Department of Statistics, Baker Hall, Carnegie Mellon University, Pittsburgh, PA, USA.
Pomona College.
Brief Bioinform. 2018 Sep 28;19(5):776-792. doi: 10.1093/bib/bbx008.
RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An underemphasized feature of normalization is the assumptions on which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this article, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.
RNA-Seq 是一种广泛用于研究不同生物条件下基因行为的方法。RNA-Seq 研究中的一个重要步骤是归一化，即调整原始数据以考虑防止直接比较表达测量的因素。归一化中的错误会对下游分析产生重大影响，例如在差异表达分析中虚报阳性。归一化被忽视的一个特征是方法所依赖的假设，以及这些假设的有效性如何对方法的性能产生重大影响。在本文中，我们解释了假设如何在原始 RNA-Seq 读计数和有意义的基因表达测量之间建立联系。我们从假设的角度检查归一化方法，因为理解方法假设对于选择适合手头数据的方法是必要的。此外，我们讨论了当假设被违反时归一化方法为何表现不佳，以及这如何导致后续分析中的问题。为了分析生物学实验，研究人员必须选择具有满足的假设并为给定实验产生有意义的表达测量的归一化方法。
