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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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