bio-chipseq-differential-binding

## Version Compatibility Reference examples tested with: DiffBind 3.20+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, PyDESeq2 0.5+, NormR 1.28+, MAnorm2 1.2+, ChIPseqSpikeInFree 1.6+. 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` | | **DESeq2 (direct)** | Predefined peaks | NB GLM | Familiar; transparent | RLE on reads-in-peaks fails for global shifts | | **edgeR (direct)** | Predefined peaks | NB GLM with TMM; quasi-likelihood F-test | Cleaner small-sample inference; QL-F controls type-I error | TMM on peak counts unstable if peaks globally shifting | | **csaw** (Lun & Smyth 2016) | Sliding windows (typically 150 bp width, 50 bp shift) | edgeR QL-F | Gold-standard for global shifts; bin-TMM composition bias; loess for trended biases | Slower; requires BAMs not count matrix; window-merge step adds complexity | | **NormR** (Helmuth 2016) | Genomic bins | Binomial mixture (background + enriched) | Control-aware; identifies enrichment/depletion/background simultaneously | Bin-level not peak-level; older codebase; less integration with downstream tools | | **MAnorm2** (Tu 2021) | Peaks | Hierarchical model with mean-variance trend | Designed for cross-condition with replicates | Less widely adopted; sparse maintenance | | **SpikChIP** (Blanco 2021) | Peaks | Spike-in-aware | Multi-sample spike-in comparison | Niche; specific spike-in protocol assumed | | **SpikeFlow** (2024) | End-to-end | Snakemake pipeline: MACS2/EPIC2/EDD peaks + DESeq2 with spike-in size factors | Automated; multiple normalization options (RPM/RRPM/Rx-Input/downsampling) | Inherits experimental-design errors upstream | | **ChIPComp** (Chen 2015) | Peaks | Joint Poisson with input | Control-aware | Older; less maintained | | **ChIPseqSpikeInFree** (Jin 2020) | Peaks (post-hoc) | Distribution-shape inference | Detects global shift WITHOUT spike-in | 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. **RRPM (reference-adjusted reads per million):** ``` scale_factor_i = min(N_spike_sample) / N_spike_sample_i ``` 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=...)`: ```r spike_in_reads <- c(120000, 145000, 110000, 95000) # per-sample Drosophila read count sample_names <- c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2') scale_factors <- min(spike_in_reads) / spike_in_reads names(scale_factors) <- sample_names # DESeq2 with spike-in size factors sizeFactors(dds) <- 1 / scale_factors # DESeq2 expects inverse convention # Or directly via DiffBind 3.x (spikein = TRUE forces library = DBA_LIBSIZE_BACKGROUND internally) dba_obj <- dba.normalize(dba_obj, spikein = TRUE, normalize = DBA_NORM_LIB) ``` **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 - Spike-in saturated (always 100k+ reads); check titration linearity 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. ```r 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) ``` DiffBind 3.20+ defaults (verified via `bRetrieve`): - `summits = 200` — narrow recentering (set `FALSE` for broad histones) - `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. ```r 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$table, tol = 1000, merge.args = list(max.width = 5000)) ``` For broad marks, increase window width to 1-2 kb and merge tolerance to 5 kb. For TFs, 150 bp window + 50 bp spacing is standard. ## DESeq2 from Count Matrix ```r library(DESeq2) counts <- read.delim('counts.tsv', row.names = 1, check.names = FALSE) coldata <- data.frame( condition = factor(c('ctrl', 'ctrl', 'ctrl', 'treat', 'treat', 'treat')), row.names = colnames(counts) ) dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design = ~ condition) # Pre-filtering for ChIP-seq is LESS aggressive than RNA-seq (peaks already enriched) keep <- rowSums(counts(dds)) >= 1 # remove only all-zero dds <- dds[keep, ] dds$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. ### csaw -- Trended bias loess over-normalizes biology **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).