ReddRadar
Agent skill
bio-chipseq-differential-binding
Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/differential-binding
SKILL.md
Differential Binding with DiffBind
Create Sample Sheet
# 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
library(DiffBind)
# From sample sheet
dba_obj <- dba(sampleSheet = 'samples.csv')
# View summary
dba_obj
Count Reads in Peaks
# Count reads in consensus peaks (DiffBind 3.0+ defaults)
# 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
# Normalize (required before analysis)
dba_obj <- dba.normalize(dba_obj)
# Check normalization
dba.normalize(dba_obj, bRetrieve = TRUE)
Set Up Contrast (DiffBind 3.0+)
# Recommended: design formula approach (DiffBind 3.0+)
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
# 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
# Summary of differential peaks
dba.show(dba_obj, bContrasts = TRUE)
# Retrieve differential binding results
db_results <- dba.report(dba_obj)
db_results
Filter Results
# 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
# 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
# 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
# Overlap between conditions
dba.plotVenn(dba_obj, dba_obj$masks$control)
dba.plotVenn(dba_obj, dba_obj$masks$treatment)
Profile Plots
# Average signal profile
profiles <- dba.plotProfile(dba_obj)
Get Consensus Peaks
# Export consensus peakset
consensus <- dba.peakset(dba_obj, bRetrieve = TRUE)
export(consensus, 'consensus_peaks.bed', format = 'BED')
Multi-Factor Design
# 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=250recenters peaks (was FALSE in older versions) - Use design formulas instead of group1/group2 for contrasts
- Blacklist filtering is applied by default
Sample Sheet Columns
| Column | Required | Description |
|---|---|---|
| SampleID | Yes | Unique identifier |
| Tissue | No | Tissue/cell type |
| Factor | No | ChIP target |
| Condition | Yes | Experimental condition |
| Treatment | No | Additional grouping |
| Replicate | Yes | Replicate number |
| bamReads | Yes | Path to BAM file |
| Peaks | Yes | Path to peak file |
| PeakCaller | Yes | macs, bed, narrow |
| bamControl | No | Path to input BAM |
Key Functions
| Function | Purpose |
|---|---|
| dba | Create DBA object |
| dba.count | Count reads in peaks |
| dba.normalize | Normalize counts |
| dba.contrast | Set up comparison |
| dba.analyze | Run differential analysis |
| dba.report | Get results |
| dba.plotPCA | PCA visualization |
| dba.plotMA | MA plot |
| dba.plotHeatmap | Heatmap |
Related Skills
- peak-calling - Generate input peak files
- peak-annotation - Annotate differential peaks
- differential-expression - Compare with RNA-seq
- pathway-analysis - Functional enrichment
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