A semi-parametric Bayesian approach, iSBA, for differential expression analysis of RNA-seq data

RNA sequencing (RNA-seq) technologies have been popularly applied to study gene expression in recent years. Identifying differentially expressed (DE) genes across treatments is one of the major steps in RNA-seq data analysis. Most differential expression analysis methods rely on parametric assumptions, and it is not guaranteed that these assumptions are appropriate for real data analysis. In this paper, we develop a semi-parametric Bayesian approach for differential expression analysis. More specifically, we model the RNA-seq count data with a Poisson-Gamma mixture model, and propose a Bayesian mixture modeling procedure with a Dirichlet process as the prior model for the distribution of fold changes between the two treatment means. We develop Markov chain Monte Carlo (MCMC) posterior simulation using Metropolis Hastings algorithm to generate posterior samples for differential expression analysis while controlling false discovery rate. Simulation results demonstrate that our proposed method outperforms other popular methods used for detecting DE genes.