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https://doi.org/10.1038/s41598-020-72664-6
Title: | How to do quantile normalization correctly for gene expression data analyses | Authors: | Zhao, Y. Wong, L. Goh, W.W.B. |
Issue Date: | 2020 | Publisher: | Nature Research | Citation: | Zhao, Y., Wong, L., Goh, W.W.B. (2020). How to do quantile normalization correctly for gene expression data analyses. Scientific Reports 10 (1) : 15534. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-020-72664-6 | Rights: | Attribution 4.0 International | 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. © 2020, The Author(s). | Source Title: | Scientific Reports | URI: | https://scholarbank.nus.edu.sg/handle/10635/199324 | ISSN: | 20452322 | DOI: | 10.1038/s41598-020-72664-6 | Rights: | Attribution 4.0 International |
Appears in Collections: | Staff Publications Elements |
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