Affiliation:
1. Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA
Abstract
Background Meta-analysis is a popular approach for combining results from multiple studies investigating the same questions. Meta-analysis has gained wide popularity in genomic analysis due to the availability of large volumes of genomic study results from public databases. In genomic meta-analysis, researchers, often, tend to combine p-values related to significance testing of a gene from multiple studies where thousands of genes are tested simultaneously. The traditional p-value combination approaches aim to find genes which are differentially expressed in at least one of studies. An alternative form of meta-analysis has, recently, gained popularity where the aim is to find genes that are consistently differentially expressed in a large number, possibly a majority, of studies. An approach based on weighted ordered p-values (WOP) has been developed, in the recent past, to perform the latter type of meta-analysis. Methods In this article, we discuss the limitations of the WOP meta-analysis method due to its adherence to the standard null distributional assumptions of classical meta-analysis that can lead to incorrect significance testing results. Moreover, we propose a robust meta-analysis method for simultaneous significance testing of multitude of genes that improves the WOP approach using an empirical modification. Results Through simulation studies, we demonstrate the superiority of our proposed method over the existing WOP meta-analysis by substantially reducing false discoveries of significant genes and controlling type-I error rates especially in the presence of unobserved confounding variables. We illustrate the utility of our proposed method through a variety of meta-analysis of genomic studies in different diseases.
Subject
General Earth and Planetary Sciences