Zhao Lili, Wu Weisheng, Feng Dai, Jiang Hui, Nguyen XuanLong
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
Department of Computational Medicine & Bioinformatics, University of Michigan.
Bayesian Anal. 2018 Jun;13(2):411-436. doi: 10.1214/17-BA1055. Epub 2017 Apr 8.
The analysis of RNA-Seq data has been focused on three main categories, including gene expression, relative exon usage and transcript expression. Methods have been proposed independently for each category using a negative binomial (NB) model. However, counts following a NB distribution on one feature (e.g., exon) do not guarantee a NB distribution for the other two features (e.g., gene/transcript). In this paper we propose a family of Negative Binomial models, which integrates the gene, exon and transcript analysis under a coherent NB model. The proposed model easily incorporates the uncertainty of assigning reads to transcripts and simplifies substantially the estimation for the relative usage. We developed simple Gibbs sampling algorithms for the posterior inference by exploiting fully tractable closed-forms of computation via suitable conjugate priors. The proposed models were investigated under extensive simulations. Finally, we applied our model to a real data set.
RNA测序数据的分析主要集中在三个主要类别上，包括基因表达、外显子相对使用情况和转录本表达。针对每个类别，已经独立提出了使用负二项式（NB）模型的方法。然而，一个特征（例如外显子）上遵循NB分布的计数并不能保证其他两个特征（例如基因/转录本）也遵循NB分布。在本文中，我们提出了一族负二项式模型，该模型在一个连贯的NB模型下整合了基因、外显子和转录本分析。所提出的模型很容易纳入将 reads 分配到转录本的不确定性，并大大简化了相对使用情况的估计。我们通过利用合适的共轭先验的完全可处理的封闭形式计算，开发了用于后验推断的简单吉布斯采样算法。在广泛的模拟下对所提出的模型进行了研究。最后，我们将我们的模型应用于一个真实数据集。
