EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments
EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments
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DOI：
10.1093/bioinformatics/btt087
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发表时间：
2013-04-15
期刊：
影响因子：
5.8
通讯作者：
Kendziorski, Christina
中科院分区：
文献类型：
--
作者：
Leng, Ning;Dawson, John A.;Kendziorski, Christina
动机：信使RNA表达在正常发育和分化以及疾病的表现中是重要的。RNA测序实验允许在全基因组范围内识别差异表达（DE）基因及其相应的亚型。然而，需要采用统计方法来确保进行准确的鉴定。存在许多用于鉴定DE基因的方法，但可用于鉴定DE亚型的方法少得多。当异构体DE感兴趣时，研究者通常直接应用基因水平（基于计数）方法来估计异构体计数。不建议这样做。简而言之，估计同种型表达对于某些组的同种型相对简单，但对于其他组更具挑战性。这导致估计的不确定性在不同亚型组之间变化。计数为基础的方法没有被设计来适应这种不同的不确定性，因此，应用它们的异构体推理结果在降低功率的某些类别的异构体和增加的错误发现为others.Results：利用经验贝叶斯方法的优点，我们已经开发了EBSeq识别DE异构体在RNA-seq实验比较两个或两个以上的生物条件。结果表明EBSeq用于鉴定DE同种型的能力和性能显著提高。EBSeq也被证明是鉴定DE基因的稳健方法。
Motivation: Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others.Results: Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes.