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.

中文翻译：

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从 RNA-seq 数据中对差异转录本使用的贝叶斯估计

下一代测序允许鉴定由差异表达转录物组成的基因，该术语通常指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 (DTU)，它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 的使用扩展到 DTU 上下文，这是先前引入的贝叶斯模型，最初设计用于识别整体表达水平的变化；(b) 提出了 DRIMSeq 的贝叶斯版本，常客用于推断 DTU 的模型。cjBitSeq 是一个基于读取的模型，通过 MCMC 采样对每个基因的每个转录本的潜在状态空间进行完全贝叶斯推理。BayesDRIMSeq 是一个基于计数的模型，它使用拉普拉斯近似法针对空模型估计 DTU 模型的贝叶斯因子。所提出的模型使用最近的独立模拟研究以及真实的 RNA-seq 数据集对现有模型进行了基准测试。我们的结果表明，贝叶斯方法在精度/召回率方面表现出与 DRIMSeq 相似的性能，但提供了更好的错误发现率校准。