CZ CELLxGENE Census

CZ CELLxGENE Census

Overview

CZ CELLxGENE Census provides programmatic access to 61+ million standardized single-cell RNA-seq observations from human and mouse. It enables population-scale queries by cell type, tissue, disease, and donor metadata, returning expression data as AnnData objects or PyTorch dataloaders for ML workflows.

When to Use

• Querying single-cell expression data across tissues, diseases, or cell types from a curated atlas
• Building reference datasets for cell type classification or marker gene discovery
• Training ML models on large-scale single-cell data (PyTorch integration)
• Comparing gene expression across conditions (e.g., COVID-19 vs healthy) at population scale
• Exploring what single-cell datasets are available for a tissue or disease of interest
• For analyzing your own scRNA-seq data, use scanpy instead
• For manipulating AnnData objects (subsetting, concatenation), use anndata instead

Prerequisites

pip install cellxgene-census
# For ML workflows
pip install cellxgene-census[experimental]

API Rate Limits: Census uses TileDB-SOMA cloud backend. No explicit rate limit, but large queries (>1M cells) should use out-of-core processing (Module 4) to avoid memory exhaustion. Always use context managers for proper resource cleanup.

Quick Start

import cellxgene_census

with cellxgene_census.open_soma() as census:
    # Get B cells from lung
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True",
        obs_column_names=["cell_type", "disease", "donor_id"],
    )
    print(f"Retrieved {adata.n_obs} cells × {adata.n_vars} genes")