cellxgene-census

Name: cellxgene-census
Availability: InStock
Author: HolobiomicsLab

Query the CELLxGENE Census data programmatically for scalable single-cell analyses.

4|1|Updated Jun 18, 2025

Search Tags:#query #single-cell #cellxgene #census #scanpy #adata

Ready to run

npx skills add https://github.com/HolobiomicsLab/Toolomics --skill cellxgene-census-holobiomicslab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cellxgene-census
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/cellxgene-census
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill cellxgene-census-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables researchers to programmatically access the CELLxGENE Census data (61M+ cells) for scalable single-cell analyses, eliminating manual data wrangling and local data downloads.

Core Features & Use Cases

Programmatic access to a large, standardized single-cell census across tissues and diseases.
Supports cross-dataset queries, population-scale analyses, and integration with analysis tools (scanpy, scvi-tools) for exploration, modeling, and multi-dataset studies.
Enables rapid discovery, comparison across datasets, and model development on millions of cells.

Quick Start

Query the CELLxGENE Census programmatically to access millions of cells across tissues and diseases.

Frequently Asked Questions about cellxgene-census

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

FAQPage Schema

How do I query CELLxGENE Census data for cross-tissue single-cell analyses?▼

You can query the CELLxGENE Census programmatically to access 61M+ standardized single cells across tissues, using a structured schema and TileDB-SOMA for precise subsetting.

Can I use scanpy and adata with CELLxGENE Census for scalable single-cell analysis?▼

Yes, CELLxGENE Census integrates with scanpy, scvi-tools, and adata, enabling you to pass queried subsets directly into these tools for exploration and modeling.

What is the best way to compare single-cell datasets across diseases without downloading data?▼

Querying the CELLxGENE Census programmatically enables rapid cross-disease and cross-dataset comparisons on millions of cells, eliminating the need for manual data wrangling or local downloads.

Does CELLxGENE Census support population-scale single-cell dataset comparisons?▼

Yes, CELLxGENE Census supports population-scale analyses and multi-dataset studies by providing programmatic access to a large, standardized single-cell census across tissues.

How does TileDB-SOMA organize single-cell census data for reproducible analyses?▼

TileDB-SOMA organizes the CELLxGENE Census data using a structured schema that enables precise subsetting and reproducible single-cell analyses across millions of cells.

What are the limitations of querying CELLxGENE Census for single-cell modeling?▼

The Skill relies on the CELLxGENE Census schema and TileDB-SOMA organization, meaning analyses are constrained to the standardized data and schemas available within that specific ecosystem.