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Title: How to do quantile normalization correctly for gene expression data analyses
Authors: Zhao, Yaxing
Wong, Limsoon
Goh, Wilson Wen Bin
Keywords: Science::Biological sciences
Issue Date: 2020
Source: Zhao, Y., Wong, L., & Goh, W. W. B. (2020). How to do quantile normalization correctly for gene expression data analyses. Scientific Reports, 10, 15534-. doi:10.1038/s41598-020-72664-6
Project: MOE2019-T2-1-042
Journal: Scientific Reports
Abstract: Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split (“Class-specific”). Via simulations with both real and simulated batch effects, we demonstrate that the “Class-specific” strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the “Class-specific” strategy.
ISSN: 2045-2322
DOI: 10.1038/s41598-020-72664-6
Schools: School of Biological Sciences 
Rights: © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
Fulltext Permission: open
Fulltext Availability: With Fulltext
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