chromatin-accessibility-deviation-computation

chromatin-accessibility-deviation-computation

Summary

Compute deviation scores that quantify how individual cells or samples deviate from the expected chromatin accessibility pattern for specific genomic annotations (motifs or kmers). This transforms sparse ATAC-seq counts into bias-corrected deviation matrices suitable for identifying annotation-accessibility associations.

When to use

When you have filtered ATAC-seq or DNAse-seq peak counts (after GC bias correction, sample filtering, and peak filtering) and wish to measure how strongly each annotation (motif or kmer) influences chromatin accessibility variability in each sample relative to a background expectation.

When NOT to use

• Input counts have not been bias-corrected with addGCBias or filtered with filterSamples/filterPeaks; computeDeviations requires preprocessed, quality-filtered input.
• Annotation matrix is already in a non-sparse, dense format unsuitable for chromVAR's internal optimization; use sparse Matrix objects.
• Goal is only clustering or dimensionality reduction without annotation interpretation; newer methods like SnapATAC outperform chromVAR for clustering tasks.

Inputs

• SummarizedExperiment with peak-by-sample counts matrix (bias-corrected, filtered for sample depth ≥1500 and in-peak fraction ≥0.15, peaks non-overlapping)
• Sparse annotation matrix (rows=peaks, columns=annotations) from matchMotifs or matchKmers

Outputs

• chromVARDeviations SummarizedExperiment object with two assays: 'deviations' (peak-annotation-by-sample deviation scores) and 'z' (z-score normalized deviations)

How to apply

Load the filtered SummarizedExperiment counts object and match annotations (motifs via matchMotifs or kmers via matchKmers) to generate a sparse annotation matrix. Pass both the counts and annotation matrix to computeDeviations, which internally computes per-annotation accessibility deviations as the difference between observed and bias-expected accessibility, producing z-scores normalized across samples. The resulting SummarizedExperiment contains two assays: deviations (raw deviation scores) and z-scores (standardized across samples); these serve as input for downstream variability, correlation, or synergy analyses. The key rationale is that deviation computation accounts for GC content bias and library size differences, enabling fair cross-sample comparisons of annotation-specific accessibility patterns.