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I'm currently learning about Gene Set Enrichment Analysis (GSEA) in the hopes of using it in my analysis of differentially expressed genes, and I just had a few questions about the program, specifically about GSEAPreranked, which I need cleared up.

  1. On the ranked list needed for GSEA input, should the list include all genes, or only those that pass a certain threshold of significance (i.e. fold change higher than 2, p value less than 0.05, etc.)? Ideally I'd like to sort the genes by fold change alone as I don't trust my p values as much, so should I only include genes with high fold changes?

  2. I am comparing multiple conditions of disease with different treatments. Am I correct that GSEA only compares two conditions? If this is the case should I run GSEA for each control/treatment comparison? Would this be conventional?

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You seem to refer to the GSEA provided by the Broad institute, (there are other GSEA algorithms).

1) You can provide whatever you wish, but if you want to know if those gene sets in which side of the ordered list are they, then provide all the list (of genes) you have.

2) GSEA analyize if the order of a given list distributes in certain way the elements of another (unordered) list. In your case you would need to compute the GSEA for each treatment vs control comparison.

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Use the following R package for Gene Set Enrichment analysis of RNA-seq data: seqGSEA

There is another R package (fgsea) recently published called "Fast Gene Set Enrichment Analysis" by Alexey Sergushichev.

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GSEAPreranked is a limited version of GSEA. If your sample number is enough(more than 5,6 samples in each condition), you can use GSEA version. Moreover, you can ask this question to the gsea-help google group.

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    $\begingroup$ please add links to support your answer $\endgroup$
    – Bioathlete
    Apr 12, 2018 at 14:15
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According to the GSEA documentation:

we are recommending use of the GSEAPreranked tool for conducting gene set enrichment analysis of data derived from RNA-seq experiments

Then more specifically to your question:

Based on your differential expression analysis, rank your features and capture your ranking in an RNK-formatted file. The ranking metric can be whatever measure of differential expression you choose from the output of your selected DE tool. For example, cuffdiff provides the (base 2) log of the fold change.

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