alterlab-cellxgene

CZ CELLxGENE Census

Overview

The CZ CELLxGENE Census provides programmatic, versioned access to standardized single-cell genomics data from CZ CELLxGENE Discover. The 2025-11-08 LTS release holds 162,025,130 human and 46,299,127 mouse cells with standardized metadata (cell types, tissues, diseases, donors), raw gene expression matrices, pre-calculated embeddings, and integration with PyTorch, scanpy, and other analysis tools. Cell counts grow with each release — read them from census["census_info"]["summary"] rather than quoting a number in a methods section.

When to Use This Skill

Use this skill 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

For analyzing your own dataset (not the reference atlas), use scanpy or scvi-tools instead.

Installation

uv pip install cellxgene-census
# For PyTorch ML workflows (loaders moved out of cellxgene-census):
uv pip install tiledbsoma-ml

Core Workflow

1. Open the Census with a context manager; pin census_version for reproducibility. "stable" is the alias for the most recent LTS release (2025-11-08 as of 2026-09) and "latest" tracks the weekly build; LTS releases are kept available for at least five years.
2. Explore metadata first (get_obs / datasets summary) to understand what's available — always filter is_primary_data == True to avoid duplicate cells.
3. Estimate query size before loading expression. < 100k cells → get_anndata() (in-memory); larger → axis_query() out-of-core iteration.
4. Query expression with obs_value_filter (cells) and var_value_filter (genes); select only the obs_column_names you need.
5. Downstream: hand the returned AnnData to scanpy, or stream batches into a PyTorch dataloader for ML.

Minimal skeleton:

import cellxgene_census

with cellxgene_census.open_soma(census_version="2025-11-08") as census:  # pinned LTS
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True",
    )

Does NOT Trigger

Scenario	Use Instead
Normalize/cluster/UMAP an AnnData you already have	alterlab-scanpy
Concatenate or wrangle local .h5ad/zarr files	alterlab-anndata
Train a deep generative model / batch-integrate your own data	alterlab-scvi-tools
Browse or download a specific GEO/ArrayExpress accession	alterlab-geo
One-off gget cellxgene lookup from the CLI	alterlab-gget

Routing Guidance

• Small/medium query (fits in RAM) → get_anndata(). See references/querying_expression.md.
• Query exceeds RAM → axis_query() with chunked iteration and incremental stats. See references/querying_expression.md.
• Training ML models → tiledbsoma_ml PyTorch dataloader / ExperimentDataset. See references/ml_and_scanpy.md.
• Standard scanpy analysis / multi-tissue integration → see references/ml_and_scanpy.md.
• Need full schema, all metadata fields, or filter-syntax details → references/census_schema.md.

Reference Index

• references/querying_expression.md — Opening the Census, exploring metadata, small/medium get_anndata() queries, and large out-of-core axis_query() processing with incremental statistics.
• references/ml_and_scanpy.md — tiledbsoma_ml PyTorch dataloader / ExperimentDataset train-test splits, scanpy integration, multi-dataset/tissue integration (anndata.concat), and four worked use cases.
• references/best_practices_and_troubleshooting.md — Primary-data filtering, version pinning, query-size estimation, tissue_general vs tissue, presence matrices, the full obs/var metadata field list, and a troubleshooting guide.
• references/census_schema.md — Census data structure, all metadata fields, value-filter syntax/operators, SOMA object types, and data inclusion criteria.
• references/common_patterns.md — Extras beyond the core recipes: incremental (Welford) variance out-of-core, ontology-term filtering, batch-processing sweeps, and a common-pitfalls list.

Part of the AlterLab Academic Skills suite.