Skip to content

Latest commit

 

History

History
84 lines (64 loc) · 3.47 KB

README.md

File metadata and controls

84 lines (64 loc) · 3.47 KB
Raw

DRVI reproducibility

This repository contains the codes required to reproduce the results in DRVI paper. Please refer to DRVI repo for the model, documentation, and help.

Repository structure

All relevant files are available in drvi_notebooks directory.

  • data contains code for pre-processing of the analyzed datasets.
  • general contains the code to run DRVI, DRVI-AP, scVI, and CVAE on different datasets. drvi_runvis.py is a general Python script for running the models, and all the running configs are available at runs.sh.
  • baseline contains the code to run PCA, ICA, and MOFA on different datasets. linear_baselines_runvis.py is a general Python script for running the models, and all the running configs are available at baseline_runs.sh.
  • evaluation contains the code for disentanglement and integration benchmarking (Fig. 2 and supplemental figures related to benchmarking).
  • analysis contains the code for analysis of immune, HLCA, and developmental pancreas datasets, as well as the code required to produce the rest of the figures.
  • utils contains utils functions used all around the project. That is why one should install this repository before using notebooks.

Requirements

Install dependencies in requirements.txt and follow the next steps.

Then run the following commands to be able to run .py files as notebooks:

jupyter nbextension install jupytext --user --py
jupyter nbextension enable jupytext --user --py

Install the reproducibility package

git clone https://gitlab.com/moinfar/drvi_reproducibility.git
cd drvi_reproducibility
pip install -e .

Install Rapids and rapids-singlecell package for faster scanpy GPU accelerated functions. Read more about Rapids installation (here)[https://docs.rapids.ai/install].

pip install rapids-singlecell  # Already in requirements

pip install \
    --extra-index-url=https://pypi.nvidia.com \
    "cudf-cu12==23.12.*" "dask-cudf-cu12==23.12.*" "cuml-cu12==23.12.*" \
    "cugraph-cu12==23.12.*" "cuspatial-cu12==23.12.*" "cuproj-cu12==23.12.*" \
    "cuxfilter-cu12==23.12.*" "cucim-cu12==23.12.*" "pylibraft-cu12==23.12.*" \
    "raft-dask-cu12==23.12.*"

Datasets

The information to obtain datasets used in the is as follows. For all datasets, pre-processing code is provided in drvi_notebooks/data.

Immune dataset

Download from figshare: https://figshare.com/ndownloader/files/25717328.

HLCA dataset

Download from cellxgene: https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293

Developmental pancreas dataset

Download the full data at E15.5 using the scvelo package:

import scvelo
adata = scvelo.datasets.pancreas()

Download the data with finer annotation using the cellRank package:

import cellrank
adata = cellrank.datasets.pancreas(kind="raw")

Then follow the Update scvelo pancreas data annotations section in cellrank_pancreas_data_preparation.py.

CRISPR screen dataset

Download the data using the pertpy package.

import pertpy
adata = pertpy.data.norman_2019()

Retina organoid dataset

Download from cellxgene: https://cellxgene.cziscience.com/collections/2f4c738f-e2f3-4553-9db2-0582a38ea4dc

Daniocell dataset

Download the dataset from: https://zenodo.org/records/8133569 Cluster annotations (Table S2 of the main paper) are downloaded from https://ars.els-cdn.com/content/image/1-s2.0-S1534580723005774-mmc3.xlsx