GSEA analysis using DEG

[Sarah P]

Hi there,

thank you for providing the GSEA software.

I am trying to use GSEA analysis using my DEG data from RNA-seq. 

I currently have DEG in which the genes do not show significance based on adjusted p-values (corrected for multiple comparisons). I was wondering if I can still use this data for GSEA analysis even though the difference between phenotypes can be considered as not real biological difference based on the adjusted p-values.

 

From my understanding of the description of the algorithm on your website, it seems like only using those DEGs that are considered significant by adjusted p-value can potentially remove genes that are actually different thus reducing the statistical power.

Is it correct to use all the genes from my DEG data (even though they are not significant based on adjusted p-value) for GSEA analysis?

I will be looking forward to hearing back from you.

Thank you in advance,

Sarah

[Anthony Castanza]

Hi Sarah,

 

A study similar to this was, in fact, one of the foundational studies for the GSEA algorithm (Mootha et al. (2003) Nat Genet 34(3): 267-73), where the proto-GSEA algorithm was used to detect pathway level changes within a dataset that, using traditional single gene differential expression methods, did not have significantly differentially expressed genes.

 

To run GSEA on your dataset, you would want to include either normalized expression data (i.e. normalized counts) if wanting to use standard GSEA, or ranking information (i.e. log2FC values) if wanting to use GSEA Preranked, for all expressed genes without applying significance filtering.

 

Hope that helps, let us know if you have any more questions

 

-Anthony

 

Anthony S. Castanza, PhD

Curator, Molecular Signatures Database

Mesirov Lab, Department of Medicine

University of California, San Diego

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