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GSEA on preranked list with weighted enrichment statistic (exponent=1) in R

Hey there!

I'm currently working on a functional analysis of RNAseq data. I've used the GSEA tool by the Broad Institute to obtain quick results and now I'm trying to reproduce the results with R for visualization purposes.

The fgsea is amazingly fast but apparently there is no way to calculate a weighted enrichment statistic which is why my results differ from the original analysis with the GSEA tool. Then I've looked at the DOSE tool as this allows for a weighted enrichment statistic. However, here I can't use the MSigDatabase gene set collection. ClusterProfiler doesn't seem to provide a GSEA preranked option.

Did I miss a function of these packages that would allow me for a GSEA on a preranked list with the MSigDatabase gene set collection and at the same time gives me the option to calculate a weighted enrichment statistic? Is there any other way to solve this problem?

Thank you very much for help in advance!!

Cheers, Moritz

fgsea dose rna-seq
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updated by alserg alserg • written by cmgraef • cmgraef

Any ideas? Thank you so much!

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written by cmgraef • cmgraef

1 answer

Could you clarify what exactly you can't reproduce? Both DOSE and ClusterProfiler use fgsea as a backend, so if it's possible with DOSE it should work fine for fgsea.

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written by alserg alserg

alserg sorry for hijacking this thread with an unrelated question but I do not think this one here merits a separate thread. What is your opinion on FDR correction? Standard GSEA uses FDR of 0.25 as cutoff as explained in their manual. Can you comment if this is also appropriate for fgsea or if the more standard 0.1 or 0.05 should be chosen.

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written by ATpoint ATpoint

Actually, that's an important question. Broad GSEA recommendation for 0.25 is due to their ad-hoc correction procedure they developed because they didn't have enough accuracy for standard correction procedures. In fgsea we use standard Benjamini-Hochberg, so standard thresholds can be used. I even recommend to go for pathways with padj<0.01, as GSEA is known to be oversensitive.

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written by alserg alserg

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