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archr

Analyze single-cell ATAC-seq data with ArchR to map chromatin accessibility and regulatory elements.

Updated Apr 19, 2026
Search Tags:#bioinformatics#single-cell#scATAC-seq#peak-calling#chromatin-accessibility#archr#motif-analysis
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill archr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: archr
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/compbio/multiomics/scATAC-seq/archr
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill archr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ArchR provides a complete workflow for single-cell ATAC-seq analysis, from raw fragments to interpretable chromatin accessibility insights and cross-modality integration with gene expression data.

Core Features & Use Cases

  • Dimensionality reduction and clustering of scATAC-seq data
  • Peak calling, motif discovery, and motif enrichment analysis
  • Integration with scRNA-seq data for multi-omic interpretation
  • Visualization and export of browser tracks for genome browsers
  • Reproducible workflows and scalable analysis on large datasets

Quick Start

Install ArchR from CRAN or GitHub, then initialize a project and run a basic workflow to generate embeddings and identify clusters.

Frequently Asked Questions about archr

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

FAQPage Schema
How do I analyze single-cell ATAC-seq data for chromatin accessibility?▼

Single-cell ATAC-seq data is analyzed by managing Arrow files to perform dimensionality reduction, cluster cells, and call peaks. This workflow maps chromatin accessibility and regulatory elements to generate interpretable embeddings and visualizations.

Can I integrate scRNA-seq data with scATAC-seq for multi-omic interpretation?▼

You can integrate scRNA-seq data with scATAC-seq for multi-omic interpretation. This cross-modality integration aligns chromatin accessibility profiles with gene expression to provide comprehensive regulatory insights across samples.

What is the best way to perform peak calling and motif enrichment in R?▼

Peak calling and motif enrichment in R are performed by processing single-cell ATAC-seq fragments through a comprehensive workflow. This approach identifies regulatory elements and discovers enriched motifs to map chromatin accessibility.

Does ArchR support scalable scATAC-seq analysis for large datasets?▼

ArchR supports scalable scATAC-seq analysis for large datasets. It manages Arrow files to provide reproducible workflows, ensuring efficient dimensionality reduction and clustering across high-volume single-cell samples.

How do I export browser tracks from chromatin accessibility data?▼

You export browser tracks from chromatin accessibility data by generating visualizations within the scATAC-seq analysis workflow. These exported tracks can then be loaded into genome browsers for further multi-omic interpretation.

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