Finding common themes

The chipenrich package provides the chipenrich() function to identify groups of genes that are more frequently associated with ChIP-seq peaks than would be expected by chance. For this, it is important to determine how genes should be grouped together. In this exercise, you will be looking at the Hallmark genesets that have been defined at the Broad Institute.

Usually, you would want to restrict the analysis to differentially bound peaks to help emphasize the molecular processes that differentiate the two groups of samples. Due to the small sample size of the data you are dealing with, you will simply be looking at peaks that have a stronger signal in the treatment-resistant tumor samples.

This exercise is part of the course

ChIP-seq with Bioconductor in R

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Exercise instructions

Select all peaks that have a higher intensity in the treatment-resistant samples than the primary tumor samples.
Run the enrichment analysis.
Print the results of the analysis.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Select all peaks with higher intensity in treatment resistant samples
turp_peaks <- peaks_binding[, "GSM1598218"] + peaks_binding[, "GSM1598219"] < ___[, "GSM1598223"] + ___[, "GSM1598225"]

# Run enrichment analysis
enrich_turp <- ___(peaks_comb[turp_peaks, ], genome="hg19", 
                   genesets = "hallmark", out_name = NULL, 
                   locusdef = "nearest_tss", qc_plots=FALSE)

# Print the results of the analysis
___(enrich_turp$results)

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