title: Gene set enrichment analysis made simple.
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.
authors.affiliation: Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD 21205, USA.
authors.forename: Rafael A
authors.initials: RA
authors.lastname: Irizarry
authors.forename: Chi
authors.initials: C
authors.lastname: Wang
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.forename: Terence P
authors.initials: TP
authors.lastname: Speed
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D010641:Phenotype