cellxgene-census

Query CELLxGENE Census single-cell data for gene expression analysis.

Updated Mar 10, 2026

Search Tags:#analysis #genomics #query #single-cell #cellxgene #data atlas

Ready to run

npx skills add https://github.com/felixboehm/biochem-allergy --skill cellxgene-census-felixboehm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cellxgene-census
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/cellxgene-census
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill cellxgene-census-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires cellxgene-census, scanpy, pytorch, tiledbsoma, pyarrow, and includes references (resource) components.

What problem does it solve?

This Skill provides programmatic access to the CELLxGENE Census, a massive, curated single-cell atlas, enabling efficient querying and analysis of millions of cells across diverse tissues, diseases, and cell types.

Core Features & Use Cases

Large-Scale Data Access: Query expression data for over 61 million cells from human and mouse.
Standardized Metadata: Filter and analyze cells based on standardized cell types, tissues, diseases, and more.
Integration: Seamlessly integrates with popular analysis tools like Scanpy and PyTorch for machine learning workflows.
Use Case: Identify all B cells in lung tissue across multiple COVID-19 datasets to study immune responses.

Quick Start

Use the cellxgene-census skill to get AnnData for B cells in lung tissue for Homo sapiens.

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 across multiple datasets?▼

You can query single-cell gene expression data across multiple datasets by accessing the CELLxGENE Census, a curated atlas of over 61 million cells. It allows efficient filtering and retrieval based on standardized cell types, tissues, and diseases.

Can I use Scanpy and PyTorch for machine learning workflows with single-cell data?▼

Yes, you can use Scanpy and PyTorch for machine learning workflows with single-cell data. The CELLxGENE Census integration supports seamless analysis, allowing you to extract AnnData objects and feed them directly into your ML pipelines.

What is the best way to analyze B cells in lung tissue for COVID-19 immune responses?▼

The best way to analyze B cells in lung tissue for COVID-19 immune responses is to programmatically query the CELLxGENE Census. It provides standardized metadata to filter over 61 million cells by specific diseases, tissues, and cell types.

Does querying large-scale single-cell atlases require installing specific Python libraries?▼

Yes, querying large-scale single-cell atlases requires installing the cellxgene-census Python library, along with dependencies like tiledbsoma and pyarrow. These facilitate efficient data access and integration with analysis tools.

How do I get AnnData for specific cell types from human single-cell data?▼

You can get AnnData for specific cell types from human single-cell data by applying filters to the CELLxGENE Census. This enables you to extract targeted subsets of cells, such as filtering for Homo sapiens B cells in lung tissue.

What are the limitations of using a curated single-cell atlas for comparative genomics?▼

A limitation of using a curated single-cell atlas for comparative genomics is that analysis is constrained to the standardized metadata and the 61 million cells currently included, meaning non-standard annotations or extremely novel cell states might be absent.