R-series

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.

You already know R. Do you need Python too? An honest answer — plus a practical guide to getting your first Python environment running and your first biological data analyzed.

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.