%0 Journal Article
%T Testing gene set enrichment for subset of genes: Sub-GSE
%A Xiting Yan
%A Fengzhu Sun
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-362
%X In this paper, we develop a novel method, termed Sub-GSE, which measures the enrichment of a predefined gene set, or pathway, by testing its subsets. The application of Sub-GSE to two simulated and two real datasets shows Sub-GSE to be more sensitive than previous methods, such as GSEA, GSA, and SigPath, in detecting gene sets assiated with a phenotype of interest. This is particularly true for cases in which only a fraction of the genes in the gene set are associated with the phenotypes. Furthermore, the application of Sub-GSE to two real data sets demonstrates that it can detect more biologically meaningful gene sets than GSEA.We developed a new method to measure the gene set enrichment. Applications to two simulated datasets and two real datasets show that this method is sensitive to the associations between gene sets and phenotype. The program Sub-GSE can be downloaded from http://www-rcf.usc.edu/~fsun webcite.Genome-wide gene expression profiling using microarray technologies has been ubiquitously used in biological research. An important problem is to identify gene sets that are significantly changed under a certain treatment (for example, two different cell lines or tissues or the same cell line under different conditions). A gene set is basically a group of genes with related functions, e.g., genes in a biological process or in the same complex. There are a variety of ways by which genes, and, ultimately, gene sets may be defined. For example, gene sets can be defined according to the information provided by several databases, such as GeneOntology [1], KEGG [2], Biocarta http://www.biocarta.com webcite, and Pfam [3]. Gene sets may also be defined by cytogenetic bands, by region of genomic sequence or by establishing the functional relationships among them. Importantly, by using a gene set-based approach, a high power can potentially be achieved for detecting differentially expressed gene sets by integrating expression changes of genes inside the same gene se
%U http://www.biomedcentral.com/1471-2105/9/362