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

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

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

library(BSgenome.Hsapiens.UCSC.hg38)

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