Bio Atac Seq Differential Accessibility

Version Compatibility

Reference examples tested with: DiffBind 3.12+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, limma 3.58+, GenomicRanges 1.54+, ChIPseeker 1.38+, Subread 2.0+ (featureCounts), sva 3.50+, RUVSeq 1.36+.

Before using code patterns, verify installed versions match:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws unexpected errors, introspect the installed package and adapt rather than retrying.

Differential Accessibility

"Find chromatin regions that change accessibility between my conditions" -> Build a sample-by-region count matrix, normalize for library size and chromatin compaction, fit a generalized linear model (negative-binomial), and extract regions with significant accessibility change.

• R (consensus-peak workflow): DiffBind -> count -> normalize -> contrast -> analyze
• R (window-based, no peak set): csaw::windowCounts + filterWindowsGlobal + edgeR QL F-test
• R (existing peak-count matrix): DESeq2 or edgeR directly on featureCounts output

DiffBind is a wrapper around DESeq2 / edgeR with ATAC-aware defaults. csaw is the only peak-free option; it tests fixed-width sliding windows. The choice depends on whether peaks are stable across conditions (use DiffBind) or whether some condition has dramatically different peak structure (use csaw or rebuild consensus peaks).

Algorithmic Taxonomy

Methodology evolves; verify the current consensus practice (Gontarz 2020 DA-strategy benchmark; Reske 2020 normalization comparison) before locking pipelines.

Decision Tree by Experimental Scenario

Consensus Peak Set Strategy

The consensus peakset choice drives FDR calibration. DiffBind defaults rarely match what a chromatin biologist wants.

Refer to atac-seq/consensus-peakset for full coverage of fixed-width re-centering and the iterative overlap algorithm. For DiffBind, the key parameter is summits=250 (re-center peaks on summit +/- 250 bp = 501 bp fixed width).

Normalization: The ATAC-Specific Choice

DiffBind 3.x conflates two orthogonal choices: the normalization method (normalize=) and the library-size definition (library=). The defaults are normalize=DBA_NORM_LIB with library=DBA_LIBSIZE_FULL (full mapped-read total).

Trigger: Treatment causes global chromatin compaction (e.g., HDAC inhibitor, DNMT inhibitor).

Mechanism: Full library-size normalization is robust to background but the default still scales background reads in; under uniform global compaction the magnitudes can collapse. RiP scaling (library=DBA_LIBSIZE_PEAKREADS) makes the opposite assumption (peak signal is stable, background absorbs the shift) and so erases the very biology of interest.

Symptom: Volcano plot is symmetric about zero; PCA shows treatment effect that vanishes after normalization.

Fix: Use spike-in normalization (add exogenous chromatin pre-Tn5; scale by spike-in reads), or keep the default library=DBA_LIBSIZE_FULL but interpret with the global shift in mind. Reske 2020 documented that the normalization choice materially changes which peaks are called differential under such a global shift.

Per-Tool Failure Modes

DiffBind -- Library-size choice confounds global change

Trigger: Treatment causes whole-genome accessibility shift; cell-cycle synchronized samples; differentiation timecourse.

Mechanism: Setting library=DBA_LIBSIZE_PEAKREADS (RiP-based) assumes total reads-in-peaks is comparable across samples. Global accessibility shifts break this assumption.

Symptom: Conditions clearly differ in PCA before normalization; after normalization PC1 is nearly noise.

Fix: Keep the default library=DBA_LIBSIZE_FULL (or use spike-in scaling) and re-run dba.contrast and dba.analyze. Re-inspect PCA; if treatment now drives PC1, the global-shift biology is preserved.

DiffBind summits parameter -- Width-driven differential

Trigger: Per-rep peaks have very different widths; consensus uses union.

Mechanism: Without summits=250, DiffBind counts reads in the original peak intervals. A peak called as 200 bp in one rep and 800 bp in another inflates the count for the wider rep.

Symptom: Top differential peaks track peak width, not signal intensity.

Fix: Always set summits=250 (or 100, depending on resolution). This re-centers all peaks on the summit and uses identical 501 (or 201) bp windows.

csaw -- Window size and filter choice dominates results

Trigger: Default width=spacing=50 bp windows; default filter=10 count cutoff.

Mechanism: Narrow windows have very low counts and inflated dispersion; the global background filter discards too many windows. Results are extremely sensitive to these.

Symptom: Number of significant windows ranges from 200 to 200,000 across reasonable parameter sweeps.

Fix: Use width=150 for ATAC (matches typical NFR fragment); threshold with filterWindowsGlobal(data, background)$filter > log2(3) to discard low-signal windows. Validate by running on technical replicates -- ~0 differential windows is the expected outcome.

DESeq2 -- Apeglm shrinkage with too few reps

Trigger: n=2 per condition; using lfcShrink(type='apeglm').

Mechanism: Apeglm shrinks log2FC toward zero based on dispersion estimate; at n=2 dispersion is unreliable, shrinkage is over-aggressive, and biology is masked.

Symptom: All log2FC values cluster near zero post-shrinkage; FDR list has high p-values across the board.

Fix: Skip shrinkage at n=2 OR switch to edgeR QL test. If n=2 is unavoidable, report unshrunken log2FC alongside FDR; do not use shrunken FCs as the effect size.

edgeR QL -- Filter must be aggressive enough

Trigger: Including peaks with mean count < 5 across all samples.

Mechanism: The QL F-test calibrates dispersion across all features. Including very-low-count peaks pulls dispersion estimates and inflates FDR.

Fix: filterByExpr(y, group=group) removes low-count peaks; restore peaks one at a time only if they are biologically critical and supported by at least one rep at depth.

Reconciliation: When Tools Disagree

Operational rule: For high-confidence reporting, require concordant detection in two methods from different families (DiffBind/DESeq2-style on consensus peaks AND csaw-style sliding windows agreeing within +/- 500 bp). Report the intersection as primary; the union as exploratory.

Effect Size and Threshold Selection

abs(log2FC) >= 1 is not universal. ATAC effects in primary cells (immune subsets, neurons) often max at 1.5-fold; require log2FC >= 0.585 with FDR < 0.05 for those settings.

Hidden Batch with SVA / RUVseq

Goal: Recover differential accessibility signal when unknown batch effects swamp the contrast.

Approach: Estimate surrogate variables on normalized counts via svaseq, append them to the DESeq2 design, refit the model, and extract the contrast.

library(DESeq2); library(sva)

dds <- DESeqDataSetFromMatrix(countData=counts, colData=coldata, design=~condition)
dds <- estimateSizeFactors(dds)
dat <- counts(dds, normalized=TRUE)
dat <- dat[rowMeans(dat) > 1, ]

mod  <- model.matrix(~condition, colData(dds))
mod0 <- model.matrix(~1, colData(dds))
svobj <- svaseq(dat, mod, mod0, n.sv=2)

dds$SV1 <- svobj$sv[, 1]; dds$SV2 <- svobj$sv[, 2]
design(dds) <- ~SV1 + SV2 + condition
dds <- DESeq(dds)
res <- results(dds, contrast=c('condition', 'treated', 'control'))

RUVseq is the alternative when negative-control regions (ChrM peaks NOT changing) or technical replicates are available. SVA is preferred when no controls exist.

Spike-in Normalization

Trigger: Treatment causes whole-genome accessibility shift (HDAC inhibitor, DNMT inhibitor); RPM/CPM/RiP normalization erases the global biology.

Mechanism: Exogenous chromatin spike-in (Drosophila S2 nuclei) is added at constant cell number ratio pre-Tn5; reads aligning to dm6 quantify the constant exogenous baseline. Sample-level scaling factor = inverse of dm6 reads per sample, applied to human-aligned counts.

Goal: Preserve global accessibility shifts that RiP / library-size normalization would erase.

Approach: Compute per-sample size factors from inverse spike-in read counts, override DESeq2's default size factors, then run the standard DESeq2 fit and contrast.

library(DESeq2)

# spike_counts: per-sample dm6 read counts (one column per sample)
sf_spike <- 1 / spike_counts
sf_spike <- sf_spike / mean(sf_spike)                 # Geometric mean = 1 for stability

dds <- DESeqDataSetFromMatrix(countData=counts, colData=coldata, design=~condition)
sizeFactors(dds) <- sf_spike                          # Override library-size factors
dds <- DESeq(dds)
res <- results(dds, contrast=c('condition', 'treated', 'control'))

After spike-in normalization, log2FC reflects absolute accessibility change (not just relative redistribution). Spike-in is the most direct control for global-shift biology.

Permutation Testing for Low Replicate Designs

Trigger: n=2 per condition; parametric NB tests give over-confident p-values.

Mechanism: csaw provides a permutation framework: the null is generated by shuffling sample labels; test statistic is the count-difference per window; per-region p is the rank under permutation.

Goal: Generate empirical per-region p-values when parametric NB tests are over-confident at low replicate counts.

Approach: Fit the observed edgeR QL F statistic, repeatedly shuffle group labels and refit, then compute per-region p as the rank of the observed F under the shuffled null.

library(csaw); library(edgeR)

# Standard csaw counts (windows or peaks)
counts <- regionCounts(bam_files, regions, ext=200)

# Standard NB fit
y <- DGEList(counts=assay(counts), group=condition)
y <- calcNormFactors(y, method='TMM')
design <- model.matrix(~condition)
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design)

# Permutation: shuffle group labels n_perms times; track per-region rank statistic
n_perms <- 1000
perm_p <- replicate(n_perms, {
    shuffled <- sample(condition)
    design_p <- model.matrix(~shuffled)
    fit_p <- glmQLFit(estimateDisp(y, design_p), design_p)
    glmQLFTest(fit_p, coef=2)$table$F
})
observed_F <- glmQLFTest(fit, coef=2)$table$F
permp <- rowMeans(perm_p >= observed_F)

Permutation requires ~1000 shuffles for stable per-region p; computationally expensive but essential when parametric tests cannot be trusted.

DESeq2 Likelihood Ratio Test for Time-Courses

Goal: Identify peaks whose accessibility trajectory differs between conditions across a timecourse.

Approach: Fit a DESeq2 LRT comparing a full model with a spline-by-condition interaction against a reduced model lacking the interaction; significant peaks have time-dependent condition response.

library(DESeq2); library(splines)

# Spline-modeled time course (5+ time points)
dds <- DESeqDataSetFromMatrix(countData=counts, colData=coldata,
                              design=~ns(timepoint, df=3) + condition + ns(timepoint, df=3):condition)
dds_full <- DESeq(dds, test='LRT', reduced=~ns(timepoint, df=3) + condition)
res <- results(dds_full)

The LRT compares the full model (with time:condition interaction) to a reduced model without; significant peaks have time-dependent condition response. Use df=3 natural splines for typical 5-7 timepoints; df=4-5 for >= 8.

Hi-C-Loop-Anchored Differential

Trigger: Combined ATAC-seq + Hi-C/HiChIP datasets; want to test enhancer-promoter pair-level differential.

Mechanism: Aggregate peak-level differential signal at HiCCUPS loop anchors (or ABC-predicted enhancer-gene pairs). Combined enhancer + promoter accessibility change has more statistical power than either alone.

Goal: Test enhancer-promoter pair-level differential accessibility by aggregating peak-level signal at loop anchors.

Approach: Import HiCCUPS loops, map consensus peaks to anchor positions, then aggregate per-peak log2FC across both anchors of each loop to get loop-level effect sizes.

# Pseudo-pattern: per loop, sum DESeq2 log2FC at both anchors
loops <- makeGenomicInteractionsFromFile('hiccups_loops.bedpe', type='bedpe',
                                         experiment_name='hiccups', description='HiCCUPS loops')
peak_to_loop <- findOverlaps(consensus_peaks, c(anchorOne(loops), anchorTwo(loops)))

loop_lfc <- aggregate(res$log2FoldChange[queryHits(peak_to_loop)],
                      by=list(loop=ceiling(subjectHits(peak_to_loop) / 2)),
                      FUN=function(x) sum(x, na.rm=TRUE))

For implementation, use the InteractionSet Bioconductor package which preserves loop-pair structure during testing. Reference: Mumbach 2017 Nat Genet (HiChIP enhancer connectome).

Annotate Differential Peaks

Goal: Assign each differentially accessible peak to its nearest gene and feature class for downstream interpretation.

Approach: Pull DiffBind / DESeq2 results as GRanges, annotate via ChIPseeker against a TxDb with a custom promoter window, then plot annotation distribution and extract gene IDs for enrichment.

library(ChIPseeker); library(TxDb.Hsapiens.UCSC.hg38.knownGene)

diff_peaks <- dba.report(dba)
peakAnno <- annotatePeak(diff_peaks, TxDb=TxDb.Hsapiens.UCSC.hg38.knownGene,
                         tssRegion=c(-2000, 500), level='gene')
plotAnnoPie(peakAnno); plotDistToTSS(peakAnno)
genes <- as.data.frame(peakAnno)$geneId          # for GO enrichment via pathway-analysis/go-enrichment

tssRegion=c(-2000, 500) defines promoter as TSS-2kb to TSS+500bp; ChIPseeker default (-3000, 3000) over-counts promoter assignments. Adjust per cell type / organism.

Common Errors

References

• Stark R & Brown G 2011 DiffBind (Bioconductor; canonical reference)
• Lun ATL & Smyth GK 2014 NAR 42:e95 (csaw windowed differential method)
• Love MI et al 2014 Genome Biol 15:550 (DESeq2)
• Robinson MD et al 2010 Bioinformatics 26:139 (edgeR)
• Chen Y et al 2016 F1000Res 5:1438 (edgeR-QL framework)
• Leek JT 2014 NAR 42:e161 (svaseq for hidden batch)
• Risso D et al 2014 Nat Biotechnol 32:896 (RUVseq)
• Reske JJ et al 2020 Epigenetics Chromatin 13:22 (ATAC normalization-method comparison; ARID1A/PIK3CA global-shift case study)
• Gontarz P et al 2020 Sci Rep 10:10150 (comparison of differential-accessibility analysis strategies for ATAC-seq)
• Corces MR et al 2018 Science 362:eaav1898 (iterative overlap, fixed-width 501 bp consensus)
• Yu G et al 2015 Bioinformatics 31:2382 (ChIPseeker)

Related Skills

• atac-seq/atac-peak-calling - Generate per-replicate peaks
• atac-seq/consensus-peakset - Build the differential-ready consensus peakset
• atac-seq/atac-qc - Pre-screen and drop failing replicates
• atac-seq/single-cell-atac - Pseudobulk-level differential per cluster
• atac-seq/co-accessibility - Identify cis-regulatory connections among DA peaks
• differential-expression/deseq2-basics - Underlying DESeq2 patterns
• differential-expression/de-results - Effect-size reporting and shrinkage
• chip-seq/differential-binding - Same DiffBind workflow, ChIP context
• pathway-analysis/go-enrichment - Downstream gene-level enrichment of DA-associated genes