What is the best way to filter DESeq2 results? FDR or log2FoldChange? I tried both and filtering makes a huge difference in the GSEA output. Thanks!
There is an official guide available that describes how to handle RNA-seq data with GSEA.
Normalizing RNA-seq quantification to support comparisons of a feature's expression levels across samples is important for GSEA. Normalization methods (such as, TMM, geometric mean) which operate on raw counts data should be applied prior to running GSEA. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.
For RNA-Seq data, you will need normalize and filter out low count measurements, and perform other preprocessing as needed.
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version 2.3.6
Do you mean you are only inputting your significant genes (FDR or fold-filtered) into GSEA and filtering out the rest? Generally GSEA uses a list which contains all (or most) of your background of analyzed genes. See these answers one or two for instance. If however you are referring to the way to rank your genes, this answer may be of use.