RT Journal Article
SR Electronic
T1 NormExpression: an R package to normalize gene expression data using evaluated methods
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 251140
DO 10.1101/251140
A1 Zhenfeng Wu
A1 Weixiang Liu
A1 Xiufeng Jin
A1 Deshui Yu
A1 Hua Wang
A1 Gustavo Glusman
A1 Max Robinson
A1 Lin Liu
A1 Jishou Ruan
A1 Gao Shan
YR 2018
UL http://biorxiv.org/content/early/2018/02/07/251140.abstract
AB Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.