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Statistical methods for handling unwanted variation in metabolomics data.

Identifieur interne : 002E52 ( PubMed/Corpus ); précédent : 002E51; suivant : 002E53

Statistical methods for handling unwanted variation in metabolomics data.

Auteurs : Alysha M. De Livera ; Marko Sysi-Aho ; Laurent Jacob ; Johann A. Gagnon-Bartsch ; Sandra Castillo ; Julie A. Simpson ; Terence P. Speed

Source :

Analytical chemistry [ 1520-6882 ] ; 2015.

RBID : pubmed:25692814

English descriptors

KwdEn :
- Humans, Mass Spectrometry (methods), Metabolomics (methods), Principal Component Analysis, Software.
MESH :
- methods : Mass Spectrometry, Metabolomics.
- Humans, Principal Component Analysis, Software.

Abstract

Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.

DOI: 10.1021/ac502439y
PubMed: 25692814

Links to Exploration step

pubmed:25692814

Le document en format XML

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<affiliation><nlm:affiliation>†Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC 3800, Australia.</nlm:affiliation>
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<author><name sortKey="Sysi Aho, Marko" sort="Sysi Aho, Marko" uniqKey="Sysi Aho M" first="Marko" last="Sysi-Aho">Marko Sysi-Aho</name>
<affiliation><nlm:affiliation>‡Zora Biosciences Oy, FIN-02150 Espoo, Finland.</nlm:affiliation>
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<author><name sortKey="Jacob, Laurent" sort="Jacob, Laurent" uniqKey="Jacob L" first="Laurent" last="Jacob">Laurent Jacob</name>
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<author><name sortKey="Gagnon Bartsch, Johann A" sort="Gagnon Bartsch, Johann A" uniqKey="Gagnon Bartsch J" first="Johann A" last="Gagnon-Bartsch">Johann A. Gagnon-Bartsch</name>
<affiliation><nlm:affiliation>∥Department of Statistics, University of California, Berkeley, California United States, 94720.</nlm:affiliation>
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<author><name sortKey="Castillo, Sandra" sort="Castillo, Sandra" uniqKey="Castillo S" first="Sandra" last="Castillo">Sandra Castillo</name>
<affiliation><nlm:affiliation>¶VTT Technical Research Centre of Finland, P. O. Box 1000, FI-02044 VTT Espoo, Finland.</nlm:affiliation>
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<author><name sortKey="Simpson, Julie A" sort="Simpson, Julie A" uniqKey="Simpson J" first="Julie A" last="Simpson">Julie A. Simpson</name>
<affiliation><nlm:affiliation>†Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC 3800, Australia.</nlm:affiliation>
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<author><name sortKey="Speed, Terence P" sort="Speed, Terence P" uniqKey="Speed T" first="Terence P" last="Speed">Terence P. Speed</name>
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<front><div type="abstract" xml:lang="en">Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.</div>
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Statistical methods for handling unwanted variation in metabolomics data.

Statistical methods for handling unwanted variation in metabolomics data.

Source :

English descriptors

Abstract

Links to Exploration step

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