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

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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.**Getting Genetics Done**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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, you’ll need to install the devtools package so that you can install my Tmisc package directly from GitHub (I haven’t submitted it to CRAN). There’s a function in Tmisc called

**read.gist()**, which reads data directly from Github Gists by specifying the GitHub Gist ID (

*be careful with this…*).

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.

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