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

Abstract

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Fig. 1.

A GSEA overview illustrating the method. (A) An expression data set sorted by correlation with phenotype, the corresponding heat map, and the “gene tags,” i.e., location of genes from a set S within the sorted list. (B) Plot of the running sum for S in the data set, including the location of the maximum enrichment score (ES) and the leading-edge subset.

Fig. 2.

Original (4) enrichment score behavior. The distribution of three gene sets, from the C2 functional collection, in the list of genes in the male/female lymphoblastoid cell line example ranked by their correlation with gender: S1, a set of chromosome X inactivation genes; S2, a pathway describing vitamin c import into neurons; S3, related to chemokine receptors expressed by T helper cells. Shown are plots of the running sum for the three gene sets: S1 is significantly enriched in females as expected, S2 is randomly distributed and scores poorly, and S3 is not enriched at the top of the list but is nonrandom, so it scores well. Arrows show the location of the maximum enrichment score and the point where the correlation (signal-to-noise ratio) crosses zero. Table 1 compares the nominal P values for S1, S2, and S3 by using the original and new method. The new method reduces the significance of sets like S3.

Fig. 3.

Leading edge overlap for p53 study. This plot shows the ras, ngf, and igf1 gene sets correlated with P53^– clustered by their leading-edge subsets indicated in dark blue. A common subgroup of genes, apparent as a dark vertical stripe, consists of MAP2K1, PIK3CA, ELK1, and RAF1 and represents a subsection of the MAPK pathway.