  Identifying differentially expressed genes from RNA sequencing data remains a
challenging task because of the considerable uncertainties in parameter
estimation and the small sample sizes in typical applications. Here we
introduce Bayesian Analysis of Differential Expression in RNA-sequencing data
(BADER). Due to our choice of data and prior distributions, full posterior
inference for BADER can be carried out efficiently. The method appropriately
takes uncertainty in gene variance into account, leading to higher power than
existing methods in detecting differentially expressed genes. Moreover, we show
that the posterior samples can be naturally integrated into downstream gene set
enrichment analyses, with excellent performance in detecting enriched sets. An
open-source R package (BADER) that provides a user-friendly interface to a C++
back-end is available on Bioconductor.
