R

Your DESeq2 results table has thousands of rows, and sorting it in Excel tells you almost nothing about your experiment. Here is how to build volcano plots and MA plots in R with ggplot2 — and how to read what they are actually telling you.

You ran DESeq2 and got your list of differentially expressed genes. Now what? Here is how to use clusterProfiler to find out which biological pathways are actually changing in your experiment — with real code, real output, and real plots.

An honest head-to-head comparison of DESeq2 and edgeR. Learn the differences in their normalization, statistical tests, and run-times, and see if they actually give different biological answers.

A step-by-step practical guide to performing differential gene expression analysis in R using DESeq2. Learn how to load counts, run the analysis, and interpret your results table.

Before you run DESeq2, you need to understand what those numbers in your count matrix actually represent. This post explains what RNA-seq count data is, why raw counts are misleading, and exactly what format DESeq2 expects.