Testing for differential binding

ChIP-seq with Bioconductor in R

Peter Humburg

Statistician, Macquarie University

Comparing groups of ChIP-seq samples

  • Use statistical analysis of read counts to identify significant differences between groups.
  • DiffBind package provides interface to analysis tools.
    • Use either DESeq2 or edgeR.
ChIP-seq with Bioconductor in R

Creating a shared peak set

Counting reads in peak set:

peak_counts <- dba.count(qc_output, summits=250)
ChIP-seq with Bioconductor in R

Establishing a contrast

Creating a contrast:

peak_counts <- dba.contrast(peak_counts, categories = DBA_CONDITION)

Other supported categories:

  • DBA_ID
  • DBA_TISSUE
  • DBA_FACTOR
  • DBA_TREATMENT
  • DBA_REPLICATE
  • DBA_CALLER
ChIP-seq with Bioconductor in R

Using controls

ChIP-seq with Bioconductor in R

Running the analysis

bind_diff <- dba.analyze(peak_counts)
ChIP-seq with Bioconductor in R

A first look at the results

PCA plot for differentially bound peaks

dba.plotPCA(bind_diff, DBA_Condition, contrast=1)

Heatmap for differentially bound peaks

dba.plotHeatmap(bind_diff, DBA_Condition, contrast=1)
ChIP-seq with Bioconductor in R

Let's practice!

ChIP-seq with Bioconductor in R

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