Intro to Differential Expression Analysis - ccsstudentmentors/tutorials GitHub Wiki

Now that you have counted how many reads aligned to each gene for each sample, you can perform your gene-level differential expression testing!

There are several possible tools you can use at this step, but we recommend using either edgeR or DESeq2.

Honestly, both of these programs are great. They both use relatively similar statistical models to analyze the data. Many people run both analyses and simply use the differentially expressed genes found by both programs. So proceed however you see fit.

If you are looking for the easiest one to start with, I'd go with DESeq2. If you followed my earlier advice and used geneIDs for the read counting step, then running DESeq2 requires very, very few lines of code.

Both analyses start with the set of files containing the read counts per gene for each sample.

So, just head to the section for DESeq2 or EdgeR.