754
+ZBTB44,Cytosol,0.415904457
+SLC35F1,Cytosol,-0.125754704
+PTPN1,Cytosol,0.769401959
+EVL,Cytosol,-0.291478224
+EPHB4,Cytosol,0.017455016
+PPP1R26,Cytosol,0.747830217
+TSC22D2,Cytosol,0.318063505
+PIK3R4,Cytosol,-0.258398091
+GDAP2,Cytosol,-0.055255281
+AFAP1,Cytosol,0.656229049
+MAN2A2,Cytosol,-0.364578526
+CACNA1H,Cytosol,-0.033237608
+SULF2,Cytosol,-0.18738937
+PLXNB2,Cytosol,-0.188913504
+XRCC2,Cytosol,-0.073506187
+MYO6,Cytosol,0.623277834
+TCF4,Cytosol,0.445968861
+RABL6,Cytosol,0.016089381
+ZKSCAN5,Cytosol,0.427868368
+ZFP62,Cytosol,0.272609076
+ERI2,Cytosol,-0.058290136
+ZNF33B,Cytosol,0.416272468
+ZNF512B,Cytosol,0.563670479
+ZNF431,Cytosol,0.281877958
+NF1,Cytosol,0.490953869
+VKORC1L1,Cytosol,0.187218053
+COL27A1,Cytosol,0.287935192
+GM2A,Cytosol,0.410549164
+SNHG17,Cytosol,-1.028437003
+CD47,Cytosol,-0.597177315
+CTBP1-AS2,Cytosol,0.570379705
+C6orf106,Cytosol,0.930619754
+NHLRC2,Cytosol,0.593727278
+KPNA5,Cytosol,-0.389139666
+ZNF252P,Cytosol,0.265371979
+PDLIM7,Cytosol,-0.361744971
+SLC39A10,Cytosol,-0.012241163
+ZNF100,Cytosol,-0.240880823
+ZNF398,Cytosol,0.134521835
+GMFB,Cytosol,-0.535182484
+ZMYM1,Cytosol,-0.219852801
+MAFG,Cytosol,0.362868819
+ARRDC1,Cytosol,-0.753202815
+KIAA1671,Cytosol,0.690698638
+IGF2R,Cytosol,-0.256231191
+SLC25A29,Cytosol,0.533779516
+PGAP1,Cytosol,-0.433202362
+SRC,Cytosol,0.953044457
+PCNXL3,Cytosol,-0.129163208
+LRRC8B,Cytosol,0.351718761
+ABCB8,Cytosol,-0.290085492
+SND1,Cytosol,-0.11091582
+ENTPD4,Cytosol,0.229266988
+KANK2,Cytosol,0.808824017
+FITM2,Cytosol,0.130239974
+DDI2,Cytosol,0.688424692
+TRIM33,Cytosol,0.210103676
+LRP10,Cytosol,-0.001568832
+ZNF655,Cytosol,0.039688198
+SLC22A5,Cytosol,-0.562353249
+ADARB1,Cytosol,0.275034018
+OGDHL,Cytosol,-0.156014467
+STMN3,Cytosol,0.897526392
+SIPA1L1,Cytosol,0.350384305
+PIGN,Cytosol,-0.565144181
+COL4A6,Cytosol,-0.208107666
+GPX1P1,Cytosol,-0.39562292
+ENTPD6,Cytosol,0.063356352
+ENPP1,Cytosol,-0.124181867
+PHF2,Cytosol,0.771037544
+RPS26,Cytosol,-1.130413272
+PSAP,Cytosol,-0.51740109
+HOXC6,Cytosol,0.240036302
+EME2,Cytosol,-0.202489384
+ZNF780A,Cytosol,0.465467494
+SLC9A8,Cytosol,0.097461251
+OCLN,Cytosol,-0.225819865
+GPAA1,Cytosol,-0.791943543
+SPG7,Cytosol,-0.10285527
+ERO1L,Cytosol,-0.039479176
+ZNF121,Cytosol,-0.548961205
+MPZL1,Cytosol,-0.207806107
+VPS13A,Cytosol,0.167264269
+AKAP17A,Cytosol,0.287715837
+ELOVL2,Cytosol,0.06246341
+SNHG12,Cytosol,-1.271630596
+NOL8,Cytosol,0.072700291
+MRPL42,Cytosol,-0.559164604
+ENTPD7,Cytosol,-0.018501549
+ZNF84,Cytosol,-0.347497195
+SIRPA,Cytosol,0.128302907
+CD2AP,Cytosol,0.314310031
+NUP62CL,Cytosol,-0.655217347
+SFI1,Cytosol,-0.467073214
+ZNF248,Cytosol,0.061506385
+ZNF770,Cytosol,0.307885752
+HMGN5,Cytosol,-0.533032567
+MIER1,Cytosol,0.636193037
+MAN1A2,Cytosol,0.18777218
+RPS6KL1,Cytosol,0.387763743
+DDX42,Cytosol,0.621103419
+STYX,Cytosol,0.308225495
+UCKL1,Cytosol,-0.663115371
+ZKSCAN8,Cytosol,0.500830861
+HOXC4,Cytosol,0.476658943
+ASPH,Cytosol,-0.349040272
+WWP2,Cytosol,0.81713354
+GFPT1,Cytosol,0.466866903
+ITSN2,Cytosol,0.142562651
+MGEA5,Cytosol,0.311853708
+FAM115A,Cytosol,0.904391714
+ZNF587,Cytosol,-0.053309549
+MAFK,Cytosol,0.399327377
+DDX39B,Cytosol,-0.811869465
+NUDT16,Cytosol,0.109437845
+TLK1,Cytosol,0.052465727
+MDM4,Cytosol,-0.543954244
+KLHL9,Cytosol,0.164254223
+C6orf89,Cytosol,0.544849092
+SLC9A6,Cytosol,-0.321096844
+FAN1,Cytosol,0.301377
+CEP290,Cytosol,-0.185133046
+C1orf85,Cytosol,-0.499592579
+SMOC1,Cytosol,-0.153774116
+ZNF652,Cytosol,0.388191905
+SMURF1,Cytosol,0.48296544
+SLC5A3,Cytosol,-0.26103903
+COLGALT2,Cytosol,-0.215623094
+TMEM184B,Cytosol,-0.06695435
+LRIG2,Cytosol,-0.499282973
+FOXJ3,Cytosol,0.506494202
+ARHGAP11A,Cytosol,0.376062522
+DENND4B,Cytosol,0.128942637
+R3HDM4,Cytosol,0.18393064
+RUNDC1,Cytosol,0.655816571
+TYW1,Cytosol,-0.336929717
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+ITPRIPL1,Cytosol,0.37887057
+SMC5,Cytosol,0.497682231
+SREBF2,Cytosol,0.005413433
+DZIP3,Cytosol,0.108514187
+KIAA0753,Cytosol,0.219933623
+ATG9A,Cytosol,-0.256449619
+TBKBP1,Cytosol,0.147311644
+NAGA,Cytosol,-0.096782138
+SGMS1,Cytosol,0.15394434
+COX20,Cytosol,-0.187518771
+CHML,Cytosol,0.249073354
+TATDN3,Cytosol,-0.165988871
+RBM20,Cytosol,0.307249891
+PCMTD2,Cytosol,0.162411849
+EFCAB7,Cytosol,-0.857734594
+ZYG11A,Cytosol,0.488169583
+SYS1,Cytosol,0.108838616
+TRAF3IP1,Cytosol,0.340479331
+PHACTR4,Cytosol,0.62830007
+ZDHHC18,Cytosol,0.459171922
+TMEM57,Cytosol,-0.347736802
+ZDBF2,Cytosol,0.283110949
+BMPR2,Cytosol,0.160920808
+OXLD1,Cytosol,-0.297615051
+MRPL38,Cytosol,-0.741836416
+SDHD,Cytosol,-1.21094098
+NEU1,Cytosol,-0.449867489
+HSPA1B,Cytosol,-0.72718275
+HLA-C,Cytosol,-0.753169884
+DDR1,Cytosol,0.354481316
+HLA-E,Cytosol,-0.296875503
+ZNF616,Cytosol,-0.072749155
+CRHR1-IT1,Cytosol,0.122788366
+GABBR1,Cytosol,-0.48897464
+RANBP17,Cytosol,-0.338775667
+ZNF204P,Cytosol,-0.555452167
+TCTN1,Cytosol,-0.715487304
+ZBTB48,Cytosol,-0.169384463
+C11orf83,Cytosol,-0.728778138
+ZNF783,Cytosol,0.355096091
+SLC35B4,Cytosol,0.080619461
+TRIQK,Cytosol,-0.572692225
+PSENEN,Cytosol,-0.799736066
+LGR4,Cytosol,-0.192190181
+PDE7A,Cytosol,-0.004500551
+TMEM170B,Cytosol,-0.077208501
+TECPR1,Cytosol,0.354716901
+ITPRIPL2,Cytosol,0.221769508
+CRYZL1,Cytosol,-0.785630059
+C5orf51,Cytosol,0.314928219
+ZNF316,Cytosol,0.661728419
+NYNRIN,Cytosol,1.042627765
+AP000525.9,Cytosol,0.459623031
+PTPLB,Cytosol,-0.318064239
+WDR52,Cytosol,-0.398668445
+SETD5-AS1,Cytosol,-0.999622032
+SACM1L,Cytosol,0.140325368
+C17orf51,Cytosol,0.763211404
+ZNF580,Cytosol,-0.374777557
+DENND1B,Cytosol,-0.240683836
+SFT2D2,Cytosol,0.291253418
+FGFR1OP,Cytosol,-0.221453613
+KLHL23,Cytosol,0.318305246
+TRIM59,Cytosol,-0.399571907
+NRAS,Cytosol,0.391813538
+QTRT1,Cytosol,-1.024636883
+CHUK,Cytosol,-0.085299856
+COG8,Cytosol,0.248099578
+RBMXL1,Cytosol,0.036691383
+LEPROT,Cytosol,-0.114863867
+ATF6B,Cytosol,0.254266274
+SLC35F6,Cytosol,-0.003451936
+ZNF134,Cytosol,-0.308173082
+EMP2,Cytosol,-0.078357645
+DNASE1,Cytosol,0.558833023
+CSNK1E,Cytosol,0.47388116
+GALT,Cytosol,-0.545143695
+ITGA1,Cytosol,-0.605991703
+AP1G2,Cytosol,-0.589435943
+CARKD,Cytosol,0.170011109
+TTLL3,Cytosol,-1.057856601
+CPNE1,Cytosol,-0.326287453
+RCN1P2,Cytosol,-0.622419162
+ZBED1,Cytosol,0.823038781
+DDX12P,Cytosol,-0.548487711
+C22orf29,Cytosol,0.466578281
+NEURL4,Cytosol,0.029037003
+TTC3P1,Cytosol,0.390330297
+PEX26,Cytosol,0.744818046
+GOLGA8B,Cytosol,-1.243904479
+MIR17HG,Cytosol,-1.885363459
+TMEM242,Cytosol,-0.234498459
+TMEM167B,Cytosol,0.308701797
+CYB5RL,Cytosol,0.371705853
+CROCCP2,Cytosol,-0.484299507
+FNIP1,Cytosol,0.26624531
+RP11-204C16.4,Cytosol,0.311140489
+TENM3,Cytosol,-0.360938762
+AC007390.5,Cytosol,0.622893579
+NBPF1,Cytosol,0.30919689
+CCNL2,Cytosol,-0.81373013
+PPT2,Cytosol,-0.143462945
+CKMT1A,Cytosol,-0.664385841
+NSUN5P1,Cytosol,-0.947189079
+RP11-54O7.3,Cytosol,0.309245846
+AFG3L1P,Cytosol,-0.218432631
+EPB41L4A-AS1,Cytosol,-1.00639578
+ATXN1L,Cytosol,0.42556881
+SMIM13,Cytosol,-0.049674862
+CTD-2228K2.7,Cytosol,-0.277751418
+PLEKHM1,Cytosol,0.572991026
+FAM195B,Cytosol,0.039357402
+IPO7P2,Cytosol,0.667312519
+SLC26A6,Cytosol,-0.615470755
+FGD5-AS1,Cytosol,0.372377057
+TLK2P1,Cytosol,0.433938747
+BAIAP2-AS1,Cytosol,0.830502537
+RP11-31F15.1,Cytosol,-0.05287993
+TMEM185B,Cytosol,-0.329137171
+C14orf132,Cytosol,0.876901225
+PARG,Cytosol,0.335037372
+TP73-AS1,Cytosol,0.176935234
+SCAMP4,Cytosol,-0.022721593
+SEC63P1,Cytosol,-0.331079012
+HCG11,Cytosol,0.350125079
+DHFR,Cytosol,0.714068279
+RP11-206L10.11,Cytosol,0.34532381
+XIST,Cytosol,-0.973893627
+RP5-1180C10.2,Cytosol,-0.663056337
+HCG18,Cytosol,0.064074759
+CTD-2619J13.14,Cytosol,0.484140707
+SNHG15,Cytosol,-1.061091646
+SNHG7,Cytosol,-1.075633756
+HOTAIRM1,Cytosol,-0.771783281
+RP4-775C13.1,Cytosol,-0.060855035
+ZNF37BP,Cytosol,-0.108646311
+ZNF736,Cytosol,-0.269466458
+MAGI2-AS3,Cytosol,-0.289325439
+JRK,Cytosol,0.309983571
+GAS5,Cytosol,-1.298187348
+LINC00338,Cytosol,-1.081931198
+PPP1R3E,Cytosol,0.195991224
+RP5-827C21.1,Cytosol,-1.176498363
+NUS1P1,Cytosol,-0.066675217
+ZBTB22,Cytosol,0.866302669
+AD000090.2,Cytosol,0.137579071
+ZBED5,Cytosol,-0.377243765
+RNF103,Cytosol,0.150357476
+TMEM189,Cytosol,0.106498575
+RPL36A,Cytosol,-1.427360761
+CD302,Cytosol,-0.156388134
+SNHG3,Cytosol,-1.160317432
+C15orf38,Cytosol,0.946498624
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+MICAL3,Cytosol,0.540528124
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+ZNF195,ERM,0.301899746
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+ZFX,ERM,0.795950858
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+TMEM98,ERM,0.833985021
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+PITRM1,OMM,1.239834164
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+TMEM109,ER Lumen,0.206975911
+PANX1,ER Lumen,-0.13581931
+UBE4A,ER Lumen,-0.180622077
+DDX6,ER Lumen,-0.324070705
+PVRL1,ER Lumen,0.227208586
+HIPK3,ER Lumen,0.175879472
+MDK,ER Lumen,0.570113047
+AMBRA1,ER Lumen,0.069471992
+NAA40,ER Lumen,-0.000429416
+SLC35F2,ER Lumen,-0.027961264
+LEPREL2,ER Lumen,0.561540077
+CORO1C,ER Lumen,-0.285809876
+ASIC1,ER Lumen,-0.161590658
+CAPRIN2,ER Lumen,0.104415726
+SLC11A2,ER Lumen,0.283271388
+MLEC,ER Lumen,-0.031190598
+BCL7A,ER Lumen,0.010788857
+RSRC2,ER Lumen,-0.099323389
+PPM1H,ER Lumen,-0.286533471
+ELK3,ER Lumen,-0.137560516
+MAGOHB,ER Lumen,-0.133491873
+ITFG2,ER Lumen,0.166403838
+PARP11,ER Lumen,-0.156740082
+DUSP16,ER Lumen,-0.264529953
+ACAD10,ER Lumen,0.146954495
+NAA25,ER Lumen,-0.143586428
+DDX55,ER Lumen,-0.165330774
+SLC38A1,ER Lumen,-0.026997901
+C12orf49,ER Lumen,-0.037356171
+MDM1,ER Lumen,-0.225935402
+CPSF6,ER Lumen,-0.129512848
+GNPTAB,ER Lumen,-0.389885712
+ATN1,ER Lumen,0.497404005
+C12orf57,ER Lumen,0.863534412
+LPCAT3,ER Lumen,0.091067298
+SUDS3,ER Lumen,-0.074167322
+GOLT1B,ER Lumen,-0.036220473
+C2CD5,ER Lumen,-0.098230499
+RAB35,ER Lumen,0.056550756
+RIC8B,ER Lumen,0.173376838
+RP11-22B23.1,ER Lumen,0.8220012
+DSE,ER Lumen,-0.16996017
+MAN1A1,ER Lumen,-0.022788367
+SERINC1,ER Lumen,-0.24787418
+UST,ER Lumen,-0.292044535
+KCTD20,ER Lumen,-0.108930358
+RNF8,ER Lumen,-0.33824812
+ICK,ER Lumen,0.111732775
+RAB23,ER Lumen,-0.111711176
+FBXL4,ER Lumen,-0.021322894
+CCNC,ER Lumen,-0.221818773
+ALDH5A1,ER Lumen,-0.194456271
+EYA4,ER Lumen,0.046094747
+PERP,ER Lumen,0.058682646
+SLC16A10,ER Lumen,0.008009062
+PHACTR2,ER Lumen,-0.157084594
+SLC39A7,ER Lumen,0.274682916
+PPP2R5D,ER Lumen,-0.086721951
+PTK7,ER Lumen,0.22045893
+CUL9,ER Lumen,0.314183703
+TMEM30A,ER Lumen,0.008877085
+SENP6,ER Lumen,-0.216467001
+VEGFA,ER Lumen,0.535889026
+PRPF4B,ER Lumen,-0.217817116
+BTN2A1,ER Lumen,0.187015412
+LAMA4,ER Lumen,0.011463866
+ERBB2IP,ER Lumen,-0.176994099
+HARS2,ER Lumen,0.137855455
+MAN2A1,ER Lumen,0.02087638
+PAPD7,ER Lumen,-0.091927675
+NNT,ER Lumen,-0.074632788
+APBB3,ER Lumen,0.287765654
+SPARC,ER Lumen,-0.444489376
+HMGCR,ER Lumen,-0.133464498
+FAF2,ER Lumen,-0.057993345
+CLK4,ER Lumen,-0.086956232
+ARSB,ER Lumen,0.053336324
+CNOT6,ER Lumen,-0.274817579
+DROSHA,ER Lumen,-0.037639024
+FAM172A,ER Lumen,0.043764644
+LNPEP,ER Lumen,-0.293311052
+SLC12A7,ER Lumen,0.579544624
+NR3C1,ER Lumen,-0.005320885
+C5orf15,ER Lumen,-0.184014551
+LIFR,ER Lumen,-0.19700868
+TRAPPC13,ER Lumen,-0.338105801
+TXNDC15,ER Lumen,-0.317508468
+H2AFY,ER Lumen,0.091799564
+TCERG1,ER Lumen,-0.317604329
+SMAD5,ER Lumen,-0.076263263
+ERGIC1,ER Lumen,-0.002131339
+STC2,ER Lumen,0.437208995
+ARL6,ER Lumen,-0.201482166
+NIT2,ER Lumen,0.03951078
+UBE3A,ER Lumen,-0.211773283
+SLC25A36,ER Lumen,0.150914205
+TFDP2,ER Lumen,-0.279754415
+XRN1,ER Lumen,-0.221085325
+WNT5A,ER Lumen,0.198794981
+PFKFB4,ER Lumen,-0.07482381
+PRKAR2A,ER Lumen,-0.198293473
+ACAP2,ER Lumen,0.051229283
+CBLB,ER Lumen,-0.525801135
+BBX,ER Lumen,-0.093268241
+GNB4,ER Lumen,-0.336262165
+C3orf52,ER Lumen,0.091878487
+PLXNA1,ER Lumen,0.572934663
+CSPG5,ER Lumen,0.247512791
+SCAP,ER Lumen,0.249258449
+HEMK1,ER Lumen,0.246100689
+ACVR2B,ER Lumen,-0.273489568
+ABCC5,ER Lumen,0.297689046
+SSR3,ER Lumen,-0.18182705
+NKTR,ER Lumen,-0.014982874
+FOXP1,ER Lumen,-0.033765893
+INO80D,ER Lumen,-0.116959845
+ADAM23,ER Lumen,-0.172571715
+MOB1A,ER Lumen,-0.186203465
+LMAN2L,ER Lumen,0.251689205
+RTKN,ER Lumen,0.277297857
+PIKFYVE,ER Lumen,-0.024544567
+FAHD2A,ER Lumen,-0.01421809
+SLC35F5,ER Lumen,-0.156030509
+STEAP3,ER Lumen,0.072742508
+EPB41L5,ER Lumen,0.070651566
+GPD2,ER Lumen,-0.185177212
+ACVR1,ER Lumen,0.421203176
+MPV17,ER Lumen,0.199195265
+TTC31,ER Lumen,0.288542779
+NDUFS7,ER Lumen,0.6401037
+SPTBN1,ER Lumen,0.015941218
+CCDC88A,ER Lumen,-0.263630781
+FN1,ER Lumen,-0.12566584
+ELMOD3,ER Lumen,0.339144305
+IGFBP5,ER Lumen,-0.012913789
+USP34,ER Lumen,-0.15504808
+GGCX,ER Lumen,0.150206828
+CHST10,ER Lumen,0.076591467
+MOB4,ER Lumen,-0.28004808
+UXS1,ER Lumen,-0.023989778
+PASK,ER Lumen,0.104592659
+TAF1B,ER Lumen,-0.209239805
+DCAF17,ER Lumen,-0.253116608
+SDC1,ER Lumen,0.455027455
+SLC1A4,ER Lumen,0.41199397
+SOS1,ER Lumen,-0.165787111
+WIPF1,ER Lumen,0.055376046
+THADA,ER Lumen,-0.03621655
+TRAK2,ER Lumen,-0.164548911
+TIA1,ER Lumen,0.080902051
+PCYOX1,ER Lumen,-0.167725474
+ARID3A,ER Lumen,0.272391659
+EPHA4,ER Lumen,0.098210078
+ALMS1,ER Lumen,-0.188203493
+BCL9,ER Lumen,0.154582402
+DHCR24,ER Lumen,0.205477343
+DNAJC16,ER Lumen,0.074135314
+RALGPS2,ER Lumen,-0.280817652
+CEP104,ER Lumen,0.052213484
+FAM20B,ER Lumen,-0.205364805
+TCEANC2,ER Lumen,-0.194121358
+WRAP73,ER Lumen,0.292842271
+ICMT,ER Lumen,0.051080492
+QSOX1,ER Lumen,0.19092759
+AMPD2,ER Lumen,0.300123207
+EDEM3,ER Lumen,-0.156933786
+RAP1A,ER Lumen,-0.225980219
+S100PBP,ER Lumen,-0.138277621
+ASH1L,ER Lumen,-0.104258527
+SFPQ,ER Lumen,-0.020220177
+MEF2D,ER Lumen,0.041878482
+C1orf21,ER Lumen,-0.139165348
+LEPR,ER Lumen,0.168942397
+IVNS1ABP,ER Lumen,-0.268410773
+KIAA2013,ER Lumen,0.43060201
+MIIP,ER Lumen,0.645156812
+SLC35D1,ER Lumen,-0.088181757
+WLS,ER Lumen,-0.003678088
+PRDM2,ER Lumen,0.189287252
+TROVE2,ER Lumen,-0.377857605
+SRSF11,ER Lumen,0.049134994
+PHTF1,ER Lumen,0.170303531
+TMEM9,ER Lumen,0.033887987
+EXOC8,ER Lumen,-0.076568458
+NID1,ER Lumen,-0.251757568
+MTR,ER Lumen,-0.399826171
+BMP8B,ER Lumen,0.25313667
+RIMS3,ER Lumen,0.194880586
+AKT3,ER Lumen,0.057199205
+ETV3,ER Lumen,-0.333019142
+LPHN2,ER Lumen,0.104412132
+RBBP5,ER Lumen,-0.398169651
+ECE1,ER Lumen,0.308652577
+CD46,ER Lumen,-0.135592569
+APH1A,ER Lumen,0.099868514
+LEPRE1,ER Lumen,0.115365759
+SLC2A1,ER Lumen,0.333680748
+SLC19A2,ER Lumen,-0.275092455
+NSUN4,ER Lumen,0.153529342
+TMED5,ER Lumen,0.075278021
+DR1,ER Lumen,-0.147592193
+PTBP2,ER Lumen,-0.356677547
+DARS2,ER Lumen,-0.423162578
+DIEXF,ER Lumen,-0.46639888
+RCAN3,ER Lumen,-0.153461793
+C1orf63,ER Lumen,0.219549796
+SLC35A3,ER Lumen,-0.204809751
+RCOR3,ER Lumen,0.124974775
+ARID1A,ER Lumen,0.174195974
+CENPF,ER Lumen,-0.466645183
+ESYT2,ER Lumen,-0.111397575
+CD3EAP,ER Lumen,0.206489126
+MESDC2,ER Lumen,-0.017953065
+CTSD,ER Lumen,0.260599049
+STK11,ER Lumen,0.46706103
+KMT2A,ER Lumen,-0.15604264
+KPTN,ER Lumen,0.634221322
+KIF14,ER Lumen,-0.373863234
+ATF6,ER Lumen,-0.275572851
+FASTKD2,ER Lumen,0.034223452
+NRP2,ER Lumen,-0.310779269
+CREB1,ER Lumen,-0.124891376
+B4GALT6,ER Lumen,-0.497017395
+ELOVL4,ER Lumen,-0.205319242
+CASP8AP2,ER Lumen,-0.143673703
+PHF3,ER Lumen,-0.33246726
+PLAGL1,ER Lumen,0.108016889
+FBXO30,ER Lumen,-0.077515312
+TMEM5,ER Lumen,-0.026279992
+ZNF430,ER Lumen,-0.042716708
+DCLRE1B,ER Lumen,-0.135656408
+PKD2,ER Lumen,0.228231984
+UBN1,ER Lumen,-0.087754049
+KLF12,ER Lumen,-0.028384766
+WDR35,ER Lumen,-0.24596603
+CCND2,ER Lumen,-0.11570064
+SATB2,ER Lumen,-0.028493678
+SENP5,ER Lumen,-0.185116977
+C1orf198,ER Lumen,-0.009231038
+HEATR1,ER Lumen,-0.216521302
+PTBP3,ER Lumen,-0.16142335
+FAM206A,ER Lumen,-0.468539843
+RBM18,ER Lumen,-0.153466108
+MAPKAP1,ER Lumen,0.039357297
+KDSR,ER Lumen,0.015935545
+ONECUT2,ER Lumen,-0.08864349
+IRF2BPL,ER Lumen,0.408980992
+AREL1,ER Lumen,-0.172468641
+ABCD4,ER Lumen,0.370829243
+RBM25,ER Lumen,-0.120498736
+NRDE2,ER Lumen,-0.026114108
+KLHL29,ER Lumen,0.175886812
+DNMT3A,ER Lumen,0.306280126
+ATAD2B,ER Lumen,0.102234773
+ATL2,ER Lumen,-0.025738666
+YIPF4,ER Lumen,-0.064065415
+AFTPH,ER Lumen,0.244697884
+BCL11A,ER Lumen,-0.006787564
+SLC17A5,ER Lumen,-0.226173471
+FAM178A,ER Lumen,0.071636541
+GPAM,ER Lumen,-0.216625028
+HELLS,ER Lumen,0.073334429
+TCTN3,ER Lumen,-0.09707102
+C10orf76,ER Lumen,0.117358634
+HOXB8,ER Lumen,0.327771843
+HOXB3,ER Lumen,0.428066948
+PANK3,ER Lumen,-0.17484198
+NUP43,ER Lumen,-0.230782925
+LRP11,ER Lumen,-0.102512763
+MASTL,ER Lumen,-0.179963176
+ELF1,ER Lumen,-0.341470225
+EGR1,ER Lumen,1.313338739
+NR2C1,ER Lumen,0.037774704
+MTERFD3,ER Lumen,-3.03e-05
+CLU,ER Lumen,0.321849198
+TNFRSF10B,ER Lumen,0.148942573
+TARDBP,ER Lumen,0.030770895
+CRISPLD1,ER Lumen,-0.133691944
+AKAP1,ER Lumen,0.143430415
+TRIM25,ER Lumen,0.020104744
+KIAA0922,ER Lumen,-0.346336181
+PAPD5,ER Lumen,-0.15134127
+CEP89,ER Lumen,0.236928659
+B4GALT4,ER Lumen,-0.108015588
+KIF18A,ER Lumen,-0.070213352
+CRY2,ER Lumen,0.201621928
+ZNF639,ER Lumen,-0.096359307
+PDS5A,ER Lumen,-0.260490687
+CLCC1,ER Lumen,-0.046315608
+ACVR2A,ER Lumen,-0.026826902
+RPL21,ER Lumen,0.521071115
+MTERFD2,ER Lumen,-0.038823386
+KIAA1191,ER Lumen,-0.157172457
+RBBP6,ER Lumen,-0.191743862
+ZC3H7A,ER Lumen,-0.210292934
+FAM35A,ER Lumen,-0.243022101
+FAM213A,ER Lumen,-0.055499183
+ODF2L,ER Lumen,0.129989238
+TRMT13,ER Lumen,-0.214498937
+RPAP2,ER Lumen,-0.206545762
+FAM126A,ER Lumen,-0.148639164
+FKBP9,ER Lumen,-0.132857989
+POLM,ER Lumen,0.343496487
+SLC25A51,ER Lumen,-0.223603272
+DCAF10,ER Lumen,0.013831977
+KIAA1549,ER Lumen,0.062626713
+CALD1,ER Lumen,-0.070816317
+CHST3,ER Lumen,0.198353069
+P4HA1,ER Lumen,-0.070168059
+RBM19,ER Lumen,0.022522011
+GIPC1,ER Lumen,0.330506531
+ATP7B,ER Lumen,-0.114006003
+ZC3H13,ER Lumen,-0.293547548
+NLN,ER Lumen,-0.357251395
+CENPK,ER Lumen,-0.414817502
+OPTN,ER Lumen,-0.24211749
+SPATS2,ER Lumen,-0.161781901
+LRP1,ER Lumen,0.287843618
+HJURP,ER Lumen,0.01387833
+USP45,ER Lumen,-0.014916398
+SLC36A1,ER Lumen,0.245106453
+LPGAT1,ER Lumen,-0.231818735
+EXOSC9,ER Lumen,-0.486073024
+PLA2G12A,ER Lumen,-0.122688071
+ADCK4,ER Lumen,0.128043311
+PFKFB2,ER Lumen,-0.587262943
+AGO2,ER Lumen,-0.075897491
+MXD4,ER Lumen,0.501799357
+ACSL3,ER Lumen,-0.321075878
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+FAM210B,ER Lumen,-0.302842864
+SDC4,ER Lumen,0.577047344
+NCOA3,ER Lumen,0.104846751
+PIGT,ER Lumen,0.278632574
+VAPB,ER Lumen,-0.1103497
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+SRSF6,ER Lumen,-0.038771249
+RAB22A,ER Lumen,0.152884321
+STX16,ER Lumen,0.258699925
+STAMBP,ER Lumen,-0.271462449
+NAGK,ER Lumen,0.273472177
+PAIP2B,ER Lumen,0.054012749
+ATP8A1,ER Lumen,0.017391127
+BTN2A2,ER Lumen,-0.052879785
+ABCC10,ER Lumen,0.566196148
+AARS2,ER Lumen,0.044632577
+ZNF391,ER Lumen,-0.221939348
+CDKN1A,ER Lumen,0.732227305
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+NRN1,ER Lumen,-0.164594383
+ATXN1,ER Lumen,0.072887776
+EEF1E1,ER Lumen,-0.211479258
+LRRFIP1,ER Lumen,-0.34682606
+AHNAK,ER Lumen,0.095805762
+ABCC4,ER Lumen,-0.269742339
+EFNB2,ER Lumen,0.028135853
+ATP5S,ER Lumen,-0.474102505
+FAM193A,ER Lumen,0.456747415
+GGA3,ER Lumen,0.228575043
+GTF3C4,ER Lumen,-0.163423001
+PPP1R12C,ER Lumen,0.702374797
+MBOAT7,ER Lumen,0.173596991
+CCDC93,ER Lumen,0.101332018
+THOC2,ER Lumen,-0.208822228
+MED1,ER Lumen,-0.236449634
+GPR108,ER Lumen,0.178198865
+GPCPD1,ER Lumen,-0.377830519
+PANK2,ER Lumen,-0.132284833
+NAPB,ER Lumen,-0.00039715
+TMX4,ER Lumen,-0.136215525
+RRBP1,ER Lumen,0.311348323
+ZNF133,ER Lumen,-0.129166736
+MCM8,ER Lumen,-0.25423754
+NCLN,ER Lumen,0.570696556
+ZNF436,ER Lumen,0.142721509
+AMOT,ER Lumen,-0.104349843
+TMEM115,ER Lumen,0.404272342
+AGO3,ER Lumen,-0.016991544
+HECTD3,ER Lumen,0.086196515
+KLC1,ER Lumen,0.341551456
+XRCC3,ER Lumen,0.377118776
+TUBGCP3,ER Lumen,-0.307813842
+PCID2,ER Lumen,-0.195175453
+FRMD8,ER Lumen,0.132398322
+PCNXL4,ER Lumen,-0.165900188
+ATG14,ER Lumen,-0.285797146
+KTN1,ER Lumen,-0.251488367
+PLEKHG3,ER Lumen,0.274149683
+WDR60,ER Lumen,-0.123083231
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+SLC10A3,ER Lumen,0.536060376
+CANX,ER Lumen,-0.132830969
+CPSF3L,ER Lumen,0.55674017
+TRAF2,ER Lumen,0.403713317
+HELB,ER Lumen,-0.550318333
+DYRK2,ER Lumen,-0.053402493
+LRRC61,ER Lumen,0.361661205
+FGFRL1,ER Lumen,0.489174174
+EMC1,ER Lumen,0.03612198
+HP1BP3,ER Lumen,0.021806611
+SIN3B,ER Lumen,0.3384983
+SLC35E1,ER Lumen,0.000143487
+GFER,ER Lumen,0.169572193
+PKMYT1,ER Lumen,0.649599989
+CHTF18,ER Lumen,0.796426642
+MACF1,ER Lumen,-0.056329059
+RNF6,ER Lumen,-0.190021023
+AKAP9,ER Lumen,-0.370628476
+HIP1,ER Lumen,-0.058162391
+POR,ER Lumen,0.333158294
+PEX1,ER Lumen,-0.131142408
+LRFN1,ER Lumen,0.472804471
+SRD5A3,ER Lumen,-0.341279808
+PPAT,ER Lumen,0.055868049
+TUBGCP6,ER Lumen,0.61013964
+DGCR8,ER Lumen,0.319393923
+TPST2,ER Lumen,0.014842692
+MPST,ER Lumen,0.307236356
+SPECC1,ER Lumen,-0.500568433
+NAA38,ER Lumen,-0.303754015
+PRKRIP1,ER Lumen,0.427315093
+PODXL,ER Lumen,0.043072537
+STRIP2,ER Lumen,0.210394323
+MKLN1,ER Lumen,-0.299681479
+CALU,ER Lumen,-0.139674176
+CCDC136,ER Lumen,0.150039075
+SMO,ER Lumen,0.156071716
+KLHDC10,ER Lumen,-0.079388221
+OSGEPL1,ER Lumen,0.042179934
+HOXD10,ER Lumen,0.387405799
+HOXD11,ER Lumen,0.338040747
+HERC2,ER Lumen,-0.117447042
+TWSG1,ER Lumen,-0.039804772
+MYO5C,ER Lumen,-0.288882876
+TMOD2,ER Lumen,-0.420410617
+TTBK2,ER Lumen,-0.049301444
+IVD,ER Lumen,-0.08291437
+CLN6,ER Lumen,0.042450434
+ARPP19,ER Lumen,-0.070722179
+VPS13C,ER Lumen,0.003477333
+SUMF2,ER Lumen,-0.072436053
+SPCS3,ER Lumen,-0.175592015
+RPAIN,ER Lumen,-0.264189582
+PLD2,ER Lumen,0.211783414
+MPDU1,ER Lumen,0.02332706
+CCNT1,ER Lumen,0.006684736
+PUS7L,ER Lumen,-0.431441126
+KRI1,ER Lumen,0.247984841
+SLC44A2,ER Lumen,-0.007454656
+BCL2L2,ER Lumen,0.075059072
+PARP2,ER Lumen,-0.11982285
+TEP1,ER Lumen,-0.138251328
+MAP7D3,ER Lumen,-0.32966522
+ABHD17A,ER Lumen,0.68440057
+ERMARD,ER Lumen,-0.246296727
+SAT1,ER Lumen,-0.280477615
+GNL3L,ER Lumen,-0.125457985
+SH3BP4,ER Lumen,0.138545486
+LDLR,ER Lumen,0.418887646
+PRKCSH,ER Lumen,0.257456499
+THEM6,ER Lumen,0.243955428
+PVRL2,ER Lumen,0.501824373
+SAFB2,ER Lumen,0.136774091
+KIF1A,ER Lumen,0.371514601
+COLGALT1,ER Lumen,0.296518477
+MLLT1,ER Lumen,0.314011523
+MLLT4,ER Lumen,-0.135761162
+ACTN4,ER Lumen,0.284117812
+NDUFA10,ER Lumen,-0.078248224
+ZSWIM6,ER Lumen,-0.021909999
+PXDN,ER Lumen,-0.045907192
+COL5A1,ER Lumen,0.35348341
+ZNF337,ER Lumen,-0.101500259
+TAF4,ER Lumen,0.101294624
+LAMA5,ER Lumen,0.719808408
+EXOSC2,ER Lumen,-0.188274067
+POMT1,ER Lumen,0.028313886
+PRRC2B,ER Lumen,0.160246419
+YIPF2,ER Lumen,0.244817525
+ZC3H4,ER Lumen,0.397082283
+CLIP1,ER Lumen,-0.403783601
+HIP1R,ER Lumen,0.568230909
+PPAN,ER Lumen,0.421259467
+SLC6A8,ER Lumen,0.321133048
+PLXNA3,ER Lumen,0.846134218
+PRRG1,ER Lumen,-0.046096508
+AKAP12,ER Lumen,-0.160219138
+RBM39,ER Lumen,-0.042460209
+GGT7,ER Lumen,0.437722771
+PPT1,ER Lumen,-0.213339736
+RLIM,ER Lumen,-0.116269921
+ABCB7,ER Lumen,-0.134462581
+MRPS25,ER Lumen,-0.027226094
+CAPN7,ER Lumen,-0.155765134
+ZFYVE20,ER Lumen,-0.171021449
+SLC6A6,ER Lumen,-0.364748035
+MGAT1,ER Lumen,0.309363093
+PSMC3IP,ER Lumen,0.26980623
+DIAPH1,ER Lumen,-0.098405157
+NDFIP1,ER Lumen,-0.27689197
+ACAP3,ER Lumen,0.957654021
+C1orf159,ER Lumen,0.42007739
+MAP1B,ER Lumen,0.238490211
+IL13RA1,ER Lumen,-0.111513787
+WDR44,ER Lumen,-0.330341349
+PRKAB2,ER Lumen,0.115243309
+CLUHP3,ER Lumen,0.33578257
+CHSY1,ER Lumen,-0.05165006
+SNRPA1,ER Lumen,0.057076479
+FBXW9,ER Lumen,0.330474481
+RFX1,ER Lumen,0.19255059
+CC2D1A,ER Lumen,0.549552105
+NUP210,ER Lumen,0.099270544
+ENOSF1,ER Lumen,0.018660307
+EMILIN2,ER Lumen,-0.550451071
+PRKAA1,ER Lumen,-0.256535615
+PNISR,ER Lumen,0.092463663
+ZRANB2,ER Lumen,-0.071022199
+KDM6B,ER Lumen,0.700236147
+GPS2,ER Lumen,0.521681873
+VPS13B,ER Lumen,0.099797166
+REEP2,ER Lumen,0.155255007
+PRMT7,ER Lumen,0.295302781
+PCED1A,ER Lumen,0.451876781
+PTPRA,ER Lumen,-0.124787183
+KIAA0907,ER Lumen,0.355930443
+DCAF8,ER Lumen,-0.127495925
+IGHMBP2,ER Lumen,0.187267182
+LPIN3,ER Lumen,0.557901118
+SERINC3,ER Lumen,-0.022399024
+FBXO44,ER Lumen,0.526462474
+USPL1,ER Lumen,0.017121142
+XPO4,ER Lumen,-0.218236813
+SCO1,ER Lumen,-0.224603513
+MPRIP,ER Lumen,0.037088531
+DSTYK,ER Lumen,-0.28000664
+SLC41A1,ER Lumen,-0.093669138
+GPALPP1,ER Lumen,-0.218139352
+IRS4,ER Lumen,0.091672465
+FAM104A,ER Lumen,0.198413791
+SLC39A11,ER Lumen,-0.321583183
+EPHB2,ER Lumen,0.089569225
+SRRM1,ER Lumen,0.068321011
+SUV420H2,ER Lumen,0.42295518
+WDR74,ER Lumen,0.090994443
+RTN3,ER Lumen,-0.119686472
+MORC2,ER Lumen,0.04004945
+LARGE,ER Lumen,0.148175521
+ADCK2,ER Lumen,-0.011705108
+AGAP3,ER Lumen,0.177480961
+KRBA1,ER Lumen,0.402820044
+ZNF767,ER Lumen,0.375174963
+ATP13A3,ER Lumen,-0.00910858
+TMEM254,ER Lumen,-0.015898413
+TMTC1,ER Lumen,-0.129061422
+KRAS,ER Lumen,-0.219191326
+SWAP70,ER Lumen,-0.423753018
+ZFC3H1,ER Lumen,-0.092765852
+TEX15,ER Lumen,-0.131100285
+CTIF,ER Lumen,0.353478272
+VHL,ER Lumen,-0.164618199
+ARL8B,ER Lumen,-0.014935633
+EDEM1,ER Lumen,0.002565975
+PRPF38B,ER Lumen,-0.015807759
+SORT1,ER Lumen,-0.012457746
+PTGFRN,ER Lumen,0.184017829
+NOTCH2,ER Lumen,-0.107724333
+CEPT1,ER Lumen,-0.189501101
+AP4B1,ER Lumen,-0.034607202
+SPIRE1,ER Lumen,0.004566242
+SLC38A2,ER Lumen,0.162944797
+KIDINS220,ER Lumen,-0.063787132
+ROCK2,ER Lumen,-0.179151921
+LPIN1,ER Lumen,0.201908988
+IL6ST,ER Lumen,0.057269911
+TMEM241,ER Lumen,-0.165317734
+LRP4,ER Lumen,0.120088307
+DDB2,ER Lumen,0.151162908
+ACP2,ER Lumen,0.320393096
+AGO4,ER Lumen,-0.099161782
+HOOK1,ER Lumen,-0.212641058
+DSC2,ER Lumen,-0.13616119
+DSC3,ER Lumen,-0.212616918
+DTNA,ER Lumen,-0.406945608
+FHOD3,ER Lumen,-0.033603338
+FADS2,ER Lumen,0.457358928
+CLOCK,ER Lumen,-0.000691981
+COL4A2,ER Lumen,0.20978089
+DZIP1,ER Lumen,-0.148769668
+UBAC2,ER Lumen,0.015321432
+ARGLU1,ER Lumen,0.212243747
+BIVM,ER Lumen,-0.34019494
+ARHGAP32,ER Lumen,0.017729738
+TMED7,ER Lumen,0.090656295
+APC,ER Lumen,-0.190022821
+WDR36,ER Lumen,-0.221790037
+NAA35,ER Lumen,-0.323443493
+TMEM2,ER Lumen,0.068945871
+GOLM1,ER Lumen,-0.01398682
+TAOK3,ER Lumen,-0.418465833
+DMTF1,ER Lumen,-0.003879196
+TMEM243,ER Lumen,-0.056042387
+PNPLA8,ER Lumen,-0.309285672
+MDFIC,ER Lumen,0.043621712
+ANKRD6,ER Lumen,-0.047434264
+KIAA1009,ER Lumen,-0.37447024
+SNX14,ER Lumen,-0.273198689
+EPHA7,ER Lumen,0.042652409
+DNAJC14,ER Lumen,-0.038942107
+GDF11,ER Lumen,-0.040322725
+TROAP,ER Lumen,0.387939405
+TSPAN31,ER Lumen,-0.162147886
+TFCP2,ER Lumen,-0.145134981
+PAN2,ER Lumen,0.381451358
+HNRNPA1,ER Lumen,0.038460111
+ACVR1B,ER Lumen,0.127655521
+OS9,ER Lumen,0.041142206
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+CD164,ER Lumen,0.10258295
+NHSL1,ER Lumen,0.667572708
+AHI1,ER Lumen,-0.257867424
+SEMA4F,ER Lumen,-0.032935306
+RAB11FIP5,ER Lumen,0.258202719
+CCDC142,ER Lumen,0.770284233
+GNS,ER Lumen,-0.143618781
+MDM2,ER Lumen,-0.189179298
+KLHL36,ER Lumen,0.176788478
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+EGLN1,ER Lumen,-0.038523958
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+ITM2C,ER Lumen,0.190138884
+SERPINE2,ER Lumen,-0.123046591
+TMEM127,ER Lumen,0.20170102
+GCC2,ER Lumen,-0.429838075
+C2orf49,ER Lumen,-0.317120494
+EPC2,ER Lumen,-0.248265086
+ARHGEF4,ER Lumen,0.146790012
+ALDH1L2,ER Lumen,-0.312719901
+CKAP4,ER Lumen,-0.001296289
+NEK3,ER Lumen,-0.260433199
+RCBTB1,ER Lumen,-0.179228081
+COG3,ER Lumen,0.090974727
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+CHST12,ER Lumen,0.554937709
+KDELR2,ER Lumen,0.002730533
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+DBNL,ER Lumen,0.267155439
+TTYH3,ER Lumen,0.516577264
+IREB2,ER Lumen,-0.329739591
+RSAD1,ER Lumen,0.095082623
+VEZF1,ER Lumen,0.058692235
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+SKIL,ER Lumen,0.04544839
+RPS6KC1,ER Lumen,-0.290759394
+BIN1,ER Lumen,0.044801038
+HS6ST1,ER Lumen,0.372267249
+UGGT1,ER Lumen,-0.150317525
+DNAJC1,ER Lumen,-0.274871865
+LRRC8A,ER Lumen,0.478357497
+CDK9,ER Lumen,0.360402321
+TOR1B,ER Lumen,0.169762133
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+RALGPS1,ER Lumen,0.245760769
+FAM129B,ER Lumen,0.429394446
+SLC2A8,ER Lumen,0.271280356
+SLC31A1,ER Lumen,-0.215786076
+ZNF189,ER Lumen,-0.088583083
+STX17,ER Lumen,-0.108917388
+TSTD2,ER Lumen,-0.026962398
+LMX1B,ER Lumen,0.134618339
+RANBP6,ER Lumen,0.193966072
+TLN1,ER Lumen,0.254900115
+ALDH1B1,ER Lumen,-0.107982559
+CNPY3,ER Lumen,0.07156789
+TMEM63B,ER Lumen,0.279673109
+TJAP1,ER Lumen,0.427432159
+SLC22A23,ER Lumen,0.427244435
+FOXF2,ER Lumen,0.249807296
+RIPK1,ER Lumen,-0.267162442
+ATAT1,ER Lumen,0.277113639
+NRM,ER Lumen,0.271369812
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+PRKRIR,ER Lumen,-0.104187041
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+SULF1,ER Lumen,-0.035782351
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+PARP6,ER Lumen,0.001513193
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+RABGGTB,ER Lumen,0.105567413
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+FBN2,ER Lumen,-0.163948772
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+NAA30,ER Lumen,0.00185763
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+PCSK6,ER Lumen,0.031237742
+SCAMP2,ER Lumen,0.178582179
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+FOXK2,ER Lumen,0.22602174
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+RNF157,ER Lumen,0.100188854
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+MBD1,ER Lumen,0.324577494
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+ATHL1,ER Lumen,0.906000626
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+COL6A2,ER Lumen,1.324621053
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+SLC47A1,ER Lumen,-0.076977194
+RERE,ER Lumen,0.443771975
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+KIAA0319L,ER Lumen,0.041107268
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+GPN2,ER Lumen,0.029146307
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+MARCH6,ER Lumen,0.04617897
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+PCYOX1L,ER Lumen,0.117318164
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+FAM193B,ER Lumen,0.462994172
+RNF44,ER Lumen,0.365602096
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+MMS22L,ER Lumen,-0.155545508
+PM20D2,ER Lumen,-0.314883711
+RNF217,ER Lumen,0.239369255
+AIG1,ER Lumen,-0.205060031
+TMEM181,ER Lumen,-0.199491506
+SDK1,ER Lumen,0.056963021
+RBAK,ER Lumen,-0.285666117
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+GBAS,ER Lumen,-0.455768398
+ZNF92,ER Lumen,0.045615758
+TMEM168,ER Lumen,-0.134063374
+C7orf43,ER Lumen,0.588772285
+SLC12A9,ER Lumen,0.408223888
+GIGYF1,ER Lumen,0.554862698
+TMEM209,ER Lumen,-0.32806947
+NOM1,ER Lumen,-0.127653372
+SH3KBP1,ER Lumen,-0.14368177
+CASK,ER Lumen,-0.058295996
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+OGT,ER Lumen,0.189245457
+ZNF711,ER Lumen,-0.337151828
+DIAPH2,ER Lumen,0.151860053
+CXorf57,ER Lumen,0.06812291
+GPC3,ER Lumen,0.154538587
+BIN3,ER Lumen,0.146898954
+SLC25A37,ER Lumen,0.612117068
+CHMP7,ER Lumen,-0.162670417
+ERLIN2,ER Lumen,-0.150732037
+TACC1,ER Lumen,-0.043482014
+WHSC1L1,ER Lumen,-0.186772166
+TERF1,ER Lumen,-0.231628432
+MTDH,ER Lumen,-0.165537559
+LRP12,ER Lumen,-0.590090226
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+UTP23,ER Lumen,-0.359602158
+ZNF7,ER Lumen,0.199045795
+ARHGAP39,ER Lumen,0.581113381
+NAPRT1,ER Lumen,0.547248415
+UHRF2,ER Lumen,-0.000486598
+ZCCHC7,ER Lumen,-0.210876208
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+INIP,ER Lumen,-0.106822738
+UGCG,ER Lumen,0.223392751
+STOM,ER Lumen,0.05805322
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+SURF4,ER Lumen,0.215497282
+MED22,ER Lumen,0.31813963
+SH3GLB2,ER Lumen,0.698044107
+FAM73B,ER Lumen,0.459730033
+GPR107,ER Lumen,-0.042491788
+C9orf142,ER Lumen,0.760515035
+INPP5E,ER Lumen,0.570311701
+DPH7,ER Lumen,0.062140207
+NOTCH1,ER Lumen,1.100295503
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+FAM171A1,ER Lumen,-0.127316854
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+POLR3A,ER Lumen,-0.151689089
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+ADD3,ER Lumen,-0.07297797
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+MKI67,ER Lumen,0.036818473
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+ITPRIP,ER Lumen,0.202450141
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+ENDOD1,ER Lumen,0.02096248
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+HYOU1,ER Lumen,0.20101495
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+TMEM138,ER Lumen,0.386828918
+FADS1,ER Lumen,-0.06226409
+EML3,ER Lumen,0.412131389
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+SIDT2,ER Lumen,0.049057226
+SOGA1,ER Lumen,0.217648253
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+ORAOV1,ER Lumen,0.353943034
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+PRSS23,ER Lumen,0.101140122
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+IPMK,ER Lumen,-0.180183147
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+EXT2,ER Lumen,0.065960892
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+FER,ER Lumen,-0.045491234
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+BICD1,ER Lumen,0.058342581
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+AC093838.4,ER Lumen,0.27528931
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diff --git a/bento/tools/gene_sets/xia2019.csv b/bento/tools/gene_sets/xia2019.csv
new file mode 100644
index 0000000..c99363f
--- /dev/null
+++ b/bento/tools/gene_sets/xia2019.csv
@@ -0,0 +1,23807 @@
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+Nucleus,Blank-9198,0.235327503003725
+Nucleus,Blank-9199,0.33714369663967
+Nucleus,Blank-9200,-0.489094116100034
+Nucleus,Blank-9201,0.127445168370803
+Nucleus,Blank-9202,0.182685611457774
+Nucleus,Blank-9203,0.473402962611069
+Nucleus,Blank-9204,-1.18194049242658
+Nucleus,Blank-9205,-0.230355667748104
+Nucleus,Blank-9206,0.817555408051193
+Nucleus,Blank-9207,-0.318459763134727
+Nucleus,Blank-9208,0.540017521157375
+Nucleus,Blank-9209,0.384267674494767
+Nucleus,Blank-9210,-0.73849419282923
+Nucleus,Blank-9211,0.802894700729869
+Nucleus,Blank-9212,-0.703228317939747
+Nucleus,Blank-9213,-0.530204108637297
+Nucleus,Blank-9214,-0.138708228364055
+Nucleus,Blank-9215,-0.251507060174824
+Nucleus,Blank-9216,0.960862626294674
+Nucleus,Blank-9217,0.630902582110391
+Nucleus,Blank-9218,0.871529214963325
+Nucleus,Blank-9219,-0.210021475791696
+Nucleus,Blank-9220,-0.600963194695702
+Nucleus,Blank-9221,0.809477488053893
+Nucleus,Blank-9222,-0.558540915914844
+Nucleus,Blank-9223,-0.444428780462293
+Nucleus,Blank-9224,-0.033579241304155
+Nucleus,Blank-9225,-0.557738953207342
+Nucleus,Blank-9226,-0.291001365438967
+Nucleus,Blank-9227,1.16558771044939
+Nucleus,Blank-9228,0.569656128535278
+Nucleus,Blank-9229,-0.167701823663878
+Nucleus,Blank-9230,-0.0144634355367764
+Nucleus,Blank-9231,0.0426039215251659
+Nucleus,Blank-9232,0.0482301754460528
+Nucleus,Blank-9233,-0.977329173612598
+Nucleus,Blank-9234,0.281299283264136
+Nucleus,Blank-9235,0.159838152494039
+Nucleus,Blank-9236,1.14866544897347
+Nucleus,Blank-9237,-0.86474230390231
+Nucleus,Blank-9238,-0.382264220596183
+Nucleus,Blank-9239,0.424150620737437
+Nucleus,Blank-9240,-1.0939815218673
+Nucleus,Blank-9241,-1.2368928588099
+Nucleus,Blank-9242,0.334172618951153
+Nucleus,Blank-9243,0.0483471354217661
+Nucleus,Blank-9244,0.206148124711806
+Nucleus,Blank-9245,-0.75758773635848
+Nucleus,Blank-9246,-0.214304966278743
+Nucleus,Blank-9247,-0.150792246363797
+Nucleus,Blank-9248,0.142869821650155
+Nucleus,Blank-9249,1.06118644909868
+Nucleus,Blank-9250,0.00799803356501217
+Nucleus,Blank-9251,0.306296477921996
+Nucleus,Blank-9252,0.165422137600048
+Nucleus,Blank-9253,0.283265181721761
+Nucleus,Blank-9254,-0.406704173603237
+Nucleus,Blank-9255,-0.12830400358171
+Nucleus,Blank-9256,-0.681888092016211
+Nucleus,Blank-9257,0.488713745858562
+Nucleus,Blank-9258,0.284975556444623
+Nucleus,Blank-9259,-0.453335034045114
+Nucleus,Blank-9260,1.39446783151037
+Nucleus,Blank-9261,0.124667469699007
+Nucleus,Blank-9262,0.0498718002629359
+Nucleus,Blank-9263,0.549424594147384
+Nucleus,Blank-9264,-0.033498697119694
+Nucleus,Blank-9265,-0.20453666641193
+Nucleus,Blank-9266,0.626205179711171
+Nucleus,Blank-9267,-0.546899117659537
+Nucleus,Blank-9268,0.177428418140489
+Nucleus,Blank-9269,-0.217494092964124
+Nucleus,Blank-9270,0.500563981542479
+Nucleus,Blank-9271,0.981360193352819
+Nucleus,Blank-9272,0.907562782674365
+Nucleus,Blank-9273,-0.020642350146036
+Nucleus,Blank-9274,0.141765671914105
+Nucleus,Blank-9275,-0.366616762317613
+Nucleus,Blank-9276,-0.695531239486435
+Nucleus,Blank-9277,-1.27036536692361
diff --git a/docs/Makefile b/docs/Makefile
index 6d3fdbf..cefa9c0 100755
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -3,7 +3,7 @@
# You can set these variables from the command line, and also
# from the environment for the first two.
-SPHINXOPTS ?=
+SPHINXOPTS ?= -j 4
SPHINXBUILD ?= sphinx-build
SOURCEDIR = source
BUILDDIR = build
@@ -20,4 +20,4 @@ help:
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
livehtml:
- sphinx-autobuild "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
+ sphinx-autobuild "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/source/_static/Hind.ttf b/docs/source/_static/Hind.ttf
deleted file mode 100644
index 682abc3..0000000
Binary files a/docs/source/_static/Hind.ttf and /dev/null differ
diff --git a/docs/source/_static/Inter-VariableFont.ttf b/docs/source/_static/Inter-VariableFont.ttf
new file mode 100644
index 0000000..ec3164e
Binary files /dev/null and b/docs/source/_static/Inter-VariableFont.ttf differ
diff --git a/docs/source/_static/Nunito.ttf b/docs/source/_static/Nunito.ttf
deleted file mode 100644
index 6bc8144..0000000
Binary files a/docs/source/_static/Nunito.ttf and /dev/null differ
diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css
index f6d5218..a1099f2 100644
--- a/docs/source/_static/custom.css
+++ b/docs/source/_static/custom.css
@@ -1,32 +1,27 @@
@font-face {
- font-family: "Hind";
- src: url('Hind.ttf') format('truetype');
+ font-family: "Inter";
+ src: url("Inter-VariableFont.ttf") format("truetype");
}
@font-face {
- font-family: "Nunito";
- src: url('Nunito.ttf') format('truetype');
+ font-family: "RobotoMono";
+ src: url("RobotoMono.ttf") format("truetype");
}
-@font-face {
- font-family: "RobotoMono";
- src: url('RobotoMono.ttf') format('truetype');
-}
-
-
:root {
- --pst-font-family-base: Hind, var(--pst-font-family-base-system) !important;
- --pst-font-family-heading: Nunito, var(--pst-font-family-base-system) !important;
- --pst-font-family-monospace: RobotoMono, var(--pst-font-family-monospace-system) !important;
+ --pst-font-family-base: Inter, var(--pst-font-family-base-system) !important;
+ --pst-font-family-heading: Inter, var(--pst-font-family-base-system) !important;
+ --pst-font-family-monospace: RobotoMono,
+ var(--pst-font-family-monospace-system) !important;
}
/* Font size */
---pst-font-size-base: 15px; /* base font size - applied at body / html level */
+/* --pst-font-size-base: 14px; base font size - applied at body / html level */
/* heading font sizes */
---pst-font-size-h1: 36px;
---pst-font-size-h2: 32px;
---pst-font-size-h3: 26px;
---pst-font-size-h4: 21px;
---pst-font-size-h5: 18px;
---pst-font-size-h6: 16px;
\ No newline at end of file
+/* --pst-font-size-h1: 32px;
+--pst-font-size-h2: 26px;
+--pst-font-size-h3: 21px;
+--pst-font-size-h4: 18px;
+--pst-font-size-h5: 16px;
+--pst-font-size-h6: 14px; */
diff --git a/docs/source/_static/tutorial_img/bento_data_structure.png b/docs/source/_static/tutorial_img/bento_data_structure.png
new file mode 100644
index 0000000..7991f97
Binary files /dev/null and b/docs/source/_static/tutorial_img/bento_data_structure.png differ
diff --git a/docs/source/_static/tutorial_img/bento_data_structure_dark.png b/docs/source/_static/tutorial_img/bento_data_structure_dark.png
new file mode 100644
index 0000000..c2b76cd
Binary files /dev/null and b/docs/source/_static/tutorial_img/bento_data_structure_dark.png differ
diff --git a/docs/source/_static/tutorial_img/bento_tools.png b/docs/source/_static/tutorial_img/bento_tools.png
new file mode 100644
index 0000000..2894697
Binary files /dev/null and b/docs/source/_static/tutorial_img/bento_tools.png differ
diff --git a/docs/source/_static/tutorial_img/bento_workflow.png b/docs/source/_static/tutorial_img/bento_workflow.png
deleted file mode 100644
index ab68782..0000000
Binary files a/docs/source/_static/tutorial_img/bento_workflow.png and /dev/null differ
diff --git a/docs/source/_static/tutorial_img/clq_workflow.png b/docs/source/_static/tutorial_img/clq_workflow.png
new file mode 100644
index 0000000..4fdff29
Binary files /dev/null and b/docs/source/_static/tutorial_img/clq_workflow.png differ
diff --git a/docs/source/_static/tutorial_img/rnaforest_workflow.png b/docs/source/_static/tutorial_img/rnaforest_workflow.png
new file mode 100644
index 0000000..9ffa851
Binary files /dev/null and b/docs/source/_static/tutorial_img/rnaforest_workflow.png differ
diff --git a/docs/source/_templates/custom-class-template.rst b/docs/source/_templates/custom-class-template.rst
deleted file mode 100755
index fc92caa..0000000
--- a/docs/source/_templates/custom-class-template.rst
+++ /dev/null
@@ -1,66 +0,0 @@
-{{ fullname | escape | underline}}
-
-.. automodule:: {{ fullname }}
-
- {% block attributes %}
- {% if attributes %}
- .. rubric:: Module Attributes
-
- .. autosummary::
- :toctree:
- {% for item in attributes %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
- {% block functions %}
- {% if functions %}
- .. rubric:: {{ _('Functions') }}
-
- .. autosummary::
- :toctree:
- {% for item in functions %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
- {% block classes %}
- {% if classes %}
- .. rubric:: {{ _('Classes') }}
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
- {% for item in classes %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
- {% block exceptions %}
- {% if exceptions %}
- .. rubric:: {{ _('Exceptions') }}
-
- .. autosummary::
- :toctree:
- {% for item in exceptions %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
-{% block modules %}
-{% if modules %}
-.. rubric:: Modules
-
-.. autosummary::
- :toctree:
- :template: custom-module-template.rst
- :recursive:
-{% for item in modules %}
- {{ item }}
-{%- endfor %}
-{% endif %}
-{% endblock %}
\ No newline at end of file
diff --git a/docs/source/_templates/custom-module-template.rst b/docs/source/_templates/custom-module-template.rst
deleted file mode 100755
index 16ee6a7..0000000
--- a/docs/source/_templates/custom-module-template.rst
+++ /dev/null
@@ -1,66 +0,0 @@
-{{ fullname | escape | underline}}
-
-.. automodule:: {{ fullname }}
-
- {% block attributes %}
- {% if attributes %}
- .. rubric:: Module Attributes
-
- .. autosummary::
- :toctree:
- {% for item in attributes %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
- {% block functions %}
- {% if functions %}
- .. rubric:: {{ _('Functions') }}
-
- .. autosummary::
- :toctree:
- {% for item in functions %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
- {% block classes %}
- {% if classes %}
- .. rubric:: {{ _('Classes') }}
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
- {% for item in classes %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
- {% block exceptions %}
- {% if exceptions %}
- .. rubric:: {{ _('Exceptions') }}
-
- .. autosummary::
- :toctree:
- {% for item in exceptions %}
- {{ item }}
- {%- endfor %}
- {% endif %}
- {% endblock %}
-
-{% block modules %}
-{% if modules %}
-.. rubric:: Modules
-
-.. autosummary::
- :toctree:
- :template: custom-module-template.rst
- :recursive:
-{% for item in modules %}
- {{ item }}
-{%- endfor %}
-{% endif %}
-{% endblock %}
\ No newline at end of file
diff --git a/docs/source/api.md b/docs/source/api.md
index e81c950..651ac5d 100644
--- a/docs/source/api.md
+++ b/docs/source/api.md
@@ -5,42 +5,47 @@
:noindex:
```
-# API
+# {octicon}`code-square` API
Import Bento with:
```python
-import bento
+import bento as bt
```
-Bento takes inspiration from the success of other tools in the python single-cell omics ecosystem. The API is organized under a set of modules including:
+Bento's API structure takes inspiration from other libraries in the Scverse ecosystem. It is organized under a set of modules including:
-- `bento.pp`: preprocessing data
-- `bento.tl`: subcellular spatial analyses
-- `bento.pl`: visualizing molecule-resolved spatial data, localization pattern statistics, and more
-- `bento.io`: reading and writing data stored in the `AnnData` format, customized to hold molecular coordinates and segmentation masks
+- `bt.tl`: subcellular spatial analyses
+- `bt.pl`: conveniently plot spatial data and embeddings
+- `bt.io`: reading and writing spatial data to `AnnData` as `h5ad` files
+- `bt.geo`: manipulating spatial data
+- `bt.datasets`: included spatial transcriptomics datasets
+- `bt.ut`: utility functions
-# Preprocessing
+# Tools
+
+## Point features
+
+Compute spatial summary statistics describing groups of molecules e.g. distance to the cell membrane, relative symmetry, dispersion, etc.
+
+A list of available cell features and their names is stored in the dict :func:`bt.tl.point_features`.
```{eval-rst}
-.. module:: bento.pp
+.. module:: bento.tl
.. currentmodule:: bento
.. autosummary::
:toctree: api/
-
- pp.get_points
- pp.set_points
- pp.get_layers
-```
-# Tools
+ tl.analyze_points
+ tl.register_point_feature
+```
-## Sample Features
-Compute spatial properties of samples. A sample is the set of points for a given gene-pair. These include properties of shape(s) and points associated with each cell.
+## Shape features
-A list of available cell features and their names is stored in the dict `bento.tl.sample_features`.
+Compute spatial properties of shape features e.g. area, aspect ratio, etc. of the cell, nucleus, or other region of interest.
+A list of available cell features and their names is stored in the dict :func:`bt.tl.shape_features`.
```{eval-rst}
.. module:: bento.tl
@@ -49,20 +54,32 @@ A list of available cell features and their names is stored in the dict `bento.t
.. autosummary::
:toctree: api/
- tl.analyze_samples
- tl.PointDispersion
- tl.RipleyStats
- tl.ShapeAsymmetry
- tl.ShapeDispersion
- tl.ShapeEnrichment
- tl.ShapeProximity
+ tl.analyze_shapes
+ tl.register_shape_feature
+ tl.obs_stats
```
+## RNAflux: Subcellular RNA embeddings and domains
-## Cell Features
-Compute spatial properties of cells. These include properties of shape(s) and points associated with each cell.
+Methods for computing RNAflux embeddings and semantic segmentation of subcellular domains.
-A list of available cell features and their names is stored in the dict `bento.tl.cell_features`.
+```{eval-rst}
+.. module: bento.tl
+.. currentmodule:: bento
+
+.. autosummary::
+ :toctree: api/
+
+ tl.flux
+ tl.fluxmap
+ tl.fe
+ tl.fe_fazal2019
+ tl.load_gene_sets
+```
+
+## RNAforest: Predict RNA localization patterns
+
+Perform multilabel classification of RNA localization patterns using spatial summary statistics as features.
```{eval-rst}
.. module:: bento.tl
@@ -71,20 +88,14 @@ A list of available cell features and their names is stored in the dict `bento.t
.. autosummary::
:toctree: api/
- tl.analyze_cells
- tl.cell_area
- tl.cell_aspect_ratio
- tl.cell_bounds
- tl.cell_density
- tl.cell_moments
- tl.cell_morph_open
- tl.cell_perimeter
- tl.cell_radius
- tl.cell_span
+ tl.lp
+ tl.lp_stats
+ tl.lp_diff
```
-## Subcellular Shape Features
-Compute spatial properties of shapes linked to each cell, such as nuclei, other organelles, or regions of interest.
+## Colocalization analysis
+
+Methods for colocalization analyses of gene pairs.
```{eval-rst}
.. module:: bento.tl
@@ -93,47 +104,57 @@ Compute spatial properties of shapes linked to each cell, such as nuclei, other
.. autosummary::
:toctree: api/
- tl.is_nuclear
- tl.nucleus_area
- tl.nucleus_area_ratio
- tl.nucleus_aspect_ratio
- tl.nucleus_offset
- tl.raster_cell
-
+ tl.colocation
+ tl.coloc_quotient
```
-## Localization Patterns
+# Plotting
+
+## Spatial plots
+
+These are convenient functions for quick 2D visualizations of cells, molecules, and embeddings. We generate `matplotlib` style figures for accessible publication quality plots.
```{eval-rst}
-.. module:: bento.tl
+.. module:: bento.pl
.. currentmodule:: bento
.. autosummary::
:toctree: api/
-
- tl.lp
- tl.lp_stats
- tl.lp_diff
- tl.lp_signatures
- tl.decompose_tensor
- tl.select_tensor_rank
+
+ pl.points
+ pl.density
+ pl.shapes
+ pl.flux
+ pl.fluxmap
+
```
-## Colocalization
+## Shape features
```{eval-rst}
-.. module:: bento.tl
+.. module:: bento.pl
.. currentmodule:: bento
.. autosummary::
:toctree: api/
-
- tl.coloc_quotient
+
+ pl.obs_stats
```
+## RNAflux
+```{eval-rst}
+.. module:: bento.pl
+.. currentmodule:: bento
-# Plotting
+.. autosummary::
+ :toctree: api/
+
+ pl.flux_summary
+ pl.fe
+```
+
+## RNAforest
```{eval-rst}
.. module:: bento.pl
@@ -142,36 +163,91 @@ Compute spatial properties of shapes linked to each cell, such as nuclei, other
.. autosummary::
:toctree: api/
- pl.qc_metrics
- pl.cellplot
pl.lp_genes
pl.lp_gene_dist
- pl.lp_signatures
pl.lp_dist
pl.lp_diff
+
```
+## Colocalization analysis
+
+```{eval-rst}
+.. module:: bento.pl
+.. currentmodule:: bento
+
+.. autosummary::
+ :toctree: api/
+
+ pl.signatures
+ pl.signatures_error
+ pl.factor
+ pl.colocation
+
+```
+
+# Manipulating spatial data
+
+Convenient methods for setting, getting, and reformatting data.
+
+```{eval-rst}
+.. module:: bento.geo
+.. currentmodule:: bento
+
+.. autosummary::
+ :toctree: api/
+
+ geo.count_points
+ geo.crop
+ geo.get_points
+ geo.get_points_metadata
+ geo.get_shape
+ geo.rename_shapes
+ geo.sindex_points
+```
# Read/Write
+
```{eval-rst}
.. module:: bento.io
.. currentmodule:: bento
.. autosummary::
:toctree: api/
-
+
io.read_h5ad
io.write_h5ad
io.concatenate
+ io.prepare
```
# Datasets
+
+```{eval-rst}
+.. module:: bento.ds
+.. currentmodule:: bento
+
+.. autosummary::
+ :toctree: api/
+
+ ds.sample_data
+ ds.load_dataset
+ ds.get_dataset_info
+ ds.load_gene_sets
+
+```
+
+# Utility functions
+
```{eval-rst}
-.. module:: bento.datasets
+.. module:: bento.ut
.. currentmodule:: bento
.. autosummary::
:toctree: api/
-
- datasets.load_dataset
-```
\ No newline at end of file
+
+ ut.sync
+ ut.geo_format
+ ut.sc_format
+ ut.pheno_to_color
+```
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 18900ab..2d8618c 100755
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -18,12 +18,12 @@
# -- Project information -----------------------------------------------------
project = "bento-tools"
-copyright = "Clarence Mah. 2021"
+copyright = " Carter Lab & Yeo Lab. 2023"
author = "Clarence Mah"
html_favicon = "favicon.ico"
# The full version, including alpha/beta/rc tags
-release = "0.1"
+release = "2.0.0a0"
# -- General configuration ---------------------------------------------------
@@ -35,11 +35,12 @@
"sphinx.ext.autosummary",
"sphinx.ext.napoleon",
"sphinx.ext.intersphinx",
- "myst_parser",
- "nbsphinx",
- "sphinx_gallery.load_style",
+ "myst_nb",
+ "sphinx_design",
]
+myst_enable_extensions = ["colon_fence", "html_image", "dollarmath"]
+
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
@@ -60,9 +61,13 @@
"use_repository_button": True,
"use_edit_page_button": True,
"path_to_docs": "docs",
- "logo_only": True
+ "logo": {
+ "alt_text": "Bento Logo",
+ },
}
+html_context = {"default_mode": "auto"}
+
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
@@ -85,11 +90,4 @@
# -- Options for extensions -------------------------------------------------------------------------------
-# nb_execution_mode = "off"
-# myst_enable_extensions = ["colon_fence"]
-nbsphinx_execute = "never"
-
-nbsphinx_prolog = """
-.. image:: https://mybinder.org/badge_logo.svg
- :target: https://mybinder.org/v2/gh/ckmah/bento-tools/HEAD?labpath={{ env.doc2path(env.docname, base=None) }}
-"""
\ No newline at end of file
+nb_execution_mode = "off"
diff --git a/docs/source/howitworks.md b/docs/source/howitworks.md
index 402c268..23c41d7 100644
--- a/docs/source/howitworks.md
+++ b/docs/source/howitworks.md
@@ -1,15 +1,33 @@
-# How it Works
+# {octicon}`gear` How it Works
## Data Structure
-More info coming soon!
+Datasets are stored as `AnnData` objects, where observations are cells, variables are genes, and the X is the count matrix. Bento additionally stores molecular coordinates in `uns['points']` and polygons as columns in `obs`.
-## Localization Patterns
+```{figure} _static/tutorial_img/bento_data_structure.png
+:class: p-2
-More info coming soon!
+AnnData adapted to hold spatial data
+```
-### Spatial Features
+### Shapes
+
+Currently, shapes are stored as `GeoSeries` columns according to which cell they belong to. These columns are identified with the suffix `"_shape"`. Each element in the `GeoSeries` is either a shapely `Polygon` or `MultiPolygon`. Shape properties are also stored as columns and identified with the corresponding shape name as the prefix e.g. `"cell"`, `"nucleus"`, etc.
+
+### Points
+For fast spatial queries, Bento indexes points to shape layers upfront, and saves them as columns `points`, denoted as `"shape index"` above. For example, `"cell"` and `"nucleus"` columns are added to indicate whether points are within the shape.
+
+Metadata for points are stored as matrices `uns`. These metadata matrices are the same length as `points`, which makes it easy to query points and associated metadata. All metadata keys are registered to `uns['points_metadata']`, which is used to keep them in sync.
+
+## RNAflux
+
+RNAflux is a method for quantifying spatial composition gradients in the cell.
+
+
+## RNAforest input features
+The following describes input features of the RNAforest model.
+
| **Categories** | **Features** |
| -------------- | ------------ |
| Distance | **Cell inner proximity**: The average distance between all points within the cell to the cell boundary normalized by cell radius. Values closer to 0 denote farther from the cell boundary, values closer to 1 denote closer to the cell boundary.
-# Get started
-Install with Python >=3.8 and <3.11:
-```bash
-pip install bento-tools
-```
-Check out the [documentation](https://bento-tools.readthedocs.io/en/latest/) for the installation guide, tutorials, API and more! Read and cite [our preprint](https://doi.org/10.1101/2022.06.10.495510) if you use Bento in your work.
+Bento is a Python toolkit for performing subcellular analysis of spatial transcriptomics data. The package is part of the [Scverse ecosystem](https://scverse.org/packages/#ecosystem). Check out the [documentation](https://bento-tools.readthedocs.io/en/latest/) for installation instructions, tutorials, and API. Cite [our preprint](https://doi.org/10.1101/2022.06.10.495510) if you use Bento in your work. Thanks!
-# Main Features
+::::{grid} 2
+:::{grid-item-card} {octicon}`terminal` Installation
+:link: installation.html
+:::
-
+:::{grid-item-card} {octicon}`workflow` Tutorials
+:link: tutorials.html
+:::
+::::
+::::{grid} 2
+:::{grid-item-card} {octicon}`gear` How It Works
+:link: howitworks.html
+:::
-- Store molecular coordinates and segmentation masks
-- Visualize spatial transcriptomics data at subcellular resolution
-- Compute subcellular spatial features
-- Predict localization patterns and signatures
-- Factor decomposition for high-dimensional spatial feature sets
+:::{grid-item-card} {octicon}`code-square` API
+:link: api.html
+:::
+::::
---
[](https://github.com/ckmah/bento-tools/blob/master/LICENSE)
```{toctree}
-:maxdepth: 1
+:maxdepth: 2
:hidden: true
installation
tutorials
-api
howitworks
+api
```
diff --git a/docs/source/installation.md b/docs/source/installation.md
index 4227834..a71b25c 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -1,48 +1,71 @@
-# Installation
-Bento requires Python version 3.8 or 3.9.
+# {octicon}`terminal` Installation
+Bento requires Python version 3.8 or 3.9. Only Python 3.8 is supported for Windows.
-Install Bento with pip.
+
+## Setup a virtual environment
+We highly recommend using a virtual environment to install Bento to avoid conflicting dependencies with other packages. If you are unfamiliar with virtual environments, we recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
+
+To setup a virtual environment with `Miniconda`, run the following. This will create and activate a new environment called `bento` with Python 3.8.
```bash
-pip install bento-tools
+VERSION=3.8 # or 3.9
+conda create -n bento python=$VERSION
+
+# set channel priorities
+conda config --env --add channels defaults
+conda config --env --add channels bioconda
+conda config --env --add channels conda-forge
+
+conda activate bento
```
+## 2. Dependencies
-To enable GPU usage, run the following:
+Bento makes use of several packages for spatial analyses that require addtional non-Python dependencies.
```bash
-pip install bento-tools[torch]
+conda install -c conda-forge gdal cmake
```
+For developing docs, you will also need pandoc:
+
+```bash
+conda install -c conda-forge pandoc
+```
+
+## 3. Install Bento
+
+All that's left is the package itself. Install with pip:
+
+```bash
+pip install bento-tools==2.0.0a0
+```
+
+---
## Development
The package and its dependencies are built using [Poetry](https://python-poetry.org/).
1. Install [Poetry](https://python-poetry.org/).
2. Clone the `bento-tools` GitHub repository.
-3. Use poetry to setup the virtual environment.
+3. Use poetry to manage dependencies and pip to install the package in editable mode.
```bash
cd bento-tools
- poetry shell
poetry install
+ pip install -e .
```
-4. You have a couple options on what dependencies to install depending on what you want to modify.
- - For developing Bento, install normal package dependencies:
-
- ```bash
- poetry install
-
- # or with gpu enabled
- poetry install --extras "torch"
- ```
- - To modify and build documentation, install extra dependencies:
-
- ```bash
- poetry install --extras "docs"
- ```
-
-5. Launch a local live server to preview doc builds:
+
+ For updating documentation locally, install extra dependencies and launch a live server to preview doc builds:
+
+ First install pandoc (see [dependencies](#Dependencies) section for more details).
```bash
+ poetry install --extras "docs"
+ pip install -e .\[docs\]
cd docs
make livehtml # See output for URL
```
+
+---
+## GPU Support (Optional)
+Bento currently only uses GPUs to accelerate tensor decomposition (via [Tensorly](https://tensorly.org/stable/index.html)) GPU support can be enabled by installing PyTorch. We recommend the [PyTorch installation instructions](https://pytorch.org/get-started/locally/) for more details as installation varies by platform.
+
diff --git a/docs/source/tutorial_gallery/Data_Visualization.ipynb b/docs/source/tutorial_gallery/Data_Visualization.ipynb
new file mode 100644
index 0000000..2fbe068
--- /dev/null
+++ b/docs/source/tutorial_gallery/Data_Visualization.ipynb
@@ -0,0 +1,718 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "ded53b22-45c5-458e-9ec1-c15bc49f4b0a",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Data Visualization\n",
+ "\n",
+ "**Author**: Clarence Mah | **Last Updated**: {sub-ref}`today`\n",
+ "\n",
+ "We will demonstrate spatial visualization in `bento-tools` for exploring subcellular biology. We will explore the seqFISH+ 3T3 cells dataset, which we have included in the package.\n",
+ "\n",
+ "## A Brief Overview\n",
+ "\n",
+ "In `bento-tools` we provide a high-level interface based on [matplotlib](https://matplotlib.org/) for plotting spatial transcriptomics formatted as an `AnnData` object. [See more details about the data structure here.](../howitworks.md#data-structure) Data is represented as points and shapes, corresponding to molecules and segmentation masks. We closely mirror the [seaborn](http://seaborn.pydata.org/#) package for mapping data semantics, while replicating some [geopandas](https://geopandas.org/en/stable/) plotting functionality with styles more suitable for visualizing subcellular data. For spatial visualization at the tissue level (i.e. plotting cell coordinates instead of cell boundaries) we recommend using [squidpy](https://squidpy.readthedocs.io/en/stable/) and [scanpy](https://scanpy.readthedocs.io/en/stable/index.html) instead.\n",
+ "\n",
+ "```{note}\n",
+ "In general, plotting in `bento-tools` assumes datasets will have data stored from multiple fields of view (fov), which must be encoded in `adata.obs[\"batch\"]`. The plotting functions plot a single fov at a time, which can be set with the `batch` parameter; if unspecified, the default is inferred from the first cell in `adata`.\n",
+ "\n",
+ "If available, cell and nuclear shapes are plotted by default. Plot more shape layers by passing their names in a list to the `shapes` parameter.\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5efe2763-a777-4e88-997b-95afa5a6f6f2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Load Libraries and Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "223b1e16-87e9-4fab-86bb-500a14d3a875",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T21:13:40.158153Z",
+ "iopub.status.busy": "2023-03-31T21:13:40.157992Z",
+ "iopub.status.idle": "2023-03-31T21:14:20.246384Z",
+ "shell.execute_reply": "2023-03-31T21:14:20.245892Z",
+ "shell.execute_reply.started": "2023-03-31T21:13:40.158135Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import bento as bt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5bb23bdb-0263-4bb8-89fe-a5e991f350a2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T21:14:20.248440Z",
+ "iopub.status.busy": "2023-03-31T21:14:20.248283Z",
+ "iopub.status.idle": "2023-03-31T21:14:21.137272Z",
+ "shell.execute_reply": "2023-03-31T21:14:21.136835Z",
+ "shell.execute_reply.started": "2023-03-31T21:14:20.248427Z"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "AnnData object with n_obs × n_vars = 211 × 9506\n",
+ " obs: 'cell_shape', 'nucleus_shape', 'batch'\n",
+ " uns: 'points'\n",
+ " layers: 'spliced', 'unspliced'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "adata = bt.ds.load_dataset(\"seqfish\")\n",
+ "adata\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04044c76",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Plotting points\n",
+ "\n",
+ "Let's plot the points (RNA) as a scatterplot in 2D. This is a lightweight wrapper around [sns.scatterplot](https://seaborn.pydata.org/generated/seaborn.scatterplot.html). Refer to the seaborn documentation for more details.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cb3f8ec5",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T21:14:21.139216Z",
+ "iopub.status.busy": "2023-03-31T21:14:21.139053Z",
+ "iopub.status.idle": "2023-03-31T21:14:22.931491Z",
+ "shell.execute_reply": "2023-03-31T21:14:22.931094Z",
+ "shell.execute_reply.started": "2023-03-31T21:14:21.139203Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "![\"RNA](\"../_static/tutorial_img/rnaforest_workflow.png\")
\n",
+ "\n",
+ "The five subcellular patterns we can predict are:\n",
+ "\n",
+ "- **cell edge**: near the cell membrane\n",
+ "- **cytoplasmic**: mostly outside the nucleus in the cytoplasm\n",
+ "- **nuclear**: most in the nucleus\n",
+ "- **nuclear edge**: near the nuclear membrane, either\n",
+ "- **none**: none of the above patterns, more or less randomly distributed\n",
+ "\n",
+ "```{seealso}\n",
+ "See [more details about the spatial statistics](../howitworks.md) used as input features for classification.\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "58cfd10c-79ab-4833-a87f-c2cfacd17037",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T20:37:54.813174Z",
+ "iopub.status.busy": "2023-03-31T20:37:54.812911Z",
+ "iopub.status.idle": "2023-03-31T20:38:31.066875Z",
+ "shell.execute_reply": "2023-03-31T20:38:31.066313Z",
+ "shell.execute_reply.started": "2023-03-31T20:37:54.813158Z"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Calculating cell features...\n",
+ "AnnData object modified:\n",
+ " obs:\n",
+ " + cell_radius, cell_maxx, cell_minx, cell_maxy, cell_miny, cell_span\n",
+ "Processing point features...\n",
+ "Saving results...\n",
+ "Done.\n",
+ "AnnData object modified:\n",
+ " obs:\n",
+ " + cell_radius, cell_maxx, cell_minx, cell_maxy, cell_miny, cell_span\n",
+ " uns:\n",
+ " + cell_gene_features\n",
+ "Calculating cell features...\n",
+ "Processing point features...\n",
+ "Saving results...\n",
+ "Done.\n",
+ "AnnData object modified:\n",
+ " obs:\n",
+ " + cell_radius, cell_maxx, cell_minx, cell_maxy, cell_miny, cell_span\n",
+ " uns:\n",
+ " + lp, cell_gene_features, lpp\n"
+ ]
+ }
+ ],
+ "source": [
+ "bt.tl.lp(adata)\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e64867a5-e7e6-4d5e-808c-10a6464bcbf4",
+ "metadata": {},
+ "source": [
+ "We can view the observed pattern frequencies to get a rough idea of how transcripts are localizing.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "5ab1ce64-1c47-47ff-a88b-e59ba61153b1",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T20:38:31.068175Z",
+ "iopub.status.busy": "2023-03-31T20:38:31.067658Z",
+ "iopub.status.idle": "2023-03-31T20:38:32.342432Z",
+ "shell.execute_reply": "2023-03-31T20:38:32.341991Z",
+ "shell.execute_reply.started": "2023-03-31T20:38:31.068159Z"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "![\"colocalization](\"../_static/tutorial_img/clq_workflow.png\")
\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "c98a27de-fa0c-495c-8341-da3f4c26771b",
+ "metadata": {},
+ "source": [
+ "First lets create shapes for the cytoplasm.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "245fe3f8-5e4a-434f-b3d8-29152fb7e3f3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T20:38:34.086922Z",
+ "iopub.status.busy": "2023-03-31T20:38:34.086776Z",
+ "iopub.status.idle": "2023-03-31T20:38:34.104050Z",
+ "shell.execute_reply": "2023-03-31T20:38:34.103604Z",
+ "shell.execute_reply.started": "2023-03-31T20:38:34.086908Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Cytoplasm = cell - nucleus\n",
+ "adata.obs[\"cytoplasm_shape\"] = bt.geo.get_shape(adata, \"cell_shape\") - bt.geo.get_shape(\n",
+ " adata, \"nucleus_shape\"\n",
+ ")\n",
+ "\n",
+ "# Create point index\n",
+ "adata.uns[\"points\"][\"cytoplasm\"] = (\n",
+ " adata.uns[\"points\"][\"nucleus\"].astype(int) < 0\n",
+ ").astype(int)\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "fbbdea3b-f259-4474-9863-cf4036f8fa33",
+ "metadata": {},
+ "source": [
+ "Now we can calculate CLQ values for every gene pair -- one for the cytoplasm and once more for the nucleus.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "de003662-52d8-49ec-97d4-b846461799de",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T20:38:34.104778Z",
+ "iopub.status.busy": "2023-03-31T20:38:34.104636Z",
+ "iopub.status.idle": "2023-03-31T20:38:39.644219Z",
+ "shell.execute_reply": "2023-03-31T20:38:39.643759Z",
+ "shell.execute_reply.started": "2023-03-31T20:38:34.104764Z"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "45c05225310d458981da30dbd0403e4b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "cytoplasm_shape: 0%| | 0/15 [00:00) to recommend the optimal number of factors.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "adf52472-f1fb-4ce6-b2bf-723e0a1e46b7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T20:38:39.645130Z",
+ "iopub.status.busy": "2023-03-31T20:38:39.644897Z",
+ "iopub.status.idle": "2023-03-31T20:38:48.190293Z",
+ "shell.execute_reply": "2023-03-31T20:38:48.189649Z",
+ "shell.execute_reply.started": "2023-03-31T20:38:39.645114Z"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Preparing tensor...\n",
+ "(2, 15, 17317)\n",
+ ":running: Decomposing tensor...\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9bc4c7dcbfe143d7a42dbead4df57813",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Device cpu: 0%| | 0/5 [00:00"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bt.tl.colocation(adata, ranks=range(1, 6))\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "aad9bd1e-6631-4c3c-9f9d-f7c761c86a47",
+ "metadata": {},
+ "source": [
+ "Let's plot the factor loadings for the suggested $k = 3$. From left to right, the three heatmaps show the loadings of each factor for each dimension – compartments, cells, and gene pairs. We can limit the heatmap to show the top 5 associated gene pairs for each factor.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "f07cb11d-2a5b-4a61-8158-3c28b3a058b9",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-03-31T20:38:48.191146Z",
+ "iopub.status.busy": "2023-03-31T20:38:48.190994Z",
+ "iopub.status.idle": "2023-03-31T20:38:51.754914Z",
+ "shell.execute_reply": "2023-03-31T20:38:51.754389Z",
+ "shell.execute_reply.started": "2023-03-31T20:38:48.191132Z"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "\n",
- "\n",
- "Note \n",
- "\n",
- "`Bento` is able to utilize these feature spaces for [predicting RNA localization patterns](Subcellular_Localization.ipynb). For unsupervised analysis, `Bento` implements tensor decomposition to generate interpretable [low dimensional signatures](Tensor_Decomposition.ipynb).\n",
- "\n",
- "
"
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.11"
- },
- "toc-autonumbering": false
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/source/tutorial_gallery/Subcellular_Localization.ipynb b/docs/source/tutorial_gallery/Subcellular_Localization.ipynb
deleted file mode 100644
index 6539a94..0000000
--- a/docs/source/tutorial_gallery/Subcellular_Localization.ipynb
+++ /dev/null
@@ -1,904 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "ded53b22-45c5-458e-9ec1-c15bc49f4b0a",
- "metadata": {},
- "source": [
- "# Subcellular Localization Analysis\n",
- "\n",
- "**Author**: Clarence Mah | **Last Updated**: 6/15/2022\n",
- "\n",
- "Here we will analyze cultured 3T3 mouse embryonic stem cells in which 10k genes are spatially profiled with seqFISH+ in [Eng. et al 2019](https://doi.org/10.1038/s41586-019-1049-y). The dataset here consists of 211 cells, each with cell/nuclear segmentation masks and 2D transcript coordinates. Here we showcase how Bento enables subcellular analysis of spatial transcriptomics data.\n",
- "\n",
- "![\"Bento](\"../_static/tutorial_img/bento_workflow.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "13e3f4a0-42ef-4654-b23f-57d3aca2440b",
- "metadata": {},
- "source": [
- "## Load Libraries\n",
- "\n",
- "We will be using `bento` for subcellular spatial analysis and visualization, along with `scanpy` for handling single-cell gene expression data."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b79dfbf5-9d59-4501-89fc-f3612d7b168d",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "Note\n",
- "\n",
- "Similar to other tools in the python single-cell omics ecosystem, `bento` adopts an API organized under a set of modules including:\n",
- "\n",
- "- `bento.io`: reading and writing data stored in the `AnnData` format, customized to hold molecular coordinates and segmentation masks\n",
- "\n",
- "- `bento.pp`: preprocessing data\n",
- "\n",
- "- `bento.tl`: subcellular spatial analyses\n",
- "\n",
- "- `bento.pl`: visualizing subcellular-resolution spatial data, localization pattern statistics, and more\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "c4e068a5-2c4d-4cb5-b92f-3119cd8bbbb6",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:00:25.592940Z",
- "iopub.status.busy": "2022-06-06T01:00:25.592310Z",
- "iopub.status.idle": "2022-06-06T01:01:04.842251Z",
- "shell.execute_reply": "2022-06-06T01:01:04.839628Z",
- "shell.execute_reply.started": "2022-06-06T01:00:25.592835Z"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "import bento\n",
- "import scanpy as sc"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a591fb8-4830-48f9-9c7d-1dfa5dc20ab4",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Load Data\n",
- "\n",
- "Let's grab the `seqFISH+` dataset. This will download the dataset into `data_home`, which by default is set to `~/bento-data`.\n",
- "\n",
- "The loaded object is an `AnnData` object, structured similarly to single-cell omics anlayses, where observations are cells, features are genes, and the main matrix is an expression count matrix. To store subcellular information, `bento` stores:\n",
- "- Molecular coordinates: formatted as `DataFrames` in `uns['points']`\n",
- "- Segmentation masks: formatted as `geopandas.GeoSeries` in `obs` denoted as `{}_shape`. In this case, we have cell and nuclear segmentations stored in `cell_shape` and `nucleus_shape` respectively."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "5bb23bdb-0263-4bb8-89fe-a5e991f350a2",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:01:04.847884Z",
- "iopub.status.busy": "2022-06-06T01:01:04.847377Z",
- "iopub.status.idle": "2022-06-06T01:01:08.510518Z",
- "shell.execute_reply": "2022-06-06T01:01:08.508931Z",
- "shell.execute_reply.started": "2022-06-06T01:01:04.847843Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "AnnData object with n_obs × n_vars = 211 × 9506\n",
- " obs: 'cell_shape', 'nucleus_shape', 'batch'\n",
- " uns: 'points'\n",
- " layers: 'spliced', 'unspliced'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "adata = bento.datasets.load_dataset(\"seqfish_raw\")\n",
- "adata"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "41b1b9f6-99cc-4a7c-951c-1b24fe259e1f",
- "metadata": {},
- "source": [
- "What does our data look like? For starters, we can visualize molecules as their spatial density in a single field of view. You may notice not all some cells are missing nuclear masks for one reason or another. We can handle this with [Quality Control](#Quality-Control) metrics in the next section."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "80acf16b-27e8-42c0-8893-6fdcf100190b",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:01:08.514667Z",
- "iopub.status.busy": "2022-06-06T01:01:08.513593Z",
- "iopub.status.idle": "2022-06-06T01:01:23.776626Z",
- "shell.execute_reply": "2022-06-06T01:01:23.760654Z",
- "shell.execute_reply.started": "2022-06-06T01:01:08.514573Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "image/png": 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I+Ps2slMK2tjz9g0NjCgUCqWDMmRsAGLDMrBdebdJ/aSXlWKcn08zeUVpCg5mUrw7fgR6ujjCYDDAwNVDrdPhRn4qjmfE4NLBdGQVleI/mZC40IDuPk7o6u2AQT094TawC3LTi3By13WkJeQij06dsaCBEYVCoXRQfAJdcGLvLaBn0/rJq5SDx2HqbkhpdngcDrpY22J+l+4Y6uwBEwEfMbkF2HU7DH/fDkO2Ux40+popN0mhhNVHaGwmQmOrxOiWF9LQe5gfeg/3w5MvjkRqXA6SPj6GuLi4VntP7R0aGFEoFEoHI2+QDSylJrDv7oirZ9TQmbK1PHojTZHBKHeP0K5GUyMQ8yCWcaHzUNTUIdOzVy5Zh5E5dIp9yVpfxhojm1vsYEtUTOYHEhSTmiI9n9QHqckSZeBItQDPAL1ZTT8aJXlgfamAsMvF5DHspKT+CgAqNALWtvv5M643YXtas6cejXMOWYaQn4HnzZrjMgww4dNgBPt4wdxEDLlajb9uh2H7jTtEjiamWEzcyDnk6UNBgFFNt8HOiEI5tmpuQhTFwzz/7vjnwBkkpOTjxt0UXLyRAH45mdOKdzqEsE/dV1utI0IDIwqFQumArJw5FGdDEqDS6OpuXAe5inJwGQ5sxKbIU7CLo1Kal+5BHpi7YDAsPS1QUCHHp8fP42xcEtRgB7hNQanVYnPYLZw9F4oAXydMHdMNQ/t1Qsi1ZJw6E4nHddU+DYwoFAqlgyES8OBqa4Hv9l5qlv70MKBSq4YpX0gDoxZEKOThlTcnwM3DBhq1Don5hVh78CQKKu6N3rWQplyl1uJWWCoi47LQ2dMOU4YFIDDAGYWFciSn5EPN18OgNyDqahwqy9mr7ToaNDCiUCiUDkavzs7IKSpDThF7SqixxJbkw0dmg6Sy9r06zc3MAnKNuu6G7Qw+j4uV706GRCJETlYJKuUqrLhzrlVHbRRKDe5GZSAxNBOBAS4QCnnwdLeBgOMDkUSIqa+Ow82jobAt4OH48eMoLGzf10JjoYERhUJpNAzDwMrKqt7tKysrUVnZ/pMEPur083fD8ZuxdTdsAEWqSjhKzJq1z5bAVixBbHF+W7vRYJZO6A87exkYDoOsjCL8+etFGPq1TdUulUqLm7eSAQCXLsdXa4zcu7jAr18n+Pj4YM6cObh8+TIuXbqE2NhY5Ofnd5iM2TQwolAoD2Ss4zLWNmEvT0yd1huurlYwMRECZjzo7hPi8pSkDoJTVjP0LhQLcGbbeRz99Sz09+1zUrOD2CeY81STfWe45LyDQad76OvGNgDo1eTIQ3sVnd5/vnh8Lmw9ZbiVGYriLlXnWGxbd+0rnY68CavKSeF0VEYRRnl4VwuX9UJNnX1yjJrY3STF2ToT9jnXikg/hDqjorFOZEJHlQV5M/Y3s0NmSTmgrOnH2bWYaJMvJgurGiPialnb0uXmZBujIrIVcvJ8pR5zZ/XhvyeXsPVpmWAYBiNnD4BbkQYXDRlwspLh3cuXAR8ODEanh6lFLqa2JM+P0oZ8XZxLnk+dCVvwnj2A3GaSafTd6NIPAJAL4Doq4bBPh+KkbNg59sSql6bAycMaOq0eEWHpUCiqvjOcglKUFJQjN60QBoMBxSm5yE19cMBq/F0D2u77RgMjCoVSL3x6uGHIpJ5wCnTDrVvJ+P33i9CodYgzVRLtTDPIu6E4NLX6b9fO9pjzymj49++Mm8fv4tq/IVDKyZslpWkMntEPpUolkktKmrXfbHk5ulizMy+3NzxkFriTm93WbtQLe3cbLPxoJnRaHQ5sOonFf7yApT/ubWu36kXU3XRE3U3H2aPh0Iv5sHcwh19XJ3C5VYEYr1wBBzdrBA7oBICBnYMMsbcSce3IHSRHZECjqjuwbitoYEShUB5K0Mgu8OrihG4DO+PQbxdw4GI8Mu5PCmcqfPDORqTF5eCTp79Hr1GBeGbdDIxbMBzH/ziPpMs3kJiYyGovNhWh95hucPV1gpu/M/hCfi29ApXlCqRGZhBD+QyHfFI26Mkna+PXUUueHoOWfETvbnCFQqFAeHg4MjMza/W5LXHxccSM5RPw0q2Lzd53elkpxDw++BwOkTenPWHC58PT3BLh+Tlt7UqdBPTxxKxnB2D/Dydw/cgdPLNuBvZfj0BppbLundshOdklyMkuqbZ5SVnE63y1CiPnDMTkpaNh7WSJitKqUcwbR+8iPS4LZXklyIjLhl7X9tcWDYwoFEqtMAyDOcvHotvAzjiy7RLO7b+NnLRCqDs7NqlfvU6Pm8dCEXEpBl7d3DBuwXAEv/w3rl27Vt3GhfEGAFg5WqIgswjhF6Nxce91KCpqv2nYOFvB1tWa9L/BgRFbz2HQktMl6fp0WFhYYNq0aQgMDERkZCT0ej1u3ryJvXv3tql+imEYzH/vSWz78B8kjqg9gGwKOoMBqaUlCLCxR0huVt07tAE+1tbIqihDeS3TMu2FPsN8MXJKTwiEPPy0ehuyk/Pxxcm3odPq8fXhU23tXouhUqhxZMtZHNlyFlJLU4gkQoglQox6egg8A11hbm0KBw87MAwQeysRJ/44j3O3eNBq2dOaLQ3TQLGUgWFo9lMKpaNyv1ZFammKtZEfYOnpA8iprFmibXOVfJ4qGPLwm5A4gRxRct9K1uxiGAZeTqbg8mp0DYyVJYCqICohMhMaaymxj07EfqYr7EpqT3RGA1nCEvK3TmVW92+ZWToZTJkm16zysnM0h5VKAb6Ah57Du8Clkz12f34AMTcSHqqfagnGWCxCj6F+GDt/MD5Z+DPyZ/gTrxcNYQeUTL5R8kWR0ZO6kS2QqrCwc38wADbHXYXDH+T5BgCTRFLLUxpACvNFBeT0icqSHcAJSo0SPOaTwabahixUm/xkTUD7ZOeusDIT4ufoa0QbZzvSLy8zslZYXAk5RZhTaJQ1EoCVOZmmIL+AvCZNQ8nz4bwzhbAHjusG5wEcSCQSrF+/HmFhYej17BeYOjQAQ7p74a1N/yKtD7kUXpxMnh+VVS33a4bcpheSNreCDPhN09jXfaU9aQtKSVuWTF4LsjPxhK3o5cHqM2swmRDT/TB5/hij/FqcoqrXzSxMMGB0AAaM6QplURl2f3kI0deqjndSt7NVAhA6YkShUGrFu7s7kkqLiaCoJTAYDIi7nUxs4zjYtegxm4PcrBLkJVWVWQi7HIe+YwIx9ZVxMDEV4d/NZ3Dl4K1W84VhGExaPAJ/fn6oRY9zLS8ZS/0Gt+gxmoK/lS1uFKW0tRsELt52mPD0QEjMxHj785dx584daDRVAWJnVxuM6NUJa386AkU71ty0JmXFlTi28zqO7byOnv5WWPbNc/hp9XaEnY9qNR9oYEShUGpFZmOGzIqytnbjkeH68TBc3XMVvYIDMOetqeg+vAt+WPFHqxzbxskCAhEfCXfT6m7cBBLKCiDlC9HZzBblaF/XBgPA3cwCf6febmtXqgl+sg8mzh+E8wdDcOj3i7iRfKP6taFDh2LJ5AE4di0GpfJHU1fU0tw8dhcWduaYtHQ0Ii5Gt9px2yZJAoVCafeMfW4Ywgvav4i1vXH7VDhWjfwQTt72ePGr+fDwYE8zNCcMw+CJRcNx7Whoix4HAFR6LS7kJGKEY+cWP1ZDsTGRQCYUIb60oO7GrUDQcH888exgfLh4C/b9cg7a+6aOnJyc8Morr6BUrsCJG82bb6ojYTAYcOKP8ygvqsDQmQNa7bh0xIhCoVTDs6nRhIjNpQg/mA2pitQ5lHQy2klF5jwZHEj+0Gc4k3qNZI4r67iiQnJbmbeRlsJIR+HWlS3+tTCy+1qnEvbtIhfC7mWZTtiplZasPguUZKVygVF+m/AwX8L2+7YmT8v7r+3EzCXDcWz3KVzYcx27vqia5mpuzVHnzp3h7GmD39/eAf296RjzeHIEomIse5rGsRsZQKTlPTxRpzq/qgDqHnk8fhwzCXsHxaFQTup/7DTkZ83Rkp+b1pT70NcBgKsy0p4UkyNTBSPNCdtwL1nSQGc33MrLAHLZ2qe0YlJEk2lFXi26YlIPc38B3f8oLCFzH1leII9jszO8+m87VyvM++NF/LLmT+RH11yHA6d9Dkc7Gd58aSxCozLwxcXT0LjpUTXeBZiFkd+1Mu96rNAyMcrPVWGUg0hLynI0pDSqapsZeRyNOfm5yN3I9kV+PoStsmL7yRgVGU6aRn6XWFooOVvX5f1r1djN+fMJGD2j9QIjOmJEoVBY+PX2BE/AhVrb9AKkjys6rR5//e80Vo9eD//+nTDnzSmQWkjq3rGBWFlZoSi7pNXywuRVynE+LQXTenZplePVl+EunriYmdLWbsCvjxfe3voSjvx6FuGXyIcEG0tTLJ03BNdCkvHrzsvtNu1Be6OsWA5OLek0WgoaGFEoFBbuvg44ves6NDoaGDWVynIFtrz9N8xtZXjpm2dhYWE8ttU0nnrqKYSei2zWPutiZ0w4Bnq7wdum/uVgWhJfCxuYC0W4lJVad+MWZPS8QVj62VyEXYzG0d/PE3m1HD1t8e6rE5CdX4Y/9994bCvXPwrQwIhCobBhGGjUrZ8/pKOSHpOFH1/biviQZGzcuBHW1tZ171QPGIaBi4sLUiLT627cjOTKK3AgNApz+nZr1eM+iOmduuB0WiL0bRRtiAQ8zF0zCQMm9sTPb/2Nze/sJl4f99wwvPrdc9h3PBQ/bD3fLMe0MTFBTzsH9LRxhJeMPQ1MaTxUY0ShUGrg88HhctB3TDfs33IOlf3YNxqDFVnCQyIlba2BfN6a7niHsC+PZVd8L1GLCduFIacYStWknsNdWgRj7uQ5E/aVfFL0rNaSP3eJFWRRKUdxCavP5FJyRERlVB/r6SGXCHsbZyCrD2l8TeqBH3QFGHk3DRs2bMCrr76KioqmpUIYNWoUSkpKkJ6YD0ZQo08RZJGJaFTl7FEquQmZx0giqWNllNHr+yrC8ISrL7z7mSOiqEqkX1xKfk4OZ0gdE1NOane0zuwAkZdMCv5LhroTdmVvMtePt5kMQQ6O+OHCOfClaujF7IBeKCK39XTMIOyr5aRwTlVBao4AwPkAef1c3vsaAOCFF17Ak9OexIULF/DCjLnVST6HTPwMAPDE2G4YOzoQq97djTR7LeBaowES55HHUNxLp8QAsDeToo+LFUz4ApgJhfC2tAJHBdhKTeFsbga9wYCo7DzotHp42VjCVCREXE4+wnNzkVZcisSCIhTIK8EUkKO+CodagkcB+X3jFZHvVS8g9zGuz2YQsPsUp5O/A3yjr72eT06NqWurT1x+7/shNwU0rfegRgMjCoVCIDU3gYWtFKGX4oB+bnXvQKk3eoMBmzZtwieffIJ3330Xq1evblJ/Tz75JP7++28wBodm8rD+KHVabIm+gVcCBmLF5UOQa9sm2/QznfrgYFoEKlr5+B4eHti0aRMsLS0xadIkZGez67N5uttg2EAfrHp3N8orlHjYLVdmIsIIfzf0d3eBi7kMOoMBafJSpJaUwAADziQnQV2sgVavR0RWLv4LRbj33raJgI9AZ3t0drHB+C6dYSUxgUQgQEV51YNLpUqN+JxCpBrKEJadg9RmrqXXkjh72UEoZgerLQUNjCgUCoFXFyfISxV1N6Q0CoVCgffffx///PMPevfujVu3GpcIMjg4GGKxGKdPn0aweF4ze1k/TmcmwEtmhfeDRuPdm8db/fgjHTvB0cQMn95tvVIajnYyzHyiNyyEPbF7927s2rULRUXsEcxOXrZ4fv4Q7Np/815QxIZhgCBvFwwL8EJ3D0fczs3CmfgkRObkIq9cDo0pORLDL32wALlSrcG1pHRczqyZVrWSmEBcXrWPm40FrKUS+PnYYE73QBTKK7E1JBQ3MjIe1GWbwxfyMHflBPQY6osr/4Zi0MQerXJcGhhRKBQWcS2cKPBxp7i4GDt27MCPP/6IyZMnIyur4bXHxo4di3379kHfxiubfo66jiX+ffFB0Bh8fvs8FOrWWR3namqOZ32C8Ondk1DrW2eRQFA3N8yZHIRDp8Kx+csltY4SAYBMJsPCuYOwc99N3AqtXRBuaSrG0jH9YWMmwZmwRHx3+BKKzJt3uqhQXlld3iPvXtFWRU4MGACDPdzxYv8+6JvpjO9jrra7FXLuluZ4Y9NCqCpV+PT5LchOLcCzb01ulWPTWmkUCqWasX5voucAbwT29cTvG08g+mW2qNOvCyn0jc8htToLu14h7AsFpH5DxGX/+GdXkAIDGxNSexOdTZYIMZWQuiYAKEkxJ+zBvclMuU5GGiLjvEVptehwlEa6JDMh+eRvbmT3sSBLmwCACYec4vlh94Tqv+cFdcconQzrpn1ZvYKpPnmOJBIJzp49i6FDh0KhYI/ujeLPJmzFhJ6sNmkTyRshR0gGFyIJ6beijNQPSczJ4y7wHgh3kT3evXkcldqq4EiQTO5jaqQRF1Swb8YqM1KbUtTL6HrRM+jj4IxlPfpic9htXMgiz7mHFzspaU4peX3pdEYFhuPJHEVef5HaKI/Odug1zgROTk547bXXEBMTwzrG4ElVmiJnRwu8s2ICLiSmYtPRq8Tqs+LuVedljFsnLO4ahHOXE7DrUhg099JiGMnzICgn7888Jft+rTEh78nCUrKNce25MveaKSkTIR9LJw+Ao50M3x+5gvisqvdd7mWkIbIkrwVOrlGdvVo0Roye9EuUS9oCI82R3W+h1X+PmDkAU18ajbc/WY29e/dWB/8Gg6FVAhC6Ko1CoVDaiJ0h4dDr9Zi4JLhB+82bNw8nTpyoNShqK35NuIwCpRxre42ECY9dHLY5YAC83LMflvXoh29DruFCRkqLHOd+xkzthSWrJ+DGjRuYO3durUHR/cyd3helZQr8dPQaa0m+kMvFyp6DMM27C966cgJ/nrtTHRS1BZUqDb7cdR57r0XgzenD8eTAQLT12IdvkBeC5wzE8W0X8M8//7TJiCgNjCgUCqWN0Oh0+HbZrxjzzFDIrGtJSfwAxo8fj+3bt7egZ43j2/BLyK4sx8d9x8HNtHnzNZkLRfh86DjYmpjihRMHcCsns1n7r43A3h6YMLMvzh0Nw5YtW1BcXPzQ9oP6eMPRToavfznNSh1gbyHFRwNGg8/l4vWLRxFX3D5KlwDA+YgkrPrtMHp4OmLtkyMh5LaNyqb3qEC89OXTOLPzCg7/cqZNfABoYEShUChtSlF2CYpyShA0tnuD9istLa27UStTqdXgu/DLOJeViC8HTsTMngEQ8Zp+k+1p64gvho7D3fwcvH/lTPVUXUsybf5ALFo1Dt9+sB/H9tyqc+TCy90G0yf2xPqvjyArp4R4zdHSDGtnjURIXhY23DyPMjV7KritKapQ4O3tx5BXWoF3hw5v9eBo8NQgTH95DL5e9itO/XW5VY9tDA2MKBQKpY35+7ODCJ47uK3daDb2J0filYsH4Gdng59mTca0bv4QcLl173gfDAN093LEhwODsSpoEP6IvINtUXegbaWplWHju2H7/04jIap+wvj5T/XHsbORyCsgxTOOlmZ4a+YI7LxwFzti76I9J7w2GICfT1xHTkU53hkyrFWOaWYixLo5ozBqziB8sWQzEsPafuEHXZVGoVCq0ZqbQGcqgl7Ih9bcBMIC9s2sTEUKakd4xhP2P6nkktpKJZl/RGbCXrrM5ZA3O3MBqZ3xsC0kbJWO/dM1bSiZUfhsHin65nFILUegGblMWatnv9dcBSnKVWjI9+JnnkvYJ3L8WH0EWpA3Vo0X+d5UY3ogjMPAYC2FxfxhwJa6xdc2jCOGcCahkFM1tXNKv4t43VjAHXyQrWNhgvsQtp0zOU1UWkl+zgIjMbZGwz5f+vsKCmeo5Ngg/Ac+CnvM6BaEeUOm41pyNi6mpSK2oAC58grUJqX1DyhHkFUnBNt3g95gwLH0RHwRfxRKnRZCK8D0GDnlWNyVvHaSE8mCsQDgsZtsU2lPaqD0PDJcyTAcQE7hGHz+2xIolbUvtR9rt7T676GTe6FMqsfWsligV9W1KckCHKzM8NaTI7Dr7F1cDk+GxJo8rnUYeS0obElRs1pKniCmFjmSuJB8byoZOd4hMBpYtL5ZwuqjuBtZwPX7q9fx7YQJmOTth4OxMSwRvTFMOfuD1ErIcyo2mjm02RkOoViA1b88j4qEYoxcOg5yufyhx2ktaGBEoVAobYxOb8Ct0BTMnNIbu7a0tTfNS2xZDtZHHIKlQIJu+kBM7OSD53sGQavXQ6nTokSpRFJxETpbWcFUIISDJR/RZenYnXYZt4oSUFDe/IV36+Ldd9/Fd99998Cg6H5EJkJMeHYIlp+7SGy3kIrx1uwR2HX+Li6Gs1crtmcMAP53/TreGTYcJxIToEPzC8Qt7c3x9u9LEXohGts/OdBugiKABkYUCoXSLvh770388PlcSCSSdnWTaC6K1HIcSIzGgdhocBkGdhJT8HhcOEqlsBSbICyvKhuziVs6KnUPzmLNMMDgQE9IRFWjd3JnA1zMZLCVVAVQhvvuauVqFbZF3QFQfz3WkO6ecHCwwp49e+rV3jvQBUV5ZcgoIY+xcGwf3IrLeOSCov+IKShAQmEhJvn4Yt/15i1SPHtED0x8fjJunQzHnxsOooFpg1ocGhhRKBRKO0CvNyA0PB3z58/Hjz/+2NbutCg6gwFZFeUwMEBqaQnxmmMtQZGEJ8BT7j3hbmoJTz9bFJVVIjI1BxKRAP27ecBGIkF2eTkKFZXAvdk8AZeLnraOGOveCV9cO4zomNqTMd6PUMDD/HFBWDRnSr1u1hwOg3mvT8SfX/wL3DeD7Gtng05O1vh+f9uKiJvKX2Fh+GDkiGYNjGYMDcT4fn74c9VfuHkyvNn6bU5oYEShUKrhxWeA6yRF15lB4MVnwIXvympT0YfUMMSU2hK2yKjQKt+E1EAUlLKnRvwcSK3OxajOhD2j523CTqxgFx89kB5A2BIBeYO1EpAFTK8UehF2agl7ebmPVT5hp5ebE3a5htReGL93APg3vgthezmSfSaMril+u1UVja8XrkLREQ6Kc6tGIE4oa1mWzzAAlwPmP0FzHXpkrin7nAsLyc+R25nshM8jp0+8rUmRSGwu+bkD7CSRxp9BiYw8P9wCdr4j4ac1n4OpqRDTAlwwcGQXJFzORmR4JH6xC4GtmSmm9OkCd1sL7M69iGsFCShWV42y6Y7UXBvB3b2xdPwAPDMuEO/+erp6u0hmVLFUrQGHw+CZtycj7lhkvcq0aHycweNxwJOZIKRIAdOMmkLI0/r64vDdWJTztcB9b9EmhFxNZ6wpUslIrU65K2nzKtlaHo7RJcc1WvAm1ZCfq17MPueSLNKvwoCqNnH5BShVqtCPZ4+I+4r7lviQQaOwkO2X0w3SMV3ZQYwbNw49rDR4dfE8nDrVemVcGgpdlUahUAhibiVDKBbAK9ClrV157IgpLMC5XVcxZv6QtnalTREKeRg61BdvvzUJdo4W2Lf9Cnb/egGlRXK8MLoflozuhxsJ6Vj+2yEczbpbHRQZcyo0AZUqNSQyMWatmvjQY3bp5w0nTzts/eRAg3zl8UgRuqXUBD28HXEh6tGcQjPmSmoa+vk3vpi0o705Zk3vg48//hjp6emYOXNmuw6KADpiRKFQjKisUOL49kvoOzoQMUfa51B3RybkdARe/X4Bzu66htzU/Lp36GD4+jhg9ux+KC6WY8ff1xBzKgo9+3vj45+eRWWlGjti43A+Kgnqexmj66q5fi48EY7JFRg4qTdCzkQiLoQMWDgcBv0mdMfM5ePwz/cnoNXUX2jcNYD98DB5UBdcjkhBQXnH0IldTU/H6wH9G7wfj8fBqOH+mDKxJ85fisXzzz+P2NjYFvCw+aGBEYVCYXHhwG288/sLEJ2NgVLROkVBKVWkRGbg9F+X8eSK8fj9vd0Au/QXtBodpBYSFGWXtLp/LUUPBwcsHzgA0hIGx09EwNRUiAXPDQEzbwDUSi2+WLsH2elFyBzFnkZ9GMm5xTAtUmLv98fx7LoZOPfPNZQrdOg+qDP8g7yg0+qQmZCDvz4/jOsnGvYg4OPrgCOHQ6ttKzMTDA70xJqfDjeon/ZMVlkZrGQSSE2EKK+sX2JKhgFeXDgcpqZCbNpyDnfC0h6ZoAiggRGFQrmPYwU/Vf1RAHT6V4Q5owdh22fkj3xuPvmU7O2cR9gqo58V40Ks1jL2k7TSKC+RuS2ZJO9oCpkfSCFnjxPoVWQfFTJyqfU/GWQh1dFdogg7Lp8shguw8xj5WZJRSo6C1KqY8tjCYS6P1HhYi8j3H29KjlBwXJ1w4kw85vq7YeTS8di77mdWn7vPbofdRDNsvlt3ziMA0Pt7sLapPcnzY/w59XMgq8JHFrPzAxljUJB9FF4m9/E+S+bt4Uclw7+PFyYuGAqZlRR/LPoLwoXdMGKMN5QaDZbuP4wiOzU0ej20EwDAEjAlz7HYKE+WmVERVX6lAfHqO/jkt0/w+xUfPPXUU2AYBnczc/DlH+8iJCQElZWk/qw+aALN0H+0HzbuOo+STiKUegEzgnxxNisFCVZySGvJUyjKI4+jcCK1X4xRLi1j/VBt8Ml6yxBUkO9fKyb7FCrZnQqKyM/FJLPmnOqhQZ5KDisfGXKz84x3BQA4nau5pvkCLl5aMQa5haGYNWMJtNp6vIl2Bg2MKBRKrWzZsgXH9zzT1m48lmg1Opw9eAcvrH0CXlu9kJiYSLy+bds2HDlyBOvXr28jDxsPwwATJ/VEt+6ucLSUoLy4An9vPAqdRofpy0Yjy46HzeduIiG3ABqdHgpt82W6jo2NxYcfftgsfckkIuSVVOBuQk0Cz8Ee7vj07IVm6b89EZOTDxdLGaIeEBjdz7CRXWAwAIsWLWp3y/DrCw2MKBRKrRQWFqI4rwyjZ/fHiR1X29qdx460xDyc2HMLixYtwptvvkm8VlFRAaaty6A3gqA+nhg3oTvkciUOHbyD4svRyEkthLmNFO/9+RJibifjk+t3m7XsR0m5AmODgiASieqVsLG+uNtbolxeM7XkaWkBvcGA2Pz2Uxy2uSisUMDT2rLOdkNH+mPi1J7Y/MOZRzYoAuiqNAqF8gB0Oh3+/OJfDJzQo+7GlBbhzpUE9O3bF05OTsR2g8EAHo8HiaT1s0I3FIlQgFdHDMBv86dj/rODsevva9j45VHcvZMKiZkJXt04D29uXozj2y/hxzf+bvZaaKGxmTAYDHB0dGy2Pl1cXDBzZA9EJtfkRgqwt0N0Xn67roXWWJIKCmEmFj60jXdnezw1px9+++kcwkPbvt5ZU6AjRhQK5YFkphZCq9Wj5/AuCLkQAwBgOORPf0IGmc9GZmGko1CReVM4DPvWYaxvUdwmn06VTqQAXJLIzsVS6UZqdXTxZE0tU7LcGs5ldCdsrQnbrzQrUr+SmU/mOnI1quE2xS6U1YeIS/ouNrKFRloog6jmvZVUqrFjxw4sWrQI77//fvX2kaK5kKfqsG72Vzi14zLKJwSSfRaTuo7k4WxNlkFOni+NjNSinIrzIWyTUDFhm+exz5flvojqv3uO6AJh9zIM7jsYJ47sw7cHDiAmJqb6dWtra2w8cBnHtl3Cxpd/r94u9yM1RBJzI12SUX6lkjSyzpeVnB1YXbt2DfPmzcMHH3zAeq2+3F8bbc7L4yGU8vF3chQ0jlUjd2IeHxnFpWDuHV5eSxymtiTPIVdB+moRTV4LOgGpcRMVs9+bnk+OHHI05OdS5Et+V5x3prP6UHZxJmxpJnkcmVAHsZUBNnerriu5Hfl9PXX1HTz57E9Y9uqzOHnyJKv/Rw06YkShUB7KXxuPYuKzHafy+6PGjh07MGzYMLi4kKL3q4dDMHL2AJhIH17gszUxkYoQOMgHz747DaPnDkJERASee+45bNiwgQiKBg8ejJ07d+L49ks4vbPlp2kvXrwIGxu2uL4xDJvaG12CvPD2/hPQ6Jp3dOtRpX///ujUqRPOnTvX1q40C3TEiEKhPJSkyEwIRXxYO5qjIKukrd1hweUwEPJ4AI98zuPxufCxsYZEUPXELDCSSGiMZqEqeVpE5+dDodFA1470ERUVFbh58yaef/55vPPOO9Xbz+6+BhdfRzz12gT873YC9G3os7ezNbp6OWDM/NHIiMtBQlgadnxxGAdz/mG17dy5M1atWoU33ngDlgndWsW/vLw8+Pv7w8XFBenp7BGThtBjiC8uHgpBmqT+9dc6MgwDrF69Ghs2bIBG0zFSe9DAiEKhPBSDwYCYkBT49fLAxaw7be0OzARCDO/iCX9HW9iZmcLBXAoehwOdkAwMGB2DokoFUoqKAQA8ckYGOiPJhIOVGRykptDqDUgpKUZUXh7ClJmILs6DWt/81cUbwgcffIDjx49j/fr1hID4n6+P4IN/XkN/vQ6XQ5Ja3S8ul4PgoM6YMMgfl0KT8ePqv5ARX0vipXt4eHjgxx9/xKZNm3Dz5k2MsWidwCgpKQmhoaHYvn07hg4d2qS+XH0c8Mv7e4GnnOpu3EEQCB4cKkwdHIDc3EQcP368FT1qWWhgRKFQ6qSiTAGBkK3raS1cpDIMcnTHIEc3OJmaITY5H2HpOTgREY9KlRppRaUsjRGvhNTMiIw0RmpSmlKtMXKRyeAmM0eAnS2WePeFndgUpzMScTI9HkmKupcrtwQVFRWIjIxEv379iOmKynIl4m4nwdZK+uCdWwAeh4M5Y7phTD9fJGYWYv2vJ5FbVA7LhwRFADB27FgcPHgQu3fvbiVPa/jmm2+wcePGJvXh4G4DnVaPyvLmW932KODlYYPM7GLWdmuZBKN7d8bkEfPbwKuWgwZGFArlgRgUVTeApDvJmL5sNE5tuwCOUcJCg56cwjIupFqSR960mQoyYAEAldZo6bmsKkgJtLPH3IBAeJpY4FZyBnafDENERi6Kbe4T6AoA2APWN0k/8vuTw/p6o0R35tFke7W0yofC7DIUogwhSENFFzUcJVI84e6HD/qMQpYmG/vSryOhoioA0BnIPn5MYNc487MiC+SmV5oTtp2MTGapcDLSwoRV/Xf06FGsW7cOISEhOFG2tfplmxs6dFv4AvLza3zhGyXArC3ZYJmXUYLHMFJY7hBKfs7SE+EQmggw/aUx6DmiC65FXcDqV77E4cP1y/I8aNAgLJr+Ir5+cQtGi+YBALQB7MSTtnbkFJWxWN9YqM+zIt+H9Aa7jMoo/mwISwRwFLrj1cHv4puLDRdhV/T3xITFI3HgWhzK+rhDZUX6peHoYG9qCr2ganttBV/LXcmHC5vzZCCp9LAibGOx9X/X6P3oeeQ2SS65j/0NcjFEZTd2GRO1OfndKPIlr2vGQYTMnAoUe1Wde6ffo8AwDNb+/gJubLmI7OxsdCRoYEShUOok9EIMFrw7vdVy53Szs8ecgEA4mZlhV2QE1l883WZC1yx5OX6KvIE/YkLwdHd3rPSbhISKHPyeeBblevZTdEuxb98+TJw4Ef7+/rh27Vr19oiICEyrR46ZpmAiFmDGq2PRY5g/TKRi/LXhID7a81q99x80aBDeffddbHp9O3LT2ibPj0qhxpm/L+OJF0bh19CNKC8vr3snI8ylYkTEZtX6WnRePvo6O9f62qMMh2HQ3d0BJ8PiiO3u/k7Q6fQ4tOVc2zjWgtDAiEKh1A8G4Atb9ifDTmKKl/v1g4vMDHuio3A0Pg4avR5iXdsnM1TqtDiUeQsnsu9iknNvfNRtNvZkXMCF/Ii6d24m9u/fj6VLlxKBkUajQUudne7+zpg5oRdsrUxxa9d1bHl3N5IjMhqUvM/d3R3vvfceXnnlFTgn9ax7hxbk3M6rcPV1wh9//IFnnnmmUcHRg957YWUlXGQy8DkcaJo5F1Nb0tXRDmqtDvHZNXPRXB4X018ajevHw9rQs5aDBkYUCqVepMVmwyvQFVFomdpH4zp1wuJevbE/Jhrvnz/Tbm8uKr0Gu9Ou4nZREl7oNBJSnhj/Zt9slWMfOnQIK1euhKWlJYqKilrkGDKRENO7dUW3QVZwc7LEkbORuHw7CYod1+re2QixWIx169Zh06ZNiIqKgrOobQMjADiy+Qx8nrXBDz/8gMWLFzdbNuycigpUajTwsLREXEHHyH7NAHiyR1ecDIsntnfu6Q69Xt8qqRbaAhoYUSiUB/KfxggAygvLwKmlIOQQL/JH8+ytLoTteI4czyjpROoXuBwGS0f0QxdHW7y6/RDyyuUAH+DfNw5id1Nl1Ac7C6/KnLTNIkk9h2kOGWjxFKRYW1TEHnexMtIh5QfUJLMMhQErep3CR0HjUCQxwe6kMPRyZy8Fr9CQvnaWkgLuLAXpeJGCnBYL5jxF2GXRKnTu3JkYNTLN0MLjYE010YLuZGJAaSp7GbXZr+T5EV+5i17BAZi/dhxunw7Hntit+PPPPxsdOAQFBWHt2rW4efMm9uzZAwDgONgRbQreYBcULoondTbeARmEXWqk0eLzjRJ7OpD7AwBXWXP9FFdo8d577+HgwYM4cOAAVq5cCfsof6L9Kf0uVh/CIjU4aj0EJRoIi9RgtGT+KEbL4FpKBoI9vBCfUwiNKXtkyVgjlPGEA2FbxpPfL4ORHE9cyH5YkNuTjYxkb1AZJSk1cNjXOVdF+up0vup8DRvmC9syHkI2hcBeU3WepVIRfKaaY9eZ33C8eAurr44ATfBIoVDqRVF2MTwDXZu1Ty6HwfJRg2AqFOC1XUeqgqJHiGKVAmtvHsUIp04Y7dy5VY6p1ejA4TTvTzeHwyB47iDMXzsNf204gN/f34MtW7Y0KiiSSCT45ptv8Pbbb+Pzzz/HRx991C7rZi1btgwJCQnYsGEDpi+fAHsP27p3qoMDUdEY7uUBa4lJM3jYtjg4yDB5Uk/8sfUSNPcFRR9+MROenp5tsrKwtaCBEYVCqRe5qQUQitmlJRoLwwDLRw0Cn8vBFycuolL9aCaHK1YpsDHsAp7u3AseEvaKn+amrLACPXo0b/26kcP90WOYP75b/geu/tv4XFUSiQTfffcdMjMzMX36dFy6dKkZvWxe0tLS8NJLL2H+/Png8bl49YdFePHrZzFv7XS8/vrr8PT0bHCfeRVynE9KwbyerZOfqaUQCHhYuHAoDh0ORWpqlbZIZm6CZSvHQq3SYuHChSgrK2tjL1sOGhhRKJQ2YWKgL5wtzPDl8YuPfGmFuNJ8fB56DvPcpsHFpPmKldbGya3nMW3aNJiamtbduB6MHxOIcaMCsOurI4gLSW50P6NGjcLff/+N6OhofPbZZ9Dp2jYpZn0pLCzEzs8P4oMnv8Kd0+FIDEtFUVERfvzxR2zYsAHDhg1rUH+/3QxBLydH9G7GorWtiUDAw0svjURaaiHOno0CAFjbSPHW+1NhayfDx+v2ISMjo45eHm2oxohCoTQIrYLUpvAZMqhhpOTIT7kTqcWodNCjp50DJvTxw6rTx1Bqq0WnP8m01Eo7cp/cIFKno7RhT80wRrmQ9GLSL60p+RwoNFppz9QSm0mvkE/FDhWkX2nSmqKgkakluOB9CnPdxmNL8hYodFXvqUBJaoZOpPiyD3Qf7qlkJkq9gByly88uhVwuh1QqRUVFla6IUajBza3J/yMqIqdyRGfDWcc5q9uJY8eOwczMDDOmDUdycuODoqlTp2LhwoV46623EBb24JVKRYPIYKEwjX3SjRUw+XIyANRoSE2NscaosDs72aVtAdlHcNFTrDaKCiWuHroNAOC5OOJ/F09hwrwB+Omb2cjLLMIHobegtOKiqIcYOc5KSFOM9r+Xfkql0uB/567hxeF98fq5o8hX1OQRqnAnr0HzaPLdGuuDOEaDqMZ6IgAQF5DnUCMhO1FYkcewSGDrBMUpJQAAE4kQi1aORWFMNnZ9fQwCgwFd+3ph5lsDsXP3Jnz22WesfTsiNDCiUCitigmfj5eD+uGX0FvIq3y0NEV1cbv4NtxM3DDBYQL2ZOyBAS2jrblfs6PT6SCzlEAg5EGtqt+KQR6fi+1/bIeZmRmGDRsGubxxn4NMJsPChQsxYsQIvPDCCx1qJEGt0mDflvM49vc1BPb3xgdrRgMM8MmssZj9/Y6H7nszNRM+GTZYP3gU3rl0GrmVFQ9t3x6wd7bAK+9Owd0bSdj99TEYDAY8/foE+Ad5Yv6CGQgPZwfXHRU6lUahUFqVBYE9EVdUiIvpqW3tSotwKOsQ7EX28Dfzr7txMxAXF4fi/HJ0DaqfJobD5WDFj4ugUCgwZMiQRgdFDMPgq6++gkQiwfz58ztUUHQ/CrkK109FYu3uE4hIz4GLlQxzB9St8fotIgRHk+Px8ZBR6GLVdGF3S2LvbIGX3p6EfdsuY9eWCxj1VF98eWAF7F2s8OkLvz1WQRFAR4woFEor4mFrgWFuHnju8N62dqXF0Bg0OJFzApOdJiO+Ir7uHZqIwWBAYW4ZONyHP+daOphjzprJcO/ijOgbiVjw+gLoG5krimEYvPDCC9DpdO121Vlzk1NSjq+PXoZSrcWr4wbiTkwGYjPY5Uf+Q28wYF98FIqVCrzZbyj+iAjB6djGT1e2FN26OuOZib2x/88riApNwyvvToYJn4MNL/6O/KySx+KzNYYGRhQK5YHo1TU1yfRaLfh8Lpgy8mfjXIo3YRt05A263K9qeocB8PyIfjj+y03YnS7H/RltcvtKyOMa1atVOBrdwGvJhN25fwphx2WROXOYPDFhV7iSP/jSFHafZX5mhG2aQmqh7G6R+pbEPtZV/5cVwce0GG6i4TAYjhJt1Dry/NmakNMscmtjvx988/0PfWkZ9Pn50KZW5VGS6Um/SronYO1nn+H27dv4ZPW7uHq18Yn5AgICsGbNGsjlcrzzzjsPvXF2XfkVYed2Iv1iJOyViO6dyOSIeeWkZsi4Np+JiPxMjK8dAEAlWS9MN4wc8alwIXVcBbUtKuNWXYPfJ9zAhD5+WDl5CJZ+swfVb9/o8uFWVPl5MToVmbllWD14MLqbOGDL1VuQ31uBWdqJPHcl/uR7k6SQtoa8HAEAckeyjYAsNQdxAXkMYUFVCgaGAYLHBWLYqC7YtmY7SvJK8fIHTyEjNh0zNzzbqKzgHQU6lUahUOpF3K0k9B3fA1Zicd2Na6GThTUsRGKcPxvdzJ61Ty7khyPIyqdVjpWfUQhXv5o6XZa2ZvDv5YERU3tj3oqx+O6773DgwAG89dZbTQqKZs6ciQ0bNmDbtm1YsmQJcnNz696pg6HW6TB//z9ws7PAK1MH1WufpKJivHbkKAQ8Lr6ZPhHj/DuD18y5qBqCh5ct1rw3FT16e+DbDUeQEZeNWWsmIzUqA399vO+xDooAOmJEoVDqSXZSLopyisHnsFfG1IcJnj44lhIPvf7xGJqPKE3FAs8xMOPJUKYtrXuHJpCXVoC5b02DR4ArVJUqdOrng+SoLJSVyJEck4X1P61AdHTTAlIPDw8sWrQI8+fP73DV1BtKZnkZ/jhxC3NH9sTliBTciqtbX1Wp0eCL05fQw9kBT/UMwMyeATgQF4PTiYkoUijq3L+p8HlcdO/kiOlz/GDvYI5//rqKKxdjYW4uwZrfluLavyH4d/MZ6B/x1BnNAQ2MKBRKi8NjOOjv6IqV546AXcyjY6LWa5GrLIGtyBZlFS0bGEVejkVcSBKKc0pw93wUDv8TgrSEmtGc6NSmBUU9evTARx99hG+//faxD4r+48j1aIzs0QnvPTMGr/90GLd1ufVag3gnIxt3MrLRzckeI7t646cpkxGRm4craam4nJUKuaZ5E53yuByM6uuDCQP9USZX4uLeu7h6MQ56vQGDR/hh5rwBuLrrMg79dKpZj/soQwMjCoXyQLgyUtTA8PgwSHQwMDVaEROxmmij05IjSua2ZfA1c0QFSlEpS4VIxy6XIMkln1Kzhxs9tQqNkgWq2KNWhZWkTsnKnNTuqPuTT+XGObzlHuys3iXZpK+uJWQbfjnpV7GSzHN0tygHHHTH7fyaY4t45JL6u6lOhG3nTYaO5qVkPS07ZwvIZDIo7htlOFj6Bw6+8gfL/+Zg7ty5ePrpp/Hee+8R9dlqY0zXtwg7rqfRSIiKvOWYSMkaeACQWWRO2OamRjmuFORn4GhK5poK78MW4hT3didsgdFxtWqjXFy1zHJJpUZaptN6vPv+Pnz1/pP4cN5ohNxOwQ+/nKkeEc3vzr6etPddTnczcnAnNwcWYhH6ubliuIcnlg7og5iifITn5yK5tBihuhyo7qtPyFGzdXA8eZVeyNXSHAzDQHivuUwigp+rHfq7OaFcrsIvv55HbEIuLh5cDaCqJErfoX2x/tM3sW3btsdSZP0gaGBEoVBaHF8zR0SXZbW1G61OTGkWxjh2bdY+fbu54vjx4ygpKWnWfmuja9eumDdvHp5++mnk59ctAn/cKK9QIi2zCEIBD+bmJlj5yhj8uPksKirYAd+DKFYocTQmDkdj4mDixEOAtR06mVvhKZ+uWNl9EFRaLW5lZgEwgLlv0YGdVAJHMzNABwh5XCjUGpQqVeDeO7Reb0B8Zj52H7yN8JjM6oCNz+dj7dq18Pb2xpNPPtmhS3s0FhoYUSiUeiEUCyAQ8YFG6DJFXD6K1R0rmWN9UOrrl3CxoajV6robNRGGYbBmzRp8++23NCh6CH/uuYFnZ/XH+s8OYd6s/vjo3Wk4cToSB8pTUFLZsCK8pSolLmWm4lJmKhAJWChMYCMxgZu5OQAyu7tGp0N0bj6YiqrRrjKlCgYDICoi+zTNrrkGBXwuDh8+jISEBCxatIgYdaTUQAMjCoVSL0bPHYiM+BzkCRse4HQ2c8C/mY0vTvqoUqSqQCczezBgWiwLdksxd+5cKBQKHD16tO7GjzFqjRYajQ4GA7Btx1WcOhuFGVN745vAAGQWleF4eByuxKc2qh5guUqFcpUKSUVV9WtqnUprQGwzbmQAcnKy8dJLLzXYl8cJGhhRKJQ64fG5GDixJ7ZvOAj9jIbvzwWDksdwxChLUQIe07hVfA/CzskCKfEtW2Ji6tSpmDVrFp5//vkWPU5HJDunFN/9eBolvcTo6+WMl0cPxIvB/XE2KhG/hYRU5zBqbYKH+GFIv04YM3Jmmxz/UYIGRhQK5YHkzq4qa7FgTBBiDEpc95VgsD858lOhJcXCFRJSdBqfag+Vko+SIlMUlihhzmc/OatkRgVes0lb6USOtsjs2fN5ednmZB/pZKY/YQ+yamylkYhXW0gKpwEApqS4WlD28JtaRYQla5vlCDmmu90B7o0Y/R7T7+F92JPv3SyTXAU2cLgPVq7f9dA+msJ/BWGXLFmCrKwH68LG2i1lbYtaLiNsbi55iwnoTWZ+TiiwZvXB5ZLXh4hPnnOdkfC+QkN+jvb2Jaw+i+Vk7i2xkOyzQk9ew9wYUsgPAJVcsg+H61VZzU085eCNkuP88TeI14NPPoUkAK8xu+Dq54QRswdix/Tp+Ouvq7h0OQ4GA5A0w+gWrCY/e1E+OUKktGK5BZ1RWjHjYsjCw7cwZdlYdHW1wv9mf43iYqPqyRQWNDCiUCgPRMTnYeOSSZCr1Fi37QTkyoZrWxgAViIJtI0sP0GpwcXHAZXliha7ud0fFGVmZrbIMR43DAYDUqMy8Ns7O3GzQIm5c/rDz98Rmzefb/FjMwyw4KNZsHO3wVfP/4TKMqopqg80MKJQKA9k9ZPDIFeq8cZvR6BtZOI3CV8ACV+ApLKiuhtTHsrcNZNxfs/1Funb398fL7zwAhYsWECDohbi7t00lJZU4sUXR8LX1wEJyGuxY/m62mLG0EDYFSux4Zn/0aCoAdCSIBQKpVaCgoLgZC3DG782Pij6D61R7a7HCaaZfmb5Ah6cvO0Rfim2Wfq7H4FAgPfeew9ffPEFDYoaiFDMzlf0MFJSC3DiZASmTesNO4lpi/gU5OuC1bOHIz2vBO9N+5IGRQ2EjhhRKJRqej/3JQAg0MsBK+cOx4asg9CMJYt6WgpIEbW/lNShXCn0ImyuQAeGA3DvJWlUWrNzXzNGcRMngMytwhSQiRYrkszZzpuTuhGbu2Sn2r7k62U5ZHFSgQ375iG4Qd645E6kvoWl5/AlM1x3NrNDjlKJsDIyieP98BNIkYjTaXJk7bh8KwDg2ulrKFLnYsudDQ/sq7GsWrUKcXFxOHny5APbjJbMJ+zkFzqx2owIDCPss6H+hG2sKdIb2KusNBmkvifHqEiq2IxcAq8zkK8b64kAgGekW5IIyClh1XVSG2YRy34QkMaR1+Sx3B9hb2+Ppa/+hB+2fcZqf0pfiw7sTNW2s2eA7777Dl8ET8DLfxysfvAw1hBVeBh9MUzYDxidPq6ZVuVwGPhN0GBS7154Y9lCHDt2jO0DpU7oiBGFQiHwcrLC81MG4Ms/z+JucXpbu/NII+TyUKAqbHI/VlZW4HK5GDFiRDN4RTJ27Fj06tUL69evb/a+OzIrVqzAoUOHsGPHDvzzzz8N3n/Dhg3gc7kY3923Wfzx7eaCDzcvQJcuXbBs2TIaFDUBOmJEoVCq8Xa2xjsLRuPvE3cQlpgNdG96n2q9DgIuF1ZCCQpVj9eSfVeJJZT6+mdBfhDr169HfHx8syfk6969O1asWIGXXnqJJvurJ1KZGCPHd0PQCBu8++67jc7zlJGRgY8PnMWHT45GVEYuEnIbH0APGReI6YuG4NTe21j23kJa3qOJ0MCIQqEAAJYuXYoFs4dj84FruBqR0mz9qvU6pJYXw0b8GAZGJpZIq0xqUh/Ozs7o1asXZsxoRAKphyASifDee+/ho48+QkJCQrP23VFx97bFyvenAADeef+1Jie/TMorwv9OXMHrE4dg49FLCFU1PMP44HEBGDOjN9a/vB15WSU0KGoGaGBEoXRQjDUhAMB4uRJ2xpgqbcX8wT0xoYcvTlTuRn6/UHjfS7VTombrNa7nuRN2Txtyus3HjFxpkyuXokiXjz6OMuQjCWUu7D4leeSPuSKL1PYIHciASitl/3QZp1HMftIoB1ElmafIyrmEsEvL2X5VOpNaE5cdaYRdONKNsOV5pD7Gp7sLvryWipiiztXbeBVGeYqMitXroxOr/2YYBm99/RYuXLiA1NRUln9NYdmyZbh79y4uXrxY6+tjzJ4j7JivySkfjpA9wnTumlFdOBmp61KU1ZIryghhKXl+rP3IZJbZ+WSupBIe+bmpytkaNlUleXWoKshCs51+jidsfUXN9cYwDEbNGwTBAgH8/HzwwcdrsHfv3mYZYYv8/DVEAkgYPhzLly+H6o1IaDU11230cjuivc15MjeXq505Rs/zwZtvv4wT50402R9KFTQwolAeY2zMJFjzxDDo9Hos/mUPvMaHtshxMioL4SJhJ/PryLiZmcNMIERiSePTFLh3dUanTp3w2muvNaNnQI8ePTBixAjMnNl8WZAFHC6kYlIkbxBVBUYVGjWUupapG9dSSC0kGPZkP4yeNxgA8N4Xb2PlypXQaJo/c/WFCxcwffp0fLnzJWx8YxdS4nLqtd/4Af44ePAgTp8+3ew+Pc7QwIhCeYyQysRw8bDBqEk94ORqBa6DCf69E4OtF0Na9LjRZZkIdggEA/YqpI5KNxsH3MrJhKYJiS2D5wzGoUOHEB4e3mx+iUQirFu3Dp988gnKyxtREfg+nCUyDLT3QIClPTrJbCBXaMmpHF7V31KBEAmlhbiSnYpLWSkoUrVvPVPwnIEInj0QSeFp2PvdMVzYdxNHS3e02PF0Oh1efvll/PKpO1Z8+hS+WrMTqfG5D92ni6c9Ar0dsfHNvdDpHt90GC0BDYwolA4Ih8OBtaMFhkzrg+5D/WFhJ4NBb4Ca4SAtKQ+ZqQXYueUCYnuLUK5suji4LpIr8iHhCWEjlKJ5J4TaL/0cXHA+I7nuhg/Azd8J3j3cMffNH5vRK+D1119/6BRaXTAABjm6Y5ZvAOxNpLiUk4yzWYn4NuIScjPI0RTDvak0K5EJAqzsMdjBA4v8gxBemIO/4kIRWdRyCQ4bg8zaFIvenQYBn4NvXvkd2cmt55/BYMCezRdg52SB1zbMxLdr9yAaDx5lWzypPxIzChAVFdVqPj4u0MCIQukgBCz/ChYSMVZNHgI7c1NUGrTIK5djw60wRN+o+oHXilBT430oA50TmXcnttiWsI3rVAGAi7SEsCOKHQhbrSV/VtzMqvKspFXGY5yLI77wYAtMLWPIJ16JUZ0zTQmZc0hnXo9RGKOcL4piMhGfgkdO+5jVUn9NEEa2Ke/jQtiFAWR7cXrVezcXixBgZoevblwCx4TUzAiKyVEzu6Okbkl77+nfwd0G0dfimnXq5vXXX4enp2et1dWNa5/Fr/QmbHEyA38HWzw/pA+4HAb/7grHtbBk3D9AZGZFvleuukrvY4AOYcjE+YGJEHJ5mOjmh7d6D8f1vDT8FHkN6vsSgGpMSb1ZdjR5Ter55OvmdmR5FOuvyc8MAAR5RiVUMsmAR1chh62rNd794wXodXr0Ce7Z5NG0xnA0/jMcnfcZPv/8czz7zjAEns7Fsd03oaisyrtkyKn67vQf3x02OgYbn/+Diq1bABoYUSgdBEtTMd6cPgLhqdn4/MB55PGUrDZt+RN6tTAET7pMAHC8Db1oHab27ILLCamQq9QA+z5dJ+Y2ZpjwfDAO/tB852r58uXw9/fHsmXLUFlZ2eD9p/XogondfLH1SgjOxyfDLLFxV5NKp8WepHCcyojHsq4DsLbXSHx0+zQRHLUmDMOg/xO9MHHxSBz77RxO/3WpTYKi+3n99dfRp08fvP3K1/j090U4fyQMx/65if8k4TOWjcaRPy6gorThnyOlbmhgRKF0ABiGwaxB3ZFVVIat5+7phWQP36e1SapIBZfhwNfKGjGFBXXv8IhibiLCKD9vrNp9pNF99HuiF2JuJOLG0dBm8cnJyQnTp0/H5MmTIZc3LGWCnakpXurfF+ZcEdbuO4Gs0uYJGkrVSnxy5yxWBA5us+DIuZM9Vm9aiNSINPz9+UGEX4xp1eM/jBs3buDH9YcglYkx58WReO9/86EsrQCHywFfyMfF/bfa2sUOCw2MKJQOQHBwMHp6OmLVH/+2tSsPRA8DQoojMcXHH59eudDW7rQYU3t0wfXk9EYHEG7+znhiyShsWrWtWfxxdHTEpk2bsHHjRhQVNWyFnJu5Od4LHoGzScn451IYNE2smWeM3mDAxrCLWNltCFZ0G4wNd841a/8PgsPloGs/b8x5/QlcOxqKvz7YDb2+fU5JlZcq8NMnh8HncyFWVAW1GpUGleXsEWFK80BLglAoHQA3NzfEZxeiRN6+fyzP5V1Bb0cnuJi1s+GsZsJcXDVatOtW41eRjZwzCLdPhiHycvOMXjz33HM4evQo9u7d2zA/vDyxYdwYbLl1G1tD7jR7UPQfeoMB/4u4AnepJSa4Nk95jIfB43GxZP1MPLF4BH77cC/++uLfdhsU3Y9Go0NpQTlKC8ppUNTC0BEjCuURZbRoXvXf/ry+iKssguI+narGuu68MUGepPDXy5QURkeWksJqAKjQkiLmobZk1uSIMkfCthHdP3JSjnMlIXhhqC++jqmZasqstCH2sQ0hb8J6PilYzu/BXvZvVEsUeg65gV9CJvnj+JAjOmVZZNI/AJCakceRO5J9aI0K1y6z643rVxOhuF0K83vbTLLJgqWCBDJHjTarxvYIcIVFDzHmzZuHQm3Ta6yJxWIMGzYMCxYsYL1mnMAx7vUasfVIL0/M8+mGd7YdR0ZhKcRg4HSSHG3SWLLFU9IkciqMl0ouOTfJIxNilj37X2FWBX5MPI61AU/gbkQhsu/T+Bhfx6IsUpgv+Yr8jI4lvMny6z8GDBiAlStXIjzqCtYtXQd9E1IptAbHoj9paxceS+iIEYVCaVX2p9+Eq4k1+lt3rrvxI8RQJw+4Olhi57HG54SasmwsvvvuOxQWNj0oAgA/Pz/k5uYiPb3+xYBHenliTmA3fLDnNDIKS+veoZlIrijAsaxwvDSgb7P3LRaLMX/+fKxbtw4bNmzAO++80+6DIkrbQQMjCoXSqqj0GmxOPIO57oNgzpfUvcMjgJjHx9LAvvh9/zUoVY3L8Ozq6wQHT7tmq4oeEBCAjRs3Yt++ffXe57+gaO2pU8gpaf2VWfvS7sDNQoZAB7u6G9eTAQMGYM+ePXj55ZexbNky3Lhxo9n6pnRMaGBEoVBanajSDNwtScVkl95t7Uqz8Kx/T0QU5iIqsX6lHGpj4gujcOy3s802kvHqq6/il19+wZ49e+rV/v6gKLuNlqtrDTociIzBmM6dmqW/WbNmYc2aNfjyyy8RHByM+Pj4uneiPPZQjRGF8oiiV9doVww6HRgdwLlP8iK0YpddUGeSIzTGeiFjTZGIyx79SC83J+xD5WTh0CdcIwj7QAqZBdFEUOXkwYwbWN9tDv7NvAWlP5mAL19vQdjW4WSwYHub5RZ0AiM9kD2pB7KOJN9LaRaZNNKklnhETsqlYNmbTAyov26L/h6uGGDhitX7j0Ntxn7WNNtHZiY+ptzOajNq1Ci49LfBsk8XsZ1oIAKBAKtWrYK5uTn+/PNPAEAw5ylWu5jvaqYyBzq4YZ5LN3zw92mUlFRADAYOF8jgyGCk2TJw2Dqv3L7k9WUHcuRHnE2KhnOiyM8ZAE7aJ+DnnpNhk2CCbHk5GCX5OXp8QQrbj5X9xuoDqAqKZs2aheeffx65uQ8vr0Gh3A8dMaJQKG1CvqoM1wvjMdGpV1u70mhcLGRYNKA3vj13FcWVjav/xTAM1q5di88//xxZWVlN8odhGGzcuBEWFhZYvXp1vbIiu0nNsaRLP/xw8mqbTJ8ZU6isxKnUREzr7N+o/R0cHLBv3z48+eSTWLJkCQ2KKA2GBkYUSgdBLODX3aidsT/9BvpYdUJns+bTlLQWTmJLrBs/Ar9evYXwrMbdfBmGwZYtW6DVahukBaoNHo+HX3/9FX369MHq1auRlJRU5z4upjJ82Hc0fom8joj09hNAXMhIQaCNfYP3c3BwwKZNm7Bjxw5Mnz6dBkWURkEDIwqlA5AalQEfW+u2dqPBFKrLsS35PJb7jYaXqU3dO7QTvE3t8br/ZPx29TYuJ6XVvcMD6NevH2QyGSZPntzkmmjr169H165dsWDBgnqNFIm4PKzsPgT7k6JwMTulScdubqIK8uBgKgV7su7BLF68GL///jv+/PNP7Nq1q8V8o3R8qMaIQnlEOaWv+fEPi7fFc4IlGA0n3I6sWpqdJzZl7SOUk7ea6EiyKKqVO6n1cTQtQ12c7kFqPMaFzX9oewsxWd8prvIuTuWp8GHPcQgricOhrPMoGZhCtLEPJv24kMgW5wrDxISttiCDg3Jn8ueu3J183eDATprXxSWbsMOTnBHs4o2Fnr3xfcg13A3Nwv0qLfN4FauPE7Voiv5j3rx5OHLkCCoqKh7Ypr54e3tjzZo1CA9nJ5dUTgoibKGAh4+9RiI9ugSnT8fDzCCEeRKpwZK7kXqhcmfyOVqawRZlGW9TOIgI2zSBXP5vlsxelSgffe/64OogtKmEwyYh8frxWjRFK1asQPfu3bFs2TIqsKY0GTpiRKF0APLy8nDhZgJenDsETEMes9sJt4uj8EHkT+AwDN7tsgRTnYbBlNeI6qstiJRngpU9BmNe5x5YdfkILjVxlGX48OGwt7fH77//3iz+aTSaemmUOBwGyxeNQGZhGTafvoH2WJzdyUQGTT3rptGgiNLc0MCIQukg7DxyG3HJeXhieEDdjdshCp0Sf6YewZcxW2HGN8V7XRZjmE0vcJm2/ZliwGC4bRDe8l8MhVaD5ZcOIVNe90jaQ/tkGDz//PP46quvoNM1vXBq//79wefzkZqaWmfbscO7gMfjttugCACshKaILctFXe69+uqr6NGjB5YtW4byNkoxQOl40Kk0CqWDYDAAW/65inXLxuFmeCry8GjeKHJVhfgj5TCcxbaY5Toa/a0DcC7/EJLlya3ui5uJA6Y6jwSP4eKHhJ04EtE8P5nDhg2DRqPB5cuXm6W/4OBgHD16FArFw1fGmYgFmDCyKz757hgMwoc2bVM8pFao0Kgf2sbBwQEzZ87E2LFjaVBEaVZoYEShdBAk/9yAAsDejCKsWTYWv3z8LzISjVblyMjcPTpTUgPCLSVHL3IGerCOI5hN9vlzSSDph4DU2QxxSCTsyFL2aiPjfEkRufZIgRaXMo5gjKM/FvnOQKGqCOfzLyJJngKRmH3TlA4lb47FclJzVGhJvleRBakpUldWreqzEIoxwN4dgx3c4W1hjoMZITiYEQoDDBBIyLxPOgFp869EsvwyxsLCAmvWrME777xTZ9v6MHnyZEzoNwUfz/m2Ol+RaiKZODNtdNX/T/p0xg11Dq4GlMDxHDnnWuFI3g6UVuRxhKT8DMU+7JE863Dyc5TEkKVN5L5kpypzVhew3WYClydkyCsrhe15E5w7+TqrzaRJk/DSSy/h66+/RllZ00bvKBRjaGBEoXQwbhwNBQC8/t18XD8ZgZ3fHYdO++jWhTqeFYUS5hSCLHvhCcfx4DAc/M5E43R2DHSG5ntfPaydMMalM3rbOiOiMAenMxKwMT4KSn3TVosZ4+joiLy8PNy8ebPJfZmZmWH16tX4Zs5mVJY/fLSIATDa0xtb7taSHbOdodPpYS6rXWPm4OCAF198Edu2baOrzygtAg2MKJQOyI2joUjJqsCbPy5A31Fd8eM7uxFzO6Wt3Wo0OoMO1wpv4FrhDXQ29cYw+3F4yr03LubGI7o0G+nyYug5JajUPXz65T9M+QLYiAWwEJrAS2qNgbZesBGY4WBKFH6KuoZiVVWQIbNo3qAIALp169ZsIuEPP/wQ+/fvR3563UVnn/ILQJlKhWtZ9S8o21aEx2Ri8dzB2HnwFrF9ypQpWLp0KTZv3ozdu3e3kXeUjg4NjCiUDkpRbhlen/Y1XtkwC0s/fBJ3LsRArtEjNT4XEbdTUFnBXlr+KBBXkYC/E/bB18weva3dMM21J0x4AtiIxCjVyFGgKkeWohgKNZnwUqvgQSYUwVtmBUuRGEW6cugMesSW5uJMdiyOJiZB1wpq5L59++L48eNN7mfSpEmwtLTEa6+9huGY/tC21mITTPfpgjVnm37c1qC8QgWZVEyssJRKpXj77bexYMGCWlMSUCjNBVOfRGD3YWAexbXAFMpjDIfDgZeXFwIDA2FmZobg4GCYmZkhPj4eV69eRWhoaPUIxmgJmYOI8XJl9Re3htTuWFjICdvfktQgJZWRuhIzITtfUF0Y51syCNhTaG4uWkh5IrhKLGHKE4HHIdtUKITQGwyIKc1FiVqBUjXph6mEHSiWFpPTOdxMUqfEM5q9cj7Lns46feHtGh/d3LBlyxZMmTKlSbmLPD09sWnTJrzwwgtISkpCv9lfEK/n9iV/p9/xH4ziCgW2nqmZRrO5TX5uFe7k51rk+/DVgBZx7HuHwoo8rmkW+Rnw5KSGTWHLfjYfnFaOeWun45+vDuHG0dDqfF1ffvklrK2tsXDhQmi17Bp+lI6PwWBolQCEjhhRKB0cvV6P+Pj46uDn999/R2BgIGxsbDBgwAA888wzyMzMxOHDhyGJlKEot7SOHtsnZRoFyjQKZCqqVMICLnkTLipr+7xI8+bNw549e5qc0NHHxwfh4eH1K/thJkNPLycs27S/ScdsDcylYsx9axR2fLIPd85UFSOWSCR488034eHhgZkzZ9KgiNLi0MCIQnnMMBgMuHv3LgDg1KlT4HK5mDZtGkaNGoUx701E5LV4bP94HypKKuvoidIQxGIxRo4ciSVLljSpH19fXyxfvhwffPBBvdo/4eODk6HxKFe0/6nTSYO7IuJyTHVQBAATJ07EwIED8eSTTza5bAqFUh9ogkcK5TFHp9Nh9+7deOWVV7B22pfo1N0dnxxcDXv3R6d22aPApEmTEB8fj4SEhCb18+233+Ljjz+uVw4kDsNggIsrrkbXnfixreHzuOjX1Q3ndl6p3ube1QWrVq3CypUrUVBQ0IbeUR4naGBEoVCqKS0ox8rR67H9k/14eeN82DtZtLVLHQaxWIyIiIh6FXh9EA4ODjAzM8P58+fr1X6EhyeKFAok5tS9aq2t6dvVDblF5UiPrSlr8sSSUfjtt98QEhLShp5RHjfoVBqFQqnmhHxr1f//bMXNirH49I0v8emzP6Aop0Z3JIzpSexjNoy86Q63iCHsqdakyHdHbj/WcYOtogh7S/JAwjZ3JXVPJXlkokoAKC8nxcMDPEj9zcV0H8IO6kK+niU3Y/U5wJ/Mtn1C4EvYklNkEVR+VBqrj//w9/fH2bNnH/h6fXjnnXfwv//9D2P93iS2J3cmn3F59yRM4906YX9IFGyvszNDq+zI86XnkbpWrlHmA9MMMqCTxbATK4otyT755WQnFa6kzsvqdM1I1sRJQ3Bl6/VqsXX37t0h8mawdc1W1nEolJaEjhhRKJRaOXbsGPIzivD+P6+Bw6GrUZsCj8dDv379cPXq1Ub3YW1tDR8fH/z111/1am8lMYGXjRWupbT/vEVd+nhCam6CqydqluEvX74cp06darJQnUJpKDQwolAoD+TbV35HcV4ZJr80pq1deaSZO3cu7ty5g5KSkkbtLxaL8eOPP0KpVNa76Gygoz3uZmZD3QxFalua8XMH4sTOa9Drapb3d+nSBYcPH25DryiPKzQwolAoD0RVqcLxP85jxMz+sHGxqnsHCguxWIx58+bhiy++qLvxA/Dz80NlZSUmTpxY730CHOwQkZ1bd8M2pmtfL5hZSnDleM1okYuLCzQaDWJiYh6yJ4XSMlCNEYVCeSAnlNtxYud2aDoVYPlf8zB8+HAM+pHU1cRadSbsbxXDCfsJ1wjC7mvBzr3zTzapW1JqyJ8mOympkRE4sEdBCktMCVtrIJ/7BgfGEvbVVHfC5vPZfUZwHAjboCf7tL5ZQtjHCn5i9eHu7o6KigqkpzduSovL5WLRokUICwurFm7HLCWDVK6S1P/wNRz0dHbEkag4AIDSgdT+AABHRSZf1PO4hG0eT75umkxqxTQyMtklAHA05D7Z/cnPxDyZPMdn8jbhuae24eNv38aR1H3V2xcvXozt27c3SahOoTQWOmJEoVDqZPLkyQCABQsWtLEnjxZ8Ph9vvvlmvZbWP4hJkyYhKCgIW7fWX4RsYyqBTq9HQkH7Xo321FNPoaKiAvv21QRFNjY2GD58OHbs2NGGnlEeZ2hgRKFQ6mTq1KnQ6/UIDg6Gu79TW7vzyPDyyy+jrKwMX375ZaP7WLx4MV588UXk5+fXex8LEzEU7TwZormZGEuXLsWnn35KbF+zZg0YhkFxcXEbeUZ53KGBEYVCqZOMjAz069cPd+7cwatfzkXX/t5t7VK7x8/PD2PHjsUHH3zQpCkhExMTREdHN2gfd0sLROfWP5BqC2ZPCcLhw4erS9X8h1QqxRtvvNFGXlEoVGNEoVDqicFgwLp163Ds2DF8/vnnCEyyw/z58zF243KiXX4wWfB1+7A+ZEfFgjqPxejJ9AAaV1L/oq+lluRIb1JDFF9mTdjWIrLESSd7MnAwF7ALwN5MI4vouvxO+nEitPayHAzD4OOPP8ann36K8nJ2DqH6MmPGDKglDvCd8yH09wVXJWoy0NILSVtroYVOqofasWrUSCsi/QYA0zyyiK5ObHSOjfIaVXiQ2jJZCFvYrfAitU86o/J0JulVS+979PGEt60FFn/zDfF6//794ezsjOvXr7P6plBaCzpiRKFQGsTVq1cxcuRIeHt74+LFi+Dy6M+IMaNHj0ZZWRnOnDnT6D54PB4WL16M7/ddIoKi+pBSVgw/y/ZZ0kUo4mPyzD7Y99c1VFbWBKteXl749ttvsWHDBloTjdKm0F80CoXSYFQqFYYOHQqdToc3v38G42b3p0kg72FqaopVq1axtDMNZcSIESgvL0dGfmndjY0oUinAZdrnz/uoid2Rm12KkOuJ1ds4HA6WLFmCO3fuNEmoTqE0B+3zm0OhUNo9Go0Gw4cPx1/fHkdAPy+88d18TJg3ACN7dYKZhL2U+3GhV69eiI2NbbAuyJiBAwc2aCXa/RgMBkj4AnCY9hWsOjpbYtjortj7J5kBfOPGjTAzM8NLL71U7wSWFEpLQTVGFAql0RgMBvxwdDU2HefAz88PwcHBGMV7AsFBAdj/v+NICE2FzWGyrpne2ZbVD6Mhb4Y5QywJ22FjCWGnzCe1PwBwKr0bYUuTyKAgbwQ58qJUkFonV1v20nanbULCFpy5w2pD+OnggMWLF+PAgQMPbfcwRg5ZD3MLCZ6YOBnxUdYQPZnKaqPLJov7cgrJ95JbWQGdQQ83qTmSy4qhE7IDJJ2I/PkX5asIW88nn5u1Rhok8Nm3j6zBpB9WkTV5jWwtpVi6tC/2fnEQeRfCwKAqiPzuu+/A5/OxYMECOoVGaRfQESMKhdJk9Ho9IiMj8c033+CnN3agJL8Miz+Zg9Hzh0BmbVp3Bx2APn36YOvWrTh+/Dj27NnTpL66dnVGYkIusrNLGrW/AUBEYS4CrO2b5EdzYWspxeqFwTi+7SKu/nsHXB4X7v5O2LhxIyIiIjB27FiEh4fX3RGF0grQwIhCoTQrqkoVtry9E9s+2gvv7m744M+X0HOYH4Qmda9GexRhGAajRo3Cxo0b8cEHH2Dbtm3Q6/V17/gQegV54PLFuCb1cSc/C8EubZ9WgcflYPWCkThyIRKXD4WAx+fiqeXj8NoPC7F582YsWbIEhYXtOxEl5fGCTqVRKJRmx2AwIOJyLGJuJGDw7MHoOyoAUxYPx8/r9iBN2XHKPPj6+uKFF16Avb091q1bh4sXLza5T4YBLCwlKC1lpw9oCKfSEvC0bw90tbJDPtom8OBxOXh57lAkpBfgVkQapiwJxsjZA5AQmoKPn/0RW8Map6GiUFoSGhhRKJQWQ6vR4fyB2zh/4DaGT++Dlz6dhTt3M/Dv3lsob+KNv60ZOCUIfRfMx86dO7Ft27ZmEw139nGAWqVFWmpBk/oxADiVnoAZ3l3xI843i28NZemswVCqtbh4OxFrFgUj+1ws/v7iMK4cDmkTfyiU+sA0MCOrgWlnqxwoFErrMloyn7AZLinKPV722wP3tbCwwA9rfkX34V1x63godn91GJVlCuiHdCfaCSKMBMfmZoRpKGSXi2DE5Eq43Cc8CbsogJzeGt6HLG575Vggq0+3D28S9knNDsyaNQvPPPMMlEolli1bhszMTNZ+DSGY8xRh9/96Pjq52eCHv6tGn9RPF7H2KcwjzwfURqqIewkyBRwufhw9Cb/evo2rGWQRW/Noch9RkVHR2DQyIaaxWDt1PClMBwCvnWXVfwdP6IbOPTlQKBQQi8X4888/sXv3btY+FEp9MRhqyezaAtARIwqF0moUFxdjy1s7YGImxpMrn8Dav5fj8v6bOJZYDI2mfS/TZhgGU6ZMwTPPPIOlS5ciJSWl2Y/B43MxfmgX/LDjQrP0p9br8NPdm1jWqx8yysqQXtbwnEgNhcfjYvLMPug9wBupWWHg8XiYM2cOkcyRQmnP0MCIQqE0CqFYACdve3D55IhRWIIDsrOzH7pvZZkCf6zbBRcfR4x5bjg+WhCMxMRc3L6dgtu3klvS7UZh42KF5z54CmmqeLzyyistEhQBQP9JvZGZW4L41Oarc3YjOwMHRdFY0W8A3jpzEkqtttn6NsbKRor5S4YjaGAn5OeVYtu2bdi3b1+LHY9CaQloYEShUOoNh8PBxMUj0HNEV1jYmCEzMRdabc1ID8MwmOwwEOnp6YiMjMR333330Nw06bFZ2PzGn7CfNRy9gzywcNFQ9O7tgW0rtkLbTkaQbJwtsfiTObh5PBSrf32xRY81a/Vk7AtNavZ+98VEwd7UFO8PHYl15083e3DE53Aws1sA5j3hD6VSg+sX4/Ddp4dx7CYNiiiPHlRjRKE0EWONCACc0u9qA0+aTm3vxdLeHFNeHgcnb3uY25ghJasMh3ZcQ1Z6IXRaPfIGksVaXS8XoHOgC4KG+cLLzxFZMRnITMhBelw2bp4IA3TspewnlNsBVFWk/+KLL+AgdEF6bBauH72DzLhs5BaRyQd1pWWsPiqf6EnY+XPJqRsOQ/7WOX3NJ2x+OlvLc1f5F77++mscO3YMv/32YO1UUxjruAxAVVD59b+rMPoSqcMJ6pHA2ie+mDzn5eViwtYpyGdejlALBsBS/wHoYeOE78IvId1A9ss1Oj95iWRBWJiSwVSnX6rsPv29MfXJIMjMTZCXn4jPPvsMhw8fbnLKAgrFGKoxolAeQaQWEvQe0w2WWiAvLw9XrlyBWq1ua7caBMMwGDglCF0G+sDMSgonb3sc/fUMjmw+jfKiCiidHB+6v0qpQfiNJITfSIKVnRm8HE3h0cUZ89dOw5CpfVCSV4qk8DTo7wuQzNVqhIeHIzo6GlOmTMFM1yVw9XVE0NgemLFiIrJSC6HV6hB1o6q+lr5SgeykPBRkFaGokUkQ6zoH3Qd6Y9nMX7B///4WC4pYNLBYbIO6BvBD1BUMc/TCGz1GIEvVFQczbyOqNBM6Q8OCGD6Hg66BLhg3sRssLE2hVmmRnVmMqTMmoaiIHWBSKI8SNDCiUJqIQMSHT5A3eo7sij7jekCr0UEWwoO/vz+EQiEqKyvx888/459//kEDR2hbDU9PT0yYMAGBJn1h5WgBp04OOLL5NPLSCpAcnkZMa3Gd6t9vYW4Z8kPjce3IHez++gi8urnB0cMW1k5kyQ8vgxdWrVqFrKwsAIA1HJEcnoaE0GSEnA6HS4A7XDo7IHBAZwCAQaPBuOeGgy/iI+ZGAkoLypEkE6O0QgmDwYCElHwADRf7OrhawdbJAgPGBMCnmwv+t+ULbN68ucH9NIaA/t4oKaxo8eOcy0rE5ZxkPOXnhQVewyDk8HC3OA0xZRmILs1AkVoOA8jrlMMwEPMECLS2R6CVPQY4uIHnpsGFs9Hw6+IMlUqNg3tv06CI0iGgU2kUSiOxsbHBjBkz8NK8V5EalYGYmwm4dfwu8tMLq6fSrK2tERwcjFmzZsHJyQmHDx9GUlKNhqS8vBznz59HcTF7+XlL4+LigqFDh0IkEuGpp57Cvn374FDUCYpyJcIvRkMpV9W6H9efzKZsPJVmd5oUXuuzc8kOHjCVZmlpCbG4akpojPlTcO/qAkdPe5jbmoEjIpfiG1QqcHhceAW6QalQoTCzCNIAV/C4HHB5HDjaypBoKMD1glREFGchs7IElVolitQ1wZLT13xIpSKYyUzg7mGN4X29IJYIkZ1agIykfJzYfROHYjc8/CQ2A/9NpU1/YSQKc0rwoz+ZjLG5ptKMsbCQAwA8TW3RReaMLjIn+MocwWM4iC7LQmVpVaZyMY+PTpZWMHAMiCspQHhhDsIKsqH6PBPPvxQMpVKN3385D73egFOX1zbw3VMo9ae1ptJoYER57BljsYjc4EQWOdWbVt2UeTwO/Lo6Q+AsRe9u7vBwtUJoRAb+To9BZjGpeTFLI2/+OgGD4D6dYW0ugcEAKC2rvkfuVubo4mCLjIJSHLkTi9zScsRmVyX2c/ohlOjjhLx5sgRzOBzMnDkTixYtwvHjx1FZWYnr16/j5s2brLajRfPYHfiS+YEyxpgTtuMFOWHz8slzo0vNqNNHrp0NYeeNcyfsoiFKAICzRAYPM0t4mVkByprVcU5SM3S2soK3uRWEPB50ej0EXC7K1CoUKStRrlZBlKCCqZkI+dmlyErJx98nv29S8dfGMmTiZ2AYYMULo3AzNAXb7MhSINaO7CX2xnmMeGJS4M7hkL/rWrVRAVgAjFHqI628KpiSCURwNzOHwLSmz8TyAsg2ke2H9VNAKBRi7dq1VE9EaRWoxohCaQdweRx08nfEyLEBcHKxRH5uGQrVaty4k4z//XYWWq0ehX71+xqdulFzwytzr/l+SwR89LZ1RB8vF/g52aKgXA6FWoNclQXS47KRFJ6GipLmywGzYsUKjBo1Cl988QWOHj3abP22BRnyUmTIS3ExOxmGUqNabBwD+BwOzIRVga2TTIqn/bsDAHwsbZCWkY3rsdm4fDwcJQUVOJbc+kHRf1haSODoYI6rvyQCE9vMDQBAqVqJuwU5EGrIEUMZqs4vl8Ng2BBf9OslxuTJk2lQROlw0MCI8tjD4XLg36dqGgUAeA5W6NLDDd5+juDxuMgrrMDl8zE4tOc20lMLoLQV1dFjw5CrNbgcl4rLcamQioRwtDCDrUyCbkI+Rs0dBGtHCxz86RTO7tvx0KXv9WHMmDEYMmQIli1bhoQE9hRNR0Oj16NQURVUFmrkeP3CMQBVGaEXhtvB0d0ab333NJKis3D6+W+bfH4bC5/HhV6vh1bb/oOMoN4eCB7RBfPnjoVC8WiXdaFQaoMGRpTHDgcHB3h6eiIgIABSqRQzRj2N4rxSFN5b3WQwNUFseAb2br0MeYUSCi57GqKlKFeqEJudj9jsfCTdm0rz6eWJsc8Oxd8z/8bRo0cRFhaGlJQU5OXlNahvS0tLvP/++1ixYsVjERQ9DLVeh3MH7wAAdm86iw9+XYjnnnsOP//8c5v4M2taHwDtX6bg7WmLGVOC8Mvv5x/7a4jScaGBEaVDM9ZuKYCqUgsB/Tsh4MXB8HSwQkZ+KWIz8qDR6rAuPwQxRQXAvdJPMpcSwAXAiCpbfVVG9KnqSj4l+zulsY6bVWHG2nY/SiONiL19CatNvFO3qv8BHC4LwYQYewQEzcLkGcvg7mqNiCN3cHH/LSSGVR3/YTXKAGDkyJHIulEEyXUXBHNcALDzLY3izyZsfS2pBrJHmRO2IpA8H0k2JuR7u0YKg/leRvlxAOT1JKfBVEZ96tTktI69DTuPkYUrOd1YrCD9yCuUEnbhkJq0A8cT0qBtwYzQD6N4vBh2gVZ45dhR5AdzwfDJc86qiwbAypZ8/yWlErIBh0yOqS83mmasBZ6MPK70hClhR9xaj7Wr/sayF2fh+vXrdfZHoTyq0MCI0uEZPbs/Bj3RE+VFchyKTcdPR66hVK6sfr3Et/1PXwBAWFQmwqKqCpaKRXw808cLr377LHZ/cxQX97GF08aYmpoiIzarpd2kNBBPC0uklpQgv1Jed+M2gmGqtGlHjx6lQRGlw0MDI0qHpXv37lj55nxY2smw9dNDiL2TgpzpndrarWZBodTgj4/24ebJcMx9YxJgMOD41lZKQkhpVmRCYYMTLLY2M4Z2g5U8EevXr29rVyiUFocGRpQOhUAgwOjRozFnzhxYWVnh7JabuHMhpt0mVmwqUdcT8N2KbXhjyxJEamZjx44dbe0SpQF4eHhgca/e2Hj1Slu78kAcrMwwrLsXnp32IhVbUx4LaGBEeaT5T0Pk5GmLwAGd0D+4C/LSC3HihwtIvJuC1Oe7A3NrEhLqB7Nzwsh4pB6jLMGCsE37kskXTfmkFqVCU7d+ozCF7NPWg8wQnBdLJuwDAL05uULKsLyA9NOkS9X/AN7ddhYr5q3GcO+p2PP9CQDA8WJ2xmaOxAQ8yxpfxqifI15nzEgdTukAV1Yf8kAlYXOM8uHAiXw9a7KRqLiYfb4MMlJDZC4lb8DFOaRfBcaaGgClleRqQVUauQ+vgvRDUF7zufOUrRc436/j6t+jF0LKU3GXSQTHoWqb8enk89nFdIsKSf2Pr2sOYWeWkro4Nx92RmoRl7yOw6PcCNt2XywA4M3fluDYx/8iNjaW/WYolA4IDYwojzQMw2D8M4MxdHIv3D4bhU2vb0f6Y6ijycgpwYYlm7Hmp0VQVapx+Ndzbe3SI4WttRRXS9lBc0vj0dUFpe14NNO1swMYhsH142Ft7QqF0moYP5xQKI8UT708GgH9vPHZi79h57fHH8ug6D+UchW+fW0bBj7RE35BnnXvQKnGxsoUUVFRrX7cAZN6I7okt+6GbYDUQoJ5aybiXD2E/RRKR4IGRpRHlhUrVsAvyBM/rt2FghaosP4oUpxXhkObz2Lpp7NhYUFO38lkMug07GmZxx0ulwOZVAx1LakJWhpVpQqXcpPqbtgGzHx1LMKvxOPigdtt7QqF0qrQwIjySBIQEIDx48fjm5V/orSg5SuSP0pc+fcOLuy/hY0bN+K/2oY8Hg+jRo1CYkTddcoeN7r7O6OgqAKJiYmtelyPABfodPp2uSJtmIsHnDvZ4di2S23tCoXS6lCNEeWRYbRkfvXfS5fORfThTBRrOYClefV2nZ8LsY84OJ+wC4pI0SoAGK+z0fNIzccgJ/KJ/nRKZ8LW1VLXsLMFedxkzsNvfj7d2Uki5Uai7qxCUlCrHU+KZ7Viv+q//8zLwYaBQ7Bq/KcIuxiDrgM6w9vBHz87p6JCUiNGzxhB+m6QkYJvKxu2aNeQQ/rBS+aTDbqUE6ZSToqix/Rn61ViSsnCvTo9+cxWwifPn3GRVACwMSMD5Awb0i9DJZloUnI8HAAgNTAoEwtZ/bUU+v5dAQBjXh6FE1eTYN+3kHhdriZ9cZKytU+hyc6EnScnr2uVhvxpTy2wZPWhLieP4/dROgDA2l6G5RsDMG3+WCRnJT/wfYx1foW9UVojiheK+ODlktnZRTITjJ47CK6+jrCyN4dBX/U5ikwE0On0+PL5X7Dl7mcPPCaF0hrQwIjyyGEiFcG7mxt+e+8fwIq9mosCyBVqnNh+ESNn9kfYxRhYOZjjztlIFBa33ySCjxP29jJ06eqMXzadAfq2tTckkxcOxal/biA5+cFBkTEB/bzh4esAiIQQSwRw87aHtb0Z1CVGo7kcBnEhKTj0yxkkhadDr6qavuTxuVj40Sys3rIEnU5Z4o033mjOt0ShNAgaGFEeOWxcrJCfUQSlXAUOu7oE5R4xt5Iwet5gAICjhy0Sw9mjUpTWh2GA55eORER4OjTtTPPl6G4Dry7O+GfTmYe2GzBgAIL79kG3AZ3g4m2H/KwS3DwTCb0OKCupxPWzMchOL4IimhxtZQRGo3jqmhHKb5b9CoGIj3UnX8T333+Pl19+ucPmH6O0b2hgRHnk8ApwQ05qft0NH3NKC8phbmOGzj094ObnhLTHeMVee8LRyRJcLoMfvz/V1q4QcDgMxs7pj5tno1BaSI70cLlczJkzBw4ODujVqxcAQJ4kxoWDdxB2LQFqpaYqiJGy80s1BLVSgyFDhmDTpk147733sG7duib1R6E0BhoYPeIEc54ibI5YxGpzQr61tdxpUf57uuRxgIL0QhjUGhT0I6fSCruR+3DKyCdyZzsyWSMASIyKdhZbkcVHb+WRSQ51WlL/UqlkJyxMLCH9kqvINvISUu9SLCVtAHC1JH21syC1OwU88iaU15dL2OaxLohLKYTXuJ6w6eyIdPCRNpEstGoML5/009GDXay1kEMWNVU6krokQSyZWJFrQj71n7oSyOpT7Ea+N3kpeR0LTcnPSFXKvs4zteQqPL3R58QzknlxxFXnnCMUgiusO0lnczFrxTCoBVwUBFYdvyyHHPYUisnzGV7mwOrD25nU7lQa6dEqQOqH1Pns68trV81xOBwGg5faI1sTiVUfrYJGo8EYi0Xw7OqCkTP7wd7VGoVaAyLupuHM0UKkJOWj0vze7cOvRh8muUWOSnKcSd/1GdmEbQhgl+gZmrcEBz6LwtpNC5Bxno9fzrzFakOhtCR0VRrlkYJhGPQY0RXZSe0z90t749zJSPTs7Ql5hRIlRVRfVBsZCTno3N0dZmbsKvbNjYmJCUyEAnx/qH2VAJk+IwgmJiZYtaoqKAKAsU8PwvJv5iM5MgM7vvwX3315DGdPRSExPhc6XcuupKusUOHAbxfwwrpp4HK5de9AoTQjNDDqYNg4WWLp53Mx/Ml+EElab6VNa2HtbAkHT1vcOkEz8daHiLtpsHOQQSIVwcKyadMcHZXslAKU5JfBw8OjxY/Vt29fFJTJkZhTWHfjVsLGRoq+fb3w7rvvVgdFc+fORdCoQPywZgdO/X0VCWGtr0+7fjoSRfllGDFiRKsfm/J4QwOjDoSNixVe/uYZJIalYdxzw/DJgdcx943J6NKlS9WTai3/OKxCV+2bic8H4+zOq23txiODXm/AH7+ch06rx8ixAW3tTrslKzkf3bp1q7thE5kwYQKS2lFQxDDAvPkDcfRoGErvlURxcXHB0qVL8cs7uxBzq22TT/67/TLWrl3bpj5QHj+oxugRRz+kOwDA3NwEr66ZiN80d3ByaB625x9FkI0LZMEi/D7hMPj8muHo0qKaZbhyuRz//vsvVCqyiGdmZiaOHj3aKu+hvvRYOgGeg/3x+UeHYOjtDwDI709qiCb3DiHssxmkhiG/jJ3HKNcoD1GgPamDuJPhRNimEvJclRaTmiSAnWen0ihnjEFLHlNVyNaAJGaQIzx6oxkFrhXpR49Acnl1jkdVWZBrJfmYIeKg7wgfPHW8GCdC46rbKIeS2h61kFw1lFpC6nYAQGxGFomVmZB2voTUGJlKyNfLMthTVvIi8hx6uJFTpToDGcCn5bPPub7CaITUjMzzZPqQ3JbFeaWQSqUPbtAMTJs2DUOHDsXClHOoGFijmerlmk60i8q3q7Mv47xEOi15cViZk+Jp8RH29XXm3FsYP3488vPtsfadhRgtmgcAeOWdZ3Fpcxhy9DzAxb66vdqC1DEJC9mZwvWlpCbN+MGLY0dq7wyxKaw+YFGTJys2NA2iyuF4tstKZCVWXRMnlNvZ+1AozQgNjDoInp62SE8vxEld1U2vTKPE6ax4AMCNrdFE2wtH11T/7evriyFDhrDm8Z955hm8/vrrxHLZiooK7N27F/n5+TAYDDh//jwqKx8u5m1ORo4JwKG9t1Fa0nrHbClMeHzwOVXnXKHVQKNvOc1GpVKDPSfvYsqIAPi52BKBEaWKuxdjMWv9FBw+fBipqaktcgxHR0f8+uuvqBjiXHfjVoDD4WDx4sX48MMPq7/nMhszeAS4YvvH+wAzdlDc2hgMBtw5G4Fn35uBzxZsgradpTegdExoYNRBGD7CD6GhaYBXw/aLiYlBTEwMa/svv/wCiYQcsXB2dsaUKVPg6ekJa2trLFu2DEVFRdBoNDh8+DD27dvXYnlHgoKCIDEV4s6tlBbpvyXwktqgq7kjxFwBbHlWsDOpGq1iwMBFYg65RgMGgIDLRWZ5OZJKixBekIPIgjzkK5pXKB0Rn4W5E3qjh6djs/bbUchMykNISAi8vLxaJDBiGAZTpkzBggULwBuypNn7bwzz5s1Dbm4uQkJqRllnrpqIi/tuoCinFEw7CIwAYO+3x7Dh6JuQWpqiOJedBZxCaW5oYNQB8PF1gLWNGS5eiG1wYPQg9Ho9ysvJaZbo6GhER9eMPnl7e4PH40Emk+H555/HE088gZiYGPz8888oLmYvi28Kr776Ko4cCIFara27cRvia26DoY6eGOLsBp1Bj+jSHKTJixBVnIt/ksLwX9yYW6pAmbpqKszexBTWQgkCrO0xys0br/QYgJC8LByLjsfd3GzomiHYzC0sx9ZD1/HK/OEYHuCFs+GtWxfsUUCpVMLNza1F+p44cSIKCwuRlpYGzxY5QsNwsJNh3sx5mD+/psyOjYsVAgb64K1Jn7ehZ7WTeDcFgUP8cH73tbZ2hfIYQAOjDoCrqxVu3kiqChoy2fldinxJPcvoPDJpmv4OOdV2Sr+rXsdNSEio/vvOnTvo168fhgwZghMnTuDWrljcPBaKjPgcAI3TBYx1Ww4AsLAxQ7dO/fG+Zj9U/qTOoY8fKQ5NqyS1F91syaSGJWq21iI2l6zTpTU8XJCuMdJz8FOrzvnMHgEY798Z5xOSseHyZSTkFVYHQhwjOQZXBZijSs+jhArRnkpEpxdiFyJhIRJhmLsHXhzYGxyGwcmMePybGo2SInK6TVdKnoswkFooRwUZUF29mICls4dgyai+uHM3DXKFGqUVZB9mFuQ0ZW01yfRGmqxiOXlOh3jFE/bZW10IW2zPHg1Tq0htU04pqUPi88gpFI6S/RnxXUhdjbFuq6Qz+V5kPchVaMePH8TChQvx22+/sfpuCqO7v4sZ44Yi/lYBRnd/F9lGmqv0cnPCdpSRoyL5crYurqTAaBtDvjflcRvCvnn4NcJev349tm+/jJycnOpts9+fhYunoyE3kYJjIoVWSv6WiLLJz41TwB69YU0I88nPtWgQOWKptCBzhAGAzkgq5nJYjCPHovHSO5NwJ74IoLERpYV5tJYkUR5C26bOV6vVuHDhAj766COsW7cOaoUaS798GjNWjIelg3mT+p6+eBgSIjOg0rXP0SIhj4vF/Xujv7srXtlzGL9eD0F8bk1Q1FCKlUrsi4nGovO78X3EZbhIZNg2YjY+HjwKg5zcwC5ZWz90egO2HL4GqViIUb07173DY0ZISAicnJzQr1+/Zu3XzNwE3YI8EB2WXnfjVqBv374YPXo0du/eXb3Ny8sLnbo4Yc/mC23o2YPJSi1EYnQ2uvVpD+NtlI4ODYwozc6RI0fw7+Yz+GzBJvD5PLy/ewWeeuqpund8ADaOFjh/MKTuhm2AkMPD26OGwU5qinVHT6FUqap7pwYQUpCJz+6ew5zTf+J8Rgqe6doD3wU/gS7WtnXvXAtHr8VAqdZg2lB29unHHaVSiWPHjsHb27tZ+3Vxt4Zep0d+LjuLeGsjEAjwwQcf4JdffoFCoaje/tlnn2Hf7xfbdW2yjOR82Du3D90TpWNDAyNKi1FaUI4dnx3E7+/9g2XLluHll19ucB/2LlaQmpsg7m77K4Aq5PDwRsBY5FfIsf7EuWYPiu5HrlXjeHI8nj++H/vjo/B2v2GY1sm/wf3oDQbsOnMXzjbmeHHqwBbw9NHGYDCAbzT901RGTghEenJBs/bZWAYOHIicnBz8/PPP1du6du0KU1NTXDkR0Yae1U16Uj48fdnlUSiU5oYGRpQW5/apcCxfvhwzZszAm2++CYap/2SQi7cdEiMzoNe3vyfZ1V3HoEBZge8vXmu1iUwDgFOpiVh97hhGuHlinn/DkxIevxGDgpIKDOjq3uhpuY5KUlISunfv3qx9qtU6nDve9kGHQCDA6tWrsWnTpuptIpEIr7/+OrZu3Qq1qn1OVf9HVmohnD1s0KNHj7Z2hdLBoeLrRxzu5XBwPMzBmEvAvRwOD31XVpuEBaRYONaHFG667+tJ2GNvs4ertUXkKjOePTmVo+5stAy8hBSY2ipc8NXy01jy5lQM3zMd//x6EUlHyAzWxqLv3LGuCHpmII7eiEbuWFdwxQoYczPenbA9XMjimhVGxTVLlWzxtbkp2W9ktj1ha+VkH9pCEeb4B0KqN8Nb5y7A+QY7B5EkhsxurHI2J2xeOanGtqslmbfaklShpo2vCWOyVOV4f+8ZvD9+JHguHOy4HQYVSD8LAtg1phwuqwCoUV5QCblchb46d9y5r7Cn8WQPU4v42qAzStpXQo6wnM0lxdaMUTJLRQn7MxDKyOvFWOCtDCOvSYMZ+5yrjJM+ikjBNmOUIbOgK3m+Rl+cB7MYKwx6dQTGiJ+GwWBocjJBMzMz2PvaouB2LCq8qwTlOj252tNCTAres8tJ4bmZiDw3AFApIX03LhJr9y2pUA4H8PTTTyM8PBxXr1ZdbGOtl2Dc04NgqXVB0QkpKvuSgm2dgPwMRAWkzSlnlxziyIwKDHuTfWpMyD4E5ezrS20Urpd0ryqyWwLgZHgSBg0ahDt37rD2o1CaCzpi1EHo1LPl6zw1FZVSg+/e249LJyLwyntTIBQ/vKK5gMeFvaUUMen5reRh/ehp54iJ3r546/xJ6NtQk1GmVGHdkdPo4+aM2b0arhk6czYKkwL8WsCzR5f89EJwuBx0H9bwacramDRpEjJzS5GY1rZTaV5eXpg5cyY+/7xmKb6JqQhDpvbG3xuPtmtt0f0kphfA1rZx+joKpb7QwKgDkHg3BW5+TpBZt2xJg+bAYDDgxvlY3L2ehPf3v44py8Y+sG1Xd3tkFpSipII9UtRWSHgCrOk3GJ9evYBSFftJvrUpU6rw3r3gqCHTagzDID29CLZSWljWmN/f240xzwxplr6eeOIJHDzV9gWPhwwZgiNHjiA/v+YhY/yzQxB2KQ6FOSVt51gDMRgMj1x9R8qjB73COgDJ4emoLFOAL2xe0WhLsu37U/j6hV8wYHIQpr4yvtY2Qb4uyC0ur/W1tuK5Lr1wJzcbYfk5dTduJUrvjRz1c3TFZO+6R4Ds7WUwGAxISsqHtUQCPr3REMTeTISlgwXMbdk13RoKwzBQKDXN4FXjkVpIMG7cOMTH1+SX8vHxQc9hfjiy9WIbetZwMnNLERBAiyFTWhaqMWpGgjnkknSu0Xz78eLNzX7Mk5odAIAC3QL0ftkTK1euZLXx7/8VYVd2JUc6UiaTAZVwgA+rD55ReTKtkZxDIyOH4t0PkjqBSgcZjKkstcCGv67gtRdHIdXcFKfeIzVGXAc+ovLzUeJT1beXI3tKLaPYnLAzi0jbuLimkw07I7dOTwYGmmzyzTEWVXogJ4kZhjl74J3X98P5vlyCknCy4CkAKDqT2gqVJflV07mQ04iSLPbNU5RNJiy0vUHqbMruy40nV6jwxY2L+HToGNzOzUJGeSmEfYtYfQp3qBAY4ILIS3HgJReAqzDAIkYPra5Ks5NrSvqlt6zlpq4nP1szb/KclqSYk81NjJIz8tn1riwk5KhgTpIVuY8peX2ZuLIDZkUK+X3jFZCfPdefVFCpK8n29+uJPP42xcCVvtj1KuswDUIgECBnsB6Z5TWaKKHu4YGowijZZW0aI2NNkd/X5FTdsXt6PZFIhD9++ANZVxTQXnHAGItFAIAXVszEkb+uoLRMCQiqjqfnkZ8rV0HquPQ80m9j3RwACJNJAbdxrlRBBfk5Fvuw5f9Co8tWJavpJMegYJUqolCaG/qo2EH45JNPoNW271UltZGdW4offzuPUcP88OWXXxJLpXUGA/IqKh6yd+syu1MPHE+PRXlF20+h1UZKWQn2xkXilZ4PT1AoszJFXmZJ6zj1iLJ582YMGDAAPF7jnx2Dg4NRWVmJHHnbjXqOGzcOMpkMR/+oGRnqPsQX9m42uHkqss38olDaMzQwaiKOjo7o378/+vfvD//+nYl/fn28iH/W1tZt7W67JCklHx9/dQRarRYbNmxo9jwyzYGzRIZ+dq74JzG8rV15KPvio2AvkaKn3YOLxfYe4oPU+PYzFdge0Wg0uHDhAl555ZVG9yGVShEREQGNnr2CrjUQiURYsGABzp8/X73N3s0aM5ePw5b3/oGqjaf4KJT2Cp1KayR8Ph+rVq3CtGnTUFhYiNjYWLgwnYg2HEHNtATDYfDE6p3V1ey///77Zl8JYmNjU3ejdopOb8Bbb72Fjz/+GJ999hlWr17d1i4RzOrUHUfTYlCiVkCC9he4/YdGr8eu2HDM9e+GD+OjWa/3HNQZIhMBslKrpl64HAZcLqd6Kq29YyUWY6SnF4b4OsLVtKYunkKrRrR7SdXfGg1iCvJhqDQgLCsHGaWNyzi9fv16HDp0CFu2bEFp6aNV1Z3H4+F///sfbt68iU8//RSjzRcCAIZN74OLB24jPS4HjJhdV5FCodDAqJpR/NmEzQjIm59eoQSPz4W5rQzdR3TF8NmDEB+aiu1rjiHhbiq0Gh203clpn4zhpA7A+yQXrs6WGDN2AUaOXYB9r/2OzPhsok19C7gak5OTg4CAAPj7+yMqKop4zfVvMmt09OvOhM0vM9IO2LA1ICqJ0TSdnLx0eGWkniNtNJnjxDSD7XOZK9lHV/fPsTOTwctPDMKmfy//n72zjo7iet/4M6vZ3cjG3d0hIbi7QwulLVKkpe6uX6h7S0sLbSmUtvwKRYq7QyCBAAlxd9dNNpv13d8fAcKdCQmBTbKB/ZzDOczmzp07/s69z31eaDUUWCoKbEWrDqGo1oZRh70Vecx9LEn/oJRa0im3qpE5c09P08zQ9SvCJHuMGu2L5X/sgEBuBvNsUs+h9Gb2BDb60ryPaNY9Un/yGLsdYXoOaUSkLktYQXofqSzIY6zq1zrEd1ySgodCgxHCDsX5CvLcWwXXY8+Rzdia8CEA4M3EORjSpEXiqUwAQNV00vaBU8L0HLKKIPe/Lp92XnhkkMUWdD7EW1VD6n149eTx8LEUY1yQH8YH+SKhqAw7d2Xicl4ptNeMPz3sxXDQt77oXR3E8LQUghckwKNDIlDTIsOO7HTEt6RDe9PHiNKF7DEZMfMrZrsuN2Llo19j/2/HAHR+f04UtWWrf2zCAhSklgIqmq4tjbwG80nbJ8b1WJpG+moBQOAfZLCnyS0glmfMmoGIiAi88sor0Ov1oNhseAa5IGpMKL56egMoNhulj5J5xwS15IdafSj5XOA1ku2yymcG03o2+XHGbSKPsVkZ2W5hBfN+lLmS9451Yts9LTTnAzMYq5gwYVBMgVEncHkchAz2w6gHYuAT7okWaQuunkrH+hXbUZRR1qW6pM0KpGWWIyevCq+/OAnvbnoBXy75GcVdrKc90tPTcfXqVaMchuoKWp0evxyMw8/PPICCFklvNwcAMNTPA2llVWiUd1/KD0Oi1umwMfsiHg3ozwiMFAoFLly4cGM58XQGpjw2EomnmL1LvQ0FYISvF6YEB8BTLMbZ3AK8uv0AKhql4NNijeIaCaoaW1/sKXmtHxvVTTrw2WwMcnbHvKAwPG81ALuLU7C3OBUyjQq3wz+f/ocPd76B+ooGxO293KX2+/fzxob/bQNWBnVpvbvF1d8ZUx8Zil9++QVNTW2ByII3pmHzt/v71PR8EyZ6A1NgRMNMyId/tDd4IgHcA50xbEY0SnMqcH7nBfz47O83UlOwze98ZoRKrcVn3x7AbA8x3tr4HL5augZF6e10qXQRpVKJwMBAXL169a7r6k0UKg1+3BOLzx6fiv3p2b3dHPg52uJS4d2fn54krroQy/yGIsrBBVeqy29Z7tLxVAyd2q/nGnab2ImEeGHkENgKBdiRnI4LOcVQqLs+uUCp1eJMaSHOlBYi2EuEh32i8NuwR/BN6gnEN3ZuHCqpacK3y3/BWxufg4uvE459d3s9uo4edqAoQKth9r52Nw+8MBmxCcexYcOGG78F9PcCm81G0tmsHm+PCRN9DVNgBIDP5yNmQiT6jw1D4AAfFKSWoKVZCUmtFJ8tWQNJdRN0csPPRDqw7ji4PC6e+2EpfntzE3ITCzpfqQO++eYb/Pzzz5BKpTh48KCBWtk7XC2oQKNCganBAYgtKOrVtgQ52yM2u7BX29BVNHodduenY6KHf4eBEQBQRuZjNNk9EE+MGoQD6dn45Eoy1DodWAbQCedJa/HZ1SMIFTvh9fBxUDefx+VOjg0AFGeU4aula/D6+mew0nIlVq5c2ek6MZMicPq/C9CoezYwsne3hVugC3545QdCwzjhkSE4tCm2R9tiwkRf5b4NjLhcLkJDQzFz5kwMHToUigI2Lh9Pxf99sx/yZgUo6+v6Dg5YdjaM6Xu6KqbFf30wU49xM8KrJcTyId1WHFu9FRMyJ2Dpa0vx6wKmxqErFBQU4JVXXsHPP/9MBEaHilYR5UJKSF8jhT2pLdDzmNoBbgWpZ9HxyHUEnXx8a5lplcCjzWJmTyWPabBFLIJt+mGOezKgOge/E0sYdVjyyYA1NseXrJNHvpi0KqaWx8aW1Cmpad5HbEsW8sQNkPFb386UhCzPETB3TmFL/kbff3YzeUVJfJl+LlwZuWwlJ/fFNoU0l6ofQt7O5zNL8ci0SFjIBJCp248stuf+iCe4U3GFsxs1NTUIyCWvDY+RxYx1cnLIGW88R7IdykaaviyBvC+aIphtEWVxweew8ca0kbCzEOHn9/ajuKgW1xViHAnpc0S1E3DohKQ2RVBH9upKXVtzBBahGT/GncObD4/CXlkG9lxq1eSVPsx0WLdya7U+qAXw2ekEPDP4AURvHIu9f5xBcU4lNJVkbj4EtWq0KCc7tCg1QJA3WLSmavxp+6IhrwV9Cemj5dBOWjBWNWn2c+Sa9umd+e/g/w5sxDibJ278zdnTDj7jgrChqABVVm0eZcrB5HUspeU+8/cm9Y+5pWQqDstC5nVvlkx+xOhV5LnWNZMXNZcfwKjDOjGPrOOmj1KRhy1UqtsbBjVh4k4xrk/FHuTbb7/Fzz//jJycHDz33HNY+9Y/uHgkGfJe8Kg5deoUGhsbsWLFii5lnm+PrKwsVFVVYcSIEQZqXS+iq4O+5W9QoiUAxUxs2xNYcgWw4plBrul7U5srm5uRXl2DsT4+tyyjVquh0WgQFNSzOhg6bjZW+OrRqaiXyfH6PwdQXNS9ucVyymrx4fZjGBboifnD+t3WOimFlfjsqQ3ITirCaz8swuzlY+76fjUky5cvx4MPPojjx4/f+I2iKCx6awb+PZWEwiqmuWlfw6+flymBrIlu574KjNzc3DBnzhzs2LEDbm5umDZtGjZv3oy8vLzOV+5G1Go1Xn75ZTg6OuL999+/6/piY2MRGMh0r+6TaPMBdTZgNrFXNm/BNUNVSzOa1X3zKzW2qAj9nW/taQQAhYWFcHNz67BMdxJl74IPZo3F3isZWHs8Hpoe8v0prW/ExzuOY6CfO0aH3Dp4vBmNWoujWy/g4yfWwTPACcs+fQT27raMcp4+Dj3qE/Tyyy/jySefxLx585Cc3Jabbdj0flAr1dgbl97B2n2HiBFBjFm3JkwYmvsmMLK1tcXOnTuxbNkyrFu3DnPmzIFEIuntZt1AqVTi5ZdfxqRJk+Dr69v5Ch2QmJiIhx566K5ce40DLcAJgl6+BZTZVFhw2xmP6wHM+vBxTK6qRKQTc7r3zWRlZcGpkzLdAQVgYWA/vNx/GNafTsCxtFz0dJL3ZoUKvxyNx5LR0TDn8Dpf4Rq15RL88sF2VBfX4YN/X8Ejb82GhXXr0J2FpQAePvY4d6JnZvpFjQzCggULsHLlShQUtOkU3f2dMG3xSOz/s2/lQ7sVrn5OCB0SYAqMTHQ790VgdL0nZv/+/Zg9ezYOHTpkcHNFQ6BUKvHJJ5/gp59+gre3d+cr3IL4+HiYmZn1+ZxCeuVZUILpgLYGUCdjumdIj7ehSt4IIYcHWzNh54WNkBqZDBwWhY4GfPbu3YvJkyf3eHLOZaEDEGnvjLfOHcLF/N6b9ZdZXoO0kirM9g7r0npKhRp7fzmCLxf/DM8QV3xx6D0MG9OaxFer0ULTAzPSrO0tMH3JCHz//ffYv3//jd+9gl3x7OcPY9vqI8hO6t3JC4YiYIAP4vcn4sqVK73dFBP3OH33U7gLfPHFFygsLMRXX30F9S1EqJQTKSzU80gBLqUiH3LSmf3aqYRcVNnQhgSsSDOz8ZVk0lkAOHRoK6ytrfH888+3mxD2domNjcWMGTOwadMmxt9cYkmxbMEM8qVvnsP0QpI70pJ4VpA7q6IlIqeLsc3LmS+JJg/yGGtOkkaJj9uV4yV/Leqo73Eo7xAe9R8BSpCFtKa2LOEaWgJYupBaxCOHwBRq5r6F2pIi07MFZI+dQqYGv44Lnqx1XY0nea2orJk9DTraZuhidZaKnmSXGajb0FJZ8RrJfdGakbcvt4zsUdOY66AHUNYsRbCzPdJqaEJhoDWpaC0gL+RgnMt8HKaNGOakMYfYLD1IF2hHC1JFX8oWkyuMJc8Jq8AKofYOGOfsi5cO7UedXA4RTYBMF1vThdUaIfM80o0BLYpIvaBjGpnsN+Mdxxv/316fjle8huHvi2m4+Uw0hJD3MHcGeQ9LLvugCMD5ossIkhfh42fGIW+XHiorDqqGtE7gEBWS7ZRakOfNPIPcF/Ny8lqwjiUnbQCAprIaYgcrPP/RYzi16TRq8oMwYfBHAICps/pj0MIIfJUQj7jhKmC4L/RmTKsDSkG2w9uTPD70BM1W1jSx/2xmnQ0BpPu/9zZSK8auJw0elVZM922ujyvZzvxWnzexszUaGmSM8iZMGJp7vsfotddeA4vFwhdffAG5nDnrxBhJSUlBUFAQLC0tOy98C44dO4bp06f3ecPHv4v+ho+5D8w55jhYcRpLvB9AlHVo5ysaEJ0eEJv13fQJNTIZuEY0JZ9FUXhx0BD8kZSIOiO5J5NrK9GiVmOM5+1pjdojs7oWG49fwmsPjER2efeKxy2sRXj5l+U4vS0eJ/5pnYZPUcCCZSMQPdAXLxzah7hSZkDVV+HyOYgaE4r0C72rBzVxf2A8T8tu4NVXX0VERASee+45KJV9w7UYAFJTU1FaWorQ0DsPAE6ePAkejwcPDw8DtqznqVfV40DFATzs/jBym4vxTeZ6THMejX7i4B5rQ2Z1DXxsmelI7iVkTS2wd+2ZmX9D3T2g1GhxosC4XnJ7cjIx3vvu9H2nU/Ih4HPRKOve2a1jHhmGhkoJERTNXzoC9vaW+PKjXUYTcBqK4Bhf1JbVoyizc98pEybulns2MIqOjsaIESPw3HPPobm5ufMVjIzExETMnTv3ruq4V/w+LjVcQpWyCg+6TUCVsg7/FO/FAs8ZiBT3zBRzhUYDHofpf9QXsbOzg66dWV9FmeXwCHBuZw3DM9nXH8cLcmFsKr/TxYVwNjfHCHfPO65DDz20On23ahgpisKEx0YhMyH3xm9TZ0XB0ckKa1YdhkrZdYdwY4fFoiBrureCPRPGyz2pMeJwOHj11Vexdu3aWwZFkx2fIZZ1QlKfQdFeHrXDyCm5Ok47clbas5AtI+PO3EVkgkWfj9rp6r72oXnw4EGMHz+e+XcDQF0kxSvsSTHEsrwdvQuvgdxfHU1WY1lIrtPiQJbnyJgao2ZvsgylJpevboi48f832Ek4/u0YBPPtUCH9Hc1NF/Giz0uot7+EJsXJG+XmnCHPq5mjhFhWqJmX/MVy8kWorSINCQv5EoQ7OWJvZms6hYqhpCZLWM08XnSnZrac3Dc9rRlqy3aOOc0AU+5EtsushuwFFdE+pqlr+iuPURbg5elhV85B5COR+Oijj26UOdzwOwDg0q6d+O+//+CyiYfmlrZ6W/yZL9mmSlJn05IpJpbp1wbfr80/J9DSGcGUDdavPwFXdds+m6eSOq+aUeQsOW4LeXzktsxvOo6cLNPoSw5/2taT97jNZfIk1MUosS49AYsjo3CmJh8AQHHIOusk5sSy62DyoPNk/uBbcpBsUYHGQa03M6+AbAe/hBze5pGyG1ifJsXSugbJjf+zWBSe/HI+KsobcfxYNjjuLgiIcEfU/DC8d+ooih/iAeAxEvm6ejGH9qQK8nhUS8nzKq8lr3N7H1Kj9qAnM/XQhpahxHLuY+Rz0+MQuQ2VmHk/clrIj5AjDb9DLR0LG4X2xvVqwkR3ck/2GC1duhTV1dU4cuRIbzfljmluboaNjQ28vLx6uylGgUarQ0b1c7ATToa39duQqq4ir24F7C2WwtJsTLduO6WiCtFuLmAZkZnf7UJRgL2VObJLa+DjbIO6ujpUVVUxytXV1UEikcDKvHu1VPM8Y3DwRCpUPZwq43Y5V14EK54ZfKzubOiUTVFQajVQd5MX07QnxiJkkD9+/2wPAIDH52Lx69OwOiEOxU2NnazddxEKhe32dJow0R3cc4GRu7s7Hn74YXz22We93ZS7oq6uDvv378fChQt7uylGg1JbgZy692BlNhDOFguh0JSgpP592Fssgaft92BR5p1XcgeUNDaiulmGaNeOjRKNFd21YZ1gD0dcunTpluWSkpIQ5O14y7/fLc4CK/iYO+B4bGa3bcMQpNVVwU5wZ/YMT4UOBpfVPcOuHC4bvuEe2PLNXtRXt3YzLXt7OtIuF+BiRVm3bNNYiI6ONjlem+gx7rnA6PXXX8fGjRtRU9N55mxjZ/369ZgyZcodr6/T6cDj3b5pXV9AoSlGWtUTsBdNg6P5PKi0xcirWQa9XgM/+z8hYHfPLLxjOXmYHdZzgu/uoJ+vS4ceMJmZmQigWREYkonOYThfk2u0vUXXkaqUd9xjRIFCeUsTpGrDTvZgsVl4+qsFkDXJEbe/NUDwC3ODk7sttq45ZtBtGSNRUVG4fPlybzfDxH3CPaUxGjFiBJydnbF582bi9/Espl+Q3pEc+1Y4k2aIOj4ZM8rtyGGU9rJ9K2mTeuhl6N4++nDS8wMAJiQ9euP/VAuF5uZm+Pj4ID8/n7nBTkhMTMTgwYORkUE68B5Vk8dnZFw/YrlsFDO40NNCaC5N/yLxp+mFaL3eTd7MAE1MMwY2Lyf1LGUjyS/vhcefvPF/f3EyPh7yNMp0VahT5iKtcgsmu32O9ROm48OULZBqWoWalXVWRB1aJfNrvr8fmSjVwyOLWN6fE4oDikQ86BSIsChLJOWSOacoDbNOFk33TtcLMa8N5jeKkmw6LAvJStmNpK+MsJocBhOVyOHoZAU0quGcqkCw2AYnT57ErSgvL4d5iQTiA20nhiNnppYpnUWeJ7UNqcPhWpDtbE5vvTFiQoPx+anTYGmYeiqtHak9sb1MDgvVDBYTy5p28jXrWeQ1aFlMSyBsRfYCqWkdjNS1U5DTWAt3CytQLECvIevUKsjruFhO6gYtQ1jgsLXIVefC4tr5a3IiH7OcenKZnmBYEUz2THJP1cC7nxcCo7zxyfwfoZUrUfGlD96fNhO/JF5G6ovOCI8oJNYpaRITy1UN5PEFAHMRGbzZisiL1M6eHHLNaSB9xnaXMA1BeULywnaNJh96pSrSF0sjZF4LARuZk0b4fD6ampoYv5sw0R3cMz1GFEXh6aefxurVq6HVGvcX6e2i1+uxZ88ezJo1647Wj4uLQ3R0tIFbZRzkSOrwQ9J5jHJ6Az4WY9CirceB0reQ1VSGFeGPgEMZdjhDpdPiv8JkPOLT36D1djduHrbIya6Ata05lEoNWlpaOixPsbpHR+VnawONTofsmu719+ltLLgC1KsMa0JIURQGTumHS0euorKgGkJLAV4dPAyXKspxqqig8wr6OBMnTkRjYyMaGvp+ElwTfYN7JjAaNWoUdDodzp69N/ICXed6GhNz867rZ5RKJVhGZOxnaOIqinGs/EMEWk7BKKe3IGCLsaPkPKy4IsTY+hl8ewdK0uFpboMox56Z1m4IIvt7oiCvBp5edsjJrOiwbH5+PsIG+2P4zCiDtyPMyREZ1dVGN0X/VtyJTsiaL4Ad3xy5UqbD+N0QOSoYniFu2PzFbgDAYx/MgYDLwc8J8QbdjrEyaNAgHDt2DBrNvWdDYMI4uSfemhRFYfny5fjtt996uykGp7CwEHK5HHx+7yRQNXaa1OU4XPYuymSXMcTheTzhOxG7SuIwxTkaPJZhR4pVOi1+yzyP56OH9IkZahQFBAQ7IzmxCMGhrkhP7TgfWUlJCc7sugRbJ7HB2+IptkZBfd/44i+SShBi03Wt1UhXL1hyzZBUX9x54S5g7SRGUXoplHIVRjw4EGHDg7Ap5Wq3zXwzNpycnJCcnNzbzTBxH3FPaIyCgoJgZ2d3y94iKprpIK1jky82tQX5haiwJmNGuteIup1JK3Qvn0Zfcht0DZJOwDz8XBsxsTyx+TFYwwkPeC1HsaIcR2R/MTfcRSaakTPdWDm04Y0xzEzrPAm5LzLaBC0BbQa4/VVSv6DlM2NwlZg85mw5+aD3OEwu03VOALAmefaN/4v4F/HcU4Mwyn4AGpQteMR1Kv7Q7CPKN7Ywp6OnlJE9QHW2NJ8iQZvm4XJTJqaoQ/Fo/1D8k9MqgpU7MnsXHK6QbRdUk7oJlpIc7m3yY15QojKapkhOfjE3hpMC4Yph5PpzD/CgrmtC1bEkOM6KRNwZWvK1dqgqqYNfRJtbumBXAqOMjcNgYrl5HOkVppKROhw2C3C1ssDh3BzoWQC7hTnUTfdo0gho9+dt5EMWVZLHXGlFXjCCyk4CCVbr/VurlIHDYgEsPeycyBu/Lp885paZbXq8of4eaFFqEZckhUrbJhCzSSfbISNTgUFF05JxWtrOc0Q/D0xbMQHvnD0M7pZJGNB/CL7KTsLkfn9g8k3r7K8m9T7+1uQ9XcIRM3bX3UJCLKfXkDMS/S3JOuxF5Hn2Nq9n1JnJo+UR1NLy+YWR2jH3VcwPPqpWQvvF2TRV30SP0ud7jPh8Pj799FOsXr26t5vSbZz57yJGPzS484L3OTKlCh8mHsT7l/fB19IOQxy84GfODPLulh+SYzHZPRCRtsY9pDZ0Wn/EHbwKvV4PR3dbVJfWdbrOpWMp8AxywagHYzot21W0feTlxqYoRkLozrASmmHmwBDElRZDZUCNo3+gE86WFoKiKLw1cBSOFuXieLFxpVIxYeJeo88HRk8//TTS0tKwb9++zgv3UYozy+ET5gGhRd9NZNqTlLU04uf0s7AzM8cHYXeXVqU9ylua8Gt6PF6KGAFnIXO2jzHgI7ZG0ABvnNmVABsnKygVajRLOhZeA4C8WYnkc1nwj7zztBh0uCwW3KysIO0jKWqCbRxQLuvaDKiZMSGQyBT46lyswdohFPIwfFQQshpq8O6gUfgz7Qr+y0nrMzotQyEQCLo1xYoJE3T6fGDUv39/bN26tbeb0a1kXc5H1uV8TFg4oreb0mc4Up6J8pZGWPPNMdrhzpPx3opzlYU4WpKNVyJHGlXmegDgsFh4OWYo9qw7CXmzEt4hbqgu6by36DrJsVlw9XWEjaNV54VvAx6bDS6LjVqZYWdrdRd8FgcVMmnnBa9BUcDU6CDUNBkuJyOPx8HTL4xHbU0THgmKwIfnT9yXPUXOnnYIDw/H1avM9CMmTHQXfVpjJBaL4e7uDr+KGDiy2sz3OIFkhuyqKOZXPVtFfoGoRbTcXrS8S4I6chig2YWpK6HXYU7TurbQTIVlrszxdaskMis3S9Cqvbh4Ih0PPnNnudPoPk46+pd7KTlbidIwh4i4tPyNCtqoiMtJCbGstST3TeLP3Fe6n43GjfRPaggkj6fTReYQhdMZcruFtuIb/3+uYD+2L58PB6kPapNa/VQcoyoZdVTUkAGASkPeFk31pMBFkNeqodmdkwXPcbZYOX0svks4hypZ24uxcjAZLFkUkr19Otrl4xzHfKkqrcljprQhdThaHnl8RCWty/MGRUDdqMF7G56GTqfDlBGr8OOWz3C04ShjGwy0WuReKUB+SjFGzIzCrh+Z6UN4Utp5Kyf1UYJact9VLA1qJM3w59sgp7quXb0Zt4nUT5lV0Wcgkfve6MOsg37/sWhVUDRjSbqPEaey9bwKbPlggQ1OJQ+N1aTfGcxpOcg2ZSFiWAC4dQooJTJwGylw5WTb6sPJdVi0y9g3gnxQFHu5Y824WQi2tUd+YwPipD8h3L8YN6uIvombTKwzJow0BROwST8hugcRAJTLLIllBwsyGDyeS3pY+TiRnkRJtTSxFIDaLHI7nuGkI7ecrj+TtWMId9NQ5JgHorFp0yao1e2UM2GimzCuT90u8uGHH2Lnzp2Q1hvuS81YkdRKYeskhkDQjrOdiXaRyBU4mJWNZTFRMOMY/htAp9fjx+PnkVJTic9HTYSzqPeH1TztxJgZFYw1x+JuCFa5XC4aG7uWR+vi4WREjggCZaDZdy2qvvNiC3awR34XZtCFDfbD6f8SYCbkQ3aX++klcsLBB5cg0MYOr57cj4UHtqJCYdhZbn0JnxA3nDhxorebYeI+o08HRkFBQfjnn396uxk9QkNVE1Ljc/H222/3dlP6FN+ePgeNTov5/SK6pX6dXo9NaVfxb0YyPh89EVGOvZdPjc2i8PyEodh8PgmVjW0fC15eXu0mju2IrMv5sHO1wZRlow3WPjNu3+igFvF4aJQrOi94jYAoL6jVGtRXNd6xwNyOb4XFXpPxfvBitGjUeOroLsRXdmyvcD/AYlOor2fOfjNhojvp04ERgPtKlLfrl+MYPXo0vL29e7spfQatXo8qqQyzwoK7pdfoOocLcrH2ygW8HDMUi0L7ddt2OuLBAWFQqNXYf7UtpYmbmxs4HA6Kioq6XN/uX47B2ccwSWXzausR6tx9CWoNSbCDPbJrb8+he/qyUbC0Nsfe30/B3EqIbx+cClEX8hN6i62xLuYtfNfvBYx3HACNXou3zhxCWp1hTSL7Itb2lrC0FkEul3de2IQJA9LnA6P7iZZmBbZt24Zly5b1dlP6FFKlEsUNEriLDSMmvhUXKkrx+omDiHRwwpcTJsKiB005R0f7YUKYP9YcI92Qp0+fjkOHDt1RnYkn09BvVDCsDSDCTq+oQqhL9yWoNRRe1mIoNGo0KW4vCax3iCv2/XEaAPDOnFVIKqvAc6MGdbgOh8XCIFc3fDJmHP6bNx8KrQr7y+PwZ+EhPHP5W1NQdI1pS0bg+LYLph4jEz1O3+jb7gQW7QutMZwUTCrF7ayjIrUTFmUdd4E3epFqWbpZIwDwJORyC81Ch0OblEM3kQSAyk/IGVR+m8mvpZJ/CjDrx1H4bNFqnNxynpEQlqirshK+vr6I5b0KhaKDoQHan4Yk9WMU4baQx4dNS8aqdCQFuHIHUkjdXtJPlqZjEz8O7UOx0ZspeFdZkC9tz32kgFTpKID4QS6UdS2YJ/DFTxoyQSwAWFqRG/IXkyLT6jpSO6Sj5djlNLedx/pmOd7bdxQLx4Rh3dwZ+Cn1HGIrCyBlk0GS3oYUwGeHM4MoTjW5IT2H7B3VWmoRZGOPRSH9YGMmxA+/nIC0ogk3a4rt7OyQlcXc51txuOmPtoUm4NmSRyGyMEN9edvLSS0kz5vOitTV8PLI+1EtAjKKqvHiqKEw07HRTOYRbW1ngoRYlgaQwmCelLz+nOKYWp5md3K7lvnkedXTZg+yade9jgtM9ffHubwiUJpWKyOVDbldz73kcsG0FOxM+j/E1cYBABSHI/HZ41Mxxc4HxVUNmD44GBGDWtehKAoOfCdQsISQzUOtshnr809h9ZXEm2q0x4gI8nw1qZk2HeF+JcTyqawAYtnFUUIsS+qYDpk6a9ozkLYZWzGp3XQTkXUqtczXh1sYOZGDbvAoukQ+CKrbiSGdi1snSNg4WOHisVRmARMmupk+3WPE6cahEWOlRSrHnrVHMebhoTATdtwjUVJSgri4OHzwwQc91Drj5MjJNMiVagyJ8QGb6v5LXqvX44+sBHx65RiWBcXg3f7jYCdsxyr9Lgi0s8PHw8ZjxZCxSKquwMsn96OoghQMi8ViTJgwAbGxd+6t09wgQ7/RIXfbXEhaFKiWNiPIyb7zwr2EnUiI0f4+2JmSflvlBWZcREZGoqCgLZFro0yBL7ecwKyhofjmqRmYMzISIo45hGwRBCwhMptS8XPmcSw89yvmnf0ZfxWc667d6dOI7Szg5ueIkmzmLFITJrqbPhtZDB48GLW1tZBIJIABRiwoCgjycUSovwv8Pe3BomUZVwso1DQ1o7xBiqoGKZKaq1En69wwrzvIvJgLNoeNITOjsffvjR2W/fTTT7F9+3ZERETct/mGyislsBGLYGttDg+RLQqaazpfyQCk1Ffi2bP/YUlgDNZOm4WrlRU4kJONpKoK3Kk3cqCdHeZHRsDf1g7b81PwafwpKLTX56WTPWqRkZEoKSlBRUXHyWM74ujfZzH1ibHY++uxO67jOnnV9XC3tUIOjPNlNzcyFGfzClElvT2/paeXjca+ff+ispLcHxdbSzhaW+BsagH+79gV+D1yhvj7+QqTRrAzHN1tUZpbBbns9oY0TZgwJH02MAoODsaZM2fuWnwt4HEwpX8QZoYGQqFUIzGjFEdiMyCRkt3wCgc2Al3tIeBxMDbCD0+4DYKkRY7zBcU4mJaN2h4Okvb+ehQPvTINX+0VtwaHt0CtVuPYsWOYM2fOfRsY5eRVY9G8IWCzKAzm+/VYYAQACq0Gv6THYVtCBsZ4+eDJ6BhwWBRO1uTiXEUhKlqabgps2kdsZoZ+Ls4Y7e8FP1tb7ErPwOenz0Am7PilMWbMGJw6dequ2p9xIQdPfb0QTt4OqCy4O+1LZkUN+nk44wRuf2ivp7A1F2KMvw9e2HF7Dvq+3vbw8rTDw3O+J393scXSSTHYcOgiTiblQq8H/LqjwfcBWm3fSCFj4t6jzwZGUVFROHLkCACAZU6On9OTR7LayUTAkwODgz2xcHw0iqrq8dWe08gpr8X1OIui3ZO8Cj1Krra+GPYCUDlz4O9sh/ERflgzZwaSiyux92AyCivatBhmErIOhQ1Nm9HO0RdUkW0veIAcgnFyjgIAnADQr1mBkJAQnD9/nlnRTfzxxx/4+eef8dBDD2Hbtm2Mv9MNIDlJzMChagw5o8i8gnyZN9PMGTk0/QbdFBAAlGLa8aBN5hHSZpjzG5kPSroRZ9UQUg/Ek+pRDDm+3n4KLz00En4aH0iv5JDb6UeKO69UkiIYbi6pi1A6kv09Tl5MV2m6LqmGJ8XW8qvYWn4VUfauGOXqhRUDJsCcy0OprBEsNRuXytvM8PxtbGFuxgOLouBuaYXUmmpUcVOxvSgJSoEaHv2BskZSX2UT13bQLa2FmPbidIwdO5bRtq6gt7BARWkDwif0Q832iwAAcQ55cuuiyXPPbyTPif5a72tadgWWDImC1pMDhZq8fmy5ZG+XRR6pFZO7kvc4pWNeT8JKUndEqcjzVDqRPCdUTJu304P+EThVn4ty+1rgptE+UTJ57s8cfAPu7u749qP/8NGKlzAm6M3WuigKETFemPvcMKzPO4oEj3zAo1WndOxgNFGHzp/skernTU7Lv1BCpmPRqNpJUmxLHh+9lDwHMmvazUT7O8BMnF2WSxvmpGnaFGrygaXWMNslb+o4bZEZKR2DZRrzntbU1IFqsYdOroCm5vYd202YMBR9MjDi8XgYMGAAXnnllTtaX8Tn4aXpQ+FoY4E/Dl3EpexS6Jn3eIeotVqkl1YhvbQKQj4X06KC8eZj43DsYhb+O9kzPTNlVRI8/PDDSEhI6NAZtrm5GYcPH0ZYWFi7gdH9QFW9FHroEersCBuhAPUtvTcF+EpNGa7Utr4MbfgCWPMFsNNYwkvcpug/W1yEXGnrS6GmRYYmlRLBoSXt1tceI6ZEYvv27ZBKbz+1xa048s95zHxiNE5cC4zulKrGZmSV1WBcoC/2pxpPr5GvhT2G2vvgpdi9nZblcDhYuXIlysvLsXPnTkyKaJ0sERjmisdfnoTvcg8hoT6/u5t8zzPj6Qk4sz2+84ImTHQDfVJ8zWKxoNVqodF0PATRHiI+Dx/MHYvaRhneX38Ql7Lv3kStRanGtrhkrFx3EOG+zlg0ZcBd13k7bNt/BTweD1OnTu20bEVFBaZOnQpr63am090H1EpkiEstgq1IiHAj8tOpV8qR11SPhPIybEtPvfHvaH4u8iT1yJPUo0nVNZ2FmZCHiXNjcPr0aYO0MeNSPqwdLGHvevfXzum0fEwM8TdAqwwDm2LhhaAx2FJ4CVXyzh30Z86cCYqiMGfOnBu/jZocjmfenobfvzMFRYZAaCmAnYsNzu++1NtNMXGf0icDo9DQUBQWFnZ5PYoC3pszFqklVfjjcAKjO/9uqa5vxld/n8CAEA8MDvcyaN3todPpkZaWhvfff7/TsidPnkR+fj5iYmK6vV3GyvYTVyFVKPHyqKG93ZRu5aEnR6OhVor4eMN8cctlSpTnVyNiaEDnhTvhXFYhOCwWhvl6GKBld89y/+GoUkhxuDyt07I2IgGefvpprFq16sZH2ZOvT8aMhwdiww9HkHypsJtbe3/gFeqOioLq+8q814Rx0SeH0kaMGEHmz7Egs0FaFJOiorqQ1vH2mQNapx3/39lEcGlD2xbF5FCUVkDGjDwJGUTJnJhT4TQCClJo8NWOU3hz3hjo5BpcTm8b/hDStKvtaYwoHam7UdG89Rr8yTG/NV+vwYIFC+Du7o6Sko6HWj788EOsXr0aJSUlyMhoSzpJsWmeRG5ixrrWWaSupDaC1BLYZJDHnH78OHJmzi0dh9yuliZPUNA6KOg+NACgor1fzWhecDfruhRQYfr6v3D+hacw1McD5wpbc1A1VNJ0STT/INtB5IlT0JLMWguYwvtKmtGVnna9UbRzrxUx56mZ25H1NshJvZkmjtzGodxXMW7cOHj1n40HH7qzhMN0rms8ko8lIzTaG0d/OYiafmSPj9sRsu1VA8hzrTUjX3DbrqRgXnQEzucX39D01cSQ58D+Ctl7Q08y217yUZWYPD70OhX2ZDsmWUUixMIDr8bug0xlBlE68572WN825DdgXCjqB9rCLHIxxg8zwyvzR8MxyAJLYrdCMkEOTODDnc/MsdZgQZ58O0vyvCame5Er8MnjyRMy97U6naYHsiHLuFtKiGW5M1Nj5GTVRLaTRx5jHpt2XsvExDJFf4gCYNParpGRFzr98Aiqmb2hNR55OJOjwzHdVsbfTJjoCfpkj5FQKERDw+0neQRaZ5/NHhiKP04moLs/RIqqGvDV1pNYNHMgIgKZGagNiU6nw+bNmzF79uxblhEIBPD19YVUKsXx48fx448/gteFtAX3EgqNFjm1dVg+uGeGO3sSiqLwwQcfYNWqVSgvLzdo3Uf+PAWeGQ/9x4V3XrgTTucUgKKA8UG9N19riJs75vlF4N24Q2hUdZ4XzdXXEXOem4DUvArYWAnx4xsPIjmnHA+f+BMSlSllhaEQifhYtmwZDh482NtNMXEf0ycDI29v7w6nqLfH5P6ByC6vQU5Fz8xyKKpqwIad8Vj2wGCYd2LEeLc0NDR0mAX96aefxpYtW7B+/XrY2tpCLBZj9OjR3domY+Zkbj5sRYY1XDQGHnzwQYhEIhw4cMDgdSvlKsTtvYQZz0wEh313jw29HvjpVDwWDIyEu3X3pmlpDx9razw9IAY/Jp+7LV0RAIQO8kVtuQT1TS34+qVZ+OfQFezsoUkW9xPOLmIUFRUhMTGx88ImTHQTfW4oLTg4GA4ODl0SljqKzTEjOhgfbz/ReWEDkpxVhpScciyYPgC/bu09h9vrvWuff/45LCwscObMGVy4cKHX2tPbSJVKsDsIJPsibtZWeOONN/Duu+9CJrs9g8KucnZ7PGY8NQETI/1x4MrdzSrLrqrFjsQ0vDR2KD7YcxSAYfV+t8JGIMBHY8bhp4sXkCi7vV41vpCH4TOisOf3k1i2YjZW/3sGSVllna/Yg3BZLASI7RBi4wgKFNzs2ywnKhV10CpaH/XNGiWq5Hc/U7G78PC0RXl5Sm83w8R9Tp8LjCZOnIiDBw9Cp7tpfLuFHLNXW7TtFptF4e1JI3H4RDqqU+tw3Q1FT/voVdiRh8Iyl6yToo2/uR5mPlykQTbEstKKwt8nr+Czp6YhvJ87LpSRD1N2O/5KLaPIL1hdOdmz0Z5WpzM2btwIuVyOr776Ct999x0kEgnRw0TxSP2BVsj0Lqj3J48PhyYNYKlJvQH9+PIkTA2NWkSKipS03GmiSpqPijVz3+keVXR/pSYu2W6Xs2rw+UpY9OMiIJGF5mYlQj8hv/xjy3yIZXsheU5ya+2I5Yw85nCppR25TnMBrWfEjjyAejZz3yzMyDKNLeTx8trQ6sfE5XHwxs9LsH79Lzh69Cijnq4w2fEZ8gczsrfzzx+P4bG3pqNpdz4Kc6ug0+rQGE5e956HaPn9xpFeQArn1mthe00aAv3ssWBCf/x0mRSKNwaS1z23kbygtGbMXlj6Nae1Iy8OmzIR3h81CidT83AlpRQWTWQdLr8lMeqEsyOe+WwuCvJrMHjmAGyvyMARy1Lg2hwGCwG5jYISZqJcegwuPU/qg/g0+Y/ShtwRlZ55bZg1UhBwORju54UIVyf0d3OGRK5Aenk1JC1ySCtaEzVy2WwMdOmHAR6tdbhZWEKmViG5ugo5yMSV+kIoda33jIhH7otMRQ63s2U0TSAz/Rp4FuQ1q6X7K7mR97TL7jbPNA6XjQnDA/DUax8xKzZhogfpU4GRQCDAtGnT8MQTT9xWeYoCHntwMBqlcuw70/msk+5AplDhQFwGxkT5MQKjnuTff/9FdnY2Zs+eDUtLS7z00kt48cUXkZ2d3Wtt6i2UKg0USjWsLARobu77KQcWvzsT0gYZ1q1b1+3byrhSiM2/ncIz70xDk0SO43sScU4uQVNz5zodOnoAqy6dxycjxmNJeH9sTOm+4ZNQG0c8EzUYGZU1+PtC0m2v5+7rAO8gF+z5KxYRg3zxb1rv92b4Wtli1qgQDPHxQEFtA+ILirEtIQUlDW2GlVzad1uTX+uHC5fFgq+1DcLsHTElqB+W+41FYn0hdpdeRrWK5qjawwhEfHC4HNMwmolep08FRv369UNBQQGKi4tvq/ywaF8EeDvgg7WG11x0hYsZRZg3th9EfB5kyna6iXqIxMREJCYmgsPhYO3atVi1ahWys7OR+nclijMNK9Y1ZsyFfEgkLfD1cUBZhaS3m3NXhMT4wN3PCd+99FePbfPqxXwkJxTg0adGY+TkcDwU6ICSigb8tu0cymsaO6/gJprVKrx/9hg+HTEBATZ2+CLuTJd9mzrCQWCOx0MGIMLWCTvOp2FfSteGAF/46EFcOJmOEVMjsWXNcehHGqxpXSbSzhmP+kfCw0KMc6nFeGfXEZReC4boTv23Qq3TIbOuFpl1tTipOgs7vgXGOIZgRcQcpDUW4O+CU5BqekdM7hPsgvLCnkvXY8LErehTgRGLxYJCcXtfpjZiIeZNi8LqP09DaWC/oq7SIJWjqLIeUb6uOJte0PkK3YxGo8Hy5cthaWmJ2bNn473fVmLtG5uQfiG3t5vWI0waH4rUjHKwWX1bZ8TlcTBr+Rgc+OssGutuT0TcHo6Ojhg5ciScnZ0RYTcaPiGuYF23UmhPaE2zWaioaUKYvws2fLIAAFCUX4PYs9koK61HZno5StDxNNBmtQqvHD+AxyOi8cvkWdiYfAUncvOhu4vpo3ZCIR6KCMEYV18cK8nBkuPbQeV2TTTuE+YOLp8DebMSDTVSZCYVAyN73hxUxOHh7ehR8LK0wbbcZPzvwlGw8rpo1X8LapVSbCu+gAPlSXjUKwafRi7ELzmHkN50+y7rhoDFojB4Qhiyr97eR68JE91JnwqMQkJCbsvYkc2i8MTDw3Ducj5yCqsBfu+/AC+kFyHG361bAiNbW1ssX74cixYtglwux48//ggASEtLI/yK6DQ1NeGvv/7CzAGPYvmnj+CV8Z8YvG3GxpQJYeByOXB2tMT5Ph4IjpkTA1mjHBePpt72OiwWC05OTrCwsEBwcDBiYmIwePBgnDp1CoWFhWjQNWHvhTxIaq+NxfCZtg56mjasOVAMAOBx2YgMcsOsoSGYNCUCGrUWFEVh0Z5daOgkBYtOr8e6q5eQUFGKpRHRmB8cid1ZGThXUoTalttL0MyiKIQ5OmJaYACiXVxxrDIHz5zaheprM8/McPsWFRwuG6+seRxNkhb0H+aPn1fuvO11DYmIy8VHg8Ygr7EeHyecgErXqs8yg2ECo+vINEpsKjyFFEkRnvSbhAPll7CrOAP6ToJaQ+Hm4wBHdxus/7zztCwmTHQ3fSowmjBhAj7++GPG7zpnUsw4a1wE+Bw2tu29BEoPqCyYX4p0c0X6soBm8serJ7v3debMZIl0szJhWVv/dkljIR4cHQmuAjd8lERVzJ4sRRJpVsmitcv5RC2xrPrgO0x9ZDoqKArvHT2Fx80DMHnU06BYFN590xsUBRw/mIKzJ9LRImsdxmMXVxJ1zH99FjZt2gTP+XysW7cOg6y+ZbRLWEs+IDVmZLCpp/W+cJvJfdPymQ9ytpKsky8h6xDUkYJtVjtJK4UV5NAkr5oUVzR6kUJplr8I+3KyMTrYB2e5tVBHcHEwPZQo4+5MukSmlzkTyxyaER7YzHEMXxvSFiJFTr6U9TraNVnDFBNLLcjfHDa0CZI93WwQPscZy5bNR0ll+1/3E7iPAgAcPe0wbFYM2Fw2QidHgcNho0WmRElRHS6aN+KtuAw0wxrwskbyIDJYbJG3k+STMdTVpk2JQzl2lxxFuJUnXg6aCTu+Jf5eOhcPbtuMZvW168+KOZysu7b/V8ur8HL5AcT4uGCipx8W9otEZYsUafXVyGusQ628BSqtFmXNjfC2ahV9W/L4CLd1wkArd2h0WhzOyMH605cgK20NxiyvBUR04a/7MTJYu250yuVx8MrPS1GcVQGzES74X+xx5D/CBeAAkRN5fXE55DVKNzgEAI2QbtpKLrNUtA83YWsdIg4PHw0Zi9rj1Ti09SJuTm3ML6DpFWnBo97Rllh23cy0KZGM9SWWKwZRqEALEq+exIrRY+Bq7Y/vTsRCe+2BpbMhrwVK047hqpJ8brJEpPGkKJV8oOmqWp9nNmFOqM6tgKq0d3VOJkwAfSgw8vb2hkgkQmpqx1/HPgGOmDgyGG9+vrPbjRy7QkVZA/hcDiwEfDS1GE5D8WhMBMJcnfDVodNolCuxbef5G3/buz0B46dGIDTSHcPHBKPqmp6mPqcc//1yHGpV6wtepVLhqaeewpEjR/DXXz2nVekNbM2FCHN3xMGrWVBrmS+xvsLUiRH455+1Hbqdc3kcLHjvAYQPD8L5PZdRU1KHTb+fQW5WW2BcMtniluvfDSmNRVh+4SdMc43B0z5T8b+RY/Dm8cO3vf6FyhJcqCyBkMNFgLUdfMU2iHFwA5vFgq2ZEF6W1kitq4RSq4Ver0dGQw2+TDiN7Oq2AOBO3cNmPjUO7v7O0AM4VlOF/MaumckaAl8rG3w2dBKOFOfg5Na7S97bVapkzXjp4H58NGwcHomOwP9dutrt24wcFYLsS6Y8cyaMgz4TGI0fPx7Hjh3rNH/OvKUjsHnPJUjvYJZMd5NdUgMfFzsk5RpmdpqNvQUejAqDTKnCobQcxt9lzUrs3poAAPDydYCZoPVrbnCYM97f8CRS4nJw4UgKqGoKDQ0NqKmpwRdffIEtl25TydnHEIvMMD7MH5WNUuxI6J1ZioYgItQNYUEuWPLoP7csExISgv99/QqKM8vw7vQvoZRf66XpF9RDrQR00GNv2UWkZumwZuoMtKhV+CbuHFpw+x8GLRo1kmoqcLWuAv/ldXzO+NXMtBddIWxoAAZNjoRvuAfkLUrsWnMUf43u+ckSLiILrBg0Dt8nxiK+sgQ+na9icNQ6HX46HYfPZ05CemU1EksrunV7di7WOL3NMLn9TJi4W/qM8/X1wKgjPH3sIbYW4VyCcWpHFCpmzqO7ITTKE/WyFlRLOxfeFuZVIzO1DJmpZfj7q31Y/9F/aKpvxms/PoY1a9YAAGbNmgUfHx8MjvAyaDuNBSGfByshH7+futhne4tYLAqPzhmIXQeSoFQyAwwOh4M1a9bg66+/xq6fDmHd2/+0BUW9xLnSYrx/8hhGe/ngt+mzEG7j3PlKPUx4pDuWfzIPbA4LWq0Ou9cexfl9V6DR9exHgpDLxWtRI3CwMBvxtxgi7Smqm2VYdz4Bz40YDG47eQoNhV8/T9g4iVFbXt95YRMmeoA+02Pk6el5SyGxRtyq93GLcEVqVjn0Kh0R8WmYciDwpGTPE4fWwUSf/irzbMfNjAbdYM4yU0Is6zgABBQ01/zuGnyZh59Na0eLP/kCL/iwdYDAx9wWIyMHIiOjBrlVdaCuyV6qhjCHRuiJaPmD1KiAGheQjf+qi/Fl6EM4fiQF+3ZdwanDEix/axJy5RKU1rZNvablRIUZTbLQ7E5qaMTpZLDGr2JO42YryYapRaQRoNKK1BTx65maLI6UfOm3+IiJ5SaftvMc7W0DNpdCrE0qMKitjL6WNBMsqSANC10cJcRyZV3naSzKpGQZjYw8PgIxTYzswQyaW6TkYBDvwCUs++QR6MvrcfrNvzBR9Bjxd7NXR+PxyQNRL23B/3afgzLIDAiKIcooaLlHeSHkefEWky+nxHIvRrv8/Ulrh0YFed7YLJoWRQ/sy85CsUSCz8ZNxC8D5mL1f2dxMjEXSnXr9S0JJO9HPY92M9HMP9ubnq42J+vwPEA6gKstyHPASmr18PKL9MSiudFYn5CO2aPCcDm/ArtFaugfDgc05IZk1eRzQEZL+hwTnsdo1+ULZNJdirYvaqe2c7+s30CoLjcjfn0SvK/9xi8gdYUAIA8gTyS3ibx+mr3Ic2JuwXwIanlkO2xpI2Y1o1Q4rcvDdARi/Egf7EolrQ5YSuakFq2KPG+sBvKYmzWQ5+iIYhMGjn8fX/72MbaXmZLGmjAO+kyPEYBOh9GCg11QVtbzeoDbpUoiRYCrfecFb4N53tE4UZ4JF2vLG14md4JUo8DHH+zAqeMZmD03BkEhrS7OYZ5OBmmnMWErFEKqNr4h1tvFt58XIkaF4NsnfiF+t3ezwZIVc/DOo2NxJiUfq3fH3tVU9+4iuboK8//biryyWiyeFIOfX56D4eHe4HEMO8OqK/hFeuLxj+fhj5XbERXgCooCNhy40Cv6xPHufgiyscemzXE9v/EOWJsSj8cCoyDmt/OFaQCcnJxQUdG9Q3UmTHSFPhUYdQSPx0F4uDsuJfS+T9CtkMpV4LEN8xJwFljCQWAJV7Elkorv7qGi0egQF5uNbz/fBytxa++Jk7h7RLm9jVLbu55WdwqLovDwGzOx/bt9kNa39cbZu9rgxR+WoDy/Gm/+tg8nknKNatIBnSalEm/8shf/nkyCuYCHxZMH4L1F4xFgawuxmZnB7o/b4XpQtPHDHdDpdIjwccFPO89Bq+v5A2hrJsTjoQPw49U4tLT07tAnnfymepytKMTDweEGr5vH4yEsLKzTSTUmTPQkfWYorTP69fNAcUkdGhpkgKVx7pafsy3isooMUpcF1wxjnQPwy/4Eg9QHAC0tKvy+9gRq4u9NEaSvtQ0UWsPqvHqKJ4IHQnpBhvN7LgEAKIrC+EeHYdjMaJzefgHHNp9D86J+vdvI20Sp1mJXbCoSMkvwv8UTIDLj4Y0Rw2EnEoECsP7yZeQqWof06hUtKG82fNJTfz9HPD53ADZ+uAMl2eVYseUlrNsfj8ziaoNv63Z4PnIITpTmIa2u6sYQmjHxb85VrB4+C/+XdvWG7YIhmDlzJi5dunQj0bUJE8aAcUYQNMzMzKDT6cjEsTfR6MuHx2A3XKyoRKMvn+GpY1ncudBWIyA7z6Qe5AwXYQ3NU0fJbAuvkXxg1MaQwhwdD2hiqaG+1hmjbcdvTi2m6TNoHicaFQe2fBEsuQKk1FVib0kOcJOchXMbbv6N+WJiWTaN3HeNeTv7JiHL0DVaLQ5kO4U2pD5G3Y5GyzKJ9Cyxo+mQ1C5kOzkS5s7JPSyJ5dLRt+5xCLK3x9XiGqjyyd4wLu0Yuw0ifZ7o+DiRaQvyK5nDo/VNpG7J1oncN0kjeTy0cuat6LextXfLxdUaE4O88fWvv4Pl6AAzMy7mPjUWvNmu+ConEVdH1QGjgmBhRb5clErmLC09TRMT5VRKLDdryPPm5MEUxNbISK8tpYpse3Ue6aEj9pUQy9pr9jnFqMeH5dsxx2MA3OCCi6WlsBMKsXxADFjXmm7B5YPbjvs2i8Xs1dHR9k01XwOtTg+dTg9ZixLSmsIbf9Pr9fjz+1PIqZQjaspAFBbWY7cmDyAtrcBW0HQ4vuQxtjIjr8msOua1wKJ1UgpoVj1TFb4IDrfGps9OwVNDgSvtfNYeXVPElpLtaPQiry9KyxwG40nJ+9w8l7xG68Lbnl+V9Qrk1NRjiL0Hjue3TqvXWjJ7X+mqI8tc8herHFJ7OGHpBPz999+MekyY6E36RGA0fPhwXL58+ZYaIzaLQlSgG77793QPt6xr2JmLDDLLJdreDeWyRkjVfT8Bak/BYbEQZGeHXWnpvd2ULjNpSgQuXsi7kfZjyVvTodPp8X7CESj66NDgdUpa6vFj5lH4NgWDy2oLarXmrR8iFAA2iwWwyXvfzpbZi1Rb1xbwmnN56F/nCApAiK8TGppaMHfC6Lb6tVpM9HsDAOAX6ors1FIQDoo9hLVIgEcejMHPv5+ERmPcNhnni4sxwNX1RmB0t7BYFMLCwnD58mWD1GfChKHoE4FRcHBwhzeP2EIIFkUhr4w5e8NYoCjAw1aM9HLDOLt6WdhgbYZxiTSNmUA7O/DYbBzJNU4rh1vh4GiJsHB3vPfWvwCAGYtHwNXbHp88uR6KDw0j5O9tdNDjSjmpk9OIyYCP4pCBkSvF7Mkqo80mTDpeCAAI8XHCm4vH4t1338WHH37IWM9MyINU0tIrgdET42IQl5CPnPzeGcLrClWyZowUeBqsPjNBa5e5XN47SWtNmLgVfUJ8TVEUtB34zgR5OKCoyrjHqAM9HFAhaYJae/dfhRRae0AkStMD5XaJdnFBo0IBhaZv9bA8tnQETp9Mh0KhxuwnRmP8vIH4deVOqFV904epN0jPr8Sbq/Zg4MCB7f6dononl6K3gw3CPJyw51BSr2y/q6RVVyPA1q7zgrfJ2IlhOHTokMHqM2HCUPSJHqPOHlyRdvYoTK2EqOpagsUK0r9EZSNgrGNWSo6na63IMpSC9sVKe6FS9U2MOnUO5BeryqKt3QGBTsgqqSF8isQ5zCBJ6krTOvmSZfw/l2PE87aAnRrCFVUwHyYm/q4g5R0AcMM36Tp0vZCKpmviuZLHDwD0TaQu5+Z9AwBhNflFXx9MCqgU7TxPWxxIoz967jSlJbkNhT3zPKrF9ACB3Bebq63DMwvnRaCxXg6tkHnM9c6kNqxORuoz2tOz3IytmGmwSdcYRdiR3j8nK4PJbfCZAZs5iwUfL3tsXncGr782BW7RLnjin92oGSMCIAKbR54nejstRExrAjZFlrlSSXaT0D2IxMLOg+/+DqRO6UQ9uW8zPMgZR7E1TC9nXih5HhtbSE2MtYhsh1TBTPgxJiSTWL4obuvdULNaIBIFIjIyElevthr2KN3EsLISIHCwDw7G5YDVjqZYQ7u+qqtIf6rqFvKeZ1szK+H5kcN+grTWe2nyUF+cjs8BMmpw85Wt55PasOZIpnUGXePIoeUiFNA6z3Vc5jOU30B7pqk7DrY1zVrodfo23VU7MwhZcrLtVnmkFkp/se1acHmoP/bdoxM9TPRt+kSPkb+/P8rKbp1GQ2wpREU1M1AxJjzsxCisMUyvVvQQP+RklENngN6n+wVzAQ/n0wp7uxldYsykcGSnl2PRE6OgUKgx77d/UCNlBq0mOkep02DNmjVYunQp8XtAgBMKC2pRUtqzrst8HgfDI31w6krfGtplUSyDWCqwWBR8Im5t2mvCRG9i9IGRlZUVwsLCcO7cuVuW8fG2R35hzS3/bgx42lsjq9wwbTQT8uEb6ARRO262JpjwuWywWSykFXU828yYsOELsPip0eg3wAv1dc349fsjRu1P1BfIyMiAi4sL8ZuDvSWKintemzgi0geFFfWoqDXuD7qb0en10On1cLG6e4+zQdOiUFVc0+EHrwkTvYXRB0ZjxoxBXFxcu3mhrsNiUZD3cj6ojrASmsFKZIbyBsM8BJulclSU1GPUZMMbrt2LvPjACEia5aio6xsvIVu+ED+PmAMen4OvVu7C76uPQWvqHbxrUlJS4O3tfWNoXijkISbGB8XFdZ2saXgi/F0Qm9S3ssmzrh23wnrJXdc15pFh2Pdr50nBTZjoDYxeYzR+/Hjs3Lnzln/39/eHUqmBQqO5katM7krTiLTjOaTnkt3BLJrbrNqG1IjwskmNCNr5alI6kuuwr1Xp526DmoZm8AvUuFkZUTm0Pe0U+aAQVpCxazV7PyrrxuCFV1/Ad999B6dT5qiWtGlcuO28+7k0CQxdh2R/mWxHrW07l4WQbBelpXnG0DRHdE2Rjsd8ADbS8mPp+OQyPReTtT9zuMPHivwtbV8AsSzOUSLIxgZlubUwL1TBfzTzCzWvnJzdpdaQ14aVkNTq0PUtPDZTm0HX+1wsJ2fzOLmQw6qV17QrVjwzvNd/Aqx5Aqw4dhIHA6RAQOu1RvfD4SWT13mTPXn92QUye0JkSlL7JS8kfaCsA8ggobKB/DsA6PJIH6PEMPJ6GRtBDo9kSkmNjJaeVBBAbT1Zp0BE3o/+YrK3Nb3ekVHH+RLSGlFZQR6fSdZPwJJlh0nWT0Cv12PC0wPRYKbBUX0VEMmH5wGmJqtgOXkera3Jocy6UjGxrJW24x2VSWrjOJYchPg5YcOxS9CYUdBakedNSstzZpHfwqizPozcN3EOeQ3SDzH9/gQAURn5XNTYkHXqaTMB1SIt9Gw91Dat27LMZA6pue6jufBLyAfSId1WODg4QGf/JNYmfMFY34QJY8Coe4yuD6PFxsbesoyZmRlq6qXQ9YKN/+1iZylCfoVhvkqHDBmCLVu2ID09Hb/++ivemz8OtpbCzle8j6EowMbGHCql8c9IWxIUDW9LGyTVVmBXmkl/0Z2Mj/BHfE5xj2/X3V6MyoZm1Db1Lb2YiMtjOjjeAS+99BKOHDli6i0yYbQYdWDk5eWFvLy8DofRAgMDjS63EJ1AN3uUG3AYR3NthtzOnTuRUlCJKTFBBqv7XkMsFsKMz4FOr0eVkQv0HQXmGOrsCQ7Fwtbc5N5uzj1H8EBfaDQa6PV6PPHhXKg0WhxL7nnxc4CbPYqN3F6kPYJs7ZFed3d+SwKBACNGjMCqVasM0ygTJroBow6MAHT6VWFjY4OCXhBPdoUIb2ekFxvG2JHOjthk9PN1wYpFE2ApZE5hvt8ZOtQPCQmF0GiM2/dHwOHi3QFjUNbchJT6SlyqLu18JRNdYuFbM3B+fxIGT4lE8EBfLPt5GxTqnu9FjPBx7rWcbHeDJY8Pmerucg3269cPGRkZUKmM+2PWxP2N0WuM+jqjwn0glStRUFWP7phD1iCVY+XfR/HLS3PwxJRB+PLAmW7YSt+FzWJBKmVqR4yNsa6+0Oh08LeywzvxbaZ3Ai4Hw7w8Ee7siAA7UhxGXYv1KIpCZaMUxZomtKjViC0uQqFE0oOtN34oADqtDrUVDZj15DisfnUTNIOsOl3P0DhbWSDAzR4/7b71LFtjxd/aFpn1dzez9oEHHsDJkycN1CITJrqHeyIwUgtZUFi3CQFtz9GmZbczo0dnRRNo02aM0SWUjcO9Om+HiByA95Gb4bGh/bDmj1MwL9FAx6EN0LczXq+n/WZWR/uB1sfneKK1J+q3ku2Y9+RomHE5jK9gp3hSvKkRkqedpSaPT1M200iRLuZsCCXXMauhJaIVdq4foIutOdZkAKOpI0PJ5ktMl8hEHvnbzX6EFAUM8LTG/nUnMDjQEZyzyWhZHcWogxtJikgtBpJqdQt+x4GVgMv8+q2uoZl9mpHHS1nd9lIOdXTAgnFR0On1uFRSjoz8erDBxRy7ADwytj/yy+uQmF2GQ2cz0XLTl7bMp7VOFkUhxNYePC4bPlY2+HLSREgUclxMLkVcThEKbvLPagkj94VLM/NslJLnXqtiCmz1lmTvm7ulhFjOayLPCZsi932eKzO9z2ZdDLEsaSHbkSPpWCAPABw2uR1BZts1GentDI2zJUa9NAHrjlxGQqgV6HlVW5yZWZ3NLUgjWPrxAY/cJquR+UjV3tSRG+blhPTUMmiLWnBdGcgpIJ9XIi5pKUC102tuk0qeNz2XvP90tAeYWQOzDl4VLd8c7bnBk5DaRX+lNcpT62Gb0nrs7bcz8w7q5LR75aYEwEILMwwaNAgffPABYz0TJoyJeyIwMkbsxCK8Pn88/tufiOweyIOUlVwChVxlUL+kvo4ZlwuxvSVSYjPxyJszETkqBMaWXc7dygqfTBqPixUlGOjqhrWXLiDQ1g7PDxwMbhOFjzcdRUFF68w7NU1jL5W1BShlzU3AtZlwqxPjEOXggrFCb3wweyzKGpqw7UIKkkv6jo+ToRkb6QdnWwv8sjsOFzJ6XnB9HV9bG2Rf7p5h9e6EAuBqb4Xskjt/tgTF+CEpKalDzagJE8aAUQdGERERKC8v77CMTCaDu6O4ZxrUBZ6cNQQXLhfgTHxOj21TrTL+WVc9yZgwX/CFfKiVaqx/bwteWL0U+kOJiL9S0NtNAwCYcTh4a8wIJJaXY6CbG7alp+LFQUMRaGuHv5OTcH5Pzh2bOl6pLkdWXjV+P5WACWH+eHHiMFQ2SvFXzSUk1XV8T91rmJvxMLF/AM5ezcfRy9m93Rwo+uB9GuRkD61Wh5pGZvqb22XkAzH4ceuXBmyVCRPdg1GLr8eNG4ddu3Z1WCY5ORku9j2vFeiIcQP8ITYXYFcfSQ55r+LlYI1jm84CALIu5WHzF7swZ0p/PP7IMDja3b17790yPTgQTQoFBnu4g8NiY7SXN9JrqrFk9w7sz8kyiNO1WqvDgatZeGbjTsTlFOGVyBF4JWIEhBym3869CIfFwmeLp4DNYuG3faa8XHdKmLMjknLL7viajBwZBJGVEEeOHDFsw0yY6AaMuseIw+Ggn3IUbFh+xO9s8zZ9EKecDUeRCF4Q3piOXT+ENJSziWMOIbAayTF6jQspbGVLyaSVNJkE6kKZMaV5qR7mAj7mjInEqq2nIbUndRB0nY5VFqMKcGXkk8f2TJshIUVRiPkgBrt372aueA1xvhq2+aTmhaJlYld4kOIKs1ryC1bQTm+500nyx4wXSA2N0q5jZ2a9TTuzULQ0XVIjbVadjtRXKZ06/9IWn2/7v7BJj+15O3BUvRcAcHT7Zlw6nIRpy8fj3bmDseXL3Ug+nQ6Lc+S+5PD9ieVqm062y2fOeNPTtCfcWvJW82FZYVlUFDgsFvR6Pbb9EotTJ9Kh1wPXFSZV0aSexSaTnBFE6cngRlBHXjstDm3/12p0OHg5C0erCvH4wCj8OvghrI6Nx4UqcvbbjQSh17AKYk4rV4rIfalXkGN89c3kstCMPPc/Z4xi1MnlkMdQJqGZHDqRwy9CHnN2VGUxeR5dK3WICnOFu5UlKiokyPOREfapdPNUpRXznm6qpRlPisnnApdLtlt0lhlw1w5ra6vWXAu1OQsK67ZtmduQJpp07Y/CjfnhR9cFqsTktaCjPdnFV5k+ajohqalqcafp4m5KLh3j74YLH5+CQ2zbMKRe246xKc349lDVWgBAcMBL+GPvWuh0Jgd3E8aP0fYYOTs7w9nZGTUlHRsjatRaJKUUY2C0d4flegKKApZOHYjEnDJk3cVY/K0QWZrBw8Oj3cDIL9QV3kHOUPbBbvruwNZGhLBAF1y+TAp9FTIldqzaj43/24rlXyzAa+ufgZ2zuEfbJuJy8eED42ElMINSo8Gb/x7EyePpPZILTapUYtXZOPx87gKeHzYIoz28un+jvYSLgxUWPzgIdRIZth1IhDHYCdYpWuDnypxEYMz0c3SGnVCI5Pg783xyc3PDo48+igsXLhi4ZSZMdA9GGxj169cP8fHxkDd3PtX6+OkMjBsVDDN+7w4PTBgQCBc7S/x5KKFb6re2s4C5uXm73k4zFgzB3k1xPZ4l3FgRmPFQL2m5pUYt82IuXh65ApkXcvHW2mWIGRfaI+0Sm5nhv0UL4G5jhWppM97fcQQZFT0vlk8oLcP/Dp/A0ogojLoHgyMei41XHx+L0goJCkvrcepC72uLACBPUg9XIxv674yF4ZHYkpYCrebOensCAwORnJyMjAyTk7uJvoHRBkYAoG2nq7Y98gtrkZ1bhTmzmFOxe4rhvp6YMSwUP/0X2y29NhZiIV7/7lHk5jK/2oIi3WFtb4EzB64afLv3MmqlGnt/OYKf3t6MhW9Ox8I3pnXr9qYHBeLoE0uh1elQXCfBLycu9EpQdJ2Sxka8f+Y4lkVEYZibR6+1ozuY5RcMG7EIlhZmSEwr6ZHeuNshrb4KHkY4WeRWzPAPAo/NxuG8O59E4ufnh6tXTc8mE30Ho9YYAcAx3VbGb5OtXyaWtQIW/t57EStfmY6EjBKklpDTYZv6MRNO8utJjYKWT8aINaNJnyOHK6ROQljVNj4/JNgTC6Oj8NGeEyjRNgE2rToN83LyacxvJAO9yoHMw2+VRy7rKlr3xcHZG6qmFvzfxlKMG/npjb/7jfDFlAnhOHApB/W+YnBknQeTTH0UqTWwT2J+GR7K+JxYDsv5jliW+tO2S9MHtZdiie75ouPSEtXSmkGJmLoSirYrokutSSyFPnJw6pnJN9u7nqjzbHw+/we889fzaK5swIEduVDK27ZVPYwc+mh2J9dXuTDfuqKCtt7LAEc7fPvIFFiK+KivleFEfCbsLcyRtDcX1tfKNAQyvaNE1R2/zVW0jge5A3mUxTnM9YU054gyXhO+jT2Ht0eMRHFtI6rrSX2LpJTZuzEmKo1YPlfkQ27XnNThSOWkdszJipmapbKR1NnE+BcSy2Zs8txfqXRj1OGzpfUaFIn4WPJFBA7EZyDM1xlHU3MBEQW9Ge2DRUdePHLSKgkAICgge6HlvjTvLQHZrtr+zGPOuUlfpmNTgDkb6ig+pNemrdteJu8DPe2xwGlhfmjVhdF0XDXk/eeYQLv2WcxvYCqfTKjcNCyEWB5cIMbiMeH4as0x2FXooC0nn6sUr52Euc2kdpPP52Ps2LHYsGEDo6wJE8aKUfcYdQVJkxx/7YjHE48Og5erTecrGACKApZNjsGSSQPw+eYTKKlv7HylO8DGyQoL3pmNnT8dgvJaIlQLCzO897/ZmP9ADE6cy8Lx2Mxu2XZfhaJHTJ1QllOJzxauhpWDJd779lE8+cYU2Dne/ZDHmCAffPTAeIgEPHz6+1GsWHMAMaGe2LjDeByVUqurseHKZXw8dhwczEWdr2DkzH90MBRyNZpkChSUGyZ5s6FQabXIq6+Ht9i688K9zOzxETgel4WSijvP6zZz5kwAwIkTJwzVLBMmuh2jDYzMzMxueyjtOldSS3D0TAbeXT4Jrg7dO47v72qHT5dOgYutFV76eTdKa7spKHIW48XVy3Byaxxid11CSKgrZs6OxsqP5yApsRBvfvIfziXkGc1QgbEwbm4MshILu7ROeV4V/nj/X+z86xxUSg3e/eZhvLhiNh4aFQFrCwGoLmQWpyjgkYGReGPySIh4PLz74z7EJxdi/tQBOBibjnoJszerNzlRUIBThQV4buTg3m7KXTFokC88Pe3w3vvb0T/QDfGpRb3dJAb5DfUIdWine8qIGBzoAXcnaxyJvTtdUEREBLZt2wa1+u5yrJkw0ZMY7VDa0KFDcfr06S6vd+h0OmpkLXh92Xj8uSseSZllna/UBcxFfDw8dyDCB3ph0/HLOJWUB103RSW2ViK8+ONSnNwahzPbLyBooC+efXEiLl7Iw5ef7UV1dRNgw0xjYAKwsjHHuTvQXOn1eiTG5yExPg9CER+hUZ5wnOiL75+ZCZlCjaOXs3FFVY30yvbdzNkUhQke/nh/1hgI+VwU10nw1cEzyM+vxMAwT5gLedh/Ns0ov0j+7+pVrJ8+G9HurrhcYtj7pifgctmY8+AA/Lj6CIRCHlzsLFFPs+UwBtKqqzHJzx+bU1J6uyntYiHg44kJA/Hbn2fRorjzgMbVzxFDhgzBb7/9ZsDWmTDR/RhlYGRvb4+oqCh89NFH7f69JcSZWDarJfU/SecLoJWosOyRoUgPKMd//11CYxOpe1Da0Hw/aMPlfFoHUF0IDwN83bB8/EAkFZbj2V93QqZUEUnVNLSUDU1eZBcDv4E83GyySQAAUXnrg8jWxhzvvjwZsQm5yLc1x1PbX4GTgyW+j4vH2axCwBeArwAWpWRQJqhkVqqx6FhD1OJAvqYt05jDDxO4j5Jt/4cUY2a/QXpN8XxoXiwyZgDHlZDbVfvR2l5FS2ZVw0zDq6X5BWlrWtvOZwO6htvrxTuq3kwsT8K7AIAWmRJVZQ2w8tKghi+DjaUQs6eG4nH2QFQ1NeNySTkUGs2Na8XL2QZBHg4Q8LlILa3Ch/8eRX1z6z5JB+kxdmIINmYno3KgHkFryWPM8RIz2qW2IH2wZC7k9SOk2XM10RwrWO3MAaB7aXGbyWv03+wUPDw4HPFNJQAADz9m+orzJR1bY3DZZE+vm7Wkw/IAYCEgfYryG8nh8LpKsgeYU8t8dE3v5wKWTIGaY6mY89RYnGouRpK/BLhmSyW0Ia8vbRl502uDmEGUpobUfrFpnlW6SvLvPBdmT6A+h/RCqmhogq2ZoN3zAwCUhvxDs4clo4xNBm1f+OS1wqIFNJSU+Vyonx5MLGsErT2dLz8wAiey8lC86jBudiXS0Xvw27Emu5nQGF/s378fJSUlHRc0YcLIMMrAaMmSJdi9ezekUmnnhW9BSnoZPvh8Fx6eHYNPV87B2XPZOHQ0hREgdQabRSHc2xnThobA1doSa4/EI7GgHKyujfJ1CZGIj/feno7iknpER3piyABf7D+egjV/5KLct/u2e68QNNAPXDMuijJKOy/cAVY2Ijz99nRsUWTi0yvH0KhSwMfCFoNY3hjo5YYh3h6QKpVgKwA+lw2VRoudZ1Jw5moecmQSoi5ncwu4WlgirtS4XxJH8/PwUEgYYlxckVDet3qNBk8Ix8F/Wh0+w4f44aPixF5uUfsUNUhgKxTCnMdDs6qT6KKHWTpiAEABm+OugjllpWv4RHgi8cJZg7TLhImexOgCIy6XixkzZmDWrFl3XZdcrsbGzedx6FAypk6KwBcfz0VObhUSrxYjo7IOkpu+oq7PBHFzEsPO2hzegQ6wsRAi0M0etU0yHE/Pw2eJJ6Huou7pTggMcIKDgyX0ANb9E4u0rJu9eIzulBkdfAEPVYU1UN3FMAAAzH9yNM4eScV2/7Ys4peVpUjOrcO685fgY2cDDouCsHUiHEqqGqDSXLs+LMiemCgnZyRWtvYwGTManQ6/X7mM5wcOxhN7dvZ2c24bCoCVnQVi9yUhKMoL1vaWKCy5c9Fwd6LW6VDe1IRQRwdcKLm74N2QDPP3xFB/T7y8aS/U2rtzqHb0tINfP09sfY05C9SECWPH6N6ylpaW0Gq1aGgw3EOtqroJf/wdi23/JSCqnycCA5wwZVokUUZ/7T0mV6iRX1qL4gYpruaX49cD8aiXtkDDb6fibkAsFuDN16aitKweq348gqJumul2LzN63hCkxd2dod/oqRGwsbfEr18fBN5kTqUHgPzaVjNNi7LONWbhDk5IrW5fl2RsnC8txqzAYEz1D8Bl9I2Es8sHxgAAWGwW5jw9Fn9+uQ+y0cYr+I0rLsFwL0+jCYwGhXvi4RFR+HDnMTQr774X69G3ZmP/7ydMomsTfRKjC4zmzZuHY8eOdUvdzTIlzpzLxplz2VBb0D10yLI35zLqKUR8Hla+PwMKhQr/+3AnWlpUgMAYZbrGja2LNa4cv3Nhq3+IK8ZMjcSqFTuhu8svZ6BVtxHt7IJ/UvuOyd32jFQs7ReFy0WnerspnfLK8KEY6e2FDS9uw6iZUaivliLxbBYw2nhNK0/nF+DHmdNhIxQA6N2Pn0Fhnpg/ORordxnGcsS/vzc8Q1zx+7ubOy9swoQRYlSBkUgkwty5c7Fw4cIbv032e4NRrs6BbLaCljGRIye/4FW0ZK4AwFGQZdRCcuiDnsxVIyD/rmsnXtHSepUsimnbEJF1uB+UEMuzHx0MHouF//6Og7pcCi4AqQcp3BTnkS9qy7OkI6QiwpPRrrJRpPDZLpmsw2lXPrF8qPwnRh2T7Z4ilrX1ZNv9/yJ7+PIeIcWzeldSXAsASntyWJJ+SPUc8vhZeDEf2k2lTGFqV5kc8NaN/4sszDD+kzHYUJCFuEe5AKwhEJCiXLk7/bZhGt25H28bpvXzd0RjvRx1aVKIru1l3UDSNPK66J7Yjh15ROwSyXY0BpBqf4ti8vriSZlBHUtD790i743qIa1/v9xQjmd4g+DACkRKA9lrpKeZd9ITqUpayB62iiIHYtk5mNlzJj1PTl/n0zqMnRrIdlufLAAAjJ0TgwlCF6x7dguOeAPfLh6KdccuomSeN4KDCxnbuZm6geQ1WV3NvJZEbqTOUZNMisCVHmTvipM107xSEkae26ZiK5TqpDhUlIOnhg3E5y3k7Fs7Wvxsmd3MqJPdSIq8KVvyOcFqIdtVMc2VUYd1lgox0d54aGwUVn1zGLrNp3FzqSO0SQmTXZ4nK1Axr9lDtb9i/jN/4NOfPsTOGlNgZKJvYlTdEU5OTqitrUVFRUVvN6XHEQh5GD4+GFwOG8cOJvd2c/o0rHZcfm8HJzcbvPXdo0ioLMPhgjtPgUAnMMgFGeV9YxjtOhqdDieL8zHC0TjV/uFD/PD93lcxdeEw/Pq/7SjLr8abs0ehUtKM9NK+caz/SklCmIMjAux6J6lsTLQ3HpoTg1Wrj6C8QmKQOqdNmwZ7e3ts377dIPWZMNEbGFVgBKDdBKn3A1weB0qFBmq1Fnrd/XkMDEH/sWFQtCjRVNu1GY1ObjZY/s40HNp6EZvSkwzaJjMzDkrr+p5W7FxZMQbZeYHVbkKX3sHb1RavLx6LJz54APv+jMU7j/wEiqKw8s+noVRr8d3evjMLSqHRYMPVy3h1+FCIuD2bAHtIkIfBgyK+kIc333wT7733nklbZKJPY1RDafb29l12u76XcHK1RkFuFbQG0LUYAnd3d/B4rcNwzjZtQx31lRK0GOl7XuxgiYwLOdCob/86oigKDywdjvjj6Th/NA0I750veGMjV1IHuVYNP0t7ZDf1fi/MiChfzBodhuqGZny07DdIaqUYOiUS4x8ahN8/2omDnsZx33SFw/m56GfthBeHDcGP5+Ig64GAYkiQBxaNjcaqLw4bLCiiKAqL35mF9PR0U8JYE30eowqMFi1axOiC1VkJGeW0PFqCUloPi45D11owe2AavcnOMh5NGtBCyiIYho/qdlJKmdF8EWUu9KSe5INbz23Tdyg0Wmh1Otia88DKLb3Rc2andCLW0VqQJofNQ8gEnjJHpp6KosUIgmpSf3CzpojL5WLgwIF48skn4eLigqYm8sD4+vpCJpNh6tSpaGlp0zlMyCANIMU5ZC6oOjOmwaPOjDwefFpCzhYheXk21TMPOktBnscUzXk4a50Zpo3XGc+aRyxTFIXR66dCKbbErw0XoX7WAeCRU+rpBoXVp0gtin0S0xuLI2n7jSVXQ2EHNN/04nY5ReqFlHZM80rzcvLE0TVFdM0a/Zpsz+yzth9Ns5ZN6mzqQ8nzlFJSh0C9F1LL2tqrtySPD1dMbkchJ+sQeJK9d5XJTIcch1zyWhCfaNPOmQn5mPnYUISFeWPL21uQfiEXbq9OxHuLxqCkSoLvdsajUKBFUyjZrlAr0gFzb14osazPsCCW4c7UwbVISeFg0KhCYjmjkDSbnePK9E76/uwkYpltS27nu6yzeLnfMHz9wGS8H3cU0gAyCK1PZaY3onRk23k0TRabdjFYZ7Xe8zHR3nhoUBRWfXEY1ZtOEC8AlhV5XU/mPUMsNw73IpatLrTNqOs/IhBugc74+DWmJtSEib6G0QRGTk5OCAoKwttvv93bTekVAkNdoVSoYSbiY87zE7F99eEe23ZMTAwiIiJgZ2eHqVOnorKyEn/88QcOHz7MGNq0tbXFoUOHsHHjRsyfPx8aI/fl6YyJS0bBU2yNladOQK3rez0O3U1qTTXGeHpjR2Zar2w/eIAPZiwbhfLsMnz91G9orJFi9EODMXvRGBw8l4Gth43TxLErqHU6fH3lLGb6BGPNmJnYVnoRh8vSoNYbrvecz+dgwrhQjBweeGP4zFAPf3sXMRa/MQ3//HgYiYl9/3yYMGE0gZG/vz9SUlLuyu26LzNomD8SEwpw4Lt9ePH7hchIyEdavOEEwHTYbBY++3weeDw2VOoJuHjxIi5duoS5c+eipqbmluvV1dVh/Pjx+O677/D999/jhRde6LY2djdOXvaYsnQsXkuMh7yPB3jdRVxJMZ4bMBBcFqtHA0dLGxGmLhqBsMF+OLz5PE7/06odCojyxpwXJuOzP44js4CZrqQvsyc/A2l1VXguOhoz3SPwa/ZZXK67uyS45mY8jOvnjweeCEJRcS2++/EwKisNOw4+47HhOP5fAi4c653g2YQJQ2M0gVH//v1RVGR8mbB7AhaLgqVYiOMHk1FVXIutqw7hqU/n4csnf0eZgTMGuImtsHBRNKKivVBUWIsN60/jxKkVXQpIGxsb8eKLL2Lr1q2IiYlBQkKCYRvZA4ishPjg31ew55cjyAnvWpqY+4lmtQoqrRa2AiEqZcxp492BtaUAH29+HvJmJT5/6nc01rZu17+/F15YtRh/ffIfMoXGIwg3JHmN9fggaTcG2Xljuf9wPOQZjR3NOUgoLu1SYBri7ojRET4YEuSJtOIqrF5zDPkFt/7guVMihvjBM8AZW346avC6TZjoLYwmMJowYQJeeukl5h+0TH0Q3YNIY0Y+JNkq8u8yJ+bkO7qmSOZCLgtozxAtTSJjUcp8SKnMye3wS8gyOi7ZzqMJKwAAoaGhaNGEYdXvT0CtVuPoPhaEUc149PPROPjZeaTEZra184Fooo7KYdfaw+PB38YOdi5MjZHNWR4oisLE0a12AIrcSmz9YAsSDiUBAKTqrvfSyWQybNmyBU888QQSEhJAscntmpeSeqHmGQpGHXIJ6XcjryR1EVxbch16Qk8A0Nl0LFad7PYisVy41AuuYkt88vB07EnLxnoXBfT25HYsLcjl7DxSRyKmbeNmPdF1ima1+Tg1hAkBkL4zdE0RR84cNtExPIdIrHJJrUpVDKlB0rOZ1z1dU8SrJs89R0YKz3UcIK2qBj6WNqi6lqmeRUukqtGS25kUkE4sHz4ZRbYhixnUiM+2fhRZ2Zrj2Y/nIqs4GUuWLEF9fau7+ISYDxGzcCQKyhpxsVSGxskWjDoo2u6e/HkwsazsT7ZbFEb2nJixmPd0C00vVdxAauecHMk6vj8+hVGHwIUMKO0tyeXiXFJztdD/IoCLOF+3Hf4Wg/Hw6Bl4RTAQKZJSFDXXolophUxfjWqFBECrTk7UEAxbgQBh9k6ItHcCp0aH0+ey8MH//YcGSQsEZzJAN+/X0e5ZbYgXo+03Q/dyO1e/Hs8t+AfvfPoCTqSd6HBdEyb6EkYRGEVHR0OlUqGgoKC3m9IrjBgxArm5uTemuOp0OqxcuRLh4eHY8M0mDEopQl15A6QNMhzRqtFyUw4wK74ZPhozDg4iEepaWpCtrIQeergKreBgZgEWBQQEOsBcyMehk6n46Y9T4O29bJB2b968GYsXL0Z0dDTQR3rRp0cEYf7ASBxMzcKGc4Y5Dp0hVajg42bdeUEjRdVDM0VdvOzx4pcPQ1InxYIFCxi9mHq9HvFns3qkLcaADlpkSc9hc54STgIr+JrbI8DSCcFmFgi1jgCXuh7YUCir06KwsQHJ1ZX4v7QksLc2QtfNth+vvfYaampqcPLkyW7djgkTPY1RBEYBAQGIj4+/bz2Mhg4diq+//prxe0pKCj586DsMmBgBM3MzuPg6YtW8IaAoCmZ8DuxtLMDhs1DXIkedvAUuFpZwsTEHBaCouQEHStOh0+vRsDUOjnbmGD8yBJ+8NQup3g7YufrQXR9vjUaDf//9F4MHD0ZOmpHO379Gv+EBmLl0JKrs2Xhl635UNfXMsBAA5NXUITTAqfOC9zET5g3C9MXDcWRLPPb/fa7doV2dTg+/QGecO3X/BEfXqZQ3olLeiHM1uQAAsZDspSzOJnudPLs5KOof4Iop470xd+7c+/a5beLexSgCowULFuCbb77p7Wb0CnPnzoWVlRUKCwvb/btSrsK53ZduLJ9TqbBo1kDYWovw3+EkfFkZD81N2gOWU+sDU3vTw8o53wzZ+VU4ezEP3h52eP+xkRj76DC8OfFTtEjvTl9z6dIlfP311/h+0/9BrTROU7eJDw/GmNnR2PjlPhyJotDTz/Hi+kaE2TuAAtAXXyEBtrbYnZnRLXW72VvhxTkjIKpT4JPlG1BTfuvk0Uf2JeHtjx7AQ4uGYq0kA3K1STDfG9iJRXh81mB88cErKCkp6e3mmDBhcHo9MJrp9jz8XcMgSAvCZLtA6FWk2lhp356PEW2ZNniuMifHwrVMixiwaaJmDikBgZI28mGTQdcLMevkN5JlLPLIr15WFfOhv3z5cjzzzDO3FD/rB4YiaoA3rG1EcHGzhs9oT2y+kowzxwqh0GigstGS/uXVzJ0tG3PT/1GHOSf2489H5+CVi+9jV2oGdH8PgOomQ8Sze95sty3tkZiYiKysLDzw8Xxs/TP2xu+8ejLgcl3F9DHKW04boqHl3NKV0TQzlswXIa+crDfSYhxsRFY3crtN2P0Qxnn44f24I0h+UINg3zJGHXZ80lPoXB7pDWVWTp5sul+Q0pH0BgIAuXdbkCiHGtWqJoT5iJHW0DqTSupB6qUcT9Yz6mgOIfU+5ldJXx55AJlfjKJLZNrpNWDLyWOopfmEsWj3hd5MB7HQDLkttTd8p5ysyWuVxyHrjC0jj59TPNmO89tfAwAsXboUC8ctxA8fvo69e/d22PNwXY+XX7sFixcvxlcDR+P3n0+g7iaHc04drReQFjjxG0mtWMVwUqdk5SNhbNeRtq8KDafDZVsf5nmso+VgKy2m6aOE5HX/67kx5N95TO2TIpl8QAVvJNMo6YXkQ/Fw0x+MOuieXjX9mc/am7FfEw+KorBiz5tovFKE/fv3d1jehIm+Sq8HRpMXDQdwf6YCiYqKAkVRKCtjvqwBwNraGouWjcDgof6Ii81GRbkE3+9IgUx1d1PVFBoNHv77X4z08cK8yDA88Jof4i8XoLxSAgAoTfHukt7rl19+wfYth6BUqLH73wt31ba7hXWT4Ngj0BlDnD0w78A/UGh7t3chpb4Sgx09bwRGfYVAGzvUyGVQGlBn5OvrixdeeAEuLi54+umnkZNz+7YUCQkJSEhIwJcf7cZbK2ZBrdQg4UIe9vx3qfOVTdw1T369EPWVEqx6eh1UagNPmTVhwkjo1cCIxWLBP9IT/3xzf355REdHY8+ePVAqyZlCLBYL8+fPx9KlS5GRLMd7b/wLSUNrr4ZsSsdfdV3hTH4hcmrrMLLJDk4OVhgU7Q0KFKaP/g25ubm4cuUKLl26BIlE0mGglJKSgi1/nMUDjw7GmWNpaKB/ufcgUaODceSf86AoCgvemI41yfG9HhQBwK6CVHw7dAZ25KdAouo79gB2QhFqZLLOC94GbDYLk0YEI/rxn5CRkYFly5ahufnOrpWjB5Nx+ng6rKyF+N+nczB6fAhWPr4BzY1959j2NVz9nREQ7Ys3xn3Y7cJuEyZ6k14NjOzt7WHjYImLR1J6sxm9ApvDwiOPPILXX3+d+N3W1hbvv/8+zM3NsXjxYgR5Pt2t7ahokuJMPDnEl5vwPby8vDBo0CA8//zz8PDwwKFDh3DixAlkZmZCLme+fM6dzIC1rTlmzI3BX7/23iwV9wBnpJzPwZznJkAhU+Bcg3F4Y5XIJDhRlounQgbjy6S+M4vHz9oG6XV373/jaGeBhbMHIsTPCe++8Sx2794N3V0aRqpUGtRUNeGFJ/7AY4+PxKcbHsemH47i0tns+7IHujsxN+Nh2SeP4OyOeFNQZOKep8cDo5vHtf3cvKHkcKD2c21r0LXhnBvLLcyvfYom8GHRiuhoe2VezryRFdakDklH64gRknIOmNWS3cbcepooCYDCmdSaUDQxsraq7QUz940ZsLS0JCz0bWxs8O/Pe5GXWoJdn59AqHoycmeQDdOYky8Tloo0b9FxmPuq45O/cWhypuIHyTqtIt9AA4BDAA4VNsKhLh/TR8zAJ/OfgKPYAkev5iDz33SUlbYFVLq4ZBzJzsdbfz2PSdHOiD2QjJamtgCKK2Bqn3RKL2KZW0ueV70nGYBx2/GZ4RS1rSPi86C2NYP+xRhEPjoQz67bCZ07eXw4DCEOcLWaNLEyp/kYsaLIdmhP2hDLMpd2BGcKmiaNxcafaUnYMH4uQi1dkTKU1IRYFJN1AoB5ei25XTtSq6LnkvvmcJm8JjkS5jVK190ovG2J5RZPcsisv6sTtuWmAKK29eqltGuyjBRd+f/dZhJGURRs/C/j1cdfRVZWGqY9Owf5+fnMdnUCXQ9zLO4D2nLr7M633noLw+aOxvLlyxnC4Mnct4jlujByJldTLtNSwTKMPE8B1mSQGG1FBt5Xm9wZdWRRtHuSlipOpiR1ci5W5AzPnAxX0PHZQl4bh3LJWa2TrJ8glieKHmPUwXIgNWrX86ldh3c2FQBgaWuO51ctxv6Enfjqx68Y9Zgwca/BdIDrQaYsG4OT96GNvIOHHUY8OAgfffTRjd9CQ0Oxbds25CYXY+eaY13KDt/dVEtl+Ov0Fby16SBe/mMPOGwWXnp9CmY+GI3+A7wgFLUKPRUyJX54eh3GzBuKj7e9DL6QKbjuTuZGhUKj0eK9uWOx51I65KreH0K7GZlGhXVpF/FU2CD0Bd9mZ5EF3ERWSKgu7bxwO1hYCTB30VC8+uqr+Pvvv7Fw4cI7Copul/Pnz2PWrFm4cOECduzYgXfffRdcbjtBq4nbRmQlxKL35yD3ahG++soUFJm4P+i1wMgzxA1BA/1QmG94m3pjJ3x4ILIv52Pv3r0AWr+qN27ciG+//RY71x4z6mEAiUyBP09dxpcf74G5uRmGjwzEZ988gmGzY1r/XtOElXO/hUqpxsgHBvZo2yiKQnZFLTgsFnZeMM6A+0RpHvgsNoY5e/V2UzrlgYAQnC4vgPIONFr2jlZ47/OHwONxMHnyZPz444/d0ML2WbFiBd544w2MGjUK+/btw/PPPw8XF5fOVzTBYO7LU0FRwIHfTc7WJu4fek1jtOSjh1GSXYGCvOreakKvILQUYNRDQ7Bx5bYbvw0ePBj5+fk4cOAAowvcWKmra8Y/f50DANg7WODFx8dg1ENDsO7t/0NNSR2+WPYrXly1GGqlGqe2d/9MNYoCotxdEOhgj092h0LK2QAAynVJREFUHO/27d0pOr0e/+WnYbJHAGIrCnu7ObfETiDEOA8fPHN2Z5fXdXGzwXNvTcW+HZcQezwddXV1BmlTyJAA2Lq0DndZ6bRISEhAcXFxu2VPnz6NxMRELFiwAIsWLcLixYuReqYW+7dcMAm0bwOxWIgZr8+Ad5g7vn1qHZrbG5Y1YeIepdd6jNQqDf757L/e2nyvMWXpGKTH5yD/aqs2wcLCAitXrsTPP//cyy27c2qqpfjfrK9x8WAi3tr4HJ74fD4cPeywYcU2TFw4HCIrZo4zQ+NubYWBPu74du8ZxGW3/7I0Fs6WFyDI2gG+VradF+4l5gWG4XRJIarlXZs1ZsbhYMmzY3Fw1xXEHk/vfIXbxMnbAcu/WAA3f2e4+jkjJCQE69atw/jx4+Hh4dHuOk1NTVi7di1GjRqFV155BTqdHi999CDMeniIt69hYyPCc0+PhU6nw/fP/A5pfe/NMjVhojfo8R4jjosT/MLdYOVsgyaKDzbdedmMfGg1+jFfqs1utKSxtPykbHL2OyP5IQDIHchl+yRSLKvlk+uw1OTfqRbaRgCYxZF+RBRNcFwdko7oh1/FvHnzUKAuwGS3F/HkigdQm6GEML8fJrv1Q9Ng8iGvp4mpWRravrfQROR2TG0SS07Gvxpvmqi5iDzG2gjmg1BeR5ZpCiW1G+bDB+EvqLH9xElMigjAs78tR/ylfJzOLMezm57HW3EnIaXZElAcUuw5dlwSsXwkJZRY1rcziUnj3jrMExnggmaNCiftL4G6yR9PpCP3vUrGTD5KT69Q2UCKnEOdSSV+Fo8USjf6MK8vyoIU3rs7k8Z/20ri8PqAwXjrSmvPYW0EU/jrIiHF/FIfUvRslUlmQqaL/aFlHrC6YWRqkvqptJ4AhR5OQguM8/bBs6d3AXrmvmmLSLF14KrWIJ8v4GHlH8sRm3gE//vqBcZ6XWGy3xs3/s/lsfH4uiX490wmTqe26p2UVmLUmEmw/M2v4OVig/S8SsR9cxjF2W3n6lDVWgCAWq1GbGwszDN3Y85zE/DOVw/ho8fWQlBNqqD5/SSMdtCvBVsz8nitvTqKWHayZabGkcpJs8UBLqQo/EqlG7GcW0o+nAL+YvZwadNzGb91BMuOKe7XlpYTy5yiEgyYGIl5T47G2R3xeHntcqMe1jdhorvolR6jGUtGYtva4/dd92y/fv2QnJx8wxPIM9AZzp62+L/vD/VyywxHi0qNnZfS8N4nOxEa5IKQQBc0SFrwyYzx4LK673Kb4h2Ai5V9Jz3B4fJUcFksTHAO7bxwD8KhWHg5cjh2FqShqou9RbOWjYTQwoyYVGAIRs/oj6rqJpyOyyZ+v5JZim//PomP1x1GTkkNXlu9GAH9PTusa8fPR6HX6/Hdwbc6LHe/wRfw8Mx3izH96Qn4+aU/sPeXI6agyMR9S68ERk7uNshOMg5/mZ7CO8QVCxcuxKpVqwC0mjgufWs6Dvx9Hrp2vuz7OrX1zXj3k524nFQELpeNoT6e+PGh6TDjdE8npZuFFRKrKzovaCTooMcv2afwsNdAmLGNZ+bUwsD+0Ol12JJ9tUvrxYwJQfhgv9Z8ZzWGmVDBYrMwekY/jJgcga17bu1sXV3fjGMXspFwLBVL35vdaXD02ePrAAA+TsxelPsRj2BXfHHoXagUKnw091sUpd/ZLEQTJu4Vejww8g52QUuzAnLZ/WUnP2RyBP766y+kpLSaWYaHh0OpUCPhpOF0GHcCBUDI5d74x+ewDVa3SqXB4ZNp+G7NUaw+dR6DvNzxwZQxMOcbVuMxwMEFNmYCHC7M7rywEZHdVIXC5hpMdgnr7aYAAKa6h2CYsxe+vxoLTXvjlrcgamQgZj0+Ej+/t63DJLBdIWZUEN7/aRHCBnjjxw92oOY23NQ3fb0fh//vHJ774hHMe2ESxGJxu+VUCjUuHLqKjxdNhq+T8eq8eoJhs2Pw5oZncOV4Cta/u9lk3mjCBHpBYxQ8LhwJV0qgufa1RqXnEX+nXMlEj1a5zPF1torU7vCk5EOcX0Wuo+cw4z/rTDIAYClJbY7KltQFaITkoeK0tDPu30ymTjh2LXGjn58fXAfMheI3W0yyfgJ8AQ8vv/8YTiYUQO1D6hxkTmRbKdqDilJ07IBD1xMBpMEjBSCk0Q2hzg7wshEj2MkBVgIzaK65EPM5bBTVSZBXU48TWXnIqb42o8iRPD6UjmyHegD54ipxJnUov+dcgdJMi8URUVgyNhrfxZ8Di5Y09mIVTURLM6+kBMxp45aJfDwzaBCyimsgv6SEcgR5nh7wSyaWDxSFMOpojiWN7lQBpBbq6mUyKSqXJnvTmjFfJnop2QtUrCUTwnq7t87GPFx1Bs8FzML+yDLUKcih5fx+5P7bHCG3wSolZ3RKh5HtrIlkBrlqC1pbNSywKAoPeIVhknsgPk/fBaWZFJbXbjGtjnk9WR5uO8aOthZ44JUoPPPsYiQkJDDKtsdkl+cZv9WP8wYAuDpYYcmMQeD5Ufg2JxYZogrgdcCMRx6b2nIrRh36+WLsQC5O11dhytx+2DkqHt/8fATS5lYRou5Y27Xwfx9th0OgK5b4BmL97kNQyFWoUdox6hQ/TOpwqltI3VeUBzl8m1pFargAgMshr/NKOVPndjOCdPL5RiVdYZQ5qtvaYR16OU146U62y83LDn5PO2LEiIFYtf4brF27tsP6TJi4n+jxwCgozBV7d9w/CR9dXFzwyy+/4PPPPwe73gsAMHRaf9SWN+Ds6cwea4ebhRWm+wRihLsnNC06pFdWo6heggNp2citrcP116UFnw8vsRj93V3wv6ljUStrwaG0bByS5Nx1ItGDeTl4NDQCE7x9kVhZjlNSw+y/s7UFjiT1rd6i6+TLKnClPgePBUfh+8TYHt8+h2LhjX6j4SiwwPsJh9BCt0XvhIenRGHHjh23HRS1h0DER4iPE8YPDECglwN2HL+Ko9JkyLXqzlduh1qlFH8XnMW8XBbeemky8gtrsP9ICm6WRev1emxYdRgrfliAiBhvXDyTdcft72u4etpi+WuTcfHqESxcuBCFhYW93SQTJoyKHg2Mhg8fDk8fe2RnlHde+B7A29sbW7duRUJCAo4dO4ZJ1k9gyuIRmLhgOH58+S+A3f3ThiMcHDG/XyS8rKxxqjgfH547ieLMBhD9Bjd1/kiVSqSWVyG1vApbLl3FIC93TA4NwFRxIH5PvoSEijL6Jm6b2pYWvHH8MD4bMwHPRg9C9sVylLc0db5iJ1gI+KiVGibRaW+wp+w8nvFYhIf8w7Etp+fyBtoJhXhpwGgotGq8HrcXGr0O5h13ZhAE+zgiyNsJy+etvqPtO3vaYdKjQxA60BeNfAr7zqZh/e54yOQqyD3uLCi6mb2Hk5FXWANvTzu8+uwE/J1RgtybtI2N9TLs2BiLqXNjUFXWgBrce1q/9pi9YAiK8qrx2muvQa2+++NswsS9Ro8GRuPHj0dKYjF02nt/HNvVzwmv/7gWGzZswB9/tA6pDZ3WH5MWDsev721FQXoZEO7dbduPcHTC/LAIuFla4t/sVLx39ijU14fLcHs6IrVWh9i8IpzLL0JMtDteiRmGhIoy/H41AVId067gdkirqcb7p47h63GT8NmgqXju7H+Qae5cbzbA1w0cNgtpxX3XKFSqkePd80fw+bBJiLB1wq+pF1DafPcBY0cMcnPD0zEDcaYuB39nX+6SpggA+DwO3lg2Hqs3nYZM1rWg1MnDFpPnD4V/hDtO/HcJm1cdQsUwt85XvAPSsyqQnlWB8goJFrz7ABoqJdj502GUZLcK9S+ezcaE2dF48X+z8OSGvVAYWRoZQzPnsWEQCPlY+8V+U1BkwsQtoLo4JVNPUV3L8jSB++iN/y98/0Hk18gRf6ptCIWqpLni2pMzRfRs5vYaw8kyLNr9rafJIoSVzJe42pLUgPBryTH5ysGklkBLSo5gVcB8kcRtfR0AwOFwsH//fpzcWYkr8a0aqqlzBmDI0v747OwZJFe1eq3wGjo/lgpvMmgQ5pC9TKoI8qVk12SPxZH9EebggB0Z6TiYkw3bWHIIjH48KFqg2t4xr4oRQMDj4omxMXC3FeOdi0fRdJMvkY5L1mHpRr7Y1RoyGHs3YjImuQYgo6kIP2Rvg1TTgqJa8rxqaevoK5iJaB9SeOOZhSPxzpe7UFLRALktuU7dMPL4hfswZ9wUSUgPIbompK6G7EZhNZDnQCdmvmAEluT1JJeSF1B/X1KbUtwkBodiYa7HIExwDselunxsuJiFclnb0Ba7idw3iuZpFTyUzEOmod8IAApTfLCkX38MdffATxfjUWRBzj6ryiZ1NtwmZh0577+K1atXo76+HitWrGAkeGXxyOPTMjkSAODhaoMpY0LhMdgBu/LScaAwC4pr6Ub8vckZhVIlea79xeRMN3riXwCQych1xFbkvdGYa4tx3r5YFNEPn8WehnrRfgCAmZCPxz+fjzQxB/+dIXvsuHPIgDvCluztLmgmr1mllvmt6Sgg9Xe+5uS+bD84jFj2/5P8+6GMzxl1MpLq0jRHE80WEstssSWWfzgH4+cNQtqFPGReLsDjHzwIne7+6CUzcW+g17djrNYN9NisNHOxCKFDAlBloFkrxsxTTz2FsrKyG0HRiPEhiBrsgyf37r4RFHUHY5398d2kKWhSKvHcgX3Yk5V5o5fIEMhVaqw+dB6XC0rx8ZjxEN5Fgs4vUw7jckMW7PhWmOU6HKw7TKsaHuQCnV6PihqmsV5fQ6PXYUtRHF67vAlNajm+HT0V84MiYc69+yFXChQCLDzw/aQpcLGwxHMH9iKh/M6GRSdMmIDBgwfjm2++6bQsl8fB+BFB+PStWXjmsZEoLK3Dk8d34r+8tBtBUU+h0+txND8X38Wfw7vDR8EnonVav6JFib2/HMGsYWF4euaQHm1TT+Hu74hRs6Kx+fuD+O1/22HjZIWPP/4YXf3QNWHifqDHhtKs7C3QWCdFQXZVT22yV5gwYQKWLFmCZ555Bm6iB7D4ubGwsbPAuu+PoGli9wwh2vJFeDp4KLwtbPHR8ZPINlBuqlvx7/lkOHha4smoGPxw4TzuZK+0ej0OVsRjpstwDLELw8GKCyi4g3oG9fdGYloJNJp758tXom7B/xWew35pBeYHR2L9pAexPTsVOy9ldOlYsykWAiw84G/ugTArHwjZZvg9LgOH87rmmnwzZhwOnn32Wbz//vuQSm8t1B4+ewBcfB3hE+6BGiEfv28+h4LiWgCAYmbvnqukygp8F38O7329EL++/jcKUopRnFGGp77dhjWvzIEZj4NV28/2ahsNzegHYqBUqLFv4xmolRr898txPL9mFvh8Pt58801Tz5EJEzfRa0lk70UmTpyIDz74AG+//TbMzc3x8tszkZVWht++OwytRgeAOcX4bhALzTA/bACGO3pjb3Eafkg9jaa6njmlqy6cxwcjx+D5gYPx08X4O6qjUFYJEccMdcom2PGtAHQ9JxObRaFZdmd6J2Mnr7EeH8efhK/YBs/1G4ypDwTieF4+zhYWorSJqUESsPkQcy3gJXKFn7kHIsQBqFLUI7e5BAcqzuOqJAeZee531aaFA/oh69J5HD58GADg7OyM8RNGtGbxvYazjxM8gl1xbncC8pOLcZbDgrGZKCdVVuDPH9Lx1DeL8Nsbm5CfXASFSoOXV+/Cd8/NQoSPM5Lz+45haGeMfjAGW388BLWytZeuqrgOS5cuxbPPPosVK1ZgxYoVvdxCEyaMh25/ix5VbwYA+Kv9MUE3ACgjx+y1fuSDWivqfHimM00Rv4Hsom9xogmEAFhdIXuumvqRfkI6WjPomiJBNaldeeDBARg29REcv5qD/636DTWNMvxdlI2zsiJg5vV8S+TbQWXFfFtoLcm280vJYRTNNQ8dD2srrJw8DinxxfjfmX2obZBBCDbsy5j+ShTNWVvhQGoxVBakdsUin5mqxT6RDD6KnHn45PgpfDBmNJ6PHoKfLsUTe0fXe3B4zGGTnFoBSqRK9Ld1hzm8oVZkEH/Xy8nL06EdM2ZqggJ+HBYcTqdD2iADe+oA4u/aS+TxyxczDf1EfPJcqrQ0cTqtHYzJSxTzPNpbkkGeSkhqjhQ0LUpdNZmTCwBEDq0amUrI8EF6CYY62WGMfwRmR42GHnq0qIGk+lII2TwEWDnAms9DrVKKWqUUmY1l+CltJ6rkN/fq2EAnIBtf20j6TXGayZvJ96+2+yQ02hNjRjhg9XYZJrs8DycPWzz98VxknM+CvLntumuwNMeOv+LQfM0/qOwp8twL+ExNVo2M1PTJleQN2GxO3sOeYuaQfEo9KeB2MSeDR4UnWef+F4Qo01/ByuOvIKmuHGeePoLSvGJsLNiK1z+Yje/f3IwTWaTmymZ0DrFcwSbPW72czGcHAOGO5JDlhitDyQLO5L2lzS1k1EGHYtOuUdo1eUSx6cb/IyMjweWykXk2A7rGtuvBuT4c+96Iw1dH30f+Fgn+zPi+0+2aMHE/YOoxMgBDh/lj6rRIZNTVIdrPDWv2xyGztJqRqNZQOFtaYMWksVgffwlZuwu7ZyO3gUKjwccnW4OjpwbE4JdLXfey+T79OH4ZPB+LfAbi34yMzle4iSHhXijPrUBZTiUeem0G/vjg3y5vvy+R3lSC9KYSsCkWLDgCsDUOcBO1Cse3Fl6Blt2AZk1bANYoZ4rV7xSvAEc8+e4MnNqThPmvTAabw4adsxi7159C3PbzRFlNTJDBttvdJNWV452L+/GAVzieWfEgtq49hqtxuUi5kIulb07Hieyjvd3Eu4LP5+P555+HWqFGcSZTU6ZWafD3R9vxwg9LsHXWL5DLmR9WJkzcb/SY+HrixIkoLb23cvBwOCyMHhOMx58YBaVSg7SiKrz/92Fklnbf1HERj4eXRg7BkaxcnCso7rbt3C4KjQafnz6DMV7e8LXueu4piUqOfwoSYGtmDnuBqPMVbsLZzhIXDiZi8xe7YONsjSe/WghOOy7n9xpavQ4StQxZTdU4XpGF4xVZKJaRQZEhsRAL8eQ709FQI0X/YX44tesydv1+Eh8u/Q0XjqZ2yzZ7kgxJNT5LOo41K3dg3jPjETnED399cxD2LtYY4nZ3Q4+9zcSJE+Hg4IALB5OgUbdv0HrleCoqCqrx0EMP9XDrTJgwTnrsLRIREYGtWzu2se9LjBwZiK++fgQvvjQRGo0Wa9ccwz+nEyFTdF8OOA9rK6yZOwO5tfXYmthzRoCd0axSYU9WJuaHR9zR+vtKUqDWabE4YEDnhdvbvkSG7578FTqdDo89fG/OKuotPP0c8NmGx2HvLMaJ3Yn46Lm/cfVcNvLTyqCU31v5Dsvya24ER0H9PbH3r7N4e9hICLop8XF3Y29vj+eeew779u2DStmxZ9G+dccxb948WFoyh3RNmLjf6NHP6y56JhklLIrCtNGheHTBUOj1eqxdcxxPP/kH0lLv3BH6dnCzssSKSWPxW1wCfo+/dEczwbqTnZkZiHJ2ga1A0HlhGjrosbc4GYsCo2BnxtRo3AovZxuorr2cdVodNv5vKzzcbLD4EVNwdLe4WFrgnXEj8ea3j0DaKMcLD6zGmYPJt+x1uFcoy6/B3r9j8fTKB1FX1Yh6hRxBdvadr2hkUBSFF198ESdPnoRarYakumM7i/yrRWhqasKGDRt6qIUmTBgvPf4pRE9uyG6kC33JFyPVzjRSkYz8+tFYkAJbbiO5DbMCppgYLDImNC8gpx5bZNOSptZKEBDpgXnPT4SsSY7ysgaUlNTh6JG2oQSOnBau0LyouE3ksqCdETe5PSkQFdS0Tvh5ZcIQHIvPRvrhItxsRygqo4l67ZlCc7o4XSMgf9DRrgKZOzO4oQvaHS6T+1o1XYorkmIMD3PB7sI0aGmCZa2q4xj8q4Q4POQVhUcDI7EmozVnmL6K3BdBdVsbhAIeQlzs8diW16DWXbse5ABrGhevrXsSj8f44P8+3wVBgBdRR7YfaeYIAApH8hhq62jHkEMzwLQnxbLBHkxvqrxqUrQ72oucIp/TRP59VkQSo45Empi4uJRcR2xHE3jTROPKOuZ5FJSQ58VxB3l/Ca/mYtj0fpiyYBiyzhWiLpSDTZ/uREvWTWYKbPJcsmiJnwuWk8fLXEQe3xY505dJl0r2VNgNJo9pcokrsexky3zR80RkD1ZqEWkCyapi3hs3U/xIaxLjYjTh6slTeO2DqTiXVYRIkRPS6ltF6HZc8jlRr/Allud6JjLqXXdsHPmDFXkveW4nj+f1CSsd0VmZJaGv4cEJD+OD2d9g8Yr5iD2eCZYdzUC1ijSSfPbZZ7F582YMGDAAly7dP/ksTZigc+8LMgyAlbUQD78wEc99/jBO7kxAdWk98vOr8efGnvE6mRYTDA6LhT0X0npke3fKucpCjHD26bzgLchsrMZ83yhY8W5PNKzR6hhpDdQqDb5d/hu8Qt2wcuvLsLHvQvKv+xyKAha9OR0xY0Px5xe7ERTtjS1f7UHmxbzeblqPk1VRi8uFpejv7gwfO2YwbexEjw9DVWENmiUtcPJyQFlB57pHiUSC8+fPIzg4uAdaaMKE8dJjgRGL1fdiMA6HjXnLRuC9bx4BAPy2YgcoCvAKdsH2bRd7xJvF2lyA2YPDsO7wBaPzgqFztjIfTgILuInuzK9pXWY8uCw2fC3sOi0bGe4OSWP7M2jUKg0+W/QzTvx7Hi9+MNMUHN0GFAUse2wELKxF+P3D/zB96ShcOJKC1HPZvd20XmPdyQRIlSrMiAhGsHPfGU5zd3fH0BnR2PzV3hu/3W5+yq1bt2LJkiWws+v8HjRh4l6lR6IVR0dH+Pj4IDf3zh13expzCzNMf3ggho0LwVfvbMe/q4+gorgW0x4bgd9W/gelsvvTGbApCssnDUJsWgHyK+u7fXt3i1qnQ6akGuE2zp0XboeUhnJQAJwFHQtAuRw2HpwRhaMn029ZRq/X48yOizi+76opOOoEoZCHxxePhIW5GTZ8vBPLP5yD4uxK7P79ZG83rVfR6fV4f/dRSOQKvDd5DPpK9oyFCxfi3J7LKErv+izgrKwsHDt2DDNmzOiGlpkw0Tfodo3ReNY8RISEoP5qCwY2TwZo+hY0kiZsbLqmSNGOq7EFOa2bRZtxQSnIZX11LaMKHU3rdLNhWuTIYAx7NhSpqYmYNP1ZFBUVQSgUYs1na/DD+i/w96WNiHjxO0adLU7kMofWoUHXFCna+Sijbtr92f1DYMPi49d/T0F0LeWFqIhMjCnzJI+FWsR8etM1RjInWhna3y3acQFo8iJ1IXJa23WyVm1Ucb0UfK0Z6MnazayYM5joRn823whR7t+A6c3+SF2bhYIHyK9cs4rWfXf3soNYwEPxeWYSkZuN7QCAtVEEqqEJb/5vJr578U+UFTGPj9UZct8avckDIqd1FrDtSc1aQzumfkIBub/HT0cSyzpH8rqukzGtClgscv893Mjr2NOcNDm8VE6bWs5nCqW9NpHGprYiFl5bvQTJ57KxfvVhzFw8HMpGGbas3NLaBgFzWJNuLlgzgmbYRcv6w7Umr1n6sQEA8VBSM1RS0aaH4bHY4PPa9kWj16GyjtkrGeBCbjgjjTwefFrSZm0UqRdqFjP3VWgjx96qRCzyHYyA4WZYl0EmfJ3nT2qKdpeQ5xkAhg0i/bliL5JDVYJC8qNnsuMzxLKmhpnih2NDDu9p6luvhRFzBmHa2PH49sW/QF0/d2wW1J72UFuS1ymLpjG6zqFDh/D1119j06ZNjKFqEybuB3pOfG3kw0DXCR7khyc+ewSPv7wYsbGxN35fsGABqqqqsHHjxh5pxyAvN0wK8cd33xy5p/KAdUZ9fTPs7S3B5bI7LFdaWIfaamZajPY4u/syBk4IR9SYECSCGSTfz0xeNAKnd13Cob/OYtj0/vDt54kfnvm919rDpljwEjlisG8gwm2cYGsmhKu5GFp9Ww+tWqdDbmMdrtaXIbayAFXyrqeS6QoHy9KxwGcQXgoZg2fjthrto4yiKEx4bBT+WrkN9ZWtgaaNkxV4fC7kLbefNicxMRFZWVmYP38+/vzzz+5qrgkTRkvfNOjoJkY8OBDzXp2Gde9svhEUURSFxx9/HFOmTMGrr77aI+3wtrXGkyMGYvXJONTWd+9D39Cw73K8oUWmBIuiEBHhjmyUG6hVwLEtcVi24kGcPXoG2RWm4AgA7F3ECB3kh48W/YyY8WGY++JkfLngB7RIe9b92JIrwHA7HwRbuSPcygsyjQIXy+txujwfFS1S5DTWQMdp67nwEFnDQ2SDoY5eeNinH4plEsRVFUHKSUORrAJ6A4cuBdJaZDZWItzaFV7mtiho7t4kzXfKtKfGo7a0Dulx2WBbtQ5HRwwLROLpjC4P/f/+++/45JNPcPz48XvOmNeEic4wBUbXmLhoBGY8NR6H/zqD5LOZN37/7LPPMHz4cMyZMwfV1d3naH0dIY+LtyePwp/xV5BUWgFmZi/jJsTWAdtz7nz2nB6ApLEFbE7HPUZd5WpsFlLOZeOhoRH4dMcJg9bdV5k0byBO70z4f/bOOzyKqm3j92zfbDbZTe+9h5DQCU1AujQVEVEQG4oi2FEU22vDz14BQaSJlSq91xAIBEjvvddNdrN99/sjQDgzgRBIshsyv/d6LznZmXPOzk555pz73A/MZmDE9AH448tdKMlmWg90JDZcAQLsPBEodQGf4iJa7g1viQMKmkpwoS4be0oTUKyuJqbSAOD6s6FQVYeCRgVOlOdByOVhgLM3+jt5Y5jndGiMOpyuuYzT1a0k1rtNzADW58RjWfREcCnrXEQiEPERO6UfVr62gfF3rbr9SZYTExPxyy+/YMWKFXjiiSdQVdX6tBsLy91IpwdGB01/QW+6Bx5mWxw0/QWoaBvQyhNEL5F/kDK1FybhzRPNUkqyUrORqbW43gdk7ty58BvviCHjB6KmpvltcLxwNl5d/Rx8wz3x1n2fIbruXuC6ZhtqmG+lWjk5WkLX9ujpUpRWZsjeCB2InDMlyNicBVcAPDXZd5OY/MkafMlGBK3MLtH1QLalZN9rI+n9ZI76OKSRowhaGemRIyxv7pfEKERNfhN4GrKfQhfmG6veQAY/glIFuE16lBdUo0+AC/6tIk0zKcOVOowGwGxqKd+EvdUrr/37wodbcWLfWQxLaER+Wsto1FV9xlUkNF1N5qeko7foDCnktjvKbFdeSAYY7lLy4VQ6iRSom/hMfYv7FvL7m6XkMT/xHJmTzCwgT6jwV9JBx3hFWzdgQgwienngf8u34rlPZkFRpcDZ3Rear9HrGGd8jFFH40TyeCi9yPPFvRf53UvTXUABeDA8EjMjeqG6qBGp6RVQaw3YWZ2FxKwSCIuuHh8OKLjAm3btqB1JDyLb4qsjSGYUXvnflw9xEOvujTG+oVgSeA+Oyc/jv5Lz0F7xuSqnvWbwcsjEtSIN87w3lTRf9JlFaoijhbDXOUBb0zKKe1gaQh4bDdMrKa7AjygL6skvR9dEQkr2i6qtZ9R5/Xnt7OyMT779BP+d3oq1qd/AbDZjvOdSUBSF0Y8Nx+qv9sLMYX63tryQtm3bhpEjR6JXr144cqRnC/FZehbW+frThQwdOhSPPfYYnn322WtBUWhoKN79+2Uo61R4acR7aOyi6Sw/Jzkiwz3w17buaa5mKxTAxVaCaiU9+m0faRcKENzLs+0N20llZSUO/hGH0TMGdXjd3QmKovD4ezOwY+VBLP5uHhpqlFiz7E+YjJ2jZRPxePhk9FgM8fLBkkP78c6avfj9YCK2nkjC2bRC6A0d56YdV1aE/505giXH98JX4oz/Rc9CqNSj7R3boLhegcLaeswOZ4qrLc1nn32GzMxMfPTRR0R2AYoCxDZCZKfe/pT0uXPnMGvWrI7oJgtLt6FHB0bh4eH4v//7P3z99dfXgqKxY8fixx9/xPYf9+PnV9d3aQqEcZFBOHoiA+pumoPKwUYMpVYLle7OVrKYTJ0nbz3wRxyC+/gioJdX2xvfpUTfE466SgUmPTESmRfysObdvzrtmAs5fLw3YhRKGxvx6oE9yKuva3unDiBPUYev0//DzuIELAydiGHOYW3v1AYJhaXo4+IBAadjp3nvhPvvvx/e3t748ccfGSmXBo4IRXnJndl8bNmyBb6+vhgwYMAd1cPC0p3osYGRXC7H+++/j+XLl2PPnj2gKAqPPPIIlixZgueffx6Jh7s2aziPw8GgQB+cv1jQpe12JMHOjsistE5h6lU0Ki1++2gbnvvkYdjKbj0v292CSCLE818/Dq9gd5zekYB/vt3Tqe3N9huBRq0OP5w70+Wrucww40RVGj5O/hcPeA/GaN/bd2UHgO2XU2EymfBw2O0lS+5onJ2dsXTpUnzxxRdoaiItJPh8Lu6d0ge7/z53R22o1Wrs27cPEyZMANVdjJxYWO6QLhFf29ra3vCzcZK55B9sSB2FoZWHF5e2aoY+R28oJ0XSdN3EPffcgxXPr8ChQ4ewfft2cDgcfL94LUL7B2Djoi3wzevHaFM3mhxCr25lRN1gQ05FiCtocSet2OTZsv2kwCCUGZW4bFYAPi1iJnkGOWLF0dHKtMEZqpU3f1EteUNTepBlhxRyH3FNK3ogCXmqqLzJ72qyMSIwyBHZ6hpoPPTgNJFv1VIRUwBaUkoKbM1VJTBrNDDXK0AZjHBIo+Uoy75isMTRA41NLeV2sLd6JfbuBxyH6THtvb547rnnMFT8ILENPaeUyzna8dqfRZRNCtIPBwDg7kqW60nxl/sGcuTErGtllJCWkNfoJiM/LiOPse8PGWS/aF5dfAEPT370CGzsxPhh0Vqc2XmeMcowhjOTKCsnxzC6VTaEPB4mGXkSVsc1G3rN7h8NN7UHfvpiP7xULeeL5DR5/AAADjKiqHcjTT7VjuR3bXJj6gw9DpLfpW5Ws25L0aTB/y7uxyeDJ6NOkosSdfMoikpL+leZTMwHvz6zpR9GAZCRUYGxNv7YG3e5+XNvsl8SIfN3VOeS38X9AnkNGwvIVV90TST9/nWViRMn4vjx49i/fz/jM5te7uDYCRFfUg142kFQy7z+6Pfe/ar1rX5ed5CLBz+ehvKny/HLL7+02hcWlruJTh8xWrBgAd5//30YDAZERUV1dnNt8sQTT2DRokXYvHkzVq1aBXd3d7z99tsIjPbFytc3oiSrc1fltIa9UIRZEVHYmHSxy9vuSPq4uCOp+s6Pn0alRW2FAsP73tkb/s149913UV5ejiVLlvSYN+HIoaEY/8RIrH//b8TtSGAERR2FiMfDOxNGor+PJ97fdRhKVftXRXU0+cpa/F14BgtDx0PAub33Qb3RCBuhAHI7G8ilzAS9XYm7uzvmzJmDFStWMD4TCAR49OHBSE4paWXP9lOaW4m17/2NWbNmwc7u5q70LCx3A50eGMlkMhQWFoLP5+P999/HqlWrEB4eftNRpM7AyckJb731Fu677z7Mnz8fGRkZWLVqFTZt2gSNRoPVb/2O+qpbMwzsaJ7t0x8JZSW4UFFmkfY7gmCZIwQcLlJr79zSwGQyIzU+GzJp5051LVu2DC4uLnj+i8fgGeTa9g7dGP8oH7y+ZgEuHEzGvt+Odlo7PlIZPr9/Auqa1FiyfR8atZYPiq5yqDwZDXo1xrvfnoA6t7YOpTUNUCjVWPDA0LZ36ETCw8ORmpqKrCzm6Ft0dDRsbIT445/4DmuvKLMMCoUCAQGd97LCwmItdPpU2qeffnrt31fNEtevXw+9Xo89e/agcp8JpXkV0Kn1nfIGy+NzsejZRZg7dy5UKhV0Oh3++usv1NfXY8eOHXj22WdhNpsxlv9Ih7d9KziKxYj19MacHf9apP2OYoSnP06XFcJk7Zlur0Oj0eCFF17AVy+sxuLvnkBxVhm2/rQfJTXW8zC/UyiKwqhZQzDvg4dQkl2G/z38dae1NczDF8/2GoS/Tydjf1qW1TlEm2HGP4Vn8HLYfdhfdpnhHHIrKJu0+C+tCHMn9QePy0HXLc1oQS6XY/ny5fjqK2ZaIg6Hg+nTp6OgqKbDk07//PPP+OyzzzBnzhzW14jlrqZLDR7NZjNWr16Nv//+GyNGjIBMJsPcj+dCKBQiNzcXJ0+eRDjH99r2+clFyCps5QLkkgNdRpeWvEF8ARf9Y4ZgxNR+cPKUITTGDwadAYlHknFy63mknMqA0WCEsqFZfzFW+CgAgONKmv00DCBXLVX2JdsU0/JBAYCKlqqKoq1+pvsaSXM46B/qhrSsClDpBkjBgTyD1IVw9GQllf3JkTZ6PjaVB3NayEizVqFrd1rzLaJD91OS5rZoPHhcDibcH4J3Dx4Cr775lDLS9FYl+czEcDz7mwcgHL0JPHVLPZTgSpt8HsChmsuaG+x8ixiNRiz+7gnwf+ZjxowZmPXeLMiNbqivbMC+9ceRdSEPsiTa9IE9WTa1kssKleR5S9dv3EogbqSt7uPVkLYRXh8nEeV9NC3KvHnzMHPmTDg4OKCyoRzT501Fge7m4v7oheTDtiGW6YItFJP90mdLMTowALODo/HV4VNQ/FaE6xfI82vJOjR9/Bl1KgJIvY/jZTJsafEtakZQRYqNAcBAy3Vm+zl5wWXPdEI1DJho24ghGIJUh/+Iz1NLmMmPpb3IVV18pRHmMjXMaiMkZXoUKskptdbywJlsyWvHNo7M8be3DT8hOmFhYdDpdNi8uWW/q3qgcY8NR5hrAN7KS4R+YMsxtc9larLsLt589SjHlvSQMx51gmK8Dsse/gw7Vx5s0weJhaW7YhHna4VCgZ07dwIANmxodmq9//774eLiAi7nys2MojDn3Rng2zZPpxRklCHxRAZMRjNwxayML+BBIObBv18gvHwdYS+zga1UDK7ZBJ1Gj7qqBvzfwnU4++9pxmgUPRGmpaAoQKnpnsvzrzI42AdVKhWyaqx7RdrN0Ov12Lx5M/744w88Ff06XH2dMfvNadCqddi6MwkZyR2j1+gKuFwuFi9ejGnTpuHnn3/Gk08+iYULF6KgoONXPFIA5vXrg2F+fnj3wEGUNjTCp8Nb6Vg2XbqMZSNHoq7iDMo07UsPczm5GOPH9Oqknt0afn5+SEtLY/zdI9AVo2fF4qsFa6B/yLuVPe+c/euP47F3HsDOlQc7pX4WFmvAalKCbN26FQC5KmbvmsOw8fcCxeEgekgwxswYCHc/J3C4HHA4FMQSEYwGI6prVSgvrUdluQJNKh1KE3MRt+cS1CotzGZzp4lMWQCxgIeZQ3tjbQcJxwVtuJp3NmazGYXppShML8W5fZfQf1xvPPb6NBiNJhzbm4wje5ParsSCcLlcrFixAlqtFjNmzMAHH3yAP//8EwkJHW8aSgF4MXwU5FoZ3ty7D9Uq5iiONZJRU40taamYET0W32e1b9QjPaMMD07v30k9axuKohAdHY3jx48Tfx8/dwRGPxyLP7/chcqiGgCdExjp1Dp4BLgiKMYPB+7MCYCFxWqxmsCoNQx6I6RyCfrdE46gKC8IRHx8t+RPqNU6mAGUF9bAoDcSU2kAwC3s+pVlt4u7zA66DnT+7Wom9QtHaV0DTnbAaERIXz/YSMUozatE5KCgDujdnZOw/zJSa/QI7+2FyQ8NQN/BgbhwNA0pFwpQfSWDubUgk8nw7rvvwmg0YtmyZZg5cyYGDBiANWvWdEp7C8NHQiYQ48PdR6BrJe2ONfNfRgaeHDwZUp4NGg3dI6ADgKlTpyI8PJzQbjo7O2P0w7H4bvFvKMluZY6/AynPr8LJbedw3zP34udzn7a9AwtLN8QqAiMej4fevXuDy+UiUhgK90BXuPu7wMXHCS6BHog/mIz9f5xBXloptBo9Q2PUnXGV2SKlqHNvZp0Fl0Nhcr8wfPTP4Q45kyRSMQrSS6Ftsq6pxSaVFufjcpB4Ng99BvojItwDE2YOxNH/LmHfP9bx2hw2MAiPPDMeFRUVWLJkCdzd3TFr1iwsWLAA58+f7/D27nUPRS+ZB16M/xM6Y/czytQZjVAbteBYaVLY1hg6dCgWLVqEF154AQpFS1A+e/Zs5Fwu7PSg6Cpbv9uDz/YuBY/Hg+EW8hWysHQ3OjQwGm/3BONvlIsTJFIRxJJmBbC5XgFbOxt4h7qDw6Hg1NsfUX190dighqZJB7NGj4riOuQV1uDsxSTkN5yFVnvl4nNtHhniKsg3PG4ZTdsiItXGtXOZQ99GISk4NtAEyqJ6cvqNLnLW2TOqhKiCrJNLe77T6+BqzfC2tcPFuiIIG5rbM/HJGzW/lvyuLgmkqFkRTAoknZKY+a44WtrfuGQ/pflkRxv9mB4tlX3IU0UvAUb4+6BC34Qk1EBYR9ap86aZC/KZIwrXm+cBwLnGXZBp9NhXtxoBTXx4CKZDL2k5HlcNC81aHWA0MQwMO4r9mo3kHy62/HPvlRhDLpfj559/RrQR+PbbHdBoaH1pY8kT3cSvNc3bNbH5FQzZpGj3oOkv2NnZYdKkSbjv8eHYvHkzNm7cCJPJhPfffx+nfk2BQ1oExkkirquTFDnXTg0nypW9yQcdp4qZ3HZYQARmuPXG0j0H0dgoRuB28sTm1dKSONPOt7pQsg8Ac6GCwZb87kYxeV1UDZIx6uA3kdesqI52e3MlfyOVToCcGkfUapvPd/MtpIpr9OHDZMOBUUihwU8AikN+9/oaZtLrsO/qibKhNbH+LTBx4kT8+OOPSE9PvybeH3xfX4weMhTf/XQMCGsRtRtol/D1ixiuwqUtIgBNbmWi5Ty8JrbWA+PP98d3332H559//ra+CwuLNdMpI0YcLgc+oe4I7RcAmZ8b+gwNRpPyyk1Jp4dBb0ROUhGMBiNqqpX45dsDKCpovlnQgx6Ds5Re/V2Fk0yCzKLuufT13qBA7M1oxcX4NrGx6T4jD3V1dViwYAHee+89rFy5EpcvX8a5c+dw4sSJLtO0hYeH44svvkB6ejpeffVVpKamAgAee+wxyGQybN1zusPb9LWXYUH/gVi09z+UNrbi+H2X06jWokmjg7uDtEu1iwsWLEBMTAw+//zza3+TOdth8vwx+HXZH1CBGcB2Jj///DPWrVsHOzs7NDRYxv+NhaWz6NDAyNnLAWNmDcHgSTGoLKpByplslBXW4NT+ZJTkNT/8zTVkKgRTUOeIBFk6l0HeXvCR2+PzYyc6rM7evXtfe7h3B+rq6vDyyy9j4sSJcHR0xJNPPomnnnoKBw8ebPOh2YsziPwDhwOYzci+mI+C1JuvgKMoCot/fhrySBusXLkS69e3WAGMGDECixcvxtSpUxGlHnvb3601RDwelt0zEj8nnO2RQREAGE1mFFcrILMVw9iJyY6vRyAQYOrUqXjhhReIIGTGK/fhzK4LyL1cCKp3SJf05SqpqalQKBQQCoVtb8zC0s3okMBIIpHg/fffx8iY8Ug/l4s37vsc2isZ4ikXpn8NS/dGxOdhbr8YrD13ASrdzb1Q2gOHw0F9fX2H1dcVmM1m7N69G0Cz9cTEiRMREtL2Q0pKkSOhFIcCT8DD6y9OhLZJC61aj7g9iTiw8eS1a0nqYAsbqRhvrH0eIokQ0+dOIZZtOzo64vnnn8fGjRtRVlaGKOaszh0xq1cU8uvrcCgvp2Mr7mYEuDt2aRqZRYsW4eLFi8jNzb32t35jouAd6oH1H/zTZf2gc+zYMUydOrXTxP0sLJbijgKjq/Pczy9/HHwBD/dMG8zI8jye+zRRpmsp6Pogg68LUdY4M99IuLZkt0tnkcEXl+YbaNPKIrUmN7Jsn0O+/RnEtMShaeQcfaMXU7Rpoq00N9KkFBRNZsNTAzA36yuuaiz49WTnVQGkmIluEim/RI7ANYbIGP0SVZAaIpOQ/A3oGhC+iqlHENa17PPAkHBU6lU4XJMLXJEpGGhyBV4e+VTWeDL1QHyabjOIFw2OQA6NyA7h/IFQ6kxEX67qcsxGY7MNgxWuhNqzZw/27Ln9jPWS7yWQSCTw8fHB66+/jncffRZZWVkIDg6+tk184iksWbIE/ZUT4MWPga1MgjnLHkTIyEgc3HMZqfGOGDv4Q3DKahn1V40hR2gbfcnf3iaPvLaMV2Zo/BxkGO8bhKU79sOdNkiop12P3Eby/NK4k6akojrmSAs9+TH9PDfxyH7SNUkAIGgk/6iRk/3gi0g9kMnAgU4phFbTfB4F+zJvFE4iUmfT+8XzCAt8EPnK83joxQPYnEPqF2UHmamO6MmOb5QUtjW4XC5GjRqFhQsXEn8P7R+AgxuPQ6tqvq7qepMXIP34ld7DDOSCc8k0OGNqyQTC9Hs1XUN66NAhvPrqq9fSKrGw3C3c8ZIMZ29HBET5YMVrGxhBEcvdh4ejHSYNCMMvl6xjNdbdhkqlQmVlJRISEjBr1iysXLnyWlB09OhRTJkyBfPnz0ddXXNAzOFQGDd3BIQ2Arz+/Hrs2nqhw/vEoSgsvCcW/yamoLj+7tGTcDjtH/URcWzBowSo1Oa1vXEHMHv2bOTm5iIvr6W9gIAARI0IR2ZC7k327HxOnz4NvV4Pf3+mkzkLS3fmjqfSxjw6DImHk68N97O0D5PZDJmtCA0q63/j4nE4eHPGKPx5/BIKtdbl4XM3Yjab8euvvyIjIwMnTjC1XEExfnjkzWlorFPht3f/gsHHs1P68WRsPzRqtNiZnN4p9VsCN7EdBBwuGnTtu+4oigODWQ+TufOXqQcGBuLxxx/HnDlziL/PmTMHBzeeQHnenSdsvlO0Wi1Eoq4VfrOwdDZ3NGIklUvQ994o/PvN7o7qT48js7AK3q7ytje0AmbdE4PiagX2J2Zauis9BpPJRARFPB4PixcvxurVq/HSz0/j4KaT+GbBatRXdc5IToSbC4YG+OK7Y3HdKkFwWwg4XFSrVdCbbmGNvgWIiIjAxo0b8fnnn6OsrIz4zM/PD6XZ1mFie+jQIUyfPt3S3WBh6VDuaMTIRipCfaUC6sbmKbTWEmPuq1tNlOnbmGtpfi5epF6oJpLp7+KUdHOPIS7tJVBc04qHjoSsVyelaJ+T20vKyTalxcwbasVAuj8QWabrmvgqCrWqJvh4OOBEaj4AwMyjJastIacnOWpSg2RwIDtqd568iQKAyZ7chldK6rqaIsjkmQ0+zGM+cLw/osO98NaR/agPM4GjIfspKSG/a2Mgecy5POZD1SSg6UqURpgMBph0OpgNRkJ7BbRoHvhiASiKAsXlYpzgMbJSmvmnmSYOp3sDAcxkmbpwUofDv0ROWZhp/kkM3yO0JPW8ERwnh5t+DgBG95ZtYvr7wd9dhrDePtDrDTj63yUk/PUP8jPKwPW4ohWpJleKZS1gZi2jJ/flN5C/m3xUywOXAwrPOE7E9tPJUJWqcVUyJ00ntUuNYeR30TiRgb6wlhxdETQyr0czbVpL5UH+TrJM8iKnTEztIcdAnk90rZxc0lJHlJMP1FQTJLKWv+WWOzPqLOCR383fpghGMwf1BhuUa+3RlC4jPvfcw3xp2NvK+XEzOBwO1q1bh/Xr12P//v3EZ3369IG9vT1+ufAl1KaWvvdyGExsZ/Ylj5e4leS2eTPIvgdWkzcoU109uYORPJ5jODOBOHuMf3gyTvFSYTKZ26WfYmGxVu4oMPIIdIWiumcu2+0ockpqMCI6wNLduCkBrg6Y17sPPjhxBNXqztOReYd5IG5n2zm9xj8+EomHk1v9LGxAIJw85bCRitF7WBi8gloEpgVpJbhwJAVG2g2eQ5sKMHg6EmWur4wo04MtqY6ZgT5axDQV5Qv4iBgchKDevuDcQk44M7/l8qyva8Lp3Zdw/mQmslJKUJhTCTQx2+1IRrj0AmWisPtCRqe2YwnC7D2RUl9q6W60yuuvv46mpib88ssvjM+EQiFKSkqgVnfub3+rlOZUoL6yAWGDgpEax44ks9wd3FFgFDulP84fuNRRfemRZBZX4Zkpg8GhKKucqghwdcAbD4zE12fjkFl7e469t0KIkxPkrjIUZzJHvK7Hr5c3vEPcsenjLdf+5urjBEdPOQZN7AOvUHdkns+DXqvHzl8OISchByaTGRQFRN8TgeABzBxsdDdoowu5EpBuSm3WkyMgTYZg0PEUuDH+ZjKacGrHeax+50+Y7OwYn9MxurWMVphMJnDqu+5hKOGJMNNnKD5Y3XE+VdaCkMPHQMcgvJW/87b251OCTtEYURSF+fPnY9CgQZg3b163WelVU1oL7zAPNjBiuWu47cCIoihEDA7GilfXt70xyw2paWhCWU0DYiN9cSo539LdIbgaFK3aH4+z3JubDt4pEa7OOLsnsU0R/6QnR+Hi0RQMuq8vQvsHQu5qD7GtCKW5FShML8XGT7dBr2t5aJmv+/f5g0m4cJwpILbIVJpN29oW+shWVzLRvS9SFIXIoqfbuQsY6BiEQlU1ClVMO4O2EHNtIOKKkaTo2Pxznp6e+PDDD0FRFF577TViFZq1c2jTScz/v8ewb+1RS3eFhaVDuO3A6PHHH0d2ZQb26TZf+9sYzGRsR9cUXcu3c3UfDrnPgTPvEuUJircYdRZNJrUAdM8OUS058qK1Z2rMzbRRAL6CPlpDah5qQ8kdOK28MIppgx10HyMxbREJT9Pc5olz2RjdOwjxCXmoDyPTYtDzrclPkr5FfA05rWOWMLUX9VHkCIjKTUaUm7xouhMFEOjkgFfHjcQPJ+ORoCiBWE8eQ7UruY9uiJKsI4M0MBQUMPOvuZ5r0UsF2AqgdJADsb2v/METZk7zb8u7orsav2QGBk0fhKryeqReLMTxk1lQltSgKLuixWnazo5cUaChmVrxmVNYtaN8ibJBRPOw0pKfN3mS32VcNnMqlKMkR3eMTrTUNhW0VX2tWF1ws8lgdG/1SqI8JvZ/RDnvJXJ/eykt+RWAmmIZUdbRdF61ShvwKQ5GOvfFW+e3QZ7DPNHpmiKeitQM8TQ3XwZfF8i87dBzE0pKyfNa60heTDZF5PkGAI2B5DEuHUF+Loxv9kgbNn0Adp/MBFdCnsM+LswgMC+HHPk7eTQaC/252LEjGgDgdYrmEaZsI0leK4wYMQKvvfYadu7cidWrV99SqpFxIlJbV30v+Vs7Cdsebeo3OY0o71aTOiXvXfVE2ZxCpv+5qidyrnLGgxjD6otY7hpuOzBydnbGn3/+2ZF96bEcv5CD0QNCMLxPAI4lWdabBGgOipaOG4mfT8YjobBzR4qAZj+Z0aPC8eHSFhdfe3sxpk+MwrSx0ZDb2cBkNkPC52Hrpjjs23oe9VcSlVIK5gOS5c6IdvBCSVM9ClS1cEb3yV93KwQ4OcDDXoq43EIgytK9AR544AG88MIL+Oabb7BzZ9tTey4uLqxfHAtLJ3PbgRGfz+82c+DWjk5vxF/7E/HEtEFIyC6GyoKeUKP9A/BkZD/8eOJMlwRFADB1Sh/U1zchIMgV73/yEBob1KA4QEFpLQx6I84k5kHRoEbKzmQU5zNHQVg6lki5B1Lrb6716q48O3QAtl5KhcZggCWzfPn6+mLOnDmYMmUKFixYgAsXbs2Y84EHHsDKlSshRWAn97B9uLi4QC6XXzMeZWHpztxWYOTo6IjRo0djw4YNHd2fHsvFzBJczizFc/cPxbd/HoOhi/UlPA4Hc3rHINbLB58dPIaMiq4JQNzd7TFr1mCUldWjV29vnDqRgZ1bL0DdpEWjIzntZZvPmkp2Bd42chyvyGp7w25GrL8P7ERCbLmYYrE+yOVyzJ8/HzNmzMC6detw7733Qqm8tVFPZ2dneHt7Izk5GbFWFBgpFAo0NTXhvvvuw8aN7bMmYGGxRm4rMJo0aRJOnDiBoqKiju5Pj+a3nfF486mxmD4iCv8c6brVfmGOTlg8eAiqVCosPbwfioquGQnkcCgs/+xhZGaW44sv90BVxU6LWQN+to5YlXnS0t3oUEQ8Hp4Y3Bfr4i/AUms/Bw8ejO+//x4ZGRlYsmQJDh8+3K79hw0bhpMnT6KxsRGwIrNpnU6Hf//9t0sT6/ZEpFIpeDzmI1soFCI8PBw2Njee9s7NzUVxcXHzucPSJu0OjBwdHTF37lzMnz+f8Vmr4jvawAddbE1PVEgXcBuz85l9SJYRZbXLzb+G0oN5wdING5Ve5DaSUvJzFa0Oo4h5exXWtZ3o8nroppIA8NHFk/ho/BhUyfXYlpIGAW2QRDGXNO1zvkSKY+sDmMeioQ8pQJZebJ5EcJJK8ODgXoj18cI/2xJx7Gw2eAA86piCW8pIft+qPuREhLGMJi6mzVPwW9GkvvLJVNg52OClL3bD4GWGyY+8sHWkZhwlI2WMOmwLaEaJtGNO0X4mYyvzJxzazKVTMvmHkpG0hLhuZCPUYFpHAcjTyL8JaeJ+nhP5ZLO5QCYaBQBlLCnqDo/8iigXxJCBZLgrqe73ljCnNXZXkb+T0Jb8rvpsKUwxXDTliKBXA+JC5iid3oEUn9MTwNaFkt9NKyP3F9YzqoRtMSm2NoppRp10A0hfZrJWjZy2yELQch7PCI9ACbcacZJkcK5oizztye+WX8M03fTdQZa9FSXgzzEj8JeCZpG0lDw36GaO9vb2GNpvKJ566ikEBwdj3bp1+OGHHxjt3AoURV2TL3DspTfdtlxB2kF4yesZ29ToyOstcDypb8zsSy5ycV8VTVawj1xIw9J+GIbHVxJjC8UCBMb4wa+XN/h8HqQOtoid0g/5tTmMOmQmZxRnlEGlaNaeUXzyGcARi+AX5gEnDxni9l6GQkQuZFCptCiNz0VFSR0a65vrMNeQ9w6jgnTWv9uF9u0OjMaOHYv4+PhutZy0O1HbpMY7+w7io/FjAAC7T6e1sUf7cbZrDoiGhfnhaEou3v9uN2rq27+a5k6Y0C8UwyL98cLPW2EwWp9/E8vdQ5DMEfcHR+DVC3e+WKRJqYVWrce7a55CwtF0xJ3MvraKzNldBnHY/ZDJZIiIiICvry/8/f3R0NCAH3/8ETt37oRer2+jBZaeCEVRCIz2xaiHYxERGwKYgfzUIuReKoCmSQtNkxbL5/2IX1O+ZuxLH2zgiMkXE45cBgBw83FE1OAgGFzJlzY3dxn6zI6Fp78TuFwOeHweko6n4tyBJKSezYFO0/PO2XYHRuHh4bcsFGS5Pa4GRx+PHwMvvi3+PJsEhfrOprds+QIMdffDUDdf9B7ujuNpeVj8607UKJsgre9iPROXg1fvH4HNxy4iKd86cj6x3J3YC0X439AxWH72OCoMdz6N4OBqh+ykYvz6yU48+vIEDJvSFzpt84ND2aDGwbhiGAwG7Nu3D0qlEomJidBqtW3Uemv4+PhYbWBlMBjg48NMQcNyc9z8XTBtwTj49/JGfXUDzuxMwPoP/oZOrYPJ1LEvjOWFNSgvrIE2xJ3xmbCwFhwuB1wuBYGQj7ETIzF0cl888up9UNQosXvFPpw/cLlD+2PN3FZgtGnTps7oC8t11Dap8fbeg5jXKwYrHp+OnRfT8Pe5JOjoc0VtIBUJMSsyGpN9w5FUU46TZflYeTAeDU0dc7NuL1wOhaUzR6NJq8e+uzDVBIv1IOHz8W7saOzLz8LZ8mKIndrepy2G3ReDo9vPoyi7Ap+9sI45lZb26Z03cgNmzJiBhx56qNPqvxM2b96MjRs3IjIyEikplhO3dwciIyMxb948BAYGwssmAEf+PI0/v9iB+sqGa1NplsBkNMFkBPQ6I7b+fBAA4OQhh6O7DHOXTMHst+7Hytc3IDsxjyFZuNtod2DE5XI7VXRNHwZsDZtLZPv6YX5EuS6U1Bq0piuhQ9fy1NI8TkQV9D2Y+iCGpohWtqmkGU/KmHVcnxC3Qa3GL//GYbssCc9MHIQVs6fjYEEuTubko7i+AUaTCdVR5E+os29uw04oxOigADzcOwqX88vx7OptqG9qHnXy+7sChAKBZkpnbs2kjqYFs3UNI8v5pKFhbS/ygeEcVw0Ol4PF701HiJMzLh5IQ+2Jcsiu20agIPUuFQNJHYmohmnUSU/2q/YgD7rJntRLUVzmFe2+k5xzF2VXEWWxnye5A4fshyyLWWeDL7kNXcOmcaW9DU5lvm3LPMiTUqMgtT1DvQuIcm4DmeOtQtWKDsVIS1IsJEcgRL2rwJfq4BBRA66+CeYtTEGn0YY8F/Q2ND0QPYUK7WczML0+YZDQDVTJ4yOqJM+v8sFMjZHSj9znjeChqKhQ4vdjlyEAD2oDebLUiMmXA94pZpoW4b5zRDlviAFHMo7gQPEB5pfoRNzd3aHX61FR0XwjaurrR3zuYUveE6s45PEJtiPPaQDIaiA1RDwOeR4/H3GcKP8WeN8N+1dXV4d//vkHEyZMYAOjGxAVFYXXHlyGyCEh+G/lQWxbdQTlBf8Qhp48BzIBM2hJr8crn2DUS9Gc+40+LkS5Ipo8F1QezL4ZxaSRqcGJfIu4+O0+DBkYiBc3LYZSpUXY+7HIvNxyzpnKGA/IVjMCdBfaHRitXbvWahIY9hQq65X4ePMhRPi4ov9AX3x43xiI+XwYTCZklpI3PKMQcLSxgaedFJfLyrFs/0EU5FuHt0jsyDAEhLihsUGNLetPtRZbslgBFCjwOdy2N7RinunbH65SW3y4r30rv26GUCzAoEGDcOTIkQ6r81aZPHkyduzYAZPJel/VlUolXFxc2t6whzFgwAC8//770Ol0yN5Zhm3f70Fj3RWDWnoiRivm9NkcJCTmo09vHzy7bBoaFU24HJeDi6ezkN1KYNSdaXdgFBcX1xn9YLkFUgsrkKCrwIqTZwEA7nZSuArIN3q9LWA0mZBWVXUtKS3fCiKQ/sNCMHHGAGg1ehTnV6OitB7wbDuRKkvXU9xUg2CpB6q13XOqc36//gh2cMQH/x1GUwdqcjyCXGFnZ4fLl7teayEUClFb2/7cbl1JdnY2Zs+eDQ6HY9UBXFcgk8kwfPhwTJw4EZ6enti6dSvWrFmDeynrnAq9VXR6I+LP5yFp/Rn4h7kjLMYHT701Gdn3hqK2rA6H/4yDoqqh7YqsnHYHRgoFa7JnLZQ1NKJSSwpKr06lWQvhvq6YNjQS0c5OKMiuhL3cjJ8//c/S3WK5CdXaRjgImFNV3YFn+jYHRe8dPQydvpWEhndAQJQP9u3bd206qyvh8/m3lEPNkiQmJsLZ2Rkvv/wyvvzyS0t3p0txc3ODh4cHoqOjwePxMGnSJOTn52PLli04fvw4dLorMgHLv6N2GHnpZchLL8OJPZcR4mMHr2A3vPXbApRkV+D4lrM4sJ+y+nP2RlDt7Lh5nGD2tQI9IWxH0JbPEcDUIZki/Ily/mTypk7XDwEAh/YiqaEJM0U042c9Ta6h9mbedG3yyDjTREsi6/vFRaJcM4vmCwKmXxJFa4aeVLYxlNyA3gcA8P2DTO1hLC4l26DNY3PETBGIobYOUgdbhA0MAkUBoE+1mEjRIMXjwzfCE31H98LRv+JQUtaA5z56CL9+uBWXTmUy6gfA0DEZ/Ml578JxTL2LgPZyQvc+chpMrnorySZ1FQBgn062a59PHtMmZ1q/xORvpGZWybgBivuSb/siHtmG3sTUTzVpyBPIwZbMkcWlaUK4NJEbvQ0AKGskR+nqK2knNteE4e5+eCCwF14++R9sMpkCPbt8mieTmqbr4pFfnn687PKYKyy1DuR3bfAhj3kjTT8kT2M+YeaM64cgDycs33wYTVr9tSTNV6nqQ24vriDr8DjO1NZxEltGzV5dNR9Lv30Z58+fZ2zXmYyTzMWbaxfg7693Iedys99V6XMxxDYxD5G6Ho2R5hrPZx7zUwWkT9YgmmZNzCVvkie3kW16f8lcneww3YDhw4fjhRdeYH6Ru4yJNo/BI8gNUUNDMWbOCJRklSH3UgG0Gh3yk4uQEsd0j6ffa9uC/vyjHJleWwYPUpdUH0Lev6uGk/cBXjXzGWFwJR8sZlrScI6QvL+LJOT23Ljm+4qznQQRnq6YGB0CqkKHS5eLcOJUJjQaPXiHyPPldryQzGZzl4SWt50rjeXuxs3XCXZyCSIHByEwyhtmvR6O7nJkns+FXqsHRRMgm2lD5xSXi/pKBT59/Cc01iox+92HsG3VkRsHRSxWw9mKIrwcMxzetvaogfXnQ5SIBFj0wDCIBPxrQVFnwOFQFtFX+oR5QGInRm6S9WcaKCoqslpLgY5CIpFg4sSJeOvZN9BQp0TG2Rx8+8Ia5F3KJ7brTvqhjqKqQYVjDbk4nVmAcRx39I3xxbixvbB95wWcP5kEfSddmx0NGxixXEMoFiDmnnCEDgpBeP8AVJbUIi+lGP/8sB+62nrUldffUDRIX2bKEbSMAAT39UdYf3+knGO6trJYH1qTEbvy0zErOBo/Xoi3dHduCEUBg8J9MG/CAJxOKcDmQ4nQGzpnubNULoHc1d4igdGwaf1x9O8z3XZa4m7B1tYWY8eOxbPPPouLFy9ixesbkJ9SbOluWSV6oxFnz+fi7Llc+Hg74qEHB+CB/97E0b9OY9/aozB20nXaUbCBEQsAwDPIFU+8/xBqy+uRl1GOv+etgLapZbjUUHt7K9sEIj4eWTIVR7ecQ9Lpuy8x6d3KPzlJWDN6BvY4ZyG3yvpEv76ucsyfMhhiIR8/b4/DpZzStne6TWxlNpj91nTkXCrocsd/Pp+P8IFBOLH1XNsbs3QKDg4OeO+999CnTx8kJCTglVdeQWpqKiOdB0vrFBbV4Mtv9sIrtxjL/ngJMmd7/P7JFkt366a0OzC6XldEPzFuR3PUlp15a5hoLtDcvDKibFMeTJS1NGsIgKndMdGOBF3LY6AtspDkMA+dxpl8o6NrGDI/7X3TPgCAmUfTjTTRtCccso3gdWRHeRnMUZmbBTU8Phd9Fnli5MiR+PKnj7F9+/YbbkvQxqKTcXgMgdG+mP3WNORcKsSRjccZb7xmXRvDqr6uRJHbyqyOjrawjZ7DrrqRZnTElPLARJv219qTo2HVQ2k/lIasRFDLHDLX25EHyJamByqvIMVQ/l5MnxkjTXdkJyQPQIOWvFaEfLKfdD0RAJhM5DkZHkjqz3Iqm8V2auixKfcsnpswGEv274Pa0FI3t4msw7acLPNV5Hc1Cml6IV/mNV4fRNbhM4LMHdeQ5AUKwFBvX8yM6AUPOynWpSTiv4sZMMnMQD9A7EJqhMJcyNxxdnpSx0SNI0XUrd179ms34a/v/8LZhKNYtWoV4/POJiQkBKoGNYpzKolR2kZ/8hhXa8nzvERBnl++MuY9INKd1N8lVTMdka9HE0GOlnFo/jkAUFJSgoiICHh4eKC0tPOC1Y6G/hwCgNABgZi+cAKcvRxxYstZfPrWKhj0RngiGp786NsyY6SPtDOeZUF+N92/aDJT0GigSS81rmS/eJXkDc6+N01AC6CmkrxX2BSQ+6hdyX4L5TS94yhmnUXOpK9aZU0AntxzAO89fC8G9pbh4EzrzbfGjhj1YAKjffHIW/cjpSoR06dP77BpAoFAgAcWTUBQjB+KM8vx+6fbGElAWayf3cUp8DG54bWhw/C/Y0ct2hd/mRxPRPeFu60Um1Mu40hZ7jU7is5kzJgx4HK5WL58eae31RpcLhcGXceurutM8vPzkZWVBS8vr24VGNEZPXsYxj1+DzZ9tAWpcZkwGow9UjPU0dSp1Phg6yF8NGMcdsfE4OLFi5buUquwgVEPZMycERjz6DColVrsXXsEH+98s0Pr/7//+z+MGhqLDR9twbl9l6+MFLGBUXfDDOCX8wn4bOw4PB7TB+suJnZ5H7zEzpjsMRR+ocHYmp6K/504Ar3JBLOg84MiHp+L2fPmYcWKFZ3e1o0YO3YscpOtX3R9t0BRFJ7438NwD3TFF0/+jOoS65tG7u7UqdQ4k12IiRMnsoERi+VxcJNhynNjETowCF8+sxJVRTUd3sbEiRMREhKCZQ98ifrK7m/01dPRGAx459BB/G/0vRAPGIDfEhMBdL5w0kvsjPs8hiBE6o2jlYl49/ClDjVrbAuBiI+XvpsHjbQOBw50bfqP64mKisKZlWkWa78nETogEFMXjAMAfPXMSqiV1r8is7uy53ImPp82DcXFxdiwYYOlu8Og0wMj+txtm94FRnLuvLV5XK6vF1E22ZOTrBpyahOcVrU8ZFlM82xrLZ/M9egcWnk48Mi3WJ2GbISi6Tta07uIqsg/0g0bA/4l53Z5ueRwtVHBDEaOUP9iwYIFuP/++7F582Ys+no+6uo6Nk3IGM5MSOUSLHvtFXw1fyXqnZ0B55b5cE5FK+3RlvibauuJcnVvUsPAa2WmT07LU1YxkDzG2iry3BBWtOKLRXve8tTkMbdNI+fb6bm/tH2Z/jcxnqTuraSR1HwE+5AnXIQ9qfcAgEwemV6Brhmi+xQpNKR/iURAE8oBcLFREuXUClLHRdcgcTyb0IQmvJe2Ey9HjcQHk0fgszMnUaNpOQ8l48nf1tGWPAcTU/2IspnDHO2xcWj+cQUcLuYGDcYI51Bsz0vF+7nbodTr4OjbiOsVQC4SJaOOrHJSf5FSRvpg2Rwj/c1cuaQuYl/D2mv/nj56OvKUqVj81GKLrwYzc3mgBKQ+SuZXT5Rzq8mbXoQreX5VNjENO/VG8lqgn0+NGtLDysmBPOZmN9qNFgCYUpNuQfjQcMz/7BFs/WEf4nclYlfDeuZGNF3lONFj5Mc65vVGh5KT9wFuKzqt60lbTPoWmXnMlwMnek7FKtKbzGTfdr/C/cnnSJkTea9xFpFBoqOIfA7l1TP9leBLbtPk3nK+FUKDP5fswcI3X0blnwYYDcbb8jXqLG4riazRghmAWdqHzNkO37/zPfR6PWbNmoWqKqbItyMQiPh46tPZOL39HMrzKsHp08pNk6XbojTo8FHifjwePBDfj5yCPzIvY09+BvQdmPoh1tkf84Jika+swTNH/kW91jJv7EOGDMHzzz+Pl19+GSpVKwmVuwh/f394eXmhoY5NytqZODg44IWv5mDtu38j8Qh7rLuK9Pgs6LUG9BndCwn7L1m6OwTtDox69+6NxMSu1xqwtA++kI/ew8Mw5bmxWLfrF6xevbpT8xfNeHky6ioU2P7jvk5rg8WymAH8lnUWcUVleDysDx4KisK+gkwkaM6iXn/7AYS7RIpnIocixN4VqzJOIKGmEGot03G7K+Byufj000+xcOFCi2eJDw4ORnx8POoquteUtEqlgrv7zVe4WQuxsbH45ptvcGjzaTYo6mL0OgNyLuaDJ7A+UXu7A6OPPvoIc+bMsfqEhj0ZV18nzH13BrRqHXauPIhVuzt3mfGAAQMQPTISHzz0pcWnHVg6n5SaCrxxai+indww1icYX0Y8jgpNPdIbSpCnrIRcTA6hO/q4QG0wIK22EmZz81QaRQHhDi4Y5OaN4Z6+OFyZikXxf0JjtOwKrLlz5+LixYtISkqyaD+6M1u3bsWzzz5769YfFkAsFuPhhx/GI488gsWLF8PuYpClu8RiRbQ7MIqPj8eECRPw+++/d0Z/WO6QmFGRmPXGNOxbdxRH/4zr9EBl2LBhePfdd/HLm5vQ1ND1rsAsluNSdTkuVZfjr8pGhEg9EG7viRA7D9gKtMR2YicZfOxkcBYPBGCGGc2u1ZVNKiRVl+OpA1ugsbHsixaHQ+HDDz/E2LFjsWDBAov25Sqenp4wGLrPUv2rlJSUwMnJqe0NLYBAIMDzzz+P6dOnIzExEUuXLsX58+cxTsQGRiwttDswqjsMLH77DWgO26LJTA6BjalimmTRaa9gjesgY/xN2YsUpQpryTpsi8lgoDXxtUZOikzpqen4ZNJ6hlibV8Ic/tM4ku3STSNFNMNHuokkADQGk/otn91knQfjlhHlqyabFEXhnpmDMW5xLJ57/kmcO9e5TrnP9n8LT3w4Ewa9AZuWbUO2SArEthhYcqpoB5DfSvJE2iojKsSPKBtouWyFCmaQp3IjldB000xePe13asU1gG7wSBdXK/3J30SaQ9bp4cjMUkwXWxtpJ1gTzWxwf34Yow43e3IKRW+4+ZCzM02QXEoz+QOAKgUpwn0sjDxPTlQHEmUehzn9ml5IS+5bJkFhmQIH0XwcuAKawaOulVUGNCgVeTzoSSzpZpc8itkvUzl5wricIj+3zSPPyav3Hg6Hwvin78WwiX3w0ksvWcUSYi6Xi6effhpPPvkkXAc/xfhcqyNH5YZ4k47c58u9ibJURAarAKCjia+1OvKGNdgrnygr9aQBZgNtccBVioqK0NDQgMGDB+PMmTOtbtPVjOU/Ar6Qj+f+7zFoVFr88OxOKOub4IyBmOA6kGE2O8H2WUYdZtqzir4wiO5zxHVm6izrhpG/C1dL3tPKH6S9VAjJKWpnO+aig/I6Uij98sCDRPlMPZksOK2GXHABMM1inwiMI8opSk+ifCCdvF9xeMx7s8lASx59jmyD4nJBcTigONzmY9d5So920/Ydi0ZGQi4un0jD+Mfv6Yz+sNwmUxeMRezkfvjkk086PSgSiUSY9vw4nD+YhA8f/hY5lwra3omFxQrhcDl46tPZCB8UjLlz5+LUqVNt79QFPPzwwxAIBCgsLGx7YyvDbDYjOTnZqnRGEbEhWPbHYtRW1GP10s1Q1je1vRNLj+W2luvv/e0Ylvz6HDJTy5Bylk0Makn6j+uNSU+PhkFnxPeL1mJrxdZObY/L5eKbb75BVVENdvxsOX8XFpY7hS/k45vjH+DSsVR889wqpGmtxy9IIBDgt99+s0jS2rsNZ2dnzF32IH5d9icyz+daujss3YDbCowaa5X4++vdmPr8OKSdz4PJaEVjYFYKl6IQ7uGM4cG+cLazhYfMDlQr8h+lUIdPzhxDqbKR+eF1uLu7Y+HChZg0ZApWvrERORe7ZtTG3d0d3t7e+O7lP1ihNUu3RCjkoX9sECZ+MAWJR1Kw+s1Nlu4SSychFovx66+/4vi/8WxQZGVQFAWZC3O63xpod2C0X7MRAHBgP4XhjwzDo69PxoYv9wAANFMHMLYX1NN0JBpSBMIrI0WX2kBy/lNvx+yiiUfTa7gJb/p5ow+jCtgWk2W6roRuo0XRpujFNcygwHsXqTVJrlyNiRMnYvDgwYiKioJSqcTatStQWlqK9PR0ZqcAvD7tQ3w1eSD+XLMTqXFZDA2WQCDAlClT8OCDDyI1NRWTZo9FWVlZq3V1JGM4M8HhUHh8wUw0JOlg6kWKFbkKcmjaLCKFO3oP5gVA/23pvxs9+a/GiSkQoktNBDS5j47WLF33BQDialpyW5qUR1hF02IMJuf56foXAKgokRFlnoQ87wf65RNlBxFzaD8pj5zXpwvhXFzJL5tLMzjk8ph+Y872ZN8PVoQSZQmfPN/ya5jGbWY1eRBdvEmDx0AZ6fIXd55swzOETO4KMJP9GmiahQYFqR8q2svsl28G2XdRAdkvY14hnL0cMOfdGTAZTVj43rNISEiwugB/DGcmIjkDIaKEGMOZiZwxTG2ZMJHUlaTZkLovDs1EU2NgnviNjeQxlUrJ0Sm6pqheR5b1rrTspdeRkZGB+++/H1u3du4IdmtM8FoEoDmly0PP3wt1oRD7DmaSxsAa8oZO2ZDfxejETMBc3Zc0TtTTPDPp9xa1O/O8EvqQL7z2NqRXV19pPVEuaCBvgoUlTN1Sn0AyZcym/IFEOVROXm+KOubv1sgjz4XNJvJZXp5LtuviTz63q7KY/aKn7XFII6/PXJ+zsAubj++WfASFnqnTtCS37XxtNpvx22c78f5v8zF4XC+c2Z/ckf3qtkhsRegzOABj7ouGgTsGu3btwrp16xAfHw/9LaQ02PvbMZiMJjzz6SNI2J9ECPy4XAov9Z2LjIwMbN68Gf/991+X3tQDY/zgFeKBz+Z8D0SFtr0DC4sV4OnnhBGTeoOvUSN8YBBO70jA9p/245yuc7V4twtFUYgZFYlT26yzf7fCf//9h9deew0ikQgajWWMOifMjoWzuxyrP9pmkfZZbg5FUSgpKYFCYV1BEXCHKUGalFp8t+RPLPhwBuIP9FxzLFdHKSID3DB2li/kjhLUVDViy8Y4rPxrMRobbz4l1hr7N5xAQXop5C52MNOW6/6xYTWysrI6quvtQiAWoL6qAXqdgU0Jy2LV8Plc3DutLyL7+SIw0hO7N8ejNi0P237Yi7oK67sRX4/QRgD3AFec3HrW0l25bbRaLY4fP47Zs2fj119/7fL2o4cEY8j43vi/xRvQpNQCIssYhrJ0T+44V1pxTiU0TVpMmjMUf6b2nNVJQgEPE4eEI9jXGd4uMiTnlCM9uRiXE/KRn10Js9l8W0HRVTLONYva6VNpWSbLBEV2jlI89OoUHNp0wiLts7DcDA8HO0QPbJ7+CAhwwcD+/si9UIjs1FLs+iMeOamlMOZ1jxVeY+fcg+STrU+1dyfS09MhFovb3rCDCYjwxIwF9+KX/21DHd02hMVqkMlkVuvTdUeB0d7i7wAApUuOYs2aNYjPqEB5aT2xjcaV5l1Am4jVBpO+DoJGUjSikTP1G3T/H46enE6SZ2hoZWbfNU6kbwq/kfyBxEk0EdKVKS2+gIeg3j6YsXQQ8vPz8dPHn+LkyZO3NE12K1zVcFkbrjMEOJK0D+/93eyjNC6J9KOi6MkQBaTGiCNhvrHxlOS4kyKQ3MYgoSVzLWSOUzUOInURNqfIGzGflqlCWszU3RgFZL0aB/Kco59v3DOkuKAkmHnzp0Q07yOafuNsvh9RDvGgZTEG4OlOamTqm+hJYkmdRHUFaaoX2Z+5YpSeNFZPS3Ts5Uq2aSNimm3pa0n9hYKmbSo7RQq7wvNo+fn0NK+uIFdEThbC0bFFp+DO9SfrNJM+PW6xvkTZwU2G9AsFMJlNqEkuwqfLt6M2j0zMS08+PUH/ElHW+TN9efjVtMSpPPJ4cRTkCWauZ45GmdXk/aita9w2KgDpBdXAoCgAgMmO+fAI60++hF4+Q/pPGW3J+6iLL9NAk6EpUpHXX6mA/J3Hu5Or9v7u1Yp487qMQIWFhXj66afx559/oqamhrltB3F9onLPYHc89dYUbF11CHmXW45RUxSp1yt4mLy3eHqQx0elY97P3aX5RJmuv6P7jpUrmDolug9RhYLULSmayOell7yeKLv6MwM9egLXPi7ks+tUAelj5OFK1gkA5TXkNetvT/5eda7kvYeuoXRMZT6nHVLJ84t7nnwQB8+Owfnz5xn7WQN3PGIEAJcvX8Z3332HF195Fx++/Q+0WuuMAu8ERzd7PLhgDCIHBaGsoBpff/0Z/vnnH0t3q8uYNWsW5syZY+lusHRTZI62oKhmQezYaX0hvhIoC8V8uHk74uf1n+P06dPXth8mnErsf1K7gygP4Uwkysp6FRo07dPbhfT2xrBJMZA7S+Hu68RYMAAAFC2IA9USRNfVKFGR25zm5BpaLcoLqnHqvwvX/mTWCNFYq4SBXtcNkNjeHdM+hw8fxnPPPYeXX34Z77zzTqe3J5VL8NQnj2DHmqNIOJza6e2x3D4URWH48OFYvXq1pbvSKh0SGAHAhg0bMPHeZzHlgf74Z7N1uJ12BO5+Thjz0CBEDQrEse3nsW75TmibdNhb0TOCIg6Hg4ULF0Kn06GkpMTS3WHpRrg4SxE7KAhBcim8A5yhu/LCdPl0FlISWkaAspOL8e/ltcS+viJypWWGhnzb9OZEM9rj2jPf0K8ndkwk3Lwd4OblABdPOcQ2fBzakoCTuy+iILMcGh9mGgt+DW1E6MqIEUUBbp5yOIpoI0gaNcY+MgQDx/fG1YiJy6Gg0+iRfCoDmiYt4tZuv+E0u6enJ0LC3PHnhtOtft6dMJvNWL16NYYPH97pbVEUhee/fQKJh5IRf4DNc2fthA8MhFgsRlxcXNsbW4AOC4wA4I+Np7H49YmY/tAAbPu7+66oAJodcectmYzoYaG4cDwdbz/8PfS6u28krC3s7e0xZ84czJw5E0bjrb3xsvQMvOzt0Nvd7VrKnb7eHojxdAd/ZvM0jlqtx8m4LFw+m4tVn/7XMmKib/91FBsbC2/vlmn3KE4s+EI+fMI8IbJpnhanhOT0uI2YD5/w5ukzigJUGiMObj2PvPQylBfXoiKvkljVqW9tRId2zZuvm6EqzK1CcStTaReOkKMVZrUGgdG+8A33hE+YBw4fPozRo0e3GhyJRCI0NqjR2GiZlVzdEaFYgBe+fQJNDWr8t/IAuE5MKwcW64Iv5KGgoKDDJCgdTYcGRpqTl/H95Sws+m4eMNgHO34+AHFMCLGNVk4OE4sryQPT6EPe3ER1TPNIjoEcMuc3kDcvLs0riVvI9E0RJJPtGhUt88O+EZ649/NYFJUm4Z2ZC5GZmcnY/27m+jn7oAB/mKq4CCoejCD+4Gt/59A0RfQ8QhRtFQgvt5TRDo+WB08zgNSq8FQ39zUCAFSQc/IG2iyEYyrN26aE1AEAgMmWrINDS9Jmn0M+MKt7k+coBMxzlCu8eRApEpP9inXIY2xD9xhykZIPUrqGgeNOeiEl5pD6PQCgaP42nArygKmOehBlSUXztSSzt4GvZ/MDx1lHYfSk3pDai5F4JhdGRbNuoiYuHW9v2QAjr2VKymw2Q+PvCHi09FV6vwfsREL4yO3hIGn2VHkwYwLRbnXflmPsZGMDf9gj6TrNkG2eCiajCal5Vc2rjgBo3UntF1WoQP53R2AwNP8+OieblmkvCqgfTx6f2limnkrmRGonGrJJLUbAFvL48ZtaTzWRm1mB3MwKYPsFTK8bjV0bDmPV0j9QVVwL0xUNEl/IxwtvPo6E+mpU92m5vtw8mPcvei6+iIHk+VOrIb1qKupILQsAONiRfR3uT2rSilUyorw5qx9ZwVDmtYQvmX/qaK6/Pzl7O2LRj0+jML0Ea5b+AXA4MHkxtWLVvWnTpE3k9dmoIX/H1nLLZZwndW12IaQer6Cc9PIJ9KBp68DMkXh/0GWiHF9NtlFYS970PGRMDZuIRz7vjmaQz1yBDfmss+Ezz/MIT3KE9sw58t4jrKHlpPSn+es1Mqex+bmkbnKvav21fw+L/RA5OUX0XayGDg2MAKCpQY3vFv2GRd/NA0VR2HY6v6Ob6FRGzBiEyc+MwQffvYNt27ZZujsWhaIoTHp6NPavP2bprrB0EbZiIWS2IkT4uaJvsBd4Tc1BhZe7HAUlNTCZzOA26PDP+lMozq9BY4ManOp6sg4HKaT2zcGlu48jbMPd4e3lAKm0OQAN7OeJi0WlqFKqUFDbfKPXlJJCzbLglgdVnqIOa/bFQXPdSJPrcdJEEgDUdTKiLC4kH9zW4uO47ecDsHeS4qXv5+H9Wd9Be+WrB/fxA1/Ex8bDCZbtYAdiMpng5OQEiqI63HPNO9QDr65+DqlxWfj1bdaJv7vg7++P8ePH4/7777d0V25IhwdGABkc9X9wMJIvF2HLH/HXNAbWiHeoBwaMj0bU8DB8u3ANtqVus3SXLE5gjB+cvR1xZqd1rhxguXMEXC64FAUxn4/JoaGYOSMSeqMJeaU12BOfDl5185uhUqmBWmuAu6s9hJVqCEV8BIY1uy3L+RQ8/Jr1ORQo9B0VgZorXkGaJh2yqhuQk1uF8it/Ky5KRlYluerF5yA5GpbpSY5IyvXtzndt1az/aCu+PfwOhk/rj4PrjgIAxLYiqOpVVhPAdQQnT57EokWLEBISgoyMVpYHtxORSIT+o6IRNSwM/cdFY9uPe3H0rzMwme6ig3aXs3z5cqxduxalpcxZBGuhUwIjoDk4+r+nV8FrWiymzeiPD5bPREWZAkl5FUhJL0V1TSNUTcwhva6Gx+dixovj0Hd4KM7uScTXz61GQw3rfQEAk58dg0ObTkKr1oHiMtMSsHRvXuo9HA/6RkHE40HI40Gl06GpUQetXgMfVzmenRoLjq7lgaNUaVBYUguugrxuTUo18lJLrmnwEuJykHm5ZZhc409OMZQ50qYieyBmsxm//W8L5r37IE78dRpatQ5j547AwY0nAN+7Y1UaAKjVatTU1IDPZ674aw/R0dGYPHkypk+fjuqkRlw4lIT43YlIjctk703dCJlMBn9/f2zfvt3SXbkpnRYYAYDRYER+TiW+Xb4bzi52cHSWYtC4CPSJ9oFcZoO0zDLABBSV1EKhaB5PVrvwYDabUVKlQG1jE4Ta1jVGHRFUeQa6Yvr80Qjp44tf3tiIS8fYJZ5XGfPYcNg72SF+14W2N2bpFgTZOyLayQ1PRwyEt60MGoMBh7NzcTw/H3szs2AGIMsgdV1XNUbXIy4kvVjoU2msy/CtceFwCh55bTIAwFYmgVewOwrSSgDfgDb27F6UlJQgODgYycntSxslFosRExODefPmoVevXkhJScGjjz4Kn+yYzukoS6diY2ODlStXYtOmTV2S3/NO6NDAqFXjMtrK/e/XNP83JiYG7u7uoCgKffr0gZ2oWX9g1wBwuVyM7hsKmcwXDpQr6HCFfJTlVyH1XC5yLhddWy1lNJhQllcFvZAmyC1vES/ayiToN643fCY6wMvLC5s3b8SG5zdAq2WK7XoqHIEAo2cPxw8vrYNWZwJHIAAV4kduVEvqNygubapDSk6FNIYzV4oofMk3PXpCWHreEW4rC3VCN5EmfqZi2gVH6xfVypJuSki+zdrQ9C6N/qSQlW4aKSxmvg1raaLdet3Np4K2F0Ux/mYnJL9wg5Y8r9Xl5DGW5pDHs8ndDLlYhH4enpgUGoJ7/PxgMJlRUFuHVw/uxam8AjR5XQly3Jv/ow8h2xRKmaOnhTQtD59HCoHpSXVNJrJOTStCTdfvSVdqjZIUndZ4kd+1Ss9cWi95mByaL1KS+/R2JbOrS/RkABcpIH93AKjWknUoODKinDud/E3kacGMOugLSETVLS91ZlsxOCF+6Ds8BAYuF0oHR9QNJF/6yPSwzbhKyN8lt5YclXssmEwnst3EtDeorCEF2ZlcUrRMP/+aaslFCSI584IcJ5lLlPer1uPQoUOYNm3aLSeUHS9/GgPG9MIjr90HHp+HE9mlePOHeBiMJjjGzAe3/Obppxr9mUJz/nDSwFFiIK8VEZ98AfCRksJqAGgMIs8XuiGmSU/WWUy7TgCmweM/6TFE2Uy7dtwcSbF1jYpmpAvAV0b2tVFK65eJllSddh8BAJ6YFOL3isknygpaAmHl3+QiDfkxcnsARKJevoCHjRs3Ij4+Ht988w1zWyujU0eMbsbFixdx8eJFAMDu3btvuN1Y/iOMv/Fk9ug/JhJega6Y8tQ91/7OF/Lh6u2I4oIaxB1IgdlkRlZyMaqLOYgeGYmBE2MQ3McfSSfT8f2Pn+PcuXOsYK8VQvsHgKIo1JbXW7orLO1EKhIi1MMJ90QEwNHNFkN8fGAwmVCsUODXCxew8vhZMMdgWSyNxFaEuCNp0Gr0ADo+jQYFCnKBBCF2HhBx+Ai2c4fBuyXgL1TWQ8ktRlJ9AbSmjl1CXVVVBamUGay02k+KwjP/ewieAS74ZvF6FGaUoWFsWIf2h6XreWjReJSWFmL58uWW7sotYbHA6E4wm804dyAZ5w4wh2Z5fC6GPhSLgHAPSGViPPjMPdA1KCGV2+LIn6fx27I/oVXrcNbUfRM0diYURWHCvJE4uPkUdBrr9JhgIYlxcUMfVw+ERTvinogAGE0maHQGNJp1eOfAQRzMyYHpygsAh03/azVwuRzw+FzweM1u4N98cGsjKreKjO8GP0kM5AIPjHaJBo/DQZGqBpVaBUqaapFV2bycms/lIkLmimEuffFU4FgcqUjC7tIEAF3rpSQUCvHBBx9AbCvCR/NW3LJTuLXAoSiEy13AveKOLpS0jPw1GXXIbWSupOwJxIwIw4AxvTBw1KOW7sot0y0Do5th0BtxbOfFa2UenwsHsxo8AQ9F6aXsCFEbTJw4EVweB0f/tE5HUpZmBBwexvkFYUpQGMKcnGEwGSEy8rDvUiYOXM5CRmkVap3YwNaaGTUqHNlZFRg6JhKZKcUozrvzB6eIy8cgJ3884DUWYq498lWJKNdkY1NeAYqbamBGy/3v+qm0o6U5cK9SwEPsgGmeg/BFnydxqvo8DlWeg9LQujfTrdLU1ARPT0/I5XLU1TGnqK6yYMEC8Hg8rFz6Z7cJiigAHjb2GODsgyleUWgy6FGraT5eXEHLd/CRyCHli1CoLkFGQzEyG0uQ0Vh8g1rvDiiKwowXx2HYlL749uUNd5RUvaux+sDogH4z849t3T96ZmB+21ydrpTKJVj22kv4+c3NAJ9HjC1QNE2Rqa6eKHNobrM6V3LoXOXGXDnCp91v9aSUBx4naOaMiUwTRLqxJF3ndr0ZHADGChZ7Jyk8ZORlQHFa5vl1Wj1yj+SSAbUN2VFzKwlyjfY2jL9dj8GGbFPYihbRLGw+ht6+TpDKxCgf1mxg6O1gj7kDYiCRcgEK+D03DnHV2ajWVcEEMxAMUMGAudaWUadeS7br5kjerOhJLFvTI+hpWoowF9KAUGMk23ASkqKscjVzWuVEOlObcz3calLHxZ3CfMB6Skg9xqNe5Kjw54njiTLFIScVg92YhnzeNvVE2RBFakDohn2aMOYt1UhrR7Cw+YEp1RmQG5+LxokOOKltRPpzV64hUoaC2kbmuaSnaVGkQg4me8ZgokdvVGgU+HpjNs5knr2y9F8IlZcZAKnLciRzwqLaX4xqAJeRCC9ZLmaND8GboQuwuzgZf+QmwN6F7JhrK/oz9b29yD/sAHJycpCamor+/fvjwIEDjH0AoG/fvpgwYQIefvhhDDY/QHxWH0x+18r+ZBtcDTkKamxl0eMAR9JsMKWKVG6J+ORLxIVSMuEwAGirWn4HJ7ENHhdHYGCYD0QCHjKKqrBhcwIuZLekTeJet6KTogAboQDeo9zQy9UVMzyjwXPkYI/8NI5XpsF4xVKdnpy1pIbUfcn86hn9ouvLZDakVk7AJfVTdENIAMgsJbW8Ri15jUvSyIPqeYk8F0zVpIaLw6HgP08KUaAak2eORVZWFqNNa8bqAyOWrsPNzwWNdUoUpFuvv8Tt4OztCC6v+UIPGxaBqCEhoCgKYlsRnDzlKEwrgcnU8vCiqJYbsZ2jLYQS0TVfHgAALcM6+MygTwtg/54kqNWtr540ish9BK0sOQ7s7YPYe0IhsRWhurIBhmAbuEpt4W5vh7yaWvxdeBrna/OhMTbf1EV8djS0O3HPfTE4vvsyRvj44fuE+NuqgwKFoc4hmOs/AhkNZfg4eQdylVUwZ9xZWoziegW+SjkELxsZngoZipVDZmNN9jGcry1oe+dWUKuZwvbrGTBgAHbu3AmFQgHcPOWdRYl0dMG9voEY7ROAuLN5WLXrDNIKK5unqm9y+ZnNgEqjw+nCQpwuLATOAQM8PTF3eATu9x6I7UXncLTy7lgVfdUYWNafi1dffRXl5eVt72RlsIERyzUmPjUKx/+9vRu0NcHn80FRFObMmYPJMTPgHeoO1RU7iMqyehzfeg4GnQEGvQH5qSXQa2jpTGhBim+MP2yuHxUSkaMoZjHzFdUj3AOPzRt2wz6auORbLrcVPVeTxoDLCflobFRjwrS+qBNQuFBchqNHTiG5tAKCmBtPS7BYN77BrhCKBUhOyEXkE9G4UN7+lxF7vhjPBI2Gs8gOX6ftRbKi45M8FzfV44OLuzDIyQ/zw+6Bf1kq/inoeGfu6OjoW1611tVQAEa7h2ByTF/YC4U4mJ+Dp/dthSnuzlYynyspQW7SJfSW+eBBn0G4xzUCP2guI6m6+wUS1zPxyZEYNn0A7nt0bLcMigA2MGK5QnAffzh6yBG/OxGMdfLdBI8AF7w86mU89NBDMJvNEAgEOPBtHNa89Ts0Tc03Ma4tc7lrWxRm0Oa5bmEqLaWgBgf23jjLN3MqrWWqUmovRkSML+6d1hd9BgagrKQOf649iV1+zDxJLN2TEZOicey/i3BytYdS335PNiehFEsjpyFZUYxv0vdA28mSnPjqfGSfL8Wy3lMAoEODo6FDh8LR0REnTpzosDo7imi5JxaEDUeTQYeNly4ioawEOlPzwXZAxxhLXq4vRFJ9Eca4ReHN/iNwvCQfq5LO3mwAymoZfF8fjHhwELb+sM/qvYpuBhsYsUA/MhoTX5qAgxcL0DggDNW9mCMgPNrLkZrM9wq3M+TNvSaCrEPfyvC4fS556bv8RYoezGpyVcxemn7Izc0N9z90PyZPngw+nw+TyYQ9e/bgwQcfvPFF2Ureyza5nX3agG5DoR0ZDZGIjwH9/DF9Wl9kZ1fgO8VlnC+7MpIQA3hHkG9fFadJLxEu7eWM48e8tfL8Sb2PmJZQslpPBo7lFaRHEcBMRFtDS1hK1/qcig8nyo+OOsmos1ZN1kH30OndmxxRseMxV0ztKyHbKVGRfZdIyH1GeZG6h1QF0zFIbSS1TVUqUrclEZDHT6lmBsm2YtrFo9cAJhOKMkrRe3Agtiamgqdsmb71HkCKcunJgnkUBy8GTMf2zGxsyU0BYAvHU+T1xm8ifyObKua5QNGCKY8T5KhlQxHZbskIDZacPICPB02AxoWP39PJBKgAwBlNCxZ2MDYhGG/3BEaFDYY+T4R7BI8AAsAYFUj2kyaJMdiQ38U1htQPhciYSXdTatyJskZL/q6lavL42Z6QYFL/MEyNjMSGbedxKjUf7nvLcH3PTLY0WwXaKDDVioDc6QR5LpRNaOnXLpThqPteLBozBD/0n4qP/zuM+ghS91Zfw3yx49KSWDs4kOl2nESkHuhYFlPPZ1TfPBRwSiF/BE4qqffcp9mIp556ClOeG4Fl7y/Brl27blqftcMGRiwQifhwdbVDUop1r5JwdnaGq6sreDweFixYgICAABw4cAAvvPACFAoFlEol9PrutRKLoij49fLC+OfHIDjIFbl5lfjqm30oKa1D/hT28rxbkUhF0Gmbz1W98dadpXgUB69EjkG5qhFbc29udNgZKHQavB2/Fx8PmgCd3oR/ctrnZk3H3kmKsY8OxQ+vbOigHnYMsWG+mNAvFEvX7UFt452tymsPdU1qfLjzEGYO6I33p43BW7n/QG20/nvaO++8g2nTpmHRokWIi+v+K5rZOy8LXnt5AoqKalBVZV3LKW1lNqA4HPhHeqHfmCi81mcuSkqadRTHjx/H888/f831vLvB4VCIHBKKOcseRGO9CsfSSvDzqsMwGFj7xZ6Ad5ArNn+/H8MmRRNL6NtiTuAgCLk8vHv+qMWmWhQ6DT45fxhfxE5GWl0VUmor2t7pBvQfE4VLJ9JRmssc5bEUQXZOeLx3P3y742SXBkVXMZuBP89ehpfcHvNDhuHbtCNd3of24BvuiWnTpuGVV165K4IigA2Mejz9+vWDl6ccv6w5ZumugKIoxIwIg3eIOxzd7BExIAA6jR6aJi0ObDiB31f/3O2WfV6Po6MjZs6cCRcXF0we8gCMBiPWvP0HMhJyoB/JTNnAcvfC4VBw9XKAq5cDLl/JANAWg5z8MNjZH2+d3waD2bIBdGlTA35KPoOXY4Zh0fEdaDLc3qhGQC9v5Fy+vZVunYEtT4CXIu/B3/suI63IssHa94dO47vnJmKoSyBOVeZYtC83QiQRYsHns7HolWetUiN2u7CBUQ/k3hEfA2h23l24eBx+zkrEuT5qAM3z7mZOK3oEd1KfwU8n59f1UvJU8jhO6kw4ZeS8NwAYq1r+JnWwxdiP+0IsFuPYsQPQl+ox//0daGjoBIFPFxMcHIz3lq1FQKALSoprkZpSgk+3xaO8qgHwlAGe/VA6grmfczB5zKoaSH0LXSNS14v8w7gBTPH3wRNkAJbXRMvzdkVrwKUoOIhtIKxp1owYTCbUNTWv7NN6kQ/BgnxScFbURGp16FJ+uhYIAOrqSO0E14E8B3emkbnknu/DDOSNZrKlojJyyXqYDynCUtMMb+g6JwAItiM1Hg0K8rzvE0xOP2sMpO4LAJriSP+gsCgTBEIeYDKhrkKBejtyVMLNhhy5zctxgy1fgGcHjcL/xZ9ASSXgcZC83pS0ZmujaDqceOaCCq0d+TcOLX+YQyrZL72EPD7xQzMxoskbT0f3wYr0Zt2YjpYTcILTswCAAJs+UNrb4ABafIw4HA7C+vvjt3c2w3SdrYXWkaaXouUmdAsijeroerTWNDQuNL8uXSOpBePUNx/PJ2OHILesAckrzoOepVPvISPKJh75XXmNNBE9M4Ui4ZEGAE6XSRuD8kEt55dBZ8S3585iUZ+hOJpZDr2p9WCY7jlUoSJ9wng0H61Ho84x6vg9ZQBRlsST57n4OHkv2dewFmKxGMd3HMfeg9vvqqAIYAOjHgtFAc+9cC+0Wj325GZ2enscDoWASC+IJUL4hXkgKMoLYkHLTUIqt8WaLSvw448/3hXu5GKxGP3798ekSZMwcOBAnD5RhYP7k1FU2BzsqF2sIwM9BSDSwRUB9o5wFknga+MAJ7EE9kIhTGYzDE3NN1WpSIgGjQY6owkqvhZptVVQ6XXIqK9CkUKBSrXq5g2xXOORlyZg6y9HoNcyjfZuxAsxgxFfVoQLldblMfZLxmmsGPow9hanIl9Z2/YO18HlckFRFLQ38Prqavp5eGCApyee3rYVTHtHy5BQWYJqtQrjfUPwX166pbtzDYlEghUrVuDo0aNYunSppbvT4bCBUQ9lxMhw+Pk745sv9sDQr+OG5e2lYoiEPEjUHNjJbDBsdDjENkK4OdigqVGDhjoVCrPKsWv9SdRlt7xtN9Qqsb3m1w7rhyUJDg7G999/j7KyMhw7dgxffvkloiNetnS3rsHncDDS3x/DfHwR7eWKUlUD8hvrkF5XhcKaBqTXVsNoNqFU2QhhZfPbqIDLhYu0eVTHJVgCZ7EEflI5Ih1d0MvRDRxQSK+rREFDPVJKq5BQWgJVNxPCdwVCHhcyJymSz+Rg0mNDUZpfDTjffJ9oZzf0cnLFswe2dUkf20O9To2N2QlYFHEPlpzbDusIcdqPh1SKFwYOwrdxcVAbbj1g7Qr+yUrGI2HRVhUYzZkzBzweD99+++1d8SJLhw2MeiAurnaYNDkG3329DyUldUA/ZtqH9sDncnHPwAAMHxgEF0cplCotuCoD9DoDTh9NR3VlA5pyy1GYRU5lXD+Vdjfg5uaGJ598EiNHjsTu3bvx3XffEY7alkbKk+LZ/gNwj58fihsUOJqfj/9LP4IG3XVLiG+wbFdnNKK4vnlaM4c2LWo2ARK+AOFyZ4Q7OmNSUCheHBCL/Po6nC4uxImCfFQ3db2I1Rrxc5SjvroRAhEfUbFB+PzF9cBCpiXCVbgUFwv7xOK3lAu3rePpbHYVJSPWxQ8P+sdgfW33c2/mczh4bfAw7MrMwNkS61uZe66iGC9ED0a4gzPSaplpa7qageOjMWHOAMybNw/FxdZ3vDoCNjDqAdDzhY1+cgCS6mpw0a4J6CUGj/bMkmUx3wAcVpBanwz9agwbNgzR0dEYNmwI4uPj8cGST3Hu3DmrCgY6g/Hyp4kyl8dF9PBQPLhwHM4dSMLaxfuQF+OFgQ9/fm2bvH60fE4O5FspT8J813aVkLqIyjxSMyPsTX4e5UzeNPefb9blhMid8GhENPq4uiMuIR+frD2Iwqp6AIAywAwOWjQdHifJ366xlTkFgYIUT5h4AGBGOiqRjkpsjLgAe4EIvR3dMMIjAE8P6oUCVSXO12YhoTYLFy86Muo025HHozqT3MYphAzG/insy6gjwoFcHVVvS56zaWWkaiSXR7Yh5DNHCgwmUr8hl5NThmf/7U2UXS4wf0fn+BZ9xns7XsPB/Wnw6R+ItLRy1Jo4oEzkuXE23+/avyd4hUFXoceFHQWQX3e7pmuKKNolJy4jtSy1TFkXbNowJTYKaXkF80kNW0NIiw7l65Nn8eO4KdhbkgKFvkXzYwj1BgCYHKQwerUxNHYFjZxs10R7SlUlk/WYIknNEf03AoCqOlJ3I3Fouek9HhgLfW4T4n9LgYep+d5nlDC93PhVpB+QwYHUxSn9ybI0sxV9JJf8XfgF5DXrLCT1eXppcz+Op+RjqlM4So/XM6rUDCX7Nd6T9IPblETqhwrlMkYdxjpaLrSNGUTZoGw+pr2GhWHGwnF4/o1nuvVCmLZgA6MehqO7HEPC/fDdDqbJXltIpCIE9/KCi6cMr44ag/379yMuLg5Lly6FTtddB9HvDC6Pi3nvTId3sBvWfrAF2ZcLr3ziftP9ugI+h4OFfWMxyN0L27JS8Wn8MYguds2wt0KnwYmyfJwoy0dQpQIxskD0dwjGNM/BOGlTid+TL6OooWc5ebt4O0JiZ4OTB1PQu7/fLU1BTPIJw45tXe9X1F5KlY04VVyIGX59sCaLuWS7tLgOvXp7W6BnN8dH4oCRbqFY/sN+mEzWOyW0IykdP82cir9sL6JWefO8c52FVC7Bkx/NwpZvd+PUqVMW6UNXwQZGPQgOh8L7W17DvsRMxKcXtr0DAKmNEC4OUgwPDkLs2EjkppWioU6F+fPnIy+Pme2+p0BRFHrFBuPBF8aiorAGHz+xEnqd9WgTXEWOWDR2KvIV9Xhizxaor0zDiDoojUF70JkMOFubgbO1GZDyxBhIjcRXYyfiVHEhVl9IuK2UGN2RmHvCkX2pANpW8uK1hoQngLuNHZGx3ZrZkpmCT+4djfXZ8dDT7ATOnsnB60snW6hnN2aadzT2laSgosq6V7/WNamRXFaBQcE+2JOY0fYOHYyNVIwnP5kNpaIJZ/473+XtdzVsYNSDGHRfXzQ1qPHX8Us33IZLUZDaNA+rvvLYKPh7Nk/fxP2biM9f3Yy66uZh254cFAXH+GLm4gkwmcz4/ctdyLyQb+kuEQx06IXpnqOxKiENO3O6/iZ6MxoNavyWmoh/01Ixv29/fD1uEr44cxLp+u6ZbPJWCYr2weSnR+O9md8ANlKIbIRtjhhRaNZ2GdrhjG1JcuvroDRo0UvugcRaUntiMBjBpU0jWRo7vgiDnAPwesI/lu7KLRGXV4ihvt5dHhg5uNpj2a+voa6iHv976CsYWkl1crfBBkY9CP9ePti5Yj+aXFu/QQ0N8MED0ZFw49jAZDZDatO8pPz7P04g77e7/y2hLYYNG4b7778fQ8JHY8fqI7hwxPqEpve5j0C0LAQrcv7GrhymTsJaaNRp8eWZUxjjH4hlw0fil4yzOFqSa+ludQo+oR5Y9O087PntGOoqGwA/KcZO7YONKw5bumsdzqnKXAxxCWAERo0NajQ0qDF48GCcOXMGAGA0GmE2meDs7Yiqoq5fiDHAyQ8ZinKUqRWQw7btHSxMlVIFJ7v2J8G+EwaOjcLs1+7D7lX7sfuXQ13atiVhA6MewNWM8noTEDE8ApyMShiNZlw8nQWtRg9jQTHGPDYcY2JDsOXtHfh4xxIL99iyXDWlu4qNlzPGzRiIfsNDsOfPeHz83z5oNHogzBcAkDeNebMy0MTVwcGk/0xhrZwoO0mZgtGkZF+iLKogp8Hs/EljuyHycfAV++KN8zvRaNDAJY652pCWIxZuZ8hRiyZnsg1xTdu6C46e3MY+jxRnVweSSmFzcMtU0oGqTBQk1GLJwOEIdpDjt4xzMJrNMFPkramqmhTPBnkxXYnjCvyIsgPNOLGPFzklJReQn+cpSXE7wEwiW11KriCL+JdMVmyuJIXAAICHeUgrvITXf34Ger0eg6K+BOUgQqrECGWUHGInUkTvZt88rWPDFUIo0MPYSnyrJw8HbIvJ36AxgPy8NdNWfg4p+ubqyG3ohoUmPrm9sIrZsYOZxfhixER8WXcWAMBRtZzXKQn5CAkJuRYYmUwm5CUXwTvEnQiMVG5MM8rrMdqSI2h0ob5XJHP0UeJMfpfCTFcEefrifF4NVKW2cK0nr9fWfMZqepGmhxzajCj9+CmDmJmzuTqy75J0ssynHXN5Rsv5V5ZVAY9pdmiIIbfhFJEXdboHzWC1jLwP1CiYv5vnYfKYH1Wuw/LlyzFkyCD876t3sHnz5rt+Uc31sIFRD2LPuuOY+PgI+AS7Qu4kxeRHY9Gk1MDcpIa7vwu2/7QfZ/dctHQ3rQaJnRgRgwIx6anRKMqtxNdv/Y3aygYYnJk3PEsz1bMfYuS++CS5OSjqTmTWV+PlU9uxrN9YfDpwEt5L2I+7ZXG/rVyCp5+eg2XLll1LcCyTiiHgcdt80MgEtjBZOPVHeylqrAcAeEvtUdRIiuuNBhO8vUkB9vF/4zH9hfG4dCwNRkPXTdFQACKdXPCflU013wxjF4nDxSI+fvrpJ0RFReGRRx5BdnZ2l7RrTbCBUQ9CrdRgy4/7Qbk0pyhw8ZSDy6VgLCmHQW+0yHC2NUJRFGInRWP0jEFoUmrw75pjSEnIt1ojs3A7T0zwiMHbF7d0u6DoKkqDDkvid+G5iFj8b+AEfHXsDEoarVsQeyuE9vWHXq8nkmsO6xOA+KQCNLUhwvYUOyGrsXsIr69iBpBZVw1HkQ0jMDq2Lxmvfz4Fy5cvh+GKieLFIymY+eoUuPo6oTTn9pPRthdnGwlkIhGyalsZ4bNyJDwBVIbOW7AwYUQE9PpcjBs3DnV1dZ3WjjXDBkY9mMqS5pPeWGA9ma0tjUQiwSOvTIS7vzN2/XYcF4+nw2wvbXtHCyEXSPBM0L34LfcYKjTdP5BYkRqHmQHR+HT0WLx37DDy6rv3jdkn1B3r1q1DY2PLdBlFUVDcwpJrHxsXKPTdL9WK3mSEiw1zermxQQ2TyQSKIqdtcpMKENIvoEsDo1AHJ1yurIB1vurcmPS6KoTKnXGhqnMC5tg+/pg8uhemTny+xwZFABsY9Qj2Nawl/9D9n58dylj+IwAABzcZ3vh1ATJTSvDDm39Aq9YDHIoxdVZwH3nTN4mZ0x2OXvVEOSuP9DWSZJDalTIP5oNEqCBF8maaZl6Z4IK3pozD/nPFOHBBAUkpqZGxy2v74dvgT+oPNGS+U6hdmHoPHuknB6MNuY2ojZdwYTEzu6bmOhnS5pQUFDY04P17R+G9+P3Ia6wDaLqILDUzWStHQo7AVFaSv5tKS9YhEZJv3b52zAeBhqYxCv+O1AOZysiHuUlNjtjZ2YlQm0XLIcYFTBymceFVrib9dBRKkNKQD6GC+fg204KLulD670TuQ7USAegl5D5NdCsHI3luCKvJ7yYtZP6Oense0ktrECR2xiFlPgyym7vqU1wushLzERTjh2P/NuuSPI6TwWDFIPLaGBBFZprPVZDnfemFVjzEfMnJ2akyN5QWKSAua/7OOhm5ubhSCzp1oaTGiH6e07VRolrmteOQTF6TGl9SayisIC+u+iBSFN6kMwB6CrguWa9TONmRrDryIjbTEoDbnSO/BwDYbGnWfT34+Ehc3nr2rjZvvBWsa/0kC4uFcPZ2xOtrnsPedUex7tMdzUGRlTM1KhxcDoV/L1q/AWB7OV1egJ+Tz+CT2Amw4bWSprwbwONzEdI/AOfOkdnMo4I8UFxR3+b+gbaeKFN3v+ntnNpaRDjfmss1AKTEZSGkXwCcvZmO6J2F3EaMknr2DfF6AmP84Ogmw+7Vd99qyfbCBkYsPR73ABcs/uEp7Fl7BEf/ZLr2WiNeMjs8GBOJ747FwXCXrhaJKy/AkZIcvD9wLHic7nermr5wInIvF6KwsMVMVSwWw8/dAWdTC9rcn0txUadrbHM7a6NQUQ93qRQiHnNIzGQywdWVTM1SU1qHooxSBPfx66IeApEeLsiu7H5BZ2fi4uOEtPgslGSVtb3xXU73u9uwsHQg9vb2ePKjWdiz9giO/xNv6e7cMo8P6otdKRkoqru702r8ktI8vfJweC8L96R9jJgxGKNmDcHOFQcYn5lMJlipjr9DqFGr0ajVws2W6Q20cuVKLFu2jPH33KQiyLpotaevrRxGk5kdMaIxZGp/nN6RYOluWAWsxoilx8Ln8/Hjjz8i6XQmTu++CEpwZcrGnnaD5pBaAe+D5Jw9R88csakLI6cFuGPIfcwc2vSQkalH0NuT9bpdSU/k4WKPPjJX/PbjcThqW7axv0DqXTT+zKmJJjeyXa092a6AFmfVRzCXUAurSC2KhLRogpFmAcOjSZ10cmZU4JhA9kt1nYTop7qz+HT2aGRTeUiub25MVcwUxJt0ZMP+4WTH6tQ2RLmmnnxwz/U7w6jziyOTiHJoThJRNuvIKdeDpr9gZ2eHt559Fg/Mm4aUbOY0J69RD8e4lgUPmgdJ7VNBdbNmRq3noahGBqOQeW6o3cljyA2kJTitoelIBK3o4JJpCYN9yeNHtbE8XFrInG6uGdlcZ0ZTBYL97JHfSKYe2rRpEx577DG4u7ujrKzs2jWnUmkRPTwMlOAkKAPZL7tC8hwsapQRZRcJ+d2VgUwPout9woa6BiCzuAoclfnayECDD3lO14YydTgqf/L7alxonl+l5DgD3TMMANTupOZKkkuOCKp9yHsPPTmwUq+Fn0yG89Ut4mujmaYVU5Pnk0lLPuY9fiXPYQDYZ/oLr3s+hV/OfYU6U88VXV+FHTFi6bE89thjKCsrw85V3cvR9Z6BwThzKQ8arfXkZutMyhoa8WvWKbwQNgoirvXrjT7++GPs378fKSm3r/3iUxyIu8F3vRFKvQ6t2TSaTCY0NjZCICAf3qd2nIeTuwx+EZ6d3jc+h4vy+u43RQk0WyE4iGza3rCdTJ48GQ4ODte8tno6bGDE0mMZMGAA/v33X0t3o13YiPgYOTAYe45bXzqSzuR0VQ5Km+oxwTPS0l25KVOnTkVwcDC++uqrVj/38vKC8RZyn3lJ5KjXaaDQd09fqvZiMpqQdDoTQdG+bW/M0uFERERgw4YNUCqVbW/cA2ADI5Yei0AguGY0112IifBGfkkNyq08G3hnsLckBSNdQy3djRvC4XLw3HPP4Z133rnhm/eMGTNwdOfFW6qvqRNN/KyRkuwKuPnd+mq2noidUAhzB7svURSFe++9F7t37+7QerszrMaIpUfC4XDQq1cvpKSkYKiWlliqkDRP45aTUxo8O1LfYnIlvUgAwC6PplmoJHUP6ghyJICqaUUXcYGcjDAKgagIT5zNKIJRSEFUTQZ1TWHkQ0UnZb730L1WTLS0SWKa16ejH1NvUGND6iBENWQlXNrzvD6EpofRMCdZBI3kNvX2ZFlbKcGxyio86sVDDC8CiSVMgzv6MVXRNEeNjaRuxMeFXJX0zeXRjDrDvyE9Ygw0n6KDpr+u/fu9pe8hLS0NCQk3FrDy+XwoCqoBRUtgq8wLJDdy0sKg58BsomDQcaFhnl6Q5pPHsJGeBNWV/BFsMpnnl8qTPMZ8FVk288nzh6Mlgz2lF1Nc7epc1fwV7EyoNquhdWJqdaqqqvDcc8/hiy++gLGuRfujVzUBej042UVku+4hRLm2kZxKqswgfXts/ZkLEkoyXa79u46yg8wOaLpOp+V9gPxupcOZ+cTcjpGPy5oo8nOa7RO4TCskmDk3zwNnEJHHnK75C5E442BuDriqlvtLXd3NE8s6nyD7Tffa6jcmChqNpkem/rgR7IgRS4/kqvuuWt22CaI1IbezQVVtzxzuNgP4MyMJ9wdHWLorDMLDwzF06FC89957N9xGKBRi6NChyE+7NddiO4GwVZ1OdyDI1g35qtYd9ZcuXYphw4YhJCSk1c9Zbkykiyty62rb3rAdRMSG4Pfff+/QOrs7bGDEwtKNCPB0RGZhz03hcrq0EN5Se/g6yizdlWvY2Nhg6dKlWLt27U01Gp6enlAoFCgraDs/V35jLQQcHkJl3XNqiaIoNOhbf+moq6tDYmJiF/eo++NgKwaPolCq7DjhOEVRGDA+GhkZ3SeZblfABkYsLN0Ik9kMnb7rspBbG00GPY4X52N4iJ+lu3KNl156CQUFBdi8eXOb295qImIzgL1F6RjrZb2aqjshPT0dDzzwgKW70a3o5euGy5Udm0/OxccRHC4H6enpHVpvd4cNjFhYuhEU1fz/nkxiZSnCPVza3rALmDRpEmJiYrB8+fIOr/tidSmiHNw6vN6ugNPGJOCuXbsQEREBThuaG5YWBob44EJZadsbtgOKolBRUAWNpmesfrxVWPE1S4/negEtAIzRzSTKXAHNT4a24sgkYvrNGGxJ8bXfdppw9XVyOLy2jCmO5dAWzJXHmlGARjhPcsGF8lLI0mmicDU5GmEQMx86fFqydoUXuXRc5U+ORnHTW8lfJSL30Y0kV8jxeWQdpmpSpGvSMd/HuFqyr5J8chv14JZpmQx9EdxcY6F2J/sR419MlPPqyeSiAW5VRLlJTwpshReYIlZTCZlM8/pzRS6X47+3/8OKFSvQ2Hjj6Y0xnObzyZ3jClfKm2kg6kSqdMWSZuF0oaEEjjZiuJtsUNtIJkHl0Ba9cdXk8eNLyTopA/P8MtDEwkYB7TcoIKfCTDbk8aJvDwDVmU5wkUjA7SVBTqoELuLWV30WFRWhvLwcAyb2QfyuC8RnZh0pHK8JJx9Toa7kVLLGify8sJapVg+PajGa9HBxBRI84XidzyG/gTygzheZj0aBgv5d6NcfeT7SzVMBZiJoOg3e5AZOyc3HgsOhEClzxNpjieDTk0t7kNebTkX+To7b0sjtr7+f8XlAN0y309mwR4SFpRtR2tgIIZfb9oZ3MU0GHbgUBx62TPfrrmTs2LE4ePAgNmzY0Cn1601GZDdUIsLHOkbHbhV/ezlKGxugM914ytdkMiElJQUObrKu69gV9GYDXB0te+60F08POYxGE0oUPc+mwxKwgRFLj8RsNsNsNsPNrXtNVSh1WvjLWlm/3YPQm0woblRAImAuqe4q/P398eijj+LAAWYutI7kXE0+Bob6dGobHU0vF9db0sLs2rULw+4f2AU9IslRlsDHVdbl7d4JfWJ8kJbWsdNoLDeGDYxYeiQmkwl//fUXnn76aUt3pV1crqpAL2fXtjdk6VRee+01bNmyBSdPnuzUds5U5SI6wAMifvdRPfRycUVyVduBUU5ODqQOtrCRMr2OOpNKTQ3sbcWwk4ja3thKCAl2Q1JKcdsbsnQI3edqY2HpYDZu3IjVq1fjgQcewJYtW679na45An30mlYePPoLRt0Vg8ly4B+k5qNRTWo+6ElAAaCplBzutymhkF1fiRd7DYK8gg9FLLmPz3pyik3lwdQ+iepIHYSJR+6jiCGnP7x6lzHq4NIyW+YVkVM92lqyXQ6XZiToRRM6ATAKSR2Sype2Tzqp/xH14YMyUaBMLToOuqZoiHseUY6vINNN1FSSWp/wDbnMfumYDtaBgYGIiIjASy+9xPisNThXRrY4fD4oDgcNvWlL8KtILYq6uuXcKAaQGlCJAQ/4Ykdei1bE+RhN39JE1sE9Tn43eiJfAFA70fahGRJy1OQfdC7k+VjbhzlV5l0jRZDEARkXKyE2ctB0E7cBg8GAv/dthvssEVauXAeBSQmhWQeKNlVM1+UUNciIslJFXkscDnPlX241qZWr9dKCFytBdU2z6NiGZuJqU8I8YIoQ0ljSRHt6NrmQHTW0ktKMosU2yiDyd6Kf9+AIYCcWwq+XG07cIAjXNZF9F+fQtGA3mX4za7QwG3vuKtcbwY4YsfRYqqqqsHDhQjz//POQyWSW7s4tUatSo6JBiRgfj7Y3vkuRCARws7VFgaK+y9umKAoffPABvv766y5LuPln1mXMCIoCvxuIZIcE+SIhrxj6W3zY5uTkMBLKdgXJlZXo5dI9Rl4jvFyRWVYFlbZnpYixJNZ/pbGwdCJFRUX4559/8NVXX4HTDR48AHCxoBQRVrJc3RIEOTugSqWCzgJvuo6OjnB1dcWOHTvave/t2iwkVpeiokmJ+/zCbq+CLmRwoDdOZxfc8vZ6vR5+fn6d16EbkFxZgWCHVlZcWiGRXq5IKe5Y/6KreAS5waDrXvkiu4Lu8SRgYelEVqxYAaVSiSVLllxLFWLNJOSXIDaoewlyO5JId1ckdbDR3a3A4VD47LPPcO7cudvaf8i0/ijKuD0B7YqkM5gT2hdyYdfqcdqDDY8PPyc5kktu/bfZtm0bYmJiujw9SH59HSKcrd9VnKKAfgGeSCoo75T6Y6f0x5E/TnVK3d0ZVmPEwgLgzTffxPfff49nnnkGq1atate+0n/OM/5mU0ZmmOTVkz40JjMp/DSbmAGZnraiWFrYrD8oLq2EYDSFKBtvXKpquWHq7Mn3HJty5pugUUxuY6TZ23DqaZ4wTcxVe5Iisg5RGxpWHW0Rnc0h5lJpM00XYrIj+07VtegoIt1csDM3AxTt6/nLyBxSe1IjibKDI6nJsk2haTGqyKSyAAj9hVeoF6RSabsF+9QV3xj/KF/sWHUItmcyic9NY6KJstiF1GCZL9mhBEqccSjCbMc+WHMqAU20AUNxNXn8eLfg1yepILViXDU5AtcYRmq2qqNoCU5pmU0G+nuhVN+IUnkTcOU3V0eQ0z8T9iwiynuLv8PGjRvx2GOP4cyZMwAAypM85zxOkXqfuuFkvw0a8pw165nv+33DyFGslEIBhGIOPHwFKG1qQG04GXDaljIfjeJK8oSjX0scA+3aasXnyT6H/GHo7VKu5Hf1cveE2ZbCWVEZzIGA0JW8jwAARdcY0TIG0TVbB/QtDu0v+jyKvzPWMurs6bAjRiwsAJqamvD6669j+vTpeP/998HjWfc7w86TKXg0PMbS3ehy5DZiBLk4IrG065cuR8SGoPQ22w3p6wexrRAFt5hAtjX+uZCMMWGBcJBY36hRtIcbnhncHz8kxLd73+LiYojaiq47GIPZhNS6CkQ5uHdpu+3lvqBQ7M3NxK0lkmkf/v7+kMlkbJ60VmADIxaWK9TW1uKhhx5Cnz598P7770MqtV4TuENnM+EmkSLG2bpv7B1NbIA3LhWXQdVFwuercHlc3Dt7GD777LPb2l8gEqCisAZa9e0LaMsUjTidU4j7YyLb3rgLifZww0v3DMHyQ8eRVlPV9g40Ll++jD59+sDFpWt1c2cqCjDEza9L22wPDkIbDPbwxuEC5mrJjsDPzw8XL17slLq7O2xgxMJyHSqVCvPmzYNGo8Eff/yB0aNHW7pLrWIwmrA+5QIW9hkMHtVzLuOx4cE42Q5xb0fRa1gY6qsaUFHR9dqm6/nz/GWMCw+CvY11ePD4yO3x6qhhWH7oONIrq9veoRUqKipQVFQEnU7XpULsyzVlCJO5WO1qv9lBfXGyuADlKqaVR0cwb948HDx4sFPq7u5Y93wBC4sFqKurw0cffYQBAwbgnXfeQVBQENatWwetVtvq9tfP2V9lfLU/UTZzaXmoDtA8YXqRugkAkNWRZXoutPM7CnAPzxdveAzFz7viUDqCrMM+k+ljJM8gRyxEteRDwcQj+0nXBwFAkyfZjsCT1MRwLpLfjUvz2JGnM49jTS9S7OQYR/ZdoDRhQC8f2Gq4SNmTC/Rl6jeSSsjRM3s5qceoKSS/TNjqVKK8r5XfEQDEYjHeXrYAS958udXP24JycQIltwdlIwbl4gRzGRlc2RSRGhCNhjx+poCW41UILfZXZWLu9L749Nyxa3/nqcnjpaelfWstR5eyH02TVkb2Q0v77QURCqIsVDrhtaHDsCE3EZc4ZYAbQPHJc0NqT2pmtCHkbzS24hEAgDvlD/fyUPiIg9E3ohbnD6dc24Z+Ftfkk9onmQ/ZL7mYqcOhe1yhWohy6FBR34S+fH+cHZRDfMw5xZyutDWQ159eQtPa1ZEaLZ6SuWqSqyKvv/oI8rznC5p1TMF2zhjq5ov/pf0C/9CWY6jSMXPeVdSRQTKH1s/W7k/u7u5wdXXFzp07GZ+xsCNGLCw35Ny5c3j66acRHR2N9evXd7kO4lZYufsMIn1d0S/I09Jd6VQoCpg2ujd2HEmCuTMEFzdhyJAhyMzMRHx8+/UzncGvaQmIdnZHbyfLpbOx5Qnx5oARiCsrxH956Xdc3+kdCRg9exh2rjiA2Ekxd97BW+R4QR4mBnXtiri2oAA8FjgQ24suQ2loxZmzA+ByuTd80WNhAyMWlptSVVWFF154ARcvXsSmTZu6XAfRFiqNDn8cu4RHRvWBkHv3DgD3j/QFn8fFqcSctjfuQDgcDp588kls2rSpS9u9GWqDHl8knMA7g0ZByu96c0R3sT0+7jsd5SolNqVf7JA681OK4erjBHWjBs6eDhBJmCMjncGurAyEOjkhUm49Zo/DXYMgF9hge8GlTmtjzpw5uHSp8+rv7rCBEQvLLfDpp5/i9OnT2Lp1K4KDgy3dHYKTKXmoU6oxNcT6DQBvB5GAh7nTBmLz7oQuHy2aM2cODAYDjh8/3rUNt0FCZQl25abj46Hjuiw4EnJ4eDRgEP6v/wzsL0nFlxdOQm9iTgHfDuV5lchPKYbMxQ7lBdWYNG9Eh9TbFmqDAWsSz2Nh5DCIucyp565GwhPgqeBYrM2Kg97cMceWjkwmw+TJk7F8+fJOqf9u4O59xWRh6WC+/PJLJCcnY8WKFVizZg1+//13AMAYzkzGtubsQqJMiclpOI3ciShzWjFxru9N+qaoy8jLlXudXGHTxUtYMuEenDmVh1pV8/B7YwAzitBLyYcoXXtCz/9ELwMA5KROwqAjNzJ60vKt7Sd3p+uJAEARRu5jl9mid3lmwgBcKC/DCWUZ4NXcYa4vU5DK55N16A2kZsZnN3k8bpZDCgA8PT3xxBNPYM6cOTfdri1MxWUw+drBrFTBVFwGcMmD3hRIrrAT2pNeN9pG5vHiig3YmH8OXIEZ34yehC3B53GsPAumKwu7dbmkTsloy3zIStzIY9gEMm+XSdBcFwVgqJcvnu8/AFmKasw/tA1VahX4UqZPlqmc1OY0mEmxEyeCPP/cElquC63OCJm7A85sPYN7ZsZe+304OvJ8c4mnXTsBNH+hVgRVdiLymIpCW4TiSahBlEqM5UPGYEXWIZSq6xDxGHMl2KkzEWTfT5PnU30AeR0IGsnzDwCq+9EuKJp/1wsRQ3CxPg/pqjyIhUBJrYz4PNCFKXCvNJFiMC5t4eM40WPX/s0X8DB/3f34448/oFIxcxayNMOOGLGwtIN9+/bh0Ucfxbx58zBy5EhLd+caGeXVOJVZgGdGDbR0VzqUKf3D4ecix9pjTBPNzmbEiBE4fPgwioqKurztW2VdZgLWZpzDFO/e+GbQQ3AT27W9UzuIdHLBD+Mn4/HeMfjm0km8d/YAqtSd80A9sT0BY2YPRdaFXHgEuHaZC70ZZqzLPYGE2jy83WsafGwskyrk4bAoeIhl2JB3utPa8I/yhlarxffff99pbdwNsIERC0s7KS8vx+LFi/F///d/+OSTT8AXWn4IHgA2nk6El4M9xkdZl5j0donyccPUARH4dtcpNGq6VigaExODxx9/HLt27erSdm+HuIoCvHruH+wrScVXA2bgHtc7n+q1F4rwfN+BeHf4KGzNSMP83dtxtrJzA8S85CKUZFeg75goVJfWYsi0/p3a3vWYYcaO4vP4Lfc4lkROgZuoazVHfV09MDUoDN+m74fK0Hnn+tCp/fH33393Wv13C2xgxMJyG6SlpeG+++6DRCLBRzuWQCqXtL1TJ6MzGPF/u45h1uDeCHFzansHKybKxw0LJw3BF9uPo6RG0fYOHUhkZCSWL1+OTz/9FOfPd/1I1e1gBrCrOBlvX9iOWQH98fWkSRgXFIQQJyfYCgSw5QtwdfyFS1Gw5Qsg4Qkh4QnhLrbHUOdADPf1xcTgYHwwajR+vf9+SAQCzN+9HQfzczrFebk1CtNLIRALsOPn/Rg6vetHP8/V5OK33OOY6ze7y4Kjsb6BeLn/EHx4+gjKNZ13rgf09kFQjB9OnDjRaW3cLbAaIxaW26SyshIvvfQSvlnwC97c+CJWvLL+tpOEdhSFNQp8u+8UXr9vBD46ewzp1bdnumdJ+ri6Y2HkEHy14wQyStvvpHynPP744/jtt99w7Nixtje2MvKUNXgh7g+M4MSgt5sbZvWOgpDLA0/Agd5oRKmyEW62tuBxOKB4zdocM4CMhnIoec3vyfElxfg67jRqTZ2zVPxm5KcVY8bCcfh41jfwDfcEX8BDK/K7TuVcTS7+K63DXL/ZWJ//O8o1nWPqKeBwMS+6L/q6uuPD00eQVVcDmU2nNAUAiB4RjtM7z6O8vHMS0t5NsIERC8sdYDabsfinpzEydSTe+uYtvPLKK0hJSQFoSTwnOCwkynaF5Dt4UyhTyEpHPojMDlmeTY4KiSuaH2wXysvw/dE4LB86GgePpuG/vZdgMDSLb9Uu5CXPoTVb1YfWKJc5VsAtI8XAkmJSC2KiaU6rYsgyrxWJCkfX3PdZvaIwPSwcHxw9gjRBFeB3pU5aUtnW1mHp9WTDVJot2c/4LKK81/QXo44HHngAoaGheO+991pp4fYw6XQwGwwwG40w6XTgud3c8kFPS4rKEzPTn3g7ke6fhYmkj9Uh30s4VHQJuDL7pa8Rw8PWDhK+ABqjHoUNCggryeNlCL4uEJIDHJpeWy4nf7iaYhmjX57h5DlaUkoaKzYOJYMtt/Xk/kmnMjHvnfthNFO4fCIdk58bh+0rSHdmoYLsWEkGeR1E9sln9EsmINu9UO5FlE20JM5/5iqQpziLJwKfwJqc40ioyQPlSl7UysfIcj83cqoxrbYVn6nSZqG0h0SKV/sOB0eowucZv6PJXgs3e4BLO+hTfJKJ8smqAGadOnLiR5ZEjjqZdDrIXOwxYHxv/PTSb8z9WRiwU2ksLB3A0aNHsXr1avz2228IDQ21dHdwsagM732yA0H+Llj2xhREhFp3TjURj4d3RtyDId4+WPDfDqRVd/1IkbOzM1588UW88MILUKu7frSkMzEDKFE2ILOuGoUNXTs12V74Qj6C+/rjn693Yej0AbBztG17p07gbE0uVmUdxeMBQ7Ek8j742Mo6pN4oRzd8NnQCzleW4PPU7Wgydr5+7r5n7kXcjvMovIMkxj0JNjBiYekg/v77b7z++uv47rvvMGbMGEt3BzW1Snz5wz7sOZCE+U/cg8cfGQJ7W+tz7x4Y4IVVU6ahQavFGwf2oU6jaXunDsbOzg4bNmzAr7/+iuLi4k5pIzDaD0Jx1xsydjcuHErClGfHoL6qAUkn0zFwfLTF+pJYV4CXEzajQFWNLwdPxbex0zErMAa95G4QtdP3yF0sx4vRsVjSfwR+TorH7xmXYO4C9RaHy0Hfe6OQsJ81dLxV2Kk0FpYO5OjRo7CxscGnn34Ks9mMQ4cOWbQ/ZjNw5lwuLicXY9aMgfhy8XQcOZ+FnSdS0KDq+gDkevr5e+LhQb1hKxLg53NnEVdsmWXxtra2+P7777Fz505s2LChU9ooyiiFjVQEv17eyClq7JQ27hbWLvsL7//7Clx8nHBu7yXM+3Am0s7moCTbMtoYg9mIP/LjsTY5GQOcvdHHyRMvRA6Fs40YNdpG8CguEmsLESLXw1XYIthu9Gl5CeFTXEh4IhzKL8biY/+hRsPM59ZZzPtgJrh8LioLu5/e0FKwgRELSweze/dulJWV4aeffkJiYiJqa2thVpNBiPwYmSG+fLg3ox6hM3nzLC+lZfWUktoTrZzUJ9QqW8z2amHGF+fiEf33eUya1gc/LZyOlKRi7P7zLApzW6atbMpIgz61K/Ot2D6phiibaKNQXAU5DZW+oMUXRsDlYthQX0zyDYW9UITNmZdwsDAbRi0FXFcNr57Uv/A8255uMBaQKwND1pcRZbMzefzGVM0El8fFKz/MR15SEX788cc227gdDpr+AsqB0ef64KzpIBzUtBEQmkZEmEf+BsbeTDPLgiRSU+TemxQI1ypJFa+dB9PMskFEHi8X2c2zuFeWyYiyfyAzUGnQ0kYkad+NKyHdB7WDmNPO/KOXkJ9SjKFT++Pfb3fj2F9x6D82CsUZzdNAgnryvDfJSH1QUroPo85RMWTC4BAncqr2UgF5POl6NQBwdahDnrEOeRWXsaUC0NR6QczlwV4oQqC9IwqVWqQrzkBnatbDyWzIa75W14i8XNmVUnOfwx3IY2jHI/c5XE5ab4TISA0XACiSSb0UsvKv/ZPH5yJ47BRMf2QystXZjH1ZWocNjFhYOoHExEQcOnQIX3/9NZ566ilLd+caFeUKrF15FJvXncKYCVF4celk5GdVIO5YBi6ey+uUNnkcDno5u6KPmztG+wegRK3Azrx0HCrKvm4ioWvM/K5HbCvC25sXoyijFP989V+nt1dRUYGRI0ficnZd2xv3cHb9cghvbVgIlaIJJdnlmPnqZJzdexGlFho1ao2roz7Fqgak1FZCbEcGNRpO140K3YiHX5uCrKwsZGezQVF7YAMjFpZOYtmyZfjmm2/w2Wef4egX2dBr21551lVoNHr8t+0Cju64iAHDgnHfjP6Yt/BepGSUISu7Anl5VVCptMgzqWA03boOQmIrhKPMBq5eDnD3dkBIL0+4DfVCcYMCSVWV+OzUcSSry9quqJMRiviY+8FMlOZU4JclXZMgdtWqVdi4cSMq9h9ABTutcVMqC6vx1fxVePKjh/HBQ1/D1dcZU+aPwaou+q3uBuydpeg/JgrDJt5dbvhdARsYsbB0EmazGa+99ho+++wzvPTNXPz64RbUlNVbulsETSotju1LxrF9yZDaixE1OgyennL07+cHiUQIob0IWp0elbVKVNQ0a2PEw9TwCXSB5MoUmvm63F82EiGqimuhVeuQk16GI/9dwq6qM2jUXTcVZplFRtcQivh4bMFo6CpqsP6Dv2Eydk6yTjplZWX46quv8PYnH+GDxzpn2u5uorFOCYm9DewcpTj612n0HvEkJjwxEv9lsEHlrfDYm9Nx5O841NWxI5TthTK3L121uavy17Cw3C0IBAIc33oGcld7FGWUIn5PIo7vukxsU/FIBGM/6QOkWWRRKumLwnG+ue7GWMdcAeVLS6RKz7cpuUCuyNLODAOPx0WQhyME/Ob3KL7GDEWjBgVltQAA6roq9XoDuGnkcvCMp0nfI8rEvIeISsh3NI0rLamsF6mRaahlOo2HfkNOXXAqa4ny3tIfAAAvvvgiAgICsHTp0i5flk9RFI78dRqJR1Kx59cjAADDYPK31ziRv1v5YObxkkWQOi8XCakPqmkij09tA9M50MOR/J2EXHJEs0pFRrAN2aRGS+zH1C2JBKT+Z5QHOYVztprU/zRtZtpIOJ9uCXzmLByDuLSt+OGHH+Dj44M1a9Zg/VvHUVXa8rDPf4r09vEbk8+o09+WPF6OfNKTKVvlTJTjMpl+QS4u5PfV6MlzdpgnmXg2vsKXUQedYW7kPtPkF4jy9yXk6tbEi8x+hf9Ifre9aZ8iJCQEmzZtwqBBg2AydU3g3xWYzeYuCUDYESMWlk5Gp9Phw4e/gVeIG0Q2Ijy69H40KvVIPJZm6a61Sf2VlWvVipYHiUBJBlcUbaqNzOtuXQwfPhyTJ0/GnDlzLOJVZDab8f2i37Do+ycQEOWDTZ9uBTv+cWP2/HMOCz56BD/99BMKCwsRHx+PXoMCcWRrgqW7ZrVQFIVXXnkFv//++10VFHUlrI8RC0sXoNfqkZdUhLT4LKx6cxMeWjwBTh7ytndk6TACAgLwxhtv4K233kJlJXN1T1fRWKfC18+vQVCML/635VW4uNhZrC/WTnW5AsnJyRg/fjwAYMeOHZj+zCiIbFg/qBsxdOhQuLi44Ntvv7V0V7otbGDEwtLFFKaVIG5XIibNG2HprvQYfEPdsXHjRmzZsgUXLlxoe4dOpqlBjTfvW46Dv5/Cy69MQGiYO1iVQuvs3bsXH374IWJjY5GYmAgAuPehQRbulXUiFPHx3nvv4bPPPmNHi+4AVmPEwmIBprk/h892vI63H/gSKoUalB1zAqpish9RrhtKLgfm8GhTWrQ8Sy7/kn44AGB3IJ0o104NJ8uR5Pa2hczrXeNIlina/ZdHW6XcSMsD5+hVz6hTIiD9bWr3edy0Dfs8ZmpRyW7S2Xe/ZiMAwN/fH3/99Rf++OMPfPnll4z9LM3o0aPx1FNPwdnZGefOncPp5VnQ61qOGcfVibGPycmeKOc8RJaDY/OJ8ggnMk8cAGSqyOzxmfVkDreSMnJEU2hL/kYeMmZqkRoVqW3S0nQ4Ic6kf1Cpkjla5rSUpvDIyseQKf0QPjAQa5b9hXGPDMGU58Zi8fD3YDQYoZlIJvhTejAVIrWDyb5PikoiygmVpPbJ144pWC5qlBHlke7kMc1opB3PalK3BABSEakL9Lcn9UHni0k/M62C1Oc5nmP6iiWufOXav0ePHo2HH34Yzz77LGO7u4Gu0hixI0YsLBZAo9IiOS4TMxZNtHRX7mqGDRuGVatW4ddff7XKoAgADh8+jEcffRRz5swBALy+5lnMffdBOHs5tLFnz6EgrRhB0b6Qudhh37pjqCiowuhHhli6W1bF4MGD8e6772Ljxo2W7kq3hw2MWFgsxN/f7EH4wEBLd+OuZcSIEVi2bBneffdd/Pzzz5buTptUVFRg2bJl+Pe7vVBUN+L11c9i8vx7IRS1LyfX3UhJdgXqKhswdf69AIAfX1qHCfNGwj+K6RjfE3F2dsbnn3+ONWvW4MSJE5buTreHDYxYWCxEQ40SDTVKPLBwvKW7clchFAtw/4sT8Pbbb2Px4sWIi4uzdJduGZPJhIxzOdj+0358POdHOHs64N1fnsKoaX0t3TWLs+WHffAO8UDvEeGoLa/HH5/vwMs/P4P+0W0vi7/befPNNzs1119Pgw2MWFgshNlsxo+vbkTvoSGY8EgsWP3enePkKcei75+As6ccTz75JNLT09veyUpRVDVg7Xt/4/ulf6PviDAsW/kEvANd2t7xLiX7Yj5yk4sQ0Ls5EPr/9u48oKb0/wP4u+VWWpQlEqmhsYcka6KxhbFOPzNjCYMv2SYMvlkmM9YYjDGRbeyMpQnDkCzfsS9RjXRbmIokEtHere7vj0w83dSYUedW79dfnnue55znltv53HM+5/Pc8A+B348n8R9XR9SpZVzC6IpryJAhsLa2xpEjR6SeSoXB5GsiiZmZmeH4wd/hu/V33Lr4OqHzkbNY/M44Rkxi1g8WizGmtBNvKxgFFbGuVLqYGZ1XR0wQvTtCPMHU9xcL9gHAi4bio9IGCWIitGGYWJknYpJ4MjeIVf0bYnZdnJd2rPg4fe5jMWlXq7ZqYuvDauewatUqhIeHY86cOZLUKSoNPTSHQlNLE7YftcD/zfwYu771hVxDD3lv1I/SjhN/5pFTxf8LOg1SVParrycmJOfmid+TU9PExN+mdcSFaiMeqwZppsZioUlzA7EoYjUd8fd8M7HQAqgAUm6JyeaWX18t+LeJaVUM29AXfn5+BVdHlk3dhS79WsHry91QZOcCeqqP8id2Evf57CPxQYZ6tcVk66RU1QKiGWnifg2NxH3oycTPZ0MT1QpVNy40EdqWv4nJ2Fppqp+3N2nefSC0bTo1gtOk5pg3bx6Cg4OLHVsRMPmaqJJISEjAjjX++GRcV1h+WLvkAaTCqok5fHx8cOfOHcyYMaPCBEV/ycvNw82AP7BniR8mrRmFiVN7Qle38tXnTU58iQkTJmDq1Klo27YtAOD3Y8FIePAMbp6DUbWaaoXvisq4phFc5w7Ejh07KkVQVJYYGBGpgcg/HmDvutP4z7z+sGpsVvIAAgBoy7Qw8IuumLzEBR4eHpg+fTpyctRnsd737fYFOZaNXIfc3Fys9naFplblu4KfmJiIBQsWwNvbG/Xr5z9m/7P3aSgUueg2oHLkYmloaGDh3im4+OstHDhwQOrpVDgMjIjUxJ2bMdjzQwAmeQ7CpxOdoK3Fj+fbaGpqwLZLYyzdNwm16lXDUrftOH/+vNTTKhNxkY+w8ccz+CM4FuPduqO+lWqdo4rO398f27dvx+TJkwEAqS8ycPin83Do0xKNbFRvz1UkGhoamOk9BplpWTi66azU06mQmGNEpGbq1KmDQ4cOIeuJLrYv/xVPHubnP+TWESsraj0pftXsnAfxxW4HAE1bscCjZrqYd4L0Im5JFVqNPu95conHeZMyWzWPQrOKntD2f7mtyLGampro3Lkzli1bBl1dXYwbNw4hISFF9q3oZDIZxo8fj0GDBuH4oTiE/fE65yy2j7gAbE4j1d9jAzMxb+txilhktHAxwmep4m2qDvViVPZZRav4HJmQpLpCW1+WrdIn7rmJ0K7zo5jrdO6sB4D8R9R37dqFVatWISAgAAAwZswYODQajIM+xQcML9qLwVN8D/H/dJeWESpjLgSKnxW9RPGLS46BeC7VTVI9V1psEAtLahiKuUx/LXT8Nubm5jh48CCCg4Ph7u4OhaL4n3dFwxwjokrq0aNH6NatG57GP8e8TV+gh4s9qhjoljywgtLR0YGFhQXmz5+PgIAAfPnll9i1axc6d+5caYMiAFAoFFi/fj1mzJgB17Fd4dSrBbS1K8+f9MTERMyaNQtLly5Fy5YtAQAXLlyAQ9/WGDC6i8Sze/9MTExw5MgRJCQkYPLkyZUuKCpLlS97j6gcUCgU+GnZr2jc2hKus/qi12cdsHrlSTy8/0zqqZUJe3t7dO3aFVZWVmjRogXS0tJw5coVuLq64uHDh1JPT62Ehobie6/jGDy0Hdp1tIb/8WDEQvWJqIro9u3bCA0NhYeHB0aPHo27d+9i71p/9B/lgFMHriGz8BXQcqh69eqYOnUqHB0dsXnzZmzatEnqKVV4DIyI1FhEcCzmDd+ArgPbYJbnIMTce4KH95Nw9OANVLQU43a9W8Gue3Po6esip/pL/Pzzz4iMjMSSJUvw6NEjqaen1hLik+Gz9hTsO1rjC7ePkHr7Js5E3JN6WmVi8eLFcHd3x5gxY+Dj44NrZ+7gg6bm6PN5R/ht/V3q6f0rH3/8Mdzc3BAeHo7Ro0fjwYMHJQ+if405RkTlxJBa41CvkTnsnVvBqnk9XD8RjIToJ0hJTsO94FgEKPa98z57GbgK7VNpO4V2D82hKmNO54lPwfSUfS60NbS0hLYyV6xzFKDYB319fTg5OaFFixbQ0tJCkyZNoFQqsW3bNmRkZEAul+PlS7EGDv09AwcOxLwZXnj+NAXnjoUgOjIBsfX1VPo9bi/+Le/aScx/yckTf49GMrFujzxZtbREdq74XftpiphDo8gUt7eyVL36l5Qp5jLFJ4m1tRp8J+YDKW/eQf2mdTH5+zHY4rEHUbeiYVLLGEt+nYOFLquQ+CAJ2tXFBXGV2eKVpOhtVkI787nqz6vwAq6mu4OFtmahfCFlhvjzAgANHbEWUu4L8f/4m5/hTp06wdPTE6GhoZg1axby8gqtpFwJlVWOEa8YEZUTKc/TIL8WBfm1KDRsbQlbpxawd26N2pb5xQ5dkrrh7t27SEzMT6pNTEzE3bt3UdyXn3r6YmmARumNxO2aYpFJAGiUV6iPTOyjoZmf56KjpwOr5vWg9erpOpNaVWHeoDZc8rrB0tIS4eHhuHr1KrKysnDmzBncuHGDf/zfgyNHjkAZ2xIdnJri84lOiPjjAQ6E3cXDx8l4t+/B5cd9+UNs8diDccuGY8VobyQ9eo64qEcYOKk3tnjslXp676R69eoYPnw4hg8fjhUrVuDw4cP8XJQxBkZE5dC94FjcC44FkP/4rmXzegjSOotmzZrByCj/6SIbGxsMGzas2P3U0f5AaHfPaSW0zTTqq4zpqWwn9tEU16r666pybm4eYsPikJGS/835WUIygs6G4kr2Cbx48QJ//vlnSW+T/qHsrBycP3kbty5HYcSUHnAf6YTouCQc9A/Ci5QMZCkq2o1YIOpWNEIvhaPHiC7Yv/Iodi/yxX93TsEHNvXx4KFq5W911KZNG2zcuBFRUVEYNmwYPyMSYWBEVM4plUrEhD5AoCIQgYGB7zRWiltpQYqgd5oj/XOpLzPhs/QY0u1rYYBTS8z9Ty/o6cpw5moEdqTLkZKdVfJOypH/7b+M/+6cgv8duIIHEfE4vuUMRn0zFIvHbZV6aiUy+6AWlm6cgb1798Lb2xvZ2eU/cby8Yo4REVElYmVllV/vp/Mn8Dt0A/Kw/HpXqRaF8mpGinWOjHTFnJnYp9VV9q2nKz5C3qFOrNB+kG4itNMUquuaNawqPlF3M0Fc9y09XNxH/ZOvAwgdHW0Mbl0XLR2a4Jth6wAA83ZOglKRg+8nbkLK8zQAgLaFubCPOBfxymh6HdXzYsNDaUJbK1p8IKBwfa7C+UOA6peKvxgZGWHNmjWIi4vDwoULi+xDrGNERESlICYmBp6enrhyKQqfj+iE2R4fo137hlJP673Izs7BEZ/TyMrIhuMQewDAsjE+iL59H8tPzkOjtur3PmvUqIH9+/cjPj4eixcvlno6BAZGRESV0rkzYVj67REcOxqEUV84YlgfO1TRlZU8UM3l5SmxfdEv6D2yC4xrGiEvNw+7F/vi+okgfOk9FoOmOEO3ig5karAIr4WFBby9vXH+/Hl8/fXXFXqdv/KEgRERUSWVmalA2J2HWLn8GOqYVsWK6QNgZa56i6y8SYhJxHX/P+A8yrHgtR0LD2L1hE2wd26NFfun4LuD09CwuXTrqllYWGDlypXw9/eHl5eXZPMgVdKHzEREJKmY6ESs2nkOrRrXhedEZ9y5l4A1yoPIVZbfx8QTYhLRb6wTtGVa+Cv9/15wDJZ8vhbVbaxRr6EpRn3VB4snbi/zuU2ZMgWjRo2Cl5cXDh06VObHp+Ix+ZqIiAo0a9YMM2fORO0qzeC75Xc8up8EAJDPFxcxRp7quaCG2Quhract3hp6dkGsm6VpJ/YHVBevzcwRv7/XMRKTmu//ZiW06x/ILxppam6C+RtG4fs5B7DhxFdCn95VxwAAxnh+AqVSia1+4pp76XVUCzwaRSaLL8SIxSnftvDxm2QyGYYNGwZXV1e4uLjg+fPiF4ImEZOviYiozIWFhcHLyws5ihxM+XYwegy2QzVTI6mn9c4S45NxxjcQE78eBCsrqyL77F3xK5raN4SJiX6R298nW1tb/Pjjj7Czs8PYsWMZFKkxBkZERCSIjIyEz6KjWDv3EOpa1YTnhlFoaFyj5IFq5ujOS4gOj0fz5s2L3J6VkY2kR8moWsqBkaurKzZt2oSMjAxMnz4dMTExpXo8+neYY0REREV6Ep+MHWv8cU8ej9We/XA0Wo79USFIVZSf4oPnj4dg2rRpCAsLQ3R0tMr2hNinaGVrifsxT4sY/e8YGhrCy8sL1tbWcHNze+cCrCQN5hgREVGJPq07AfP2fYnLRwPx25YzeNyvpUqfhK5ilfP6DZ4I7fhb4rp6hs1UbyclJ4mLsTo1jRDaKgUfw8QFYqvLVc9pY83MkJ2lwLHNZwGIFd7t7Owwz2cH5h86VfCa+cV0lX1oPxbzoU7eXanS502GhobYvHkz9PX1sXz5cly5cqXY/lQy5hgREZHaSHr0HEuH/4BOA9piZcACtGthWfIgNXH52C107m+H6mYmKttiYmJgXbsGZFrv73SoqamJtWvX4ubNmxg4cCCDonKGgREREf0tTx8+w/z+Xji57X8Y2tsWzp2bQldH/TMyEmIScf1kCLq5tFfZlpSUBEVuLjTf090QPT09bNy4ETKZDN9999172SeVLQZGRET0t6WnZOBXn1PY7HsZ3ds3wtJpH5eLitmx4fFo3vFD2Dg0LrVjmJqaYuPGjUhMTMSECRNK7ThUuphjRERE/4iOjg4WLVoEJycnjBo1Chb6Q4Xt9+eJ54uMJ2L+UN0G4kK1AJCQZCy0TaulCO28QmkmyalVhHZ2otgGgLpn88c4trPGZ/3bYuekrbh9Mbxg+5pry+AxYTuys/LrLuUa6KrsQzsqTmjnPHudH2ViWhU2kyzQpUsX7Nq1C9u2lVzTiN4dc4yIiEitZWdnY968edi3bx9WrlyJ4eO6Qp2/PJ+/fhe/nQvFmEWfQt/odQCVm5MHUzPjYka+nalFDbhv/A8SEhIwZMgQBkUVAAMjIiL6x3JycrBmzRp88sknaNOhIUZO6AYjY9WrNuri2NlQRNy4B4fB7QpeO7LvKlxGObzTfmS6Mgxw64WvtrghYOfv2LBhA5KTk9/zbEkKDIyIiOhfy8rKwkpPP1SrbgiPJS7Q1taSekpvddTnFLoN7Qi7HjYAgKCr91DPqubfnrO2TAufzRmIeo3NsWq8Dy4dvlGa06UyxhwjIiJ6b7S0tLBq1SrY2dkh/FQ8jvichuJV7s6zwS2Evk/sVc8/Sj2xFpLWS/Gpt5pNxUKMllXFWkixL8W6RgDwLMRUaBvfAyxqmWDO507YHXAT66e74NChQ1iwYAHkcjl6Gbiq7ONU2k4YGRnB2dkZI0aMQEREBL799lukpqaq9KXSwRwjIiIqd3Jzc+Hu7o7+/fvDpKYRpn0/Ctoy9bt69OBJMrz2ncOInnbo2bMnIiIi4ODw9ttpTk5O2L17N9q2bQtPT0/Mnj2bQVEFxStGRERUKlxtZmHejknIyc7BHq8jCKgqnj+kvGL0F4taJnDrYo4TJ05g4MCBiIuLQx2tBkJ/A2N9xGf+ibVr1+LChQsq+6eyUVZXjBgYERFRqehddQxMTKtikFsPtPmoBb5YfwTZiteBjzoERgDw4qw3vL298dNPPyE8PBztdfoJ27MzFdgRymKNUmNgREREFcY333yDNmad4TN7DxRZCgBA1Namqh0f6wlNpWmW0Na9K27PNM0TxxvmqO4zW8waKbyGm8HwNJh/YIov5g9GTk4uTm70R6B/iNDndN4B1f1SmWKOERERVRgLFy5EekoG+o51knoqRYqPTsTisZtwev8VfDyhJz5x7wej6oZST4skwMCIiIhKnVKphP+O39Gpvx2MqhmUPEAigWfD8P2ETejY3w7LT8xV67lS6WBgREREpc7U1BQu7n2hb6SHaevGqHWF7OTEl5g/wAsAsPCXWahhrpq3RBUXAyMiIip1jo6OaNjKElkZ2TCzMoVJrapST6lYmWlZmNVjEa4eu4m5e76EkZGR1FOiMqJdchciIqJ/x9fXFx9++CGcnZ0xd9pcXI69DOcZbir95F6WQtvM9KXQfhkmJl/rPhO/32dpqtZM0n0qvvY02lxoG+g/FNqaVdIAAJk5SpzccwldP+0ECwsLhIWFFfXWqIJhYERERGVi+fLliIyMxNq1a7Fs2TKkX5J6RiVLe5GOBxGPpJ4GlSHeSiMiojLzyy+/YN++ffDw8ED7XjZST4dIBQMjIiIqU6tXr8bEiRMxdGpv6OnrSj0dIgFvpRERUZm7efMmth/0wYAFLTF16lSkpeXn9Th/M1PoJ59jJrSrdhQrXWteEJ8YUxaRY5TzYYbQ1r9QReygJV4jyMvIFPeZK1bjVnf16tXDzp07Ubt2bSiVSmzatAk//PADACA6OhopKSnIzc1FTk4O7O3tJZ6t+uEVIyIiksTKlSsRERGBdevWwcBAPesFGVUzQHUzY2RkZJTcWU3k5ORg5syZaN68OTp06IDJkyejadPXVcadnJxga2vLoOgtGBgREZFkvLy8EBkZia+//lrqqRSpcTtrxNyJQ3R0tNRT+dsSEhIQFBQEAEhNTYVcLkfdunX/1lhLS0vcvn27oD1z5kx4enoWbJPL5di2bRsiIiKwe/dudO/eHRcvXkRkZGSFCbQYGBERkaS2bNkCW1tbNG7cWOqpFCkrPavkTmrK0tIStra2uHbtGoD8CuSnTp1CYGAgxo8f/877s7a2xqpVq9CkSRM0adIEw4YNg4ODA7766ivMnTv3fU9fEswxIiIiST19+hQBAQFwdHTE5s2rhG09diwS2i+tTYR2qliSCLpiChIAICVJTPDOMi7cIVVoahm/Lj7ZupctniUkv3Xu6szAwAC+vr5wd3dHSkoKAMDBwQHx8fEwNTVFQEAAwsPDceHChb+9z+joaISGhgIA7ty5gzNnzgAAbt++DSsrq/f+HqTAK0ZERCS5/fv3Y+jQobC2tpZ6KgXMG9RCs3YN4b+nHBRcKkRbWxu+vr7Ys2cP/Pz8Cl6Pj48HACQmJsLPzw/t2rVTGfvmci0ymUzYlpX1+upZXl5eQTsvLw/a2hXjWgsDIyIiktz9+/fh5+eH0aNHSz2VAi06fojLx4OQmVb+bqVt3boVcrkca9asKXhNX18fhoaGBf/u1atXwdWfN1laWqJmzZrQ0NCAo6MjtLRUn/SryBgYERGRWtixYwd69uyJvn37Sj0VaGpqoN/orsjKUEg9lXfWuXNnuLq64qOPPkJQUBCCgoLQp08f1K5dGxcvXkRwcDCuX7+O48ePw9/fX2V8UlISdu7cicDAQISGhsLV1RUNGjSQ4J1IQ0OpVL5Lf6U6r4hMRETlW5s2bbBu3ToMHz4cMTEx6F1tnNjBQqxrlGotJgzpPc1W2af2szShrahpKLRlz9KFdp78HnT0ZFh9zhOzei5GRmomAhT73vWtlEuWlpY4duwYbGzUryq5UqkskwCEV4yIiEht3Lp1C4cPH4aHhweqVatW8oBSoi3TRnamAhmpmSV3pgqFgREREamVjRs3olWrVhg5cqRkc+jl6og/LsglO75UYmNj1fJqUVliYERERGrl5cuX8Pb2xogRI1C/cZ0yP75RNQN07G+HgF3ny/zYJD0GRkREpHZ27dqFDRs24Kv1X8CgapWSB7xHZh/UwoukFDyMSijT45J6YPI1ERGpLTc3Nzg4OGDSpEl48eIFnJt6iB0yCyVb56ku+Hoy9vtij9FLb4TQ7udlj0uXLuHAgQP/ZMpUSph8TUREld6GDRtw6dIl7N27F05OTqV+vEGTe6FTp07CemFUuVSMMpVERFRhrV+/HlFRUVixYgWuHI/Dn+GPEB7y4L0eo7qZMfqMcULD1paYPXs25PLKl3hN+RgYERGR2gsICEBUVBSWzvkJth2skZmRjZO+gXjx6PXiaMlPU5GanFrMXorWvk9rDHDribioBBxZH4Bz5869z6mX6B1TWioVKdJ3mGNERETlip6eHnr27AkXF5eC5So0NDRQo0YNXL16taBfSEgITp8+LYxNT09Hbm5+HpKOjg7mzp2L3r17Y+LEiQgJCSm7N/EGBkZv92bMUVY5RgyMiIioQrCxsUH9+vUB5J9QP/vsM5ibmxds19DQwPPnz3Hy5EnIZDK4uLjA0NAQc+fORUBAgFTTZmBUDAZGREREpah79+4F635dvnwZd+7ckXhGDIyKw8CIiIiokmFg9HZSBEZMviYiIpIQLzioF9YxIiIiInqFgRERERHRK++cY1RaEyEiIiIqQanfd3zXHCPeCCUiIqIKi7fSiIiIiF5hYERERET0CgMjIiIiolcYGBERERG9wsCIiIiI6BUGRkRERESvMDAiIiIieoWBEREREdErDIyIiIiIXvl/2ENafB7ogNQAAAAASUVORK5CYII=",
- "text/plain": [
- "![\"Bento](\"../_static/tutorial_img/bento_pattern_annotation.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "253130de-3dfd-41dd-8d34-d259c87dfeb1",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### Calculate spatial features\n",
- "\n",
- "These features are used by the classifier to predict patterns for each sample. The entire dataset takes ~8 hours to process, therefore we have provided precomputed features to proceed with the analysis.\n",
- "\n",
- "\n",
- "\n",
- "Note\n",
- "\n",
- "Why does it take so long? Unlike single-cell analysis which uses expression counts, we are computing spatial relationships considering every molecule! In this dataset there are >4 million molecules and multiple high-resolution polygons for every cell.\n",
- "\n",
- "
\n",
- "\n",
- "For the curious, check out the [\"How it Works\"](../howitworks.md#spatial-features) page.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "bcada814-0a1a-45f4-8181-d27bd7ac0250",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:01:41.394472Z",
- "iopub.status.busy": "2022-06-06T01:01:41.393417Z",
- "iopub.status.idle": "2022-06-06T01:01:47.241203Z",
- "shell.execute_reply": "2022-06-06T01:01:47.236349Z",
- "shell.execute_reply.started": "2022-06-06T01:01:41.394418Z"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load dataset with precomputed features\n",
- "adata = bento.datasets.load_dataset('seqfish')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ea9bb270-e8dd-49ca-bd13-586b69dc4a0a",
- "metadata": {},
- "source": [
- "### Predict Subcellular Patterns\n",
- "\n",
- "Predict the subcellular pattern for every sample (by default, only samples with count >= 5). The five subcellular patterns we can predict are:\n",
- "\n",
- "- **cell edge**: near the cell membrane\n",
- "- **cytoplasmic**: mostly outside the nucleus in the cytoplasm\n",
- "- **nuclear**: most in the nucleus\n",
- "- **nuclear edge**: near the nuclear membrane, either\n",
- "- **none**: none of the above patterns, more or less randomly distributed\n",
- "\n",
- "![\"Subcellular](\"../_static/tutorial_img/subcellular_patterns.png\")
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "58cfd10c-79ab-4833-a87f-c2cfacd17037",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:01:47.247716Z",
- "iopub.status.busy": "2022-06-06T01:01:47.246627Z",
- "iopub.status.idle": "2022-06-06T01:02:40.560059Z",
- "shell.execute_reply": "2022-06-06T01:02:40.559284Z",
- "shell.execute_reply.started": "2022-06-06T01:01:47.247648Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "a1264b86e60843e780fce6cdd0360286",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/5 [00:00"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "bento.pl.lp_dist(adata, percentage=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "41173cd8-659a-4996-bdc1-01b24c2e0076",
- "metadata": {},
- "source": [
- "We can also visualize the localization of each gene where the point position denotes the balance between subcellular localization pattern frequencies. The color denotes the gene's most frequent pattern. Interestingly, we see a wide range of variability in localization. A large number of genes are pulled towards none while nuclear enriched genes show strong bias and a high fraction of cells."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "70913097-2e28-41fd-a76f-958007d8b532",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:02:44.095899Z",
- "iopub.status.busy": "2022-06-06T01:02:44.095615Z",
- "iopub.status.idle": "2022-06-06T01:02:46.705628Z",
- "shell.execute_reply": "2022-06-06T01:02:46.704678Z",
- "shell.execute_reply.started": "2022-06-06T01:02:44.095872Z"
- },
- "tags": [
- "nbsphinx-thumbnail"
- ]
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "No handles with labels found to put in legend.\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "![\"Tensor](\"../_static/tutorial_img/tensor_decomposition.png\")
\n",
- "\n",
- "Here is the shape of our dataset tensor."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "adf52472-f1fb-4ce6-b2bf-723e0a1e46b7",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:02:46.707484Z",
- "iopub.status.busy": "2022-06-06T01:02:46.707084Z",
- "iopub.status.idle": "2022-06-06T01:02:46.714061Z",
- "shell.execute_reply": "2022-06-06T01:02:46.713082Z",
- "shell.execute_reply.started": "2022-06-06T01:02:46.707446Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(5, 179, 3726)"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "adata.uns['tensor'].shape"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "26cfe6d7-cc0d-4ea3-b43b-5dd0e84657d9",
- "metadata": {},
- "source": [
- "First we will determine the number of factors used to represent our dataset. To do so, we will perform tensor decomposition for a range of values (default [1-5], three times each) and calculate the reconstruction loss with the original tensor at each value. Reconstruction accuracy across range of decomposition ranks (1 is perfect, 0 is noise). The best rank is highlighted in red as determined by the elbow method.\n",
- "\n",
- "\n",
- "\n",
- "Note\n",
- "\n",
- "Estimating rank can be difficult; here we are using a heuristic and therefore may not generalize perfectly to a different dataset. Try increasing the `upper_rank` if the error does not flatten, or increasing `runs` if the confidence interval is too wide.\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "fc04e1f9-f6a8-411c-ae30-18858df056fe",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:02:46.715957Z",
- "iopub.status.busy": "2022-06-06T01:02:46.715631Z",
- "iopub.status.idle": "2022-06-06T01:04:25.797891Z",
- "shell.execute_reply": "2022-06-06T01:04:25.796703Z",
- "shell.execute_reply.started": "2022-06-06T01:02:46.715919Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Device: cpu\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "592143a668df45d9b471805fbc8c1565",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/5 [00:00"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "bento.tl.select_tensor_rank(adata, bento.PATTERN_NAMES)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "41a73c70-1094-461d-8e9d-328717ec65dd",
- "metadata": {},
- "source": [
- "Perform tensor decomposition at a chosen rank (e.g. best rank above)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "8378adaf-4bfc-47cf-a32d-9530dd79fb19",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:04:25.800534Z",
- "iopub.status.busy": "2022-06-06T01:04:25.800073Z",
- "iopub.status.idle": "2022-06-06T01:04:35.121650Z",
- "shell.execute_reply": "2022-06-06T01:04:35.120942Z",
- "shell.execute_reply.started": "2022-06-06T01:04:25.800474Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Device: cpu\n"
- ]
- }
- ],
- "source": [
- "bento.tl.lp_signatures(adata, 3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "428da2c8-f282-4980-a81b-91852a223222",
- "metadata": {},
- "source": [
- "Each factor is described by 3 vectors, the pattern vector (it's mislabeled as layers TODO fix), cell vector, and gene vector. Genes and cells are ordered by clustering (hierarchical clustering) for visual clarity. Higher loading values, which indicate stronger association with the factor, are denoted by darker colors.\n",
- "\n",
- "**Pattern color key:**\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "f07cb11d-2a5b-4a61-8158-3c28b3a058b9",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:04:35.122915Z",
- "iopub.status.busy": "2022-06-06T01:04:35.122712Z",
- "iopub.status.idle": "2022-06-06T01:04:38.108225Z",
- "shell.execute_reply": "2022-06-06T01:04:38.107425Z",
- "shell.execute_reply.started": "2022-06-06T01:04:35.122890Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "\n",
- "\n",
- "Note \n",
- "\n",
- "We recommend characterizing the gene and cell loadings of each signature to aid in interpretation. Refer to our paper for more details regarding these localization signatures! If you are analyzing your own dataset, we encourage reaching out the authors directly if you have any questions about your analysis or feature requests for `bento`!\n",
- "\n",
- "
"
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.11"
- },
- "toc-autonumbering": false
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/source/tutorial_gallery/Tensor_Decomposition.ipynb b/docs/source/tutorial_gallery/Tensor_Decomposition.ipynb
deleted file mode 100644
index a742530..0000000
--- a/docs/source/tutorial_gallery/Tensor_Decomposition.ipynb
+++ /dev/null
@@ -1,457 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "ded53b22-45c5-458e-9ec1-c15bc49f4b0a",
- "metadata": {},
- "source": [
- "# Tensor Decomposition of Spatial Features\n",
- "\n",
- "**Author**: Clarence Mah | **Last Updated**: 6/15/2022\n",
- "\n",
- "Here we will demonstrate how to use tensor decomposition to generate low dimensional signatures with `bento`. We will use the included seqFISH+ dataset."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5efe2763-a777-4e88-997b-95afa5a6f6f2",
- "metadata": {},
- "source": [
- "## Load Libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "8e8aee69-31aa-47ca-ad84-9a79c24d3020",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:06:06.511918Z",
- "iopub.status.busy": "2022-06-06T01:06:06.511472Z",
- "iopub.status.idle": "2022-06-06T01:06:37.429030Z",
- "shell.execute_reply": "2022-06-06T01:06:37.428235Z",
- "shell.execute_reply.started": "2022-06-06T01:06:06.511847Z"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "import bento"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9a591fb8-4830-48f9-9c7d-1dfa5dc20ab4",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Load Data\n",
- "\n",
- "Let's start with the preprocessed seqFISH+ data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "bcada814-0a1a-45f4-8181-d27bd7ac0250",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:06:37.430779Z",
- "iopub.status.busy": "2022-06-06T01:06:37.430413Z",
- "iopub.status.idle": "2022-06-06T01:06:39.622651Z",
- "shell.execute_reply": "2022-06-06T01:06:39.621955Z",
- "shell.execute_reply.started": "2022-06-06T01:06:37.430737Z"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load precomputed features\n",
- "adata = bento.datasets.load_dataset('seqfish')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "f1c26d01-93be-4239-95a1-b050c0d08cdd",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:06:39.626565Z",
- "iopub.status.busy": "2022-06-06T01:06:39.626335Z",
- "iopub.status.idle": "2022-06-06T01:06:39.633774Z",
- "shell.execute_reply": "2022-06-06T01:06:39.633106Z",
- "shell.execute_reply.started": "2022-06-06T01:06:39.626538Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Layers with keys: cell_edge, cell_edge_p, cell_inner_asymmetry, cell_inner_proximity, cell_outer_asymmetry, cell_outer_proximity, cytoplasmic, cytoplasmic_p, l_half_radius, l_max, l_max_gradient, l_min_gradient, l_monotony, none, none_p, nuclear, nuclear_edge, nuclear_edge_p, nuclear_p, nucleus_dispersion, nucleus_inner_asymmetry, nucleus_inner_edge_enrichment, nucleus_inner_proximity, nucleus_outer_asymmetry, nucleus_outer_edge_enrichment, nucleus_outer_proximity, point_dispersion, spliced, unspliced"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "adata.layers"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0d5a255d-6547-4413-8c4f-2892356ed6c4",
- "metadata": {},
- "source": [
- "## Tensor Rank Estimation\n",
- "First we will determine the number of factors used to represent our dataset. To do so, we will perform tensor decomposition for a range of values and calculate the reconstruction loss with the original tensor at each value. Reconstruction accuracy across range of decomposition ranks (1 is perfect, 0 is noise). The best rank is highlighted in red as determined by the elbow method.\n",
- "\n",
- "\n",
- "\n",
- "Note\n",
- "\n",
- "Estimating rank can be difficult; here we are using a heuristic and therefore may not generalize perfectly to a different dataset. Try increasing the `upper_rank` if the error does not flatten, or increasing `runs` if the confidence interval is too wide.\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "17c7a264-68b4-4f38-8203-581a99153cb1",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:06:39.634993Z",
- "iopub.status.busy": "2022-06-06T01:06:39.634722Z",
- "iopub.status.idle": "2022-06-06T01:06:39.645676Z",
- "shell.execute_reply": "2022-06-06T01:06:39.645078Z",
- "shell.execute_reply.started": "2022-06-06T01:06:39.634966Z"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "layers = [\n",
- " \"cell_inner_proximity\",\n",
- " \"nucleus_inner_proximity\",\n",
- " \"nucleus_outer_proximity\",\n",
- " \"l_half_radius\",\n",
- " \"l_max\",\n",
- " \"l_max_gradient\",\n",
- " \"l_min_gradient\",\n",
- " \"l_monotony\",\n",
- " \"cell_inner_asymmetry\",\n",
- " \"nucleus_inner_asymmetry\",\n",
- " \"nucleus_outer_asymmetry\",\n",
- " \"point_dispersion\",\n",
- " \"nucleus_dispersion\",\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "31dffb1e-3256-4064-86a6-67d4fcb6cda5",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:10:16.703161Z",
- "iopub.status.busy": "2022-06-06T01:10:16.702821Z",
- "iopub.status.idle": "2022-06-06T01:16:06.456320Z",
- "shell.execute_reply": "2022-06-06T01:16:06.455546Z",
- "shell.execute_reply.started": "2022-06-06T01:10:16.703133Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Device: cpu\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1f1c1d6ca0954358bac0742f1a85e38d",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/10 [00:00"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "bento.tl.select_tensor_rank(\n",
- " adata,\n",
- " layers=layers,\n",
- " upper_rank=10,\n",
- " runs=1\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fc11671c-975d-4d1a-b989-0cf60785e7ed",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "Note\n",
- "\n",
- "GPU Support: Tensor decomposition runs much faster on the GPU. See `bento` installation instructions to see how you can take advantage of this and compute signatures much more efficiently.\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4ff404e5-e852-4cf5-831a-7d179b9fb40f",
- "metadata": {},
- "source": [
- "## Generate signatures with tensor decomposition\n",
- "\n",
- "`Bento` is able to utilize these feature spaces for predicting RNA localization patterns (see the \"Subcellular Localization\" tutorial). For unsupervised analysis, `Bento` implements tensor decomposition to generate interpretable low dimensional signatures."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "1b9411d6-6005-4a07-bbd9-4de209dd2965",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:16:06.458244Z",
- "iopub.status.busy": "2022-06-06T01:16:06.457975Z",
- "iopub.status.idle": "2022-06-06T01:16:36.179394Z",
- "shell.execute_reply": "2022-06-06T01:16:36.178705Z",
- "shell.execute_reply.started": "2022-06-06T01:16:06.458212Z"
- },
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Device: cpu\n"
- ]
- }
- ],
- "source": [
- "bento.tl.decompose_tensor(\n",
- " adata,\n",
- " layers,\n",
- " 9,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c2fb8cba-99c5-4a72-bfe4-e86c5f8b9c9c",
- "metadata": {},
- "source": [
- "Visualize the signature loadings."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "e952e7c0-84a5-4a9e-b5ca-ac7193f91281",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-06T01:16:36.181030Z",
- "iopub.status.busy": "2022-06-06T01:16:36.180633Z",
- "iopub.status.idle": "2022-06-06T01:16:40.264796Z",
- "shell.execute_reply": "2022-06-06T01:16:40.264107Z",
- "shell.execute_reply.started": "2022-06-06T01:16:36.180988Z"
- },
- "tags": [
- "nbsphinx-thumbnail"
- ]
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
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