  Next generation sequencing allows the identification of genes consisting of
differentially expressed transcripts, a term which usually refers to changes in
the overall expression level. A specific type of differential expression is
differential transcript usage (DTU) and targets changes in the relative within
gene expression of a transcript. The contribution of this paper is to: (a)
extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian
model which is originally designed for identifying changes in overall
expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist
model for inferring DTU. cjBitSeq is a read based model and performs fully
Bayesian inference by MCMC sampling on the space of latent state of each
transcript per gene. BayesDRIMSeq is a count based model and estimates the
Bayes Factor of a DTU model against a null model using Laplace's approximation.
The proposed models are benchmarked against the existing ones using a recent
independent simulation study as well as a real RNA-seq dataset. Our results
suggest that the Bayesian methods exhibit similar performance with DRIMSeq in
terms of precision/recall but offer better calibration of False Discovery Rate.

Genomics;Applications;Computation