domains / families / science / cellxgene-census

cellxgene-census

combined

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis. Queries 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.

When to use

• 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
• **Cross-tissue or cross-disease expression comparisons** (e.g., macrophages across lung/liver/brain; COVID-19 vs control).

Skill page

Conflict

The three copies overlap heavily on the core Census workflow (open_soma, get_obs/get_anndata, axis_query, is_primary_data filtering, and scanpy integration), but they disagree on versioning, scale, and ML installation. K-Dense-AI pins cellxgene-census 1.18.0, uses the 2025-11-08 LTS build, and installs tiledbsoma-ml for PyTorch; aipoch and FreedomIntelligence use older/unpinned versions, the 2023-07-25 example build, and the [experimental] extra. K-Dense also adds spatial support, presence-matrix checks, and a reference to eight workflow patterns, while aipoch provides a complete runnable script and FreedomIntelligence provides more detailed inline patterns. Crowning one copy would hide the alternative versioning and ML setup choices.