A FC-GSEA Approach to Identify Significant Gene-Sets Using Microarray Gene Expression Data

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene-sets showing differential expression between two groups. In particular, unlike other previous approaches, it enables us to uncover the biological meanings of the identified gene-sets in an elegant way by providing a unified analytical framework that employs a priori known biological knowledge along with gene expression profiles during the analysis procedure. For original GSEA, all the genes in a given dataset are ranked by the signal-to-noise ratio of their microarray expression profiles between two groups and then further analyses are proceeded. Despite of its impressive results in previous studies, however, the gene ranking by the signal-to-noise ratio makes it hard to consider both highly up-regulated genes and highly down-regulated genes at a time as significant genes, which may not reflect such situations as incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the FC-GSEA method where the Fisher's criterion is employed for gene ranking instead of the signal-to-noise ratio, and evaluate its effects made in Leukemia related pathway analyses.