A Bayesian model selection approach for identifying differentially expressed transcripts from RNA-Seq data

TL;DR

This paper introduces a Bayesian joint model for estimating transcript expression and differential expression from RNA-Seq data, improving accuracy and efficiency over existing methods.

Contribution

It proposes a hierarchical Bayesian framework with conjugacy, enabling simultaneous estimation of expression levels and differential expression, and develops efficient MCMC samplers including a collapsed Gibbs sampler.

Findings

The collapsed Gibbs sampler outperforms reversible jump MCMC.

The model achieves accurate inference on synthetic datasets.

Application to real data demonstrates practical utility.

Abstract

Recent advances in molecular biology allow the quantification of the transcriptome and scoring transcripts as differentially or equally expressed between two biological conditions. Although these two tasks are closely linked, the available inference methods treat them separately: a primary model is used to estimate expression and its output is post-processed using a differential expression model. In this paper, both issues are simultaneously addressed by proposing the joint estimation of expression levels and differential expression: the unknown relative abundance of each transcript can either be equal or not between two conditions. A hierarchical Bayesian model builds upon the BitSeq framework and the posterior distribution of transcript expression and differential expression is inferred using Markov Chain Monte Carlo (MCMC). It is shown that the proposed model enjoys conjugacy for fixed dimension variables, thus the full conditional distributions are analytically derived. Two samplers are constructed, a reversible jump MCMC sampler and a collapsed Gibbs sampler, and the latter is found to perform best. A cluster representation of the aligned reads to the transcriptome is introduced, allowing parallel estimation of the marginal posterior distribution of subsets of transcripts under reasonable computing time. The proposed algorithm is benchmarked against alternative methods using synthetic datasets and applied to real RNA-sequencing data. Source code is available online (https://github.com/mqbssppe/cjBitSeq).