Using Volcano Plots in R to Visualize Microarray and RNA-seq Results

(This article was originally published at Getting Genetics Done, and syndicated at StatsBlogs.)

I've been asked a few times how to make a so-called volcano plot from gene expression results. A volcano plot typically plots some measure of effect on the x-axis (typically the fold change) and the statistical significance on the y-axis (typically the -log10 of the p-value). Genes that are highly dysregulated are farther to the left and right sides, while highly significant changes appear higher on the plot.

I've analyzed some data from GEO (GSE52202) using RNA-seq to study gene expression in motor neurons differentiated from induced pluripotent stem cells (iPSCs) derived from ALS patients carrying the C9ORF72 repeat expansion. I aligned the data, counted with featureCounts, and analyzed with DESeq2. I uploaded the results to this GitHub Gist.

Here's how you can use R to create a simple volcano plot. First, download the results file here and save it as a text file called results.txt.

After reading in the data from GitHub the next section creates a basic volcano plot. A few more lines color the points based on their fold change and statistical significance. Finally, if you have the calibrate package installed, the last line labels a few genes of interest.

Please comment on the article here: Getting Genetics Done

Tags: Bioinformatics, R, RNA-Seq, visualization