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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Vydané v:	Mass spectrometry reviews Ročník 41; číslo 3; s. 421 - 442
Hlavní autori:	Han, Wei, Li, Liang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Wiley Subscription Services, Inc 01.05.2022
Predmet:	batch effect Mass Spectrometry Mass spectroscopy Metabolome metabolome analysis Metabolomics Metabolomics - methods NMR Phenotypes batch effect mass spectrometry NMR metabolome analysis metabolomics
ISSN:	0277-7037, 1098-2787, 1098-2787
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Abstract	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.
AbstractList	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. 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.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.
Author	Han, Wei Li, Liang
Author_xml	– sequence: 1 givenname: Wei surname: Han fullname: Han, Wei organization: University of Alberta – sequence: 2 givenname: Liang surname: Li fullname: Li, Liang email: liang.li@ualberta.ca organization: University of Alberta
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Snippet	Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in...
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SubjectTerms	batch effect Mass Spectrometry Mass spectroscopy Metabolome metabolome analysis Metabolomics Metabolomics - methods NMR Phenotypes
Title	Evaluating and minimizing batch effects in metabolomics
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmas.21672 https://www.ncbi.nlm.nih.gov/pubmed/33238061 https://www.proquest.com/docview/2647615961 https://www.proquest.com/docview/2464607301
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