CZ CELLxGENE Census

Overview

The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell genomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of millions of cells across thousands of datasets.

The Census includes:

• 61+ million cells from human and mouse
• Standardized metadata (cell types, tissues, diseases, donors)
• Raw gene expression matrices
• Pre-calculated embeddings and statistics
• Integration with PyTorch, scanpy, 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

For machine learning workflows, install additional dependencies:

uv pip install cellxgene-census[experimental]

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 specific version for reproducibility
with cellxgene_census.open_soma(census_version="2023-07-25") as census:
    # Work with census data

Key points:

• Use context manager (with statement) for automatic cleanup
• Specify census_version for reproducible analyses
• Default opens latest "stable" release

2. Exploring Census Information

Before querying expression data, explore available datasets and metadata.

Access summary information:

# Get summary statistics
summary = census["census_info"]["summary"].read().concat().to_pandas()
print(f"Total cells: {summary['total_cell_count'][0]}")

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

# Filter datasets by criteria
covid_datasets = datasets[datasets["disease"].str.contains("COVID", na=False)]

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_counts = cell_metadata.groupby("tissue_general").size()

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

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
query = 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']"
    )
)

# 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:

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

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 the experimental PyTorch integration:

from cellxgene_census.experimental.ml import experiment_dataloader

with cellxgene_census.open_soma() as census:
    # Create dataloader
    dataloader = experiment_dataloader(
        census["census_data"]["homo_sapiens"],
        measurement_name="RNA",
        X_name="raw",
        obs_value_filter="tissue_general == 'liver' and is_primary_data == True",
        obs_column_names=["cell_type"],
        batch_size=128,
        shuffle=True,
    )

    # Training loop
    for epoch in range(num_epochs)
how to use cellxgene-census
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use cellxgene-census on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add cellxgene-census
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill cellxgene-censuscopy
The skills CLI fetches cellxgene-census from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3
Select Cursor when prompted
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◆ Which agents do you want to install to?
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/cellxgene-census
Reload or restart Cursor to activate cellxgene-census. Access the skill through slash commands (e.g., /cellxgene-census) or your agent's skill management interface.
⚠
Security & Verification Notice
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