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cellxgene-context

Identifies cell types underlying ENCODE signals via single-cell expression profiles.

26|5|Updated Mar 8, 2026
Search Tags:#genomics#single-cell#deconvolution#RNA-seq#tissue-specific#cell-type#ENCODE
One-click install
npx skills add https://github.com/ammawla/encode-toolkit --skill cellxgene-context
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cellxgene-context
Source: https://github.com/ammawla/encode-toolkit/tree/main/plugin/skills/cellxgene-context
Command: npx skills add https://github.com/ammawla/encode-toolkit --skill cellxgene-context

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables users to determine which specific cell types contribute to regulatory signals observed in bulk ENCODE data by leveraging single-cell transcriptomic atlases.

Core Features & Use Cases

  • Cell-type-specific gene expression querying: Identify the dominant cell types expressing genes of interest in relevant tissues.
  • Integration with ENCODE data: Cross-reference single-cell data with bulk ENCODE experiments for deconvolution of signals.
  • Use Case: When a researcher finds a regulatory peak near a gene in ENCODE, they can analyze CellxGene data to identify the cell types that predominantly express that gene, helping clarify the biological context.

Quick Start

Query the expression of the insulin gene in pancreatic cell types using CellxGene Census to interpret bulk ENCODE histone modification peaks.

Frequently Asked Questions about cellxgene-context

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

FAQPage Schema
How do I deconvolve bulk ENCODE regulatory signals using single-cell RNA-seq data?▼

To deconvolve bulk ENCODE regulatory signals, you cross-reference bulk datasets with single-cell expression profiles to identify the specific cell types contributing to the observed signal.

What is the best way to find cell-type-specific gene expression in tissue-specific single-cell datasets?▼

Finding cell-type-specific gene expression involves querying single-cell transcriptomic atlases with precise gene and tissue filters to pinpoint dominant cellular sources expressing your genes of interest.

Can I use single-cell transcriptomic atlases to interpret ENCODE histone modification peaks?▼

Yes, you can use single-cell transcriptomic atlases to interpret ENCODE histone modification peaks by identifying which cell types predominantly express nearby genes, clarifying the biological context.

Does single-cell deconvolution work for integrating genomic, epigenetic, and transcriptomic data?▼

Single-cell deconvolution works for integrating genomic, epigenetic, and transcriptomic data by resolving tissue-specific cell type composition across multiple bulk regulatory signal layers.

What are the limitations of using single-cell RNA-seq for bulk signal deconvolution?▼

A limitation of using single-cell RNA-seq for bulk signal deconvolution is that accurate analysis requires cross-referencing bulk versus single-cell datasets with precise gene and tissue filters.

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