AustralieFrV1, Main, Exploration, bibRecord, 002804

Statistical methods for handling unwanted variation in metabolomics data

Identifieur interne : 002804 ( Main/Exploration ); précédent : 002803; suivant : 002805

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 [ 0003-2700 ] ; 2015.

RBID : PMC:4544854

Descripteurs français

KwdFr :
- Analyse en composantes principales, Humains, Logiciel, Métabolomique (), Spectrométrie de masse ().
MESH :
- Analyse en composantes principales, Humains, Logiciel, Métabolomique, Spectrométrie de masse.

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 recognised 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 online.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4544854

DOI: 10.1021/ac502439y
PubMed: 25692814
PubMed Central: 4544854

Affiliations:

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<front><div type="abstract" xml:lang="en"><p id="P1">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 recognised 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 online.</p>
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Statistical methods for handling unwanted variation in metabolomics data

Statistical methods for handling unwanted variation in metabolomics data

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

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