Merge pull request #192 from FZJ-INM1-BDA/bugfix_cellsGenes

bugfix: cortical cell distribution name/feat: add structure attr to gene exp

requirements.txt

@@ -1,14 +1,11 @@
-numpy
-requests
-nibabel
 anytree
+nibabel
 appdirs
 scikit-image
-click
-neuroglancer-scripts
+requests
 memoization
+neuroglancer-scripts
 nilearn
-simple-term-menu
-pandas
+importlib-resources; python_version < "3.7"
+typing-extensions; python_version < "3.8"
 pydantic==1.8.2
-fpdf

setup.py

@@ -49,6 +49,7 @@ def find_version():
         "neuroglancer-scripts",
         "nilearn",
         'importlib-resources; python_version < "3.7"',
+        'typing-extensions; python_version < "3.8"',
         "pydantic==1.8.2",
     ],
 )

siibra/features/cells.py

@@ -127,6 +127,10 @@ def imgdecoder(b):
                 break
         return result
 
+    @property
+    def name(self):
+        return f"Cortical cell distribution for {self.info.get('brain_area')}"
+
     @property
     def info(self):
         return self._info_loader.data

siibra/features/genes.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Dict, List
 from .feature import SpatialFeature
 from .query import FeatureQuery
 
@@ -25,10 +26,28 @@
 import numpy as np
 import json
 import siibra
+try:
+    # python 3.8+
+    from typing import TypedDict
+except ImportError:
+    # python 3.7 and below
+    from typing_extensions import TypedDict
 
 
 BASE_URL = "http://api.brain-map.org/api/v2/data"
 
+class DonorDict(TypedDict):
+    id: int
+    name: str
+    race: str
+    age: int
+    gender: str
+
+class SampleStructure(TypedDict):
+    id: int
+    name: str
+    abbreviation: str
+    color: str
 
 class GeneExpression(SpatialFeature):
     """
@@ -37,13 +56,15 @@ class GeneExpression(SpatialFeature):
 
     def __init__(
         self,
-        gene,
+        gene: str,
         location: Point,
-        expression_levels,
-        z_scores,
-        probe_ids,
-        donor_info,
-        mri_coord=None,
+        expression_levels: List[float],
+        z_scores: List[float],
+        probe_ids: List[int],
+        donor_info: DonorDict,
+        mri_coord: List[int]=None,
+        structure: SampleStructure=None,
+        top_level_structure: SampleStructure=None,
     ):
         """
         Construct the spatial feature for gene expressions measured in a sample.
@@ -72,6 +93,8 @@ def __init__(
         self.gene = gene
         self.probe_ids = probe_ids
         self.mri_coord = mri_coord
+        self.structure = structure
+        self.top_level_structure = top_level_structure
 
     def __repr__(self):
         return " ".join(
@@ -263,6 +286,8 @@ def _retrieve_microarray(self, donor_id, probe_ids):
                     probe_ids=[p["id"] for p in probes],
                     donor_info={**self.factors[donor["id"]], **donor},
                     mri_coord=sample["sample"]["mri"],
+                    structure=sample['structure'],
+                    top_level_structure=sample['top_level_structure'],
                 )
             )
 

test/features/test_cells.py

@@ -41,6 +41,7 @@ def test_to_model(self):
         feature = features[0]
         model = feature.to_model(detail=False)
         assert isinstance(model, CorticalCellDistributionModel)
+        assert getattr(model.metadata, 'short_name') is not None and model.metadata.short_name != ""
 
 
 if __name__ == "__main__":

test/features/test_genes.py

@@ -0,0 +1,20 @@
+import pytest
+import siibra
+from siibra.features.genes import GeneExpression
+
+test_params = [
+    ("hoc1 left", "MAOA")
+]
+
+@pytest.mark.parametrize("region_spec,gene", test_params)
+def test_genes(region_spec:str, gene: str):
+    parc = siibra.parcellations['2.9']
+    region = parc.decode_region(region_spec)
+    features = siibra.get_features(region, "gene", gene=gene)
+    assert len(features) > 0, f"expecting at least 1 gene feature"
+    assert all([
+        isinstance(f, GeneExpression) for f in features
+    ]), f"expecting all features to be of type GeneExpression"
+    assert all([
+        hasattr(f, 'structure') and hasattr(f, "top_level_structure") for f in features
+    ]), f"expecting all features to have structure and top_level_structure attributes"