An improved method for computing q-values when the distribution of effect sizes is asymmetric.

ABSTRACT:

Motivation

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.

Results

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

SUBMITTER: Orr M 

PROVIDER: S-EPMC4609005 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature