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bio-single-cell-scatac-analysis

Analyze single-cell ATAC-seq data with Signac and ArchR for QC, clustering, and motif scoring.

Updated Aug 23, 2026
Search Tags:#peak-calling#scatac-seq#signac#archr#motif-analysis#chromvar
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-single-cell-scatac-analysis-stellaromics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-single-cell-scatac-analysis
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-single-cell-scatac-analysis
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-single-cell-scatac-analysis-stellaromics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables end-to-end analysis of single-cell ATAC-seq data with Signac (R/Seurat) and ArchR, delivering QC metrics, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR to elucidate regulatory programs from 10X Genomics data.

Core Features & Use Cases

  • QC metrics calculation (TSS enrichment, nucleosome signal, fragment statistics) to filter cells
  • Dimensionality reduction and clustering using Signac/ArchR workflows
  • Peak calling per cluster and motif activity scoring with chromVAR
  • Use Case: analyze a scATAC-seq dataset to identify cell types and regulatory programs across clusters

Quick Start

Process a 10X Genomics scATAC-seq dataset to QC, reduce dimensionality, cluster, call peaks, and score motif activity using Signac and ArchR.

Frequently Asked Questions about bio-single-cell-scatac-analysis

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

FAQPage Schema
How do I perform end-to-end single-cell ATAC-seq analysis in R?▼

End-to-end single-cell ATAC-seq analysis in R is performed using Signac and ArchR to execute QC, dimensionality reduction, clustering, peak calling, and chromVAR motif activity scoring on 10X Genomics datasets.

What QC metrics are needed to filter cells in scATAC-seq data?▼

scATAC-seq QC filtering requires calculating TSS enrichment, nucleosome signal, and fragment statistics to identify high-quality cells before proceeding to dimensionality reduction and clustering.

Can I use Signac and ArchR together for scATAC-seq clustering and peak calling?▼

Yes, Signac and ArchR can be used together within an R environment to perform dimensionality reduction, clustering, and per-cluster peak calling to identify cell types and regulatory programs.

How does chromVAR motif activity scoring work in scATAC-seq?▼

chromVAR motif activity scoring in scATAC-seq quantifies transcription factor motif enrichment across clusters to elucidate regulatory programs from chromatin accessibility peaks.

Do I need an R-based toolchain to analyze 10X Genomics scATAC-seq datasets?▼

Yes, analyzing 10X Genomics scATAC-seq datasets requires an R-based toolchain including Signac, ArchR, chromVAR, and related Bioconductor packages to execute the full analysis workflow.

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