CZ CELLxGENE Census

Overview

The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell and spatial transcriptomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of public Census releases without downloading whole datasets first.

The Census includes:

• 217+ million total cells and 125+ million unique cells in the 2025-11-08 stable LTS release
• 1,845 datasets in the 2025-11-08 stable LTS release
• Human, mouse, marmoset, rhesus macaque, and chimpanzee data in the current schema
• Standardized metadata (cell types, tissues, diseases, donors)
• Raw gene expression matrices and source H5AD lookup/download helpers
• Pre-calculated summary counts, embeddings, and spatial data
• Integration with AnnData, Scanpy, TileDB-SOMA, TileDB-SOMA-ML, and other analysis tools

When to Use This Skill

This skill should be used when:

• Querying single-cell expression data by cell type, tissue, or disease
• Exploring available single-cell datasets and metadata
• Training machine learning models on single-cell data
• Performing large-scale cross-dataset analyses
• Integrating Census data with scanpy or other analysis frameworks
• Computing statistics across millions of cells
• Accessing pre-calculated embeddings or model predictions

Installation and Setup

Install the Census API:

uv pip install "cellxgene-census==1.18.0"

For spatial workflows:

uv pip install "cellxgene-census[spatial]==1.18.0"

For PyTorch model training, use TileDB-SOMA-ML. The old cellxgene_census.experimental.ml loaders are absent from 1.18.0:

uv pip install "cellxgene-census==1.18.0" tiledbsoma-ml

Core Workflow Patterns

Eight patterns, each with code, are in references/core_workflow_patterns.md:

1. Opening the Census — always pin census_version so an analysis stays reproducible.
2. Exploring Census information — available datasets, cell counts, and summary tables.
3. Querying expression data — small to medium scale into an AnnData.
4. Large-scale queries — out-of-core processing when the slice will not fit in memory.
5. Machine learning with PyTorch — TileDB-SOMA-ML data loaders.
6. Spatial Census data — accessing spatial assays.
7. Integration with Scanpy — handing a Census slice to a standard Scanpy workflow.
8. Multi-dataset integration — combining datasets and handling batch effects.

Key Concepts and Best Practices

The examples pin the current LTS build 2025-11-08, verified through the live release directory on 2026-09-30. The SDK and data release are separate versions. Resolve stable once with get_census_version_description("stable")["release_build"] and record that date; never silently switch builds midway through analysis.

Always Filter for Primary Data

Unless analyzing duplicates, always include is_primary_data == True in queries to avoid counting cells multiple times:

obs_value_filter="cell_type == 'B cell' and is_primary_data == True"

Specify Census Version for Reproducibility

Always specify the Census version in production analyses:

census = cellxgene_census.open_soma(census_version="2025-11-08")

Estimate Query Size Before Loading

For large queries, count the selected rows without loading every metadata column. Cell count alone is not a memory estimate: gene count, sparsity, dtype, layers, embeddings, and downstream dense copies also matter:

import tiledbsoma as soma
with census["census_data"]["homo_sapiens"].axis_query(
    measurement_name="RNA",
    obs_query=soma.AxisQuery(
        value_filter="tissue_general == 'brain' and is_primary_data == True"
    ),
) as query:
    print(f"Selected {query.n_obs:,} cells and {query.n_vars:,} genes")

# Query axes consume memory too; stream expression if the matrix will not fit.

Use tissue_general for Broader Groupings

The tissue_general field provides coarser categories than tissue, useful for cross-tissue analyses:

# Broader grouping
obs_value_filter="tissue_general == 'immune system'"

# Specific tissue
obs_value_filter="tissue == 'venous blood'"

Select Only Needed Columns

Minimize data transfer by specifying only required metadata columns:

obs_column_names=["cell_type", "tissue_general", "disease"]  # Not all columns

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