cellxgene-context
Guide for integrating CellxGene Census single-cell data with ENCODE bulk experiments. Use when users need cell-type-specific expression context for ENCODE regulatory data, want to deconvolve bulk ENCODE signals, or validate regulatory elements at single-cell resolution. Trigger on: CellxGene, single-cell atlas, cell type expression, Census, cell type specificity, single-cell context, scRNA-seq atlas.
npx skills add majiayu000/claude-skill-registry --skill cellxgene-context --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides a user to bridge bulk ENCODE functional genomics data with cell-type-resolved expression from the CellxGene Census, enabling deconvolution of bulk signals and validation of regulatory elements at single-cell resolution.
How it works
Outlines a step-by-step workflow:
- Identify ENCODE bulk signals and the nearest genes in a tissue context.
- Query CellxGene Census for cell-type expression in the matching tissue, using the Python API (e.g.,
cellxgene_census.open_soma()andget_anndata) to filter by tissue and gene, and summarize expression by cell type. - Map CellxGene cell types (Cell Ontology terms) to ENCODE biosamples, documenting alignments like beta cells to pancreatic beta biosamples.
- Retrieve available cell types for a tissue via
get_obsand compute cell-type counts. - Interpret expression patterns to infer which cell types drive bulk ENCODE signals, with a framework for single- vs multi-cell-type expression and potential weak signals.
- Cross-reference with ENCODE scATAC-seq data to corroborate chromatin accessibility in identified cell types.
- Present results in a table, including dominant cell types and estimated contributions to bulk signals.
When to use it
Use when you need cell-type-specific context for ENCODE regulatory data, want to deconvolve bulk ENCODE signals, or validate regulatory elements at single-cell resolution; trigger on terms like CellxGene, single-cell atlas, Census, cell-type specificity, or scRNA-seq atlas.
What it can touch
The workflow relies on the Python API provided by CellxGene Census:
cellxgene_census(module) and functions such asopen_soma(),get_anndata(), andget_obs().- It assumes access to organism data like
Homo sapiensand filters by tissue (e.g., pancreas, pancreas tissue general) and gene symbols (e.g.,INS).
Caveats
Covers batch effects across studies, sparse single-cell data, and species alignment; stresses using Cell Ontology IDs for consistent cell-type matching and warns that raw counts are not directly comparable across datasets. It notes that ENCODE biosamples may map to broader or proxy cell types and that large datasets improve resolution for rare cell types.
# Integrating CellxGene Census Single-Cell Data with ENCODE Bulk Experiments Bridge bulk ENCODE functional genomics data with cell-type-specific expression from the CellxGene Census, the largest unified single-cell RNA-seq atlas, to resolve cell-type contributions to regulatory element activity. ## Scientific Rationale **The question**: "Which specific cell types within my tissue drive the regulatory signals I see in bulk ENCODE data?" ENCODE provides deeply sequenced bulk functional genomics (ChIP-seq, ATAC-seq, Hi-C) across hundreds of biosamples. But bulk data from a tissue like "pancreas" is a mixture of acinar cells (~80%), duct cells (~10%), endocrine cells (~5%), and others. An H3K27ac peak in bulk pancreas could be driven by any of these cell types. CellxGene Census provides cell-type-resolved expression data from 50M+ single-cell observations across thousands of datasets, enabling deconvolution of bulk ENCODE signals. ### The Bulk-to-Single-Cell Bridge | Bulk ENCODE Signal | Single-Cell Question | CellxGene Answer | |-------------------|---------------------|-----------------| | H3K27ac peak near INS gene in pancreas | Which cell type expresses INS? | Beta cells (>500 TPM)
- Scientific Rationale
- The Bulk-to-Single-Cell Bridge
- What CellxGene Census Provides
- Key Literature
- When to Use This Skill
- Python API Reference
- Installation
- Core API Pattern
- Step 1: Identify the ENCODE Target Gene
- Step 2: Query CellxGene Census for Cell-Type Expression
- Basic Gene Expression Query
- Multi-Gene Query
- Query by Cell Ontology Term
- Step 3: Map CellxGene Cell Types to ENCODE Biosamples
pip install cellxgene-census
What does the cellxgene-context skill do?
Guide for integrating CellxGene Census single-cell data with ENCODE bulk experiments. Use when users need cell-type-specific expression context for ENCODE regulatory data, want to deconvolve bulk ENCODE signals, or validate regulatory elements at single-cell resolution. Trigger on: CellxGene, single-cell atlas, cell type expression, Census, cell type specificity, single-cell context, scRNA-seq atlas.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cellxgene-context --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.