# Bayesian estimation of Differential Transcript Usage from RNA-seq data

## TL;DR

This paper introduces Bayesian models for detecting differential transcript usage (DTU) in RNA-seq data, extending existing methods with Bayesian inference to improve FDR calibration while maintaining comparable precision and recall.

## Contribution

It extends cjBitSeq to the DTU context and proposes a Bayesian version of DRIMSeq, enhancing Bayesian inference for DTU detection in RNA-seq analysis.

## Key findings

- Bayesian methods have similar precision/recall to DRIMSeq.
- Bayesian models offer better FDR calibration.
- Models perform well on simulated and real datasets.

## Abstract

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.