GSEA preranking metric for RNA Seq

Question

[bipin]

I came across multiple posts regarding the pre-ranking metric for GSEA when using RNA seq data. However, there doesn't seem to be a consensus.

Some of the metrics I came across are:-

• sign of log fold change * -log10(p-value[not adjusted p-val])
• logfc shrink values from DESeq2
• Inbuilt signal2noise from GSEA. However, this cannot be used in case of <3 replicates.

What metric do you use for ranking the genes or you know is widely used?

[Balasubramaniam]

How to deal with the data that has some genes with 0 log2fold change? Is it a good idea to prefilter before ranking as they are essentially not modulated by the treatment for example?

Answer 1

[Kevin Blighe]

Edit 31st July, 2019: I gave my original answer (below) assuming that you were referring to the general process of gene enrichment (or 'gene-set enrichment analysis'), and not that you were referring to GSEA, the Broad Institute's PROGRAM that hijacked the term GSEA

GSEA (the Broad Institute program) permits a ranked list of genes, as does topGO (R), fGSEA (see my comment below), and other enrichment programs - there are too many programs.

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It makes sense that there is no consensus, as there are countless ways to do this. My own recommendation would be to:

1. Set an adjusted P value cut-off
2. Rank genes based on absolute log (base 2) fold change

I believe the most widely used method is to just set an adjusted P value and log (base 2) fold change cut-off, and to then 'throw' the resulting gene list into the GSEA without any ranking.

The lack of consensus on a proper filtering strategy may in part be due to the fact that a substantial proportion of researchers do not pay much attention to the results of GSEA. GSEA results would certainly never stand as the sole evidence in a clinical test, neither would they be sufficient evidence on which conclusions could be made in most reputable journals.

Kevin

[asalimih]

what would be your answer if the question was about the GSEA program of Broad Institute?

[Kevin Blighe]

Rank by fold-change.

[Kevin Blighe]

For others landing here, note also these excellent postings on using fGSEA:

• https://stephenturner.github.io/deseq-to-fgsea/
• https://davetang.org/muse/2018/01/10/using-fast-preranked-gene-set-enrichment-analysis-fgsea-package/

[Juan Cordero]

How bad would it be to use the -log10(p-value) * log2(foldchange)? Note I mean log2(foldchange) and not its sign.

[Kevin Blighe]

No issue - at least you maintain directionality, in that case.

[V_Vibes]

I have a query regarding the analysis of GSEA Results. I have used GSEA to obtain the dysregulated KEGG pathways. Now, I want to rank the dysregulated KEGG pathways. So, is it logical to use NES * (-log10 Nominal p-value) or NES * (-log10 FDR q-value) for ranking the KEGG pathways?