Genes that make a difference

Identifying ChIP-seq peaks is all well and good but it doesn't tell you much about what is going on inside a cell. In this exercise, you get a sneak-peak at how you can use genome annotations to make sense of ChIP-seq results. Two sets of genes have been loaded into the R session for you. The first, ar_sets, has a list of all genes associated with peaks in the primary and treatment-resistant tumors. The second, db_sets, is a subset of the first that contains only genes associated with peaks that show evidence of differential binding between the two conditions.

You'll use the upset() function from the UpSetR package to visualize the overlap between the gene sets for the primary and treatment-resistant tumor samples.

This exercise is part of the course

ChIP-seq with Bioconductor in R

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Exercise instructions

Take a look at the full gene sets stored in the ar_sets object.
Visualize the overlap between the two groups using the upset() function.
Take a look at the genes with differential binding.
Visualize the overlap of differentially bound peaks between the two groups using the upset() function.

Hands-on interactive exercise

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

# Take a look at the full gene sets
print(___)

# Visualise the overlap between the two groups using the `upset` function
upset(fromList(___))

# Print the genes with differential binding
___(db_sets)

# Visualise the overlap of differentially bound peaks between the two groups using the `upset` function
___(fromList(___))

Edit and Run Code