An Improved Method for Computing Q-Values When the Distribution of Effect Sizes Is Asymmetric

• Activity Number: 306
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Megan Orr*+ and Peng Liu and Dan Nettleton
• Companies: North Dakota State University and Iowa State University and Iowa State University
• Keywords: gene expression ; differential expression ; false discovery rate ; multiple testing

Abstract

Asymmetry is frequently observed in the empirical distribution of test statistics that results from the analysis of gene expression experiments. This asymmetry indicates an asymmetry in the distribution of effect sizes. A common method for identifying differentially expressed (DE) genes in a gene expression experiment while controlling false discovery rate (FDR) is Storey's q-value method. This method ranks genes based solely on the p-values from each gene in the experiment. We propose a method that alters and improves upon the q-value method by taking the sign of the test statistics, in addition to the p-values, into account. Through two simulation studies (one involving independent normal data and one involving microarray data), we show that the proposed method, when compared to the traditional q-value method, generally provides a better ranking for genes as well as a higher number of truly DE genes declared to be DE, while still adequately controlling FDR. We illustrate the proposed method by analyzing data from an experiment involving gene expressions in maize leaves.