A Comparative Multiple Simulation Study for Parametric and Nonparametric Methods in the Identification of Differentially Expressed Genes

Abstract

RNA-seq data simulated from a negative binomial distribution, sampled without replacement, or modified from read counts were analyzed to compare differential gene expression analysis methods in terms of false discovery rate control and power. The goals of the study were to determine optimal sample sizes/proportions of differential expression needed to adequately control false discovery rate and which differential gene expression methods performed best with the given simulation methods. Parametric tools like edgeR and limma-voom tended to be conservative when controlling false discovery rate from a negative binomial distribution as the proportion of differential expression increased. For the nonparametric simulation methods, many differential gene expression methods did not adequately control false discovery rate and results varied greatly when different reference data sets were used for simulations.