Gene set enrichment analysis made simple

Rafael A. Irizarry, Chi Wang, Yun Zhou, Terence P. Speed

Research output: Contribution to journalArticlepeer-review

Abstract

Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, choose an appropriate cut-off, and create a list of candidate genes. This approach has been criticised for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, the most popular method, gene set enrichment analysis (GSEA), seems overly complicated. Furthermore, GSEA is based on a statistical test known for its lack of sensitivity. In this article we compare the performance of a simple alternative to GSEA. We find that this simple solution clearly outperforms GSEA. We demonstrate this with eight different microarray datasets.

Original languageEnglish (US)
Pages (from-to)565-575
Number of pages11
JournalStatistical Methods in Medical Research
Volume18
Issue number6
DOIs
StatePublished - Dec 2009
Externally publishedYes

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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