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bio-single-cell-scatac-analysis

Analyze single-cell ATAC-seq data to identify regulatory elements and cell types.

Updated Mar 30, 2026
Search Tags:#single-cell#qc#scatac-seq#chromatin-accessibility#signac#archr#motif-analysis
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-scatac-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-single-cell-scatac-analysis
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-single-cell-scatac-analysis
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-scatac-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Single-cell ATAC-seq analysis workflow to identify regulatory elements and cell types, enabling streamlined QC, dimensionality reduction, clustering, peak calling, and motif activity scoring.

Core Features & Use Cases

  • Comprehensive scATAC processing with Signac (R/Seurat) and ArchR for peak calling, motif scoring, and integration with scRNA-seq.
  • Scalable workflow for 10X Genomics scATAC data, with QC metrics and visualization.
  • Use Case: researchers upload a 10X scATAC dataset and obtain cell type annotations, peak sets, and motif activity profiles.

Quick Start

Process a 10X Genomics scATAC dataset to generate QC metrics, cluster cells, call peaks, and score motif activity.

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 process 10X Genomics scATAC-seq data for cell type identification?▼

You can process 10X Genomics scATAC-seq data by running an end-to-end workflow that performs QC, dimensionality reduction, clustering, peak calling, and motif activity scoring to identify regulatory elements and cell types.

How do I calculate motif activity scores from chromatin accessibility data?▼

Motif activity scoring from chromatin accessibility data is calculated using chromVAR within the workflow, operating alongside Signac and ArchR to analyze regulatory elements in scATAC-seq datasets.

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

Yes, Signac and ArchR are used together for scATAC-seq analysis to perform peak calling, motif scoring, clustering, and integration with scRNA-seq data for comprehensive chromatin accessibility profiling.

Do I need R to analyze single-cell ATAC-seq data for regulatory elements?▼

Yes, you need R with the Signac and ArchR toolchain installed to analyze single-cell ATAC-seq data, as the workflow relies on these R packages for QC, clustering, and motif activity scoring.

What is the best way to integrate scRNA-seq with scATAC-seq data?▼

The best way to integrate scRNA-seq with scATAC-seq data is using ArchR within the workflow, which supports integration to help annotate cell types and identify regulatory elements from chromatin accessibility profiles.

Why does scATAC-seq analysis require dimensionality reduction and QC?▼

scATAC-seq analysis requires QC and dimensionality reduction because single-cell chromatin accessibility data is high-dimensional and sparse, necessitating quality filtering and clustering to accurately identify cell types and call peaks.

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