Yu Fang, Chen Ming-Hui, Kuo Lynn, Huang Peng, Yang Wanling
Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska 68198-4350,
Biometrics. 2011 Mar;67(1):142-50. doi: 10.1111/j.1541-0420.2010.01447.x.
Expressed sequence tag (EST) sequencing is a one-pass sequencing reading of cloned cDNAs derived from a certain tissue. The frequency of unique tags among different unbiased cDNA libraries is used to infer the relative expression level of each tag. In this article, we propose a hierarchical multinomial model with a nonlinear Dirichlet prior for the EST data with multiple libraries and multiple types of tissues. A novel hierarchical prior is developed and the properties of the proposed prior are examined. An efficient Markov chain Monte Carlo algorithm is developed for carrying out the posterior computation. We also propose a new selection criterion for detecting which genes are differentially expressed between two tissue types. Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method.
表达序列标签（EST）测序是对来自特定组织的克隆cDNA进行的单通道测序读取。不同无偏cDNA文库中独特标签的频率用于推断每个标签的相对表达水平。在本文中，我们针对具有多个文库和多种组织类型的EST数据，提出了一种具有非线性狄利克雷先验的分层多项模型。开发了一种新颖的分层先验，并研究了所提出先验的性质。开发了一种高效的马尔可夫链蒙特卡罗算法来进行后验计算。我们还提出了一种新的选择标准，用于检测两种组织类型之间哪些基因存在差异表达。通过几次模拟证明，我们具有新基因选择标准的新方法具有较低的假阴性和假阳性率。使用一个真实的EST数据集来推动和说明所提出的方法。
