Histogram of p-values

A good diagnostic plot is the histogram of p-values. A large density of low p-values indicates many differentially expressed genes; whereas, a uniformly distributed histogram indicates there are few. Create a p-value histogram for the leukemia study.

This exercise is part of the course

Differential Expression Analysis with limma in R

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Exercise instructions

The fitted model object of the leukemia study from Chapter 2, fit2, has been loaded in your workspace. The limma package is already loaded.

Use topTable to obtain the summary statistics for every gene.
To obtain results for every gene, set the argument number to be the number of rows of fit2.
To disable sorting the results by significance level, set the argument sort.by to "none".
Use hist to create a histogram of p-values.

Hands-on interactive exercise

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

# Obtain the summary statistics for every gene
stats <- ___(fit2, number = ___, sort.by = ___)

# Plot a histogram of the p-values
___(stats[, ___])

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