Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

We describe a powerful analytical method called Gene Set Enrichment Analysis for interpreting gene expression data

Aravind Subramanian; Pablo Tamayo; Vamsi K. Mootha; Sayan Mukherjee; Benjamin L. Ebert; Michael A. Gillette; Amanda Paulovich; Scott L. Pomeroy; Todd R. Golub; Eric S. Lander; Jill P. Mesirov


Scholarcy highlights

  • Genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge
  • We explored the ability of Gene Set Enrichment Analysis to provide biologically meaningful insights in six examples for which considerable background information is available
  • We generated mRNA expression profiles from lymphoblastoid cell lines derived from 15 males and 17 females and sought to identify gene sets correlated with the distinctions ‘‘maleϾfemale’’ and ‘‘femaleϾmale.’’
  • We previously introduced GSEA to analyze such data at the level of gene sets
  • The method was initially used to discover metabolic pathways altered in human diabetes and was subsequently applied to discover processes involved in diffuse large B cell lymphoma, nutrient-sensing pathways involved in prostate cancer, and in comparing the expression profiles of mouse to those of humans
  • We have created an initial molecular signature database consisting of 1,325 gene sets, including ones based on biological pathways, chromosomal location, upstream cis motifs, responses to a drug treatment, or expression profiles in previously generated microarray data sets

Need more features? Save interactive summary cards to your Scholarcy Library.