Modeling overdispersion heterogeneity in differential expression analysis using mixtures.

Next-generation sequencing technologies now constitute a method of choice to measure gene expression. Data to analyze are read counts, commonly modeled using negative binomial distributions. A relevant issue associated with this probabilistic framework is the reliable estimation of the overdispersion parameter, reinforced by the limited number of replicates generally observable for each gene. Many strategies have been proposed to estimate this parameter, but when differential analysis is the purpose, they often result in procedures based on plug-in estimates, and we show here that this discrepancy between the estimation framework and the testing framework can lead to uncontrolled type-I errors. Instead, we propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Three consistent statistical tests are developed for differential expression analysis. We show through a wide simulation study that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it reaches the nominal value for the type-I error, while keeping elevate discriminative power between differentially and not differentially expressed genes. The method is finally illustrated on prostate cancer RNA-Seq data.