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cellxgene-census skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

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Install the cellxgene-census skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/cellxgene-census ~/.claude/skills/cellxgene-census
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

Eight patterns, each with code, are in references/coreworkflowpatterns.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 — the Census 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

Always Filter for Primary Data

Unless analyzing duplicates, always include isprimarydata == 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:

  • celltype, celltypeontologyterm_id
  • tissue, tissuegeneral, tissueontologytermid
  • disease, diseaseontologyterm_id
  • assay, assayontologyterm_id
  • donorid, sex, selfreported_ethnicity
  • developmentstage, developmentstageontologyterm_id
  • dataset_id
  • isprimarydata (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, nmeasuredobs (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())

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