Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
DiffBind 3.0+ changed defaults: summits=200 (was FALSE), dba.normalize() now required, blacklist filtering on by default, full library size normalization replaces reads-in-peaks. Always run packageVersion('DiffBind') and inspect dba.normalize(obj, bRetrieve=TRUE) to confirm what was applied.
Differential ChIP-seq Binding
"Compare protein-DNA binding between experimental conditions" -> Identify regions where IP signal changes significantly, accounting for sequencing depth, composition bias, trended biases, and global shifts that confound naive normalization.
R (BAM + peaks): DiffBind::dba() -> -> ->
dba.count()
dba.normalize()
dba.analyze()
R (count matrix): DESeq2::DESeq() or edgeR::glmQLFTest() on a peaks-by-samples matrix
R (windows-based, global-shift-robust): csaw::windowCounts() -> csaw::normFactors() -> edgeR::glmQLFTest()
R (control-aware): normr::diffR(chip1.bam, chip2.bam, genome) joint binomial mixture
Python (count matrix): pydeseq2.DeseqDataSet()
Choice of normalization matters more than choice of test statistic (RLE vs TMM vs csaw bin-TMM on the same reference reads produce nearly identical results). Choose by which of the three normalization problems applies.
The Three Distinct Normalization Problems
Problem
Symptom on MA plot
Cause
Fix
Composition bias
Loess shifts off y=0 systematically
Few high-signal peaks dominate read counts; small fold changes look large or inverted
TMM on background 10 kb bins (csaw / DiffBind background=TRUE); NOT reads-in-peaks
Trended bias (intensity-dependent)
Loess curve sweeps from + to - across abundance
Library-prep efficiency varies with fragment abundance
Non-linear loess offsets (csaw normOffsets); use cautiously — can over-normalize biology
Global shift (treatment changes most peaks)
Loess entirely shifted off y=0; mean log2FC ≠ 0
Drug/perturbation changes the genome-wide level of binding (HDACi, BETi, EZH2i, target KD)
Spike-in scaling (ChIP-Rx); no algorithmic fix works
Why this matters: Most published ChIP-seq differential analyses default to RLE/TMM on reads-in-peaks, which assumes "most peaks are unchanged." For HDAC inhibitors, BET inhibitors, EZH2 inhibitors, or any large dosage / target-knockdown experiment, this assumption is violated. The algorithm forces the median log2FC to zero, hides the real effect, and amplifies noise around the new "zero." The result can have the wrong sign.
Diagnostic: Plot MA loess on differential results. A loess curve that sweeps abundance indicates trended bias. A uniformly shifted loess indicates a global shift. Both patterns together indicate normalization is failing in two ways simultaneously.
Algorithmic Taxonomy
Tool
Treats
Statistical model
Strength
Fails when
DiffBind 3.20+
Consensus peaks summit ± 200 bp (default)
DESeq2 or edgeR backend
Mature; integrated counting/normalization; spike-in support; blacklist filter on
Default summits=200 recenters peaks (wrong for broad marks); DBA_NORM_LIB is conservative but misses global shifts unless background=TRUE
Post-hoc heuristic only; not definitive; sanity check
Decision Tree: Choosing Normalization
Scenario
Recommended
Tool / parameter
Standard TF ChIP, local changes expected, balanced gain/loss
RLE on reads-in-peaks
DESeq2 default; DiffBind DBA_NORM_RLE
Histone marks, broad domains, local changes
TMM on background 10 kb bins
csaw normFactors; DiffBind background=TRUE
HDAC / BET / EZH2 inhibitor (global change)
Spike-in scaling
DiffBind spikein=TRUE; SpikeFlow; manual sizeFactors() from spike reads
Dosage titration, cell-cycle synchronization
Spike-in scaling
Same
ChIP target knockdown / degron
Spike-in or matched-input control subtraction
Spike-in preferred; bamCompare log2 ratio next
CUT&RUN/CUT&Tag standard
E. coli spike-in (carryover)
DiffBind custom scaling; see cut-and-run-tag
No spike-in available; suspect global shift
ChIPseqSpikeInFree
Post-hoc distribution-shape inference
Suspected trended (abundance-dependent) bias
Non-linear loess
csaw normOffsets
Genome-wide enrichment/depletion analysis
NormR
Binomial mixture
Many conditions, large peak set
edgeR QL-F or DiffBind+edgeR
Better type-I control than DESeq2 Wald
Spike-In Scaling Factor Calculation
ChIP-Rx (Orlando 2014) uses Drosophila chromatin spike-in added at fixed concentration BEFORE IP. Egan 2016 adds a fixed MASS of Drosophila S2 chromatin (matched to target chromatin by the ~27:1 human:fly genome-size ratio), not a fixed cell count.
Apply to read counts pre-test, OR pass as sizeFactors() to DESeq2 / normFactors() to edgeR / a numeric library-size vector to DiffBind's dba.normalize(..., library=...):
Rx-Input variant (Fursova 2019): additionally scale by input spike-in to correct IP efficiency variation.
Internal-control sanity check: After spike-in normalization, blacklist regions and constitutive housekeeping sites (U6 promoter, rRNA processing factors that are stable) should show no signal change. If they do, the normalization is broken — common causes:
Spike-in scaling applied to peak counts instead of read counts
Spike-in reads not deduplicated before scaling
Spike-in genome not filtered for high-mapq before scaling
Per the Patel et al 2024 Nat Biotechnol survey, improper spike-in normalization is common: of 53 datasets examined, only 27 (~51%) had adequate matched input controls across conditions.
DiffBind Workflow (BAMs + Peaks)
Goal: Run DiffBind from a sample sheet to consensus peaks, normalized counts, and tested differential binding.
Approach: Build sample sheet, count reads in consensus peaks (summit-centered for narrow marks, full peak width for broad), choose normalization based on the three-problem framework above, then test with DESeq2 or edgeR backend.
library(DiffBind)# Sample sheet: SampleID, Condition, Replicate, bamReads, bamControl, Peaks, PeakCaller
dba_obj <- dba(sampleSheet ='samples.csv')# Counting: summits=250 (narrow); FALSE for broad histones (use full peak width)
dba_obj <- dba.count(dba_obj, summits =250, minOverlap =2, bParallel =TRUE)# Normalization — choose per the three-problem framework
dba_obj <- dba.normalize(dba_obj,# default: full library size
method = DBA_DESEQ2,
normalize = DBA_NORM_LIB,
library = DBA_LIBSIZE_FULL)# Background bin TMM for composition bias / broad marks# dba_obj <- dba.normalize(dba_obj, background = TRUE)# Spike-in scaling for global shifts# dba_obj <- dba.normalize(dba_obj, spikein = TRUE)
dba_obj <- dba.contrast(dba_obj, design ='~ Condition')
dba_obj <- dba.analyze(dba_obj, method = DBA_DESEQ2)# Always inspect what was actually applied
dba.normalize(dba_obj, bRetrieve =TRUE)
library = DBA_LIBSIZE_FULL — full library, conservative
normalize = DBA_NORM_LIB — library size only, not reads-in-peaks RLE
Blacklist filtering on
background = FALSE — set TRUE for composition bias
csaw Workflow (Windows-Based)
Goal: Detect differential binding from sliding windows without committing to predefined peaks; robust to composition bias via background bin TMM.
Approach: Count reads in overlapping windows, normalize on 10 kb bins (composition bias) and/or loess (trended bias), test with edgeR QL-F, then merge significant windows into regions.
library(csaw)
library(edgeR)
bam_files <-c('ctrl_1.bam','ctrl_2.bam','treat_1.bam','treat_2.bam')
condition <- factor(c('ctrl','ctrl','treat','treat'))# Window counts: 150 bp window, 50 bp spacing (sharp marks); 1-2 kb for broad
param <- readParam(minq =30, pe ='both', dedup =TRUE,
discard = import('hg38-blacklist.v2.bed'))
windows <- windowCounts(bam_files, width =150, ext =200, param = param)# Composition bias via 10 kb bins (always applied for ChIP-seq)
bg_bins <- windowCounts(bam_files, bin =TRUE, width =10000, param = param)
windows <- normFactors(bg_bins, se.out = windows)# Optional: trended bias via non-linear loess (use cautiously)# windows <- normOffsets(windows, se.out = TRUE)# edgeR QL-F test
y <- asDGEList(windows)
design <- model.matrix(~condition)
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design, robust =TRUE)
results <- glmQLFTest(fit, coef =2)# Merge significant windows within 1 kb into regions
merged <- mergeResults(windows, results$table, tol =1000, merge.args =list(max.width =5000))
For broad marks, increase window width to 1-2 kb and merge tolerance to 5 kb. For TFs, 150 bp window + 50 bp spacing is standard.
DESeq2 from Count Matrix
library(DESeq2)
counts <- read.delim('counts.tsv', row.names =1, check.names =FALSE)
coldata <- data.frame(
condition = factor(c('ctrl','ctrl','ctrl','treat','treat','treat')),
row.names = colnames(counts))
dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design =~ condition)# Pre-filtering for ChIP-seq is LESS aggressive than RNA-seq (peaks already enriched)
keep <- rowSums(counts(dds))>=1# remove only all-zero
dds <- dds[keep,]
dds$condition <- relevel(dds$condition, ref ='ctrl')
dds <- DESeq(dds)
res <- results(dds, alpha =0.05)# match independent-filtering optimization# Optional: LFC shrinkage for ranking/visualization only — does NOT change padj
library(apeglm)
resLFC <- lfcShrink(dds, coef ='condition_treat_vs_ctrl', type ='apeglm')
For spike-in normalization, set sizeFactors(dds) from scaling factors before DESeq(dds). For background-bin TMM (composition bias) use csaw to compute size factors, then transfer.
Per-Tool Failure Modes
DiffBind -- summits=200 default destroys broad marks
Trigger: Default DiffBind 3.x call with histone broad marks (H3K27me3, H3K9me3).
Mechanism:summits=200 re-centers peaks to summit ± 200 bp, throwing away most of a 10-100 kb broad domain.
Symptom: Differential count for broad marks much lower than expected; signal concentrated at narrow centers of broad regions.
Fix:dba.count(obj, summits = FALSE, ...) for broad marks; OR use full-width consensus peaks; OR switch to csaw with 1-2 kb windows.
DiffBind -- DBA_NORM_RLE reads-in-peaks default reverses global shifts
Trigger: Default (legacy DiffBind < 3.0) OR explicitly setting normalize = DBA_NORM_RLE on a global-shift experiment.
Mechanism: RLE on reads-in-peaks assumes most peaks unchanged. If 80% of peaks lose signal (e.g., EZH2 inhibitor on H3K27me3), the size factors compensate by inflating the "lost" peaks' normalized values toward control levels.
Symptom: Differential results show fewer peaks changed than visually obvious; or signs are wrong (gain reported where loss occurred).
Fix: Switch to background = TRUE (bin TMM); for definitive analysis use spikein = TRUE with ChIP-Rx.
DESeq2 -- Pre-filtering removes condition-specific peaks
Trigger: Applying RNA-seq-style filter rowSums(counts) >= 10 to ChIP-seq counts.
Mechanism: A peak present in treatment but absent in control has near-zero control counts. Aggressive filtering removes truly differential peaks.
Symptom: Significantly differential gains-of-binding peaks missing from results.
Fix:rowSums(counts) >= 1 only (remove all-zero rows); accept the loss of statistical power vs. recovering true differential peaks.
Trigger: Applying normOffsets() (loess) when the abundance-dependent shift IS the biology.
Mechanism: Loess fits a smooth curve to the MA-plot trend; if treatment uniformly increases binding at low-signal peaks (which is biology), loess interprets it as a technical trend and removes it.
Symptom: No differential peaks detected despite obvious treatment effect.
Fix: Use bin-TMM only (composition bias); apply loess only after confirming the trend is technical (e.g., it appears in IgG-only samples).
Spike-in normalization -- Scaling factor applied to wrong layer
Trigger: Multiplying peak counts by spike-in scaling factor.
Mechanism: Spike-in factors are for read-level normalization; applied to peak counts they double-correct (peak counts already reflect mapped reads).
Symptom: Effect sizes shifted by 2-10× from expected biology; internal-control regions show artifactual signal change.
Fix: Apply spike-in via sizeFactors(dds) (DESeq2) or normFactors (edgeR) BEFORE the test; or via bamCoverage --scaleFactor for browser tracks. Never multiply peak-level counts.
IDR-passing peaks not used as differential input
Trigger: Using per-replicate MACS calls (loose -p 1e-2) as DiffBind peak input.
Mechanism: Loose ENCODE-pattern peaks include many low-confidence calls; DiffBind's minOverlap = 2 may not filter aggressively enough.
Symptom: Differential results dominated by noise at marginal peaks; high false-positive rate.
Fix: Pre-filter peak input to IDR-passing peaks (TF) or naive-overlap-passing peaks (histone) before DiffBind ingestion.
Force same normalization; differences should shrink to <5%
csaw windows + DiffBind peaks disagree
csaw catches sub-peak local maxima or wider regions DiffBind missed
Inspect IGV; csaw windows-based often more sensitive to broad/diffuse changes
Spike-in scaled + non-scaled give opposite signs
Global shift present
Spike-in is correct; non-scaled is fooled by composition bias
DiffBind run twice gives different results
Different summits or minOverlap settings
Verify via dba.normalize(obj, bRetrieve=TRUE); pin parameters in script
Few replicates, large fold changes, low padj
DESeq2 dispersion estimate unstable with n=2
Switch to edgeR QL-F or DiffBind with edgeR backend
Different fold changes in DiffBind 3.x vs 2.x
Default normalization changed
Match settings explicitly; document version in methods
Operational rule for publication-grade: Run on ENCODE-pattern peaks (IDR or naive overlap-passing), normalize with both reads-in-peaks AND background-bin AND spike-in if available; require concordance across at least two methods. For global-shift experiments, spike-in is mandatory.
Common Errors
Error / symptom
Cause
Solution
Error: dba.normalize() must be called before dba.analyze()
DiffBind 3.x change; not in older docs
Add dba.normalize(obj) step
DiffBind very slow on many samples
Sequential counting
dba.count(obj, bParallel = TRUE)
Error in DESeq() with few replicates
Dispersion estimation unstable
Use DESeq(dds, fitType = 'parametric', sfType = 'poscounts') or switch to edgeR
Spike-in size factors mostly NA
Spike-in reads not in sample sheet or wrong BAM path
Verify spike-in BAM exists; load via DiffBind spikein field
All padj = NA
Independent filtering too aggressive
Lower alpha in results() to match intended threshold
Volcano plot inverted (down peaks on right)
Contrast direction reversed
contrast = c('condition', 'treat', 'ctrl') for positive log2FC = up in treat
