Bayesian estimation of Dierential Transcript Usage from RNA-seq data

Panagiotis Papastamoulis, Magnus Rattray

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalStatistical Applications in Genetics and Molecular Biology
Early online date1 Nov 2017
Publication statusPublished - 2017


  • MCMC
  • Laplace approximation
  • alternative splicing
  • within gene transcript expression
  • false discovery rate


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