Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh‐resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high...

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Bibliographic Details
Published in:Mass spectrometry reviews Vol. 41; no. 3; pp. 421 - 442
Main Authors: Han, Wei, Li, Liang
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.05.2022
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ISSN:0277-7037, 1098-2787, 1098-2787
Online Access:Get full text
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Summary:Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh‐resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype‐related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.
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ISSN:0277-7037
1098-2787
1098-2787
DOI:10.1002/mas.21672