cellxgene-census

Name: cellxgene-census
Availability: InStock
Author: robinbarvaag

Query and analyze single-cell gene expression data from the CZ CELLxGENE Census.

Updated Jan 10, 2026

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

Ready to run

npx skills add https://github.com/robinbarvaag/poynt --skill cellxgene-census-robinbarvaag

💬 Prompt for ChatGPT / Claude Chat▼

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

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 a massive, standardized collection of single-cell genomics data, enabling complex queries and analyses that would otherwise be computationally prohibitive.

Core Features & Use Cases

Query Expression Data: Filter and retrieve gene expression data for millions of cells based on cell type, tissue, disease, and more.
Machine Learning Ready: Seamlessly integrate with PyTorch for training models on large-scale single-cell datasets.
Scanpy Integration: Use with the popular scanpy toolkit for standard single-cell analysis workflows.
Use Case: Analyze gene expression patterns across different cell types in the human brain, or train a machine learning model to classify cell types using millions of cells from various tissues.

Quick Start

Use the cellxgene-census skill to query expression data for 'T cells' in the '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 for specific cell types and tissues?▼

To query single-cell gene expression data, you filter cells by metadata like cell type, tissue, and disease to retrieve specific expression matrices from large-scale datasets. This enables precise extraction of targeted genomic profiles.

Can I train PyTorch machine learning models on large-scale single-cell genomics data?▼

Yes, you can train PyTorch machine learning models on large-scale single-cell genomics data by integrating the queried expression matrices directly into your pipeline. This supports scalable model training across millions of cells.

Does the cellxgene-census skill work with scanpy for single-cell analysis workflows?▼

Yes, the cellxgene-census skill works with scanpy, allowing you to integrate queried gene expression matrices directly into standard single-cell analysis workflows. This streamlines downstream data processing and visualization.

What is the best way to analyze population-scale single-cell data out-of-core?▼

The best way to analyze population-scale single-cell data out-of-core is using tiledbsoma and pyarrow, which support scalable processing without loading the entire dataset into memory. This handles massive genomic datasets efficiently.

How do I filter cells by disease status in a single-cell genomics dataset?▼

You filter cells by disease status by applying metadata queries to the cellxgene-census dataset, retrieving only the gene expression profiles matching your specified disease conditions. This narrows the dataset for targeted analysis.

Are there limitations when retrieving millions of cells for gene expression analysis?▼

A limitation when retrieving millions of cells is memory capacity, but the skill supports out-of-core processing via tiledbsoma to handle massive datasets. This prevents memory overflow during large-scale genomic queries.