Papastamoulis Panagiotis, Rattray Magnus
.
Stat Appl Genet Mol Biol. 2017 Nov 27;16(5-6):367-386. doi: 10.1515/sagmb-2017-0005.
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.
下一代测序技术能够识别由差异表达转录本组成的基因，该术语通常指的是整体表达水平的变化。一种特定类型的差异表达是差异转录本使用情况（DTU），其针对转录本基因内相对表达的变化。本文的贡献在于：（a）将cjBitSeq的应用扩展到DTU背景下，cjBitSeq是之前引入的一个贝叶斯模型，最初设计用于识别整体表达水平的变化；（b）提出DRIMSeq的贝叶斯版本，DRIMSeq是一种用于推断DTU的频率主义模型。cjBitSeq是一个基于 reads 的模型，通过对每个基因的每个转录本的潜在状态空间进行 MCMC 采样来执行完全贝叶斯推断。BayesDRIMSeq是一个基于计数的模型，使用拉普拉斯近似估计DTU模型相对于零模型的贝叶斯因子。使用最近的一项独立模拟研究以及一个真实的RNA-seq数据集，将所提出的模型与现有模型进行基准测试。我们的结果表明，贝叶斯方法在精度/召回率方面与DRIMSeq表现出相似的性能，但在错误发现率的校准方面表现更好。
