Maza Elie
Genomics and Biotechnology of the Fruits Laboratory, UMR 990 INRA/Institut National Polytechnique de Toulouse, Ecole Nationale Supérieure Agronomique de Toulouse, Université de Toulouse Castanet-Tolosan, France.
Front Genet. 2016 Sep 16;7:164. doi: 10.3389/fgene.2016.00164. eCollection 2016.
In the past 5 years, RNA-Seq has become a powerful tool in transcriptome analysis even though computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. It is, however, now commonly accepted that the choice of a normalization procedure is an important step in such a process, for example in differential gene expression analysis. The present article highlights the similarities between three normalization methods: TMM from edgeR R package, RLE from DESeq2 R package, and MRN. Both TMM and DESeq2 are widely used for differential gene expression analysis. This paper introduces properties that show when these three methods will give exactly the same results. These properties are proven mathematically and illustrated by performing calculations on a given RNA-Seq data set.
在过去的五年中,RNA测序已成为转录组分析中的一项强大工具,尽管用于分析高通量测序数据的计算方法尚未标准化。然而,目前人们普遍认为,选择归一化程序是这一过程中的重要一步,例如在差异基因表达分析中。本文着重介绍了三种归一化方法之间的相似之处:edgeR R包中的TMM、DESeq2 R包中的RLE以及MRN。TMM和DESeq2都广泛用于差异基因表达分析。本文介绍了一些特性,这些特性表明这三种方法在何时会得出完全相同的结果。这些特性经过数学证明,并通过对给定的RNA测序数据集进行计算加以说明。