Differentially expression analysis



I'm performing a DE analysis on 5 groups, 1 control and 4 mutants.

The pipeline so far went: Raw data --> Trimmomatic --> Bowtie2 --> FeatureCounts --> Limma-Voom. I got the Limma-Voom report with the graphs and the 4 contrasts I set up: Control-Mutant1, Control-Mutant2, Control-Mutant3, Control-Mutant4. Everything is great, I even extracted the normalised count file from Limma-Voom.

However I want the differential gene expression tables with fold difference in expression. I can obtain that by processing the normalised counts and this is the blurry bit at the moment.

Question1: Do I need to calculate this separately for the 4 contrasts and basically get 4 tables at the end (each having the p-values and the log fold change)? I know this can be done in R with DESeq but can't I just extract it from Limma whilst doing the graphs?

Question2: Just to make sure I have the correct start here, all I need is the normalised counts, right?




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