Version Compatibility

Reference examples tested with: DESeq2 1.42+, GenomicRanges 1.54+, Subread 2.0+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Differential Accessibility

"Find differentially accessible regions between my conditions" → Identify chromatin regions with statistically significant changes in accessibility between treatment groups, cell types, or timepoints.

• R: DiffBind or DESeq2 on a peak-by-sample count matrix

DiffBind Workflow

Goal: Identify differentially accessible chromatin regions between experimental conditions.

Approach: Load sample metadata and peak files into DiffBind, count reads in consensus peaks, normalize, define contrasts, and run differential analysis with DESeq2 backend.

library(DiffBind)

# 1. Create sample sheet
samples <- data.frame(
    SampleID = c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2'),
    Condition = c('control', 'control', 'treated', 'treated'),
    Replicate = c(1, 2, 1, 2),
    bamReads = c('ctrl_1.bam', 'ctrl_2.bam', 'treat_1.bam', 'treat_2.bam'),
    Peaks = c('ctrl_1.narrowPeak', 'ctrl_2.narrowPeak', 'treat_1.narrowPeak', 'treat_2.narrowPeak')
)
write.csv(samples, 'samples.csv', row.names=FALSE)

# 2. Load data
dba <- dba(sampleSheet='samples.csv')

# 3. Count reads
dba <- dba.count(dba)

# 4. Normalize
dba <- dba.normalize(dba)

# 5. Set up contrasts
dba <- dba.contrast(dba, contrast=c('Condition', 'treated', 'control'))

# 6. Differential analysis
dba <- dba.analyze(dba)