Question: GSEA related to other functional analyses
gravatar for sup230
2.3 years ago by
sup23050 wrote:


I need clear explanation on how GSEA is related to other functional analyses in general. I understand that it tests whether an a priori defined gene sets appear significantly different between two phenotypes, but how is this different from GO over-representation test or KEGG pathway analysis? I remember reading something about the latter two needs a pre-specified threshold whereas some analysis does not require any prior statistical threshold but only looks at relative difference between phenotype groups.

I have done DE gene analysis using DESeq2 package to get significant gene list between two groups of phenotype (in my case, it is seizure history of yes/no, from 475 total samples and involving about 30k genes). From the significant gene list, I have done GO term over-representation test (using gprofiler, and cluster profiler) and also used GAGE package to find some KEGG pathways with significant p-adjusted values.

On the GSEA website I read that Molecular Signature Database is divided into 8 major collections and sub-collections. Is there hierarchy of these collection or are there any overlaps between these collection? To be more specific, will I get different results from significant KEGG pathways by that I would from different source?

If I end up using GSEA software on desktop or Java, should I use normalized count data or raw count data? Has anyone done GSEA from RNA-seq data with a dimension as mine (475 samples and 30k genes)? If so, any advice and brief work-flow intro would be highly appreciated!

goterm gsea rna-seq • 895 views
ADD COMMENTlink modified 2.1 years ago by Biostar ♦♦ 20 • written 2.3 years ago by sup23050

Read Tarca et al. (2013), in particular the 2nd and 3rd paragraphs from introduction should help clarify some of your doubts.

ADD REPLYlink modified 2.3 years ago • written 2.3 years ago by h.mon28k
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