Integrative gene set enrichment analysis utilizing isoform‐specific expression. Issue 6 (4th June 2017)

• Record Type: Journal Article
• Authors: Li, Lie; Wang, Xinlei; Xiao, Guanghua; Gazdar, Adi
• Is Part Of: Genetic epidemiology. Volume 41:Issue 6(2017)
• Page Start: 498
• Page End: 510
• Publication Date: 2017-06-04
• Subjects: GLM -- fixed effect -- integrative GSEA -- pathway analysis -- random effects -- RNA‐seq -- score statistic; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22052
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: Gene set enrichment analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. In the past, many studies have shown that GSEA is a very useful bioinformatics tool that plays critical roles in the innovation of disease prevention and intervention strategies. Despite its tremendous success, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results. Further, in the wake of next generation sequencing technologies, it has been made possible to measure genome‐wide isoform‐specific expression levels, calling for innovations that can utilize the unprecedented resolution. Currently, enormous amounts of data have been created from various RNA‐seq experiments. All these give rise to a pressing need for developing integrative methods that allow for explicit utilization of isoform‐specific expression, to combine multiple enrichment studies, in order to enhance the power, reproducibility, and interpretability of the analysis. We develop and evaluate integrative GSEA methods, based on two‐stage procedures, which, for the first time, allow statistically efficient use of isoform‐specific expression from multiple RNA‐seq experiments. Through simulation and real data analysis, we show that our methods can greatly improve the performance in identifying essential gene sets compared to existing methods that can only use gene‐level expression.