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cellxgene-census

Query CELLxGENE Census single-cell data via Python APIs for scalable analytics.

48|6|Updated Mar 9, 2026
Search Tags:#query#single-cell#cellxgene#census#scanpy#adata#tiledbsoma
One-click install
npx skills add https://github.com/qinyan-ai/qinyan-academic-skills --skill cellxgene-census-qinyan-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cellxgene-census
Source: https://github.com/qinyan-ai/qinyan-academic-skills/tree/main/skills/05-%E7%94%9F%E7%89%A9%E4%BF%A1%E6%81%AF%E4%B8%8E%E5%9F%BA%E5%9B%A0%E7%BB%84%E5%AD%A6/cellxgene-census
Command: npx skills add https://github.com/qinyan-ai/qinyan-academic-skills --skill cellxgene-census-qinyan-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The CZ CELLxGENE Census is a versioned collection of single-cell data that researchers can query programmatically to perform scalable, reproducible analyses across millions of cells and datasets. This skill enables machine-driven discovery by exposing observations, gene expression matrices, and metadata through Python APIs, so users can build reproducible pipelines without manual data wrangling.

Core Features & Use Cases

  • Programmatic access to census observations and gene expression across Homo sapiens and Mus musculus.
  • Query metadata and expression using Python APIs (get_obs, get_anndata, get_var, axis_query) to retrieve metadata, expression matrices, and embeddings.
  • Seamless integration with analysis tools like Scanpy and PyTorch; supports out-of-core processing and dataset versioning for reproducible analyses.

Quick Start

Query the CELLxGENE Census programmatically and return a concise dataset summary of available datasets and sample metadata.

Frequently Asked Questions about cellxgene-census

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query single-cell gene expression data programmatically?▼

Access single-cell metadata and gene expression using Python APIs like get_obs, get_var, and get_anndata to retrieve specific slices from the CELLxGENE Census data.

Can I use Scanpy to analyze CELLxGENE Census data?▼

Yes, the CELLxGENE Census supports seamless integration with analysis tools like Scanpy and PyTorch, allowing you to pass queried data directly into your existing single-cell analysis pipelines.

What is the best way to access millions of single-cell observations without manual data wrangling?▼

Accessing millions of single-cell observations without manual wrangling is achieved by using the TileDB-SOMA based census_data objects, which expose observations and matrices directly via Python APIs.

Does the CELLxGENE Census support reproducible single-cell analytics?▼

The CELLxGENE Census supports reproducible single-cell analytics through dataset versioning using census_version and primary data filtering, ensuring consistent results across analyses.

How do I retrieve cell metadata for Homo sapiens and Mus musculus datasets?▼

Retrieve cell metadata for Homo sapiens and Mus musculus datasets by utilizing the get_obs API to programmatically query and extract observation-level metadata from the census.

Can I perform out-of-core processing on large-scale single-cell datasets using this API?▼

Yes, the API supports out-of-core processing for scalable single-cell analytics, enabling you to process large-scale gene expression datasets that exceed memory limits.

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