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==1.17.*"

For spatial workflows:

uv pip install "cellxgene-census[spatial]==1.17.*" "spatialdata[extra]>=0.2.5"

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

uv pip install "cellxgene-census==1.17.*" tiledbsoma-ml

Core Workflow Patterns

1. Opening the Census

Always use the context manager to ensure proper resource cleanup:

import cellxgene_census

# Open latest stable version
with cellxgene_census.open_soma() as census:
    # Work with census data

# Open the current LTS version for reproducibility
with cellxgene_census.open_soma(census_version="2025-11-08") as census:
    # Work with census data

Key points:

• Use context manager (with statement) for automatic cleanup
• Specify census_version for reproducible analyses
• stable opens the current LTS Census release; latest opens the newest weekly release retained for a shorter period

2. Exploring Census Information

Before querying expression data, explore available datasets and metadata.

Access summary information:

# Get summary statistics as label/value rows
summary = census["census_info"]["summary"].read().concat().to_pandas()
summary_values = summary.set_index("label")["value"]
print(f"Total cells: {int(summary_values['total_cell_count']):,}")
print(f"Unique cells: {int(summary_values['unique_cell_count']):,}")

# Get all datasets
datasets = census["census_info"]["datasets"].read().concat().to_pandas()

# Get precomputed counts by organism, cell type, tissue, disease, and assay
summary_counts = census["census_info"]["summary_cell_counts"].read().concat().to_pandas()
tissue_counts = summary_counts[summary_counts["category"].eq("tissue_general")]

Query cell metadata to understand available data:

# Get unique cell types in a tissue
cell_metadata = cellxgene_census.get_obs(
    census,
    "homo_sapiens",
    value_filter="tissue_general == 'brain' and is_primary_data == True",
    column_names=["cell_type"]
)
unique_cell_types = cell_metadata["cell_type"].unique()
print(f"Found {len(unique_cell_types)} cell types in brain")

# Count cells by tissue
tissue_metadata = cellxgene_census.get_obs(
    census,
    "homo_sapiens",
    value_filter="is_primary_data == True",
    column_names=["tissue_general"],
)
tissue_counts = tissue_metadata["tissue_general"].value_counts()

Important: Always filter for is_primary_data == True to avoid counting duplicate cells unless specifically analyzing duplicates.

3. Querying Expression Data (Small to Medium Scale)

For queries returning < 100k cells that fit in memory, use get_anndata():

# Basic query with cell type and tissue filters
adata = cellxgene_census.get_anndata(
    census=census,
    organism="Homo sapiens",  # or "Mus musculus"
    obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True",
    obs_column_names=["assay", "disease", "sex", "donor_id"],
)

# Query specific genes with multiple filters
adata = cellxgene_census.get_anndata(
    census=census,
    organism="Homo sapiens",
    var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19', 'FOXP3']",
    obs_value_filter="cell_type == 'T cell' and disease == 'COVID-19' and is_primary_data == True",
    obs_column_names=["cell_type", "tissue_general", "donor_id"],
)

Filter syntax:

• Use obs_value_filter for cell filtering
• Use var_value_filter for gene filtering
• Combine conditions with and, or
• Use in for multiple values: tissue in ['lung', 'liver']
• Select only needed columns with obs_column_names
• In current LTS releases, disease and disease_ontology_term_id may contain ||-delimited multiple values; inspect available values before relying on exact equality filters for disease cohorts

Getting metadata separately:

# Query cell metadata
cell_metadata = cellxgene_census.get_obs(
    census, "homo_sapiens",
    value_filter="disease == 'COVID-19' and is_primary_data == True",
    column_names=["cell_type", "tissue_general", "donor_id"]
)

# Query gene metadata
gene_metadata = cellxgene_census.get_var(
    census, "homo_sapiens",
    value_filter="feature_name in ['CD4', 'CD8A']",
    column_names=["feature_id", "feature_name", "feature_length"]
)

4. Large-Scale Queries (Out-of-Core Processing)

For queries exceeding available RAM, use axis_query() with iterative processing:

import tiledbsoma as soma

# Create axis query
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"
    ),
    var_query=soma.AxisQuery(
        value_filter="feature_name in ['FOXP2', 'TBR1', 'SATB2']"
    ),
) as query:
    # Iterate through expression matrix in chunks
    iterator = query.X("raw").tables()
    for batch in iterator:
        # batch is a pyarrow.Table with columns:
        # - soma_data: expression value
        # - soma_dim_0: cell (obs) coordinate
        # - soma_dim_1: gene (var) coordinate
        process_batch(batch)

Computing incremental statistics:

import tiledbsoma as soma

# Example: Calculate mean expression
n_observations = 0
sum_values = 0.0

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"),
    var_query=soma.AxisQuery(value_filter="feature_name in ['FOXP2', 'TBR1', 'SATB2']"),
) as query:
    iterator = query.X("raw").tables()
    for batch in iterator:
        values = batch["soma_data"].to_numpy()
        n_observations += len(values)
        sum_values += values.sum()

mean_expression = sum_values / n_observations

5. Machine Learning with PyTorch

For training models, use TileDB-SOMA-ML. The former cellxgene_census.experimental.ml PyTorch loaders are deprecated and scheduled for removal.

import tiledbsoma as soma
from tiledbsoma_ml import ExperimentDataset, experiment_dataloader

with cellxgene_census.open_soma() as census:
    experiment = census["census_data"]["homo_sapiens"]
    with experiment.axis_query(
        measurement_name="RNA",
        obs_query=soma.AxisQuery(
            value_filter="tissue_general == 'liver' and is_primary_data == True"
        ),
    ) as query:
        dataset = ExperimentDataset(
            query=query,