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. 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.Comment: Revised version, accepted to Statistical Applications in Genetics and Molecular Biolog

BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data

The BayesBinMix package offers a Bayesian framework for clustering binary data with or without missing values by fitting mixtures of multivariate Bernoulli distributions with an unknown number of components. It allows the joint estimation of the number of clusters and model parameters using Markov chain Monte Carlo sampling. Heated chains are run in parallel and accelerate the convergence to the target posterior distribution. Identifiability issues are addressed by implementing label switching algorithms. The package is demonstrated and benchmarked against the Expectation-Maximization algorithm using a simulation study as well as a real dataset.Comment: Accepted to the R Journal. The package is available on CRAN: https://CRAN.R-project.org/package=BayesBinMi

Reversible Jump MCMC in mixtures of normal distributions with the same component means

The Bayesian estimation of a special case of mixtures of normal distributions with an unknown number of components is considered. More specifically, the case where some components may have identical means is studied. The standard Reversible Jump MCMC algorithm for the estimation of a normal mixture model consisting of components with distinct parameters naturally fails to give precise results in the case where (at least) two of the mixture components have equal means. In particular, this algorithm either tends to combine such components resulting in a posterior distribution for their number having mode at a model with fewer components than those of the true one, or overestimates the number of components. This problem is overcome by defining-for every number of components-models with different number of parameters and introducing a new move type that bridges these competing models. The proposed method is applied in conjunction with suitable modifications of the standard split-combine and birth-death moves for updating the number of components. The method is illustrated by using two simulated datasets and the well-known galaxy dataset.