BADER: Bayesian analysis of differential expression in RNA sequencing data

TL;DR

BADER is a Bayesian method for RNA-seq differential expression analysis that efficiently accounts for uncertainty, improves detection power, and seamlessly integrates with gene set enrichment analysis.

Contribution

It introduces a Bayesian framework for RNA-seq differential expression that enhances detection accuracy and supports downstream enrichment analysis.

Findings

Higher power in detecting differentially expressed genes.

Effective integration with gene set enrichment analysis.

Open-source R package available on Bioconductor.

Abstract

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.