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,
            layer_name="raw",
            obs_column_names=["cell_type"],
            batch_size=128,
            shuffle=True,
        )
        dataloader = experiment_dataloader(dataset)

        # Training loop
        for epoch in range(num_epochs):
            dataset.set_epoch(epoch)
            for X, obs in dataloader:
                labels = obs["cell_type"]

                # Forward pass
                outputs = model(X)
                loss = criterion(outputs, labels)

                # Backward pass
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

Train/test splitting:

train_dataset, test_dataset = dataset.random_split(0.8, 0.2, seed=42)
train_loader = experiment_dataloader(train_dataset, num_workers=2)
test_loader = experiment_dataloader(test_dataset, num_workers=2)

Use batch_size and shuffle on ExperimentDataset, not on torch.utils.data.DataLoader; experiment_dataloader() rejects DataLoader-level batch_size, shuffle, sampler, and batch_sampler arguments.

6. Spatial Census Data

Spatial data is available for supported Census releases in a separate census_spatial_sequencing collection. Use the spatial extra and a current TileDB-SOMA version when querying Visium or Slide-seq V2 data:

import cellxgene_census
import tiledbsoma as soma

with cellxgene_census.open_soma(census_version="2025-11-08") as census:
    spatial_experiment = census["census_spatial_sequencing"]["homo_sapiens"]
    with spatial_experiment.axis_query(
        measurement_name="RNA",
        obs_query=soma.AxisQuery(
            value_filter="dataset_id == '4cceac62-9513-42a4-90e5-2878dbb0192c'"
        ),
    ) as query:
        sdata = query.to_spatialdata(X_name="raw")

7. Integration with Scanpy

Seamlessly integrate Census data with scanpy workflows:

import scanpy as sc

# Load data from Census
adata = cellxgene_census.get_anndata(
    census=census,
    organism="Homo sapiens",
    obs_value_filter="cell_type == 'neuron' and tissue_general == 'cortex' and is_primary_data == True",
)

# Standard scanpy workflow
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)

# Dimensionality reduction
sc.pp.pca(adata, n_comps=50)
sc.pp.neighbors(adata)
sc.tl.umap(adata)

# Visualization
sc.pl.umap(adata, color=["cell_type", "tissue", "disease"])

8. Multi-Dataset Integration

Query and integrate multiple datasets:

# Strategy 1: Query multiple tissues separately
tissues = ["lung", "liver", "kidney"]
adatas = []

for tissue in tissues:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter=f"tissue_general == '{tissue}' and is_primary_data == True",
    )
    adata.obs["tissue"] = tissue
    adatas.append(adata)

# Concatenate with AnnData's current API
import anndata as ad
combined = ad.concat(adatas, label="tissue", keys=tissues)

# Strategy 2: Query multiple datasets directly
adata = cellxgene_census.get_anndata(
    census=census,
    organism="Homo sapiens",
    obs_value_filter="tissue_general in ['lung', 'liver', 'kidney'] and is_primary_data == True",
)

Key Concepts and Best Practices

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, first check the number of cells to avoid memory issues:

# Get cell count
metadata = cellxgene_census.get_obs(
    census, "homo_sapiens",
    value_filter="tissue_general == 'brain' and is_primary_data == True",
    column_names=["soma_joinid"]
)
n_cells = len(metadata)
print(f"Query will return {n_cells:,} cells")

# If too large (>100k), use out-of-core processing

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 == 'peripheral blood mononuclear cell'"

Select Only Needed Columns

Minimize data transfer by specifying only required metadata columns:

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

Check Dataset Presence for Gene-Specific Queries

When analyzing specific genes, verify which datasets measured them:

presence = cellxgene_census.get_presence_matrix(
    census,
    "homo_sapiens",
    var_value_filter="feature_name in ['CD4', 'CD8A']"
)

Two-Step Workflow: Explore Then Query

First explore metadata to understand available data, then query expression:

# Step 1: Explore what's available
metadata = cellxgene_census.get_obs(
    census, "homo_sapiens",
    value_filter="disease == 'COVID-19' and is_primary_data == True",
    column_names=["cell_type", "tissue_general"]
)
print(metadata.value_counts())

# Step 2: Query based on findings
adata = cellxgene_census.get_anndata(
    census=census,
    organism="Homo sapiens",
    obs_value_filter="disease == 'COVID-19' and cell_type == 'T cell' and is_primary_data == True",
)

Available Metadata Fields

Cell Metadata (obs)

Key fields for filtering:

• cell_type, cell_type_ontology_term_id
• tissue, tissue_general, tissue_ontology_term_id
• disease, disease_ontology_term_id
• assay, assay_ontology_term_id
• donor_id, sex, self_reported_ethnicity
• development_stage, development_stage_ontology_term_id
• dataset_id
• is_primary_data (Boolean: True = unique cell)

The current schema includes organism collections beyond human and mouse. Confirm available organisms for the selected release with list(census["census_data"].keys()).

Gene Metadata (var)

• feature_id (Ensembl gene ID, e.g., "ENSG00000161798")
• feature_name (Gene symbol, e.g., "FOXP2")
• feature_type
• feature_length (Gene length in base pairs)
• nnz, n_measured_obs (availability summaries useful for checking sparsity and coverage)

Reference Documentation

This skill includes detailed reference documentation:

references/census_schema.md

Comprehensive documentation of:

• Census data structure and organization
• All available metadata fields
• Value filter syntax and operators
• SOMA object types
• Data inclusion criteria

When to read: When you need detailed schema information, full list of metadata fields, or complex filter syntax.

references/common_patterns.md

Examples and patterns for:

• Exploratory queries (metadata only)
• Small-to-medium queries (AnnData)
• Large queries (out-of-core processing)
• PyTorch integration
• Spatial Census access patterns
• Scanpy integration workflows
• Multi-dataset integration
• Best practices and common pitfalls

When to read: When implementing specific query patterns, looking for code examples, or troubleshooting common issues.

Common Use Cases

Use Case 1: Explore Cell Types in a Tissue

with cellxgene_census.open_soma() as census:
    cells = cellxgene_census.get_obs(
        census, "homo_sapiens",
        value_filter="tissue_general == 'lung' and is_primary_data == True",
        column_names=["cell_type"]
    )
    print(cells["cell_type"].value_counts())

Use Case 2: Query Marker Gene Expression

with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19']",
        obs_value_filter="cell_type in ['T cell', 'B cell'] and is_primary_data == True",
    )

Use Case 3: Train Cell Type Classifier

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="is_primary_data == True"),
    ) as query:
        dataset = ExperimentDataset(
            query=query,
            layer_name="raw",
            obs_column_names=["cell_type"],
            batch_size=128,
            shuffle=True,
        )
        dataloader = experiment_dataloader(dataset)

        for X, obs in dataloader:
            labels = obs["cell_type"]
            # Training logic
            pass

Use Case 4: Cross-Tissue Analysis

with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'macrophage' and tissue_general in ['lung', 'liver', 'brain'] and is_primary_data == True",
    )

    # Analyze macrophage differences across tissues
    sc.tl.rank_genes_groups(adata, groupby="tissue_general")

Troubleshooting

Query Returns Too Many Cells

• Add more specific filters to reduce scope
• Use tissue instead of tissue_general for finer granularity
• Filter by specific dataset_id if known
• Switch to out-of-core processing for large queries

Memory Errors

• Reduce query scope with more restrictive filters
• Select fewer genes with var_value_filter
• Use out-of-core processing with axis_query()
• Process data in batches

Duplicate Cells in Results

• Always include is_primary_data == True in filters
• Check if intentionally querying across multiple datasets

Gene Not Found

• Verify gene name spelling (case-sensitive)
• Try Ensembl ID with feature_id instead of feature_name
• Check dataset presence matrix to see if gene was measured
• Some genes may have been filtered during Census construction

Version Inconsistencies

• Always specify census_version explicitly
• Use same version across all analyses
• Check release notes for version-specific changes