Bayesian estimation of differential transcript usage from RNA-seq data

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.

Researchers from the University of Manchester 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 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. These results suggest that the Bayesian methods exhibit similar performance with DRIMSeq in terms of precision/recall but offer better calibration of False Discovery Rate.

Directed Acyclic Graph representation for the cjBitSeq model. Squares and circles represent unknown and observed/fixed quantities, respectively.

Availability – The methods are available at https://github.com/mqbssppe/cjBitSeq (cjBitSeq) and https://github.com/mqbssppe/BayesDRIMSeq (BayesDRIMSeq).