A hierarchical Bayesian model for RNA-Seq data

In the last few years, RNA-Seq has become a popular choice for highthroughput studies of gene expression, revealing its potential to overcome microarrays and become the new standard for transcriptional profiling. At a gene-level, RNA-Seq yields counts rather than continuous measures of expression, leading to the need for novel methods to deal with count data in high-dimensional problems. We present a hierarchical Bayesian approach to the modeling of RNA-Seq data. The model accounts for the difference in the total number of counts in the different samples (sequencing depth), as well as for overdispersion, with no need to transform the data prior to the analysis. Using an MCMC algorithm, we identify differentially expressed genes, showing promising results both on simulated and real data, compared to those of edgeR and DESeq (state-of-the-art algorithms for RNA-Seq data analysis).