RNA-sequencing (RNA-seq) technologies have revolutionized the way agricultural biologists study gene expression as well as generated a tremendous amount of data waiting for analysis. Detecting differentially expressed genes is one of the fundamental steps in RNA-seq data analysis. In this paper, we model the count data from RNA-seq experiments with a Poisson-Gamma hierarchical model, or equivalently, a negative binomial (NB) model. We derive a semi-parametric Bayesian approach with a Dirichlet process as the prior model for the distribution of fold changes between the two treatment means. An inference strategy using Gibbs algorithm is developed for differential expression analysis. The results of several simulation studies show that our proposed method outperforms other methods including the popularly applied edgeR and DESeq methods. We also discuss an application of our method to a dataset that compares gene expression between bundle sheath and mesophyll cells in maize leaves.

中文翻译：

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RNA-seq数据差异表达分析的半参数贝叶斯方法。

RNA测序（RNA-seq）技术彻底改变了农业生物学家研究基因表达的方式，并产生了大量等待分析的数据。检测差异表达的基因是RNA-seq数据分析的基本步骤之一。在本文中，我们使用Poisson-Gamma分层模型或等效的负二项式（NB）模型对来自RNA-seq实验的计数数据进行建模。我们推导了一种半参数贝叶斯方法，该方法采用Dirichlet过程作为两种处理方式之间倍数变化分布的先验模型。开发了使用吉布斯算法的推理策略，用于差异表达分析。若干仿真研究的结果表明，我们提出的方法优于包括流行应用的edgeR和DESeq方法在内的其他方法。