Cell Atlas Approximations Disease Compression

This repository contains scripts for compressing datasets from the cellxgene census using the scquill package.

Repository Structure

• compression/: Contains the main compression scripts and utilities.

  • cellxgene_census_compression.py: Main script for compressing cellxgene census datasets.
  • utils/: Utility functions for the compression process.
    • ensembl_to_gene.py: Converts Ensembl IDs to gene names.
    • guess_normalisation.py: Determines the normalization method used in the dataset.
    • write_to_file.py: Handles writing metadata(dataset title, collections...etc) to files.

• static/: Contains static data files used in the compression process.

  • human_gene_pairs.csv: Gene pair information for human.
  • mouse_gene_pairs.csv: Gene pair information for mouse.
  • multi_condition_datasets.csv: Contains dataset IDs that have multiple experimental conditions.

• requirements.txt: Lists all Python dependencies for the project.

Getting Started

These instructions will guide you through setting up your project environment and running the compression scripts.

Prerequisites

• Python 3.11s
• pip (Python package installer)

1. Clone the Repository

First, clone this repository to your local machine using git:

git clone https://github.com/YingX97/cell_atlas_approximations_disease_compression
cd cell_atlas_approximations_disease_compression

2. Set up Python virtual environment

It is recommended to use a virtual environment to avoid conflicts with other projects or system packages:

python3.11 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install required packages

Install all required packages using pip:

pip install -r requirements.txt

4. Running the compression script

cd compression
python3 cellxgene_census_compression.py