Bio Chipseq Differential Binding

Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+

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.

Differential Binding with DiffBind

"Compare ChIP-seq binding between conditions" → Identify genomic regions with statistically significant differences in transcription factor or histone mark occupancy between experimental groups.

• R: DiffBind::dba() → dba.count() → dba.contrast() → dba.analyze()

Create Sample Sheet

Goal: Define the experimental design linking BAM files, peak files, and sample metadata for DiffBind.

Approach: Build a data frame (or CSV) with required columns mapping each sample to its files and conditions.

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# Create sample sheet as data frame or CSV
samples <- data.frame(
    SampleID = c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2'),
    Tissue = c('cell', 'cell', 'cell', 'cell'),
    Factor = c('H3K4me3', 'H3K4me3', 'H3K4me3', 'H3K4me3'),
    Condition = c('control', 'control', 'treatment', 'treatment'),
    Replicate = c(1, 2, 1, 2),
    bamReads = c('ctrl1.bam', 'ctrl2.bam', 'treat1.bam', 'treat2.bam'),
    Peaks = c('ctrl1_peaks.narrowPeak', 'ctrl2_peaks.narrowPeak',
              'treat1_peaks.narrowPeak', 'treat2_peaks.narrowPeak'),
    PeakCaller = c('macs', 'macs', 'macs', 'macs')
)

write.csv(samples, 'samples.csv', row.names = FALSE)

Load Data

Goal: Initialize a DiffBind object from the sample sheet containing all samples and peaks.

Approach: Read the sample sheet CSV into a DBA object that identifies overlapping peaks across samples.

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library(DiffBind)

# From sample sheet
dba_obj <- dba(sampleSheet = 'samples.csv')

# View summary
dba_obj

Count Reads in Peaks

Goal: Quantify read coverage at consensus peak regions across all samples.

Approach: Count reads in summit-centered windows using dba.count, creating a count matrix for statistical testing.

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# Count reads in consensus peaks
# summits=250 and bUseSummarizeOverlaps=TRUE are now defaults
dba_obj <- dba.count(dba_obj)

# With specific parameters
dba_obj <- dba.count(
    dba_obj,
    summits = 250,         # Re-center peaks around summits (default in 3.0)
    minOverlap = 2         # Peak must be in at least 2 samples
)

Normalize Data

Goal: Apply normalization to account for library size and composition differences between samples.

Approach: Use dba.normalize which applies DESeq2/edgeR normalization factors to the count matrix.

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# Normalize (required before analysis)
dba_obj <- dba.normalize(dba_obj)

# Check normalization
dba.normalize(dba_obj, bRetrieve = TRUE)

Set Up Contrast

Goal: Define the comparison between experimental conditions for differential testing.

Approach: Specify a design formula or category-based contrast that tells DiffBind which groups to compare.

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# Recommended: design formula approach
dba_obj <- dba.contrast(dba_obj, design = '~ Condition')

# Or use categories for automatic contrast
dba_obj <- dba.contrast(dba_obj, categories = DBA_CONDITION)

# Legacy approach (retained for backward compatibility, not recommended)
# dba_obj <- dba.contrast(dba_obj, group1 = dba_obj$masks$control,
#                         group2 = dba_obj$masks$treatment)

Run Differential Analysis

Goal: Identify peaks with statistically significant binding differences between conditions.

Approach: Apply DESeq2 or edgeR negative binomial models to the normalized count matrix.

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# Analyze with DESeq2 (default)
dba_obj <- dba.analyze(dba_obj, method = DBA_DESEQ2)

# Or with edgeR
dba_obj <- dba.analyze(dba_obj, method = DBA_EDGER)

View Results

Goal: Retrieve and inspect differentially bound regions with fold changes and significance values.

Approach: Extract results as a GRanges object with dba.report, sorted by significance.

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# Summary of differential peaks
dba.show(dba_obj, bContrasts = TRUE)

# Retrieve differential binding results
db_results <- dba.report(dba_obj)
db_results

Filter Results

Goal: Subset differential peaks by significance and fold-change thresholds.

Approach: Apply FDR and fold-change cutoffs to dba.report output.

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# Get significant peaks (FDR < 0.05, |FC| > 2)
db_sig <- dba.report(dba_obj, th = 0.05, fold = 2)

# Get all results for custom filtering
db_all <- dba.report(dba_obj, th = 1)

Export Results

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# To data frame
results_df <- as.data.frame(dba.report(dba_obj, th = 1))

# Export to CSV
write.csv(results_df, 'differential_binding.csv', row.names = FALSE)

# Export to BED
library(rtracklayer)
export(db_sig, 'diff_peaks.bed', format = 'BED')

Visualization

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# PCA plot
dba.plotPCA(dba_obj, DBA_CONDITION, label = DBA_ID)

# Correlation heatmap
dba.plotHeatmap(dba_obj)

# MA plot
dba.plotMA(dba_obj)

# Volcano plot
dba.plotVolcano(dba_obj)

# Heatmap of differential peaks
dba.plotHeatmap(dba_obj, contrast = 1, correlations = FALSE)

Venn Diagram of Peaks

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# Overlap between conditions
dba.plotVenn(dba_obj, dba_obj$masks$control)
dba.plotVenn(dba_obj, dba_obj$masks$treatment)

Profile Plots

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# Average signal profile
profiles <- dba.plotProfile(dba_obj)

Get Consensus Peaks

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# Export consensus peakset
consensus <- dba.peakset(dba_obj, bRetrieve = TRUE)
export(consensus, 'consensus_peaks.bed', format = 'BED')

Multi-Factor Design

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# With blocking factor (e.g., batch correction)
dba_obj <- dba.contrast(dba_obj, design = '~ Batch + Condition')
dba_obj <- dba.analyze(dba_obj)

DiffBind 3.0 Notes

DiffBind 3.0+ introduced significant changes:

• dba.normalize() is now required before analysis
• Default summits=250 recenters peaks (was FALSE in older versions)
• Use design formulas instead of group1/group2 for contrasts
• Blacklist filtering is applied by default

Sample Sheet Columns

Key Functions

Related Skills

• peak-calling - Generate input peak files
• peak-annotation - Annotate differential peaks
• differential-expression - Compare with RNA-seq
• pathway-analysis - Functional enrichment