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chromvar-motif-accessibility

Compute bias-corrected per-cell motif accessibility deviations from ATAC data.

1|Updated Nov 20, 2025
Search Tags:#r#bioconductor#scatac-seq#chromvar#motif-accessibility#tf-activity#motif-matching
One-click install
npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill chromvar-motif-accessibility
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chromvar-motif-accessibility
Source: https://github.com/tony-zhelonkin/SciAgent-toolkit/tree/main/skills/chromvar-motif-accessibility
Command: npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill chromvar-motif-accessibility

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identifies transcription factor activity by computing bias-corrected per-cell motif accessibility deviation scores from ATAC data, enabling regulatory insights without RNA measurements.

Core Features & Use Cases

  • Computes bias-corrected motif deviation scores for single cells or bulk ATAC data.
  • Enables transcription factor activity inference, motif variability analysis, and differential accessibility studies.
  • Supports integration with Signac/ArchR workflows, various motif databases (JASPAR, chromVARmotifs), and downstream clustering.

Quick Start

Install chromVAR and dependencies, load ATAC counts, and run computeDeviations to obtain per-motif per-cell deviation scores.

Frequently Asked Questions about chromvar-motif-accessibility

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

FAQPage Schema
How do I infer transcription factor activity from scATAC-seq data without RNA measurements?▼

Compute bias-corrected per-cell motif accessibility deviations using chromVAR to infer transcription factor activity directly from a counts-based scATAC-seq peak matrix without requiring RNA data.

What motif databases can I use for motif matching and variability analysis in ATAC data?▼

You can use JASPAR or chromVARmotifs motif collections for motif matching to compute per-cell bias-corrected motif accessibility deviations from your ATAC counts matrix.

How do I correct GC bias when calculating motif accessibility deviations in single-cell ATAC?▼

Use chromVAR's built-in GC-bias correction during computeDeviations to normalize per-cell motif accessibility deviations against background peak accessibility, yielding accurate TF activity scores from scATAC-seq data.

Can I use this chromVAR workflow for bulk ATAC-seq differential accessibility analysis?▼

Yes, the workflow supports both scATAC-seq and bulk ATAC data for differential accessibility analysis by computing bias-corrected motif deviation scores across samples to identify variably accessible TF motifs.

What input format is required to compute motif deviation scores for TF activity inference?▼

A counts-based peak matrix is required as input. This matrix provides the raw chromatin accessibility read counts necessary for motif matching and subsequent bias-corrected deviation score calculations.

Does this motif accessibility approach integrate with existing Signac or ArchR scATAC-seq workflows?▼

Yes, the computed per-cell motif deviation scores integrate with Signac and ArchR workflows, enabling downstream clustering by regulatory programs and differential accessibility analysis within existing scATAC-seq pipelines.

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