cellxgene-census

Query CELLxGENE Census single-cell genomics data for expression and metadata.

8|Updated Jan 13, 2026

Search Tags:#data analysis #bioinformatics #genomics #single-cell #cellxgene #expression data

Ready to run

npx skills add https://github.com/hxk622/TokenDance --skill cellxgene-census-hxk622

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cellxgene-census
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/bioinformatics/cellxgene-census
Command: npx skills add https://github.com/hxk622/TokenDance --skill cellxgene-census-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill allows programmatic access to the CELLxGENE Census, a massive repository of single-cell genomics data, enabling researchers to query and analyze millions of cells without needing to download or manage large datasets locally.

Core Features & Use Cases

Programmatic Querying: Access gene expression data and metadata across diverse tissues, diseases, and cell types.
Scalable Analysis: Supports both in-memory analysis for smaller queries and out-of-core processing for large-scale datasets.
Integration: Seamlessly integrates with popular bioinformatics tools like Scanpy and PyTorch for machine learning workflows.
Use Case: A researcher wants to find all human B cells in lung tissue and analyze their gene expression for specific markers. This Skill can efficiently retrieve that subset of data for further analysis.

Quick Start

Use the cellxgene-census skill to open the latest stable version of the human census and query for all cells in the lung tissue.

Frequently Asked Questions about cellxgene-census

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

FAQPage Schema

How do I query gene expression data from the CELLxGENE Census programmatically?▼

You can query gene expression data from the CELLxGENE Census programmatically by using an API that retrieves specific cell subsets, such as human B cells in lung tissue, without downloading the entire dataset locally.

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

Yes, you can use Scanpy and PyTorch for machine learning workflows with single-cell genomics data by retrieving standardized cells from the CELLxGENE Census API and integrating them directly into these frameworks for model training.

What is the best way to analyze millions of single-cell genomics cells without local storage?▼

The best way to analyze millions of single-cell genomics cells without local storage is using the CELLxGENE Census API, which supports both in-memory analysis for smaller queries and out-of-core processing for large-scale datasets.

Does the CELLxGENE Census API support cross-dataset comparisons for population-scale analyses?▼

Yes, the CELLxGENE Census API supports cross-dataset comparisons for population-scale analyses by providing programmatic access to millions of standardized cells across diverse tissues, diseases, and cell types.

Do I need the cellxgene-census library to retrieve expression data and metadata?▼

Yes, you need the cellxgene-census library to retrieve expression data and metadata, as it provides the necessary API to open the latest stable human census and query specific cell subsets efficiently.