A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data.
A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data.
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基于假设检验的 RNA-Seq 数据归一化和差异表达分析方法
DOI：
10.1371/journal.pone.0169594
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Li H
中科院分区：
文献类型：
--
作者：
Zhou Y;Wang G;Zhang J;Li H
新一代测序技术使 RNA 测序 (RNA-seq) 成为测量基因表达水平的流行选择。为了减少基因表达测量的噪音并在几种条件或样本之间进行比较，标准化是调整不同样本测序深度和其他不需要的技术影响的重要步骤。在本文中，我们利用管家基因的现有知识开发了一种新颖的全局标度标准化方法。我们从假设检验的角度提出问题，并找到一个最佳比例因子，最大限度地减少经验误差和名义 I 类误差之间的偏差。将我们的方法应用于各种模拟研究和真实例子，我们证明它在检测差异表达基因方面比最先进的替代方案更准确和稳健。
Next-generation sequencing technologies have made RNA sequencing (RNA-seq) a popular choice for measuring gene expression level. To reduce the noise of gene expression measures and compare them between several conditions or samples, normalization is an essential step to adjust for varying sample sequencing depths and other unwanted technical effects. In this paper, we develop a novel global scaling normalization method by employing the available knowledge of housekeeping genes. We formulate the problem from the hypothesis testing perspective and find an optimal scaling factor that minimizes the deviation between the empirical and the nominal type I error. Applying our approach to various simulation studies and real examples, we demonstrate that it is more accurate and robust than the state-of-the-art alternatives in detecting differentially expression genes.