GSEA following DESeq2

Question

[sup230]

Hi Biostars

I am trying to use GSEA GUI from broad institute to do gene set analysis on RNA seq data. I have been reading many posts and researched GSEA website about the DEseq2->GSEA workflow and here is what I understood from it.

So if I used DEseq2 package to get a list of DE genes and if I would like to run preranked GSEA function,

1. get a table with the list of genes on the row and log2FoldChange, p-value, and adj p-values on the column
2. order the gene list by a metric -log10(p-value)*sign(logFC) and create rank file (.rnk) in R
3. load this file to GSEA software and run GSEApreranked after choosing required and basic fields (making sure enrichment statistic is "classic")

Am I on the right track in understanding this workflow?

[alserg]

As you use R pipeline, I recommend to use R implementation of pre-ranked GSEA: either through fgsea package or clusterprofiler/DOSE interface. It's the same method but much faster.

Answering your question, I normally use stat column of DESeq2 results, but your metric should also work fine.

Answer 1

[GouthamAtla]

Looks good. I would always recommend to do in your own way and get feedback. The -log10(p-value)*sign of the fold change is been used in published papers,so there is no problem with it.

[sup230]

thanks for clarifying! Could you elaborate a little bit on taking p-value (unadjusted) vs. FDR-adjusted pvalue for metric calculation, and if I use metric =(sign of log2FC)*(-log10(pval)), what is the appropriate parameter for scoring scheme (classic vs weighted (p=1))? How should I decide to use classic or weighted scheme? Thanks for your input in advance!

[bioinfo456]

are you doing this for the purpose of cross checking the significance of the set of genes selected by DESeq2?