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- DESEQ2 will take in raw count data as input, along with sample metadata. It has convenient readers to read in kallisto and htseq counts, but really you can read in your own gene expression matrix as well.
- The design formula tells DESEQ2 what condition you are testing: ~condition would test for differences among conditions; ~batch + condition would test for differences in condition while controlling for batch. DESEQ2 vignette gives you details on how you can tweak this design formula.
- DESEQ2 will perform normalization, calculate dispersion, and calculate results will contain log2 fold changes , p values and adjusted p values for every gene.
- Impose cutoffs on fold change and adjusted p value to get significantly differentially expressed genes.
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