# Bio Atac Seq Motif Deviation

Analyze transcription factor motif accessibility variability using chromVAR.

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Design
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-motif-deviation -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-motif-deviation -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-motif-deviation -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-motif-deviation -g -a gemini-cli -y`

## Description

Analyze transcription factor motif accessibility variability using chromVAR. Use when identifying which TF motifs show variable accessibility across samples or conditions in ATAC-seq data.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Version Compatibility

Reference examples tested with: ggplot2 3.5+, limma 3.58+

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Motif Deviation Analysis

**"Which TF motifs show variable accessibility across my samples?"** → Compute per-sample deviation scores for TF motif accessibility to identify regulators driving chromatin state differences.
- R: `chromVAR::computeDeviations(counts, motifs)`

Measure per-sample variability in transcription factor motif accessibility using chromVAR. This identifies TFs whose binding sites show differential accessibility across conditions.

## Required Packages

```r
library(chromVAR)
library(motifmatchr)
library(BSgenome.Hsapiens.UCSC.hg38)  # or appropriate genome
library(JASPAR2020)
library(TFBSTools)
library(SummarizedExperiment)
```

## Basic Workflow

**Goal:** Run chromVAR to compute per-sample TF motif deviation scores from ATAC-seq peak counts.

**Approach:** Load peak counts into a SummarizedExperiment, correct for GC bias, filter low-quality peaks, match JASPAR motifs, and compute deviation z-scores.

### 1. Load Peak Counts

```r
library(chromVAR)
library(SummarizedExperiment)

# From count matrix and peak ranges
peaks <- read.table('peaks.bed', col.names = c('chr', 'start', 'end'))
peak_ranges <- GRanges(seqnames = peaks$chr, ranges = IRanges(peaks$start, peaks$end))

counts <- read.table('counts.txt', header = TRUE, row.names = 1)
counts_matrix <- as.matrix(counts)

fragment_counts <- SummarizedExperiment(
    assays = list(counts = counts_matrix),
    rowRanges = peak_ranges
)
```

### 2. Add GC Bias Correction

```r
library(BSgenome.Hsapiens.UCSC.hg38)

fragment_counts <- addGCBias(fragment_counts, genome = BSgenome.Hsapiens.UCSC.hg38)
```

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-atac-seq-motif-deviation/SKILL.md