Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150368
Title: Examining the practical limits of batch effect-correction algorithms : when should you care about batch effects?
Authors: Zhou, Longjian
Sue, Andrew Chi-Hau
Goh, Wilson Wen Bin
Keywords: Science::Biological sciences
Issue Date: 2019
Source: Zhou, L., Sue, A. C. & Goh, W. W. B. (2019). Examining the practical limits of batch effect-correction algorithms : when should you care about batch effects?. Journal of Genetics and Genomics, 46(9), 433-443. https://dx.doi.org/10.1016/j.jgg.2019.08.002
Project: NRF2018NRF-NSFC003SB-006
Journal: Journal of Genetics and Genomics
Abstract: Batch effects are technical sources of variation and can confound analysis. While many performance ranking exercises have been conducted to establish the best batch effect-correction algorithm (BECA), we hold the viewpoint that the notion of best is context-dependent. Moreover, alternative questions beyond the simplistic notion of "best" are also interesting: are BECAs robust against various degrees of confounding and if so, what is the limit? Using two different methods for simulating class (phenotype) and batch effects and taking various representative datasets across both genomics (RNA-Seq) and proteomics platforms, we demonstrate that under situations where sample classes and batch factors are moderately confounded, most BECAs are remarkably robust and only weakly affected by upstream normalization procedures. This observation is consistently supported across the multitude of test datasets. BECAs do have limits: When sample classes and batch factors are strongly confounded, BECA performance declines, with variable performance in precision, recall and also batch correction. We also report that while conventional normalization methods have minimal impact on batch effect correction, they do not affect downstream statistical feature selection, and in strongly confounded scenarios, may even outperform BECAs. In other words, removing batch effects is no guarantee of optimal functional analysis. Overall, this study suggests that simplistic performance ranking exercises are quite trivial, and all BECAs are compromises in some context or another.
URI: https://hdl.handle.net/10356/150368
ISSN: 1673-8527
DOI: 10.1016/j.jgg.2019.08.002
Rights: © 2019 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. All rights reserved. This paper was published by Elsevier Limited and Science Press in Journal of Genetics and Genomics and is made available with permission of Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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