Journal
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
Volume 16, Issue 5-6, Pages 387-405Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/sagmb-2017-0005
Keywords
alternative splicing; false discovery rate; Laplace approximation; MCMC; within gene transcript expression
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Funding
- MRC [MR/M02010X/1]
- BBSRC [BB/J009415/1]
- EU [305626]
- BBSRC [BB/J009415/1] Funding Source: UKRI
- MRC [MR/M02010X/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/J009415/1] Funding Source: researchfish
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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.
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