title: Statistical methods for handling unwanted variation in metabolomics data.
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.
authors.affiliation: †Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC 3800, Australia.
authors.forename: Alysha M
authors.initials: AM
authors.lastname: De Livera
authors.affiliation: ‡Zora Biosciences Oy, FIN-02150 Espoo, Finland.|¶VTT Technical Research Centre of Finland, P. O. Box 1000, FI-02044 VTT Espoo, Finland.
authors.forename: Marko
authors.initials: M
authors.lastname: Sysi-Aho
authors.affiliation: §Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, INRA, UMR5558, Villeurbanne, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Jacob
authors.affiliation: ∥Department of Statistics, University of California, Berkeley, California United States, 94720.
authors.forename: Johann A
authors.initials: JA
authors.lastname: Gagnon-Bartsch
authors.affiliation: ¶VTT Technical Research Centre of Finland, P. O. Box 1000, FI-02044 VTT Espoo, Finland.
authors.forename: Sandra
authors.initials: S
authors.lastname: Castillo
authors.affiliation: †Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC 3800, Australia.
authors.forename: Julie A
authors.initials: JA
authors.lastname: Simpson
authors.affiliation: ∥Department of Statistics, University of California, Berkeley, California United States, 94720.|⊥Bioinformatics Division, Walter and Eliza Hall Institute, 1 G Royal Parade, Parkville, Victoria 3052, Australia.|⧧Department of Mathematics and Statistics, University of Melbourne, VIC 3800, Melbourne, Australia.
authors.forename: Terence P
authors.initials: TP
authors.lastname: Speed
mesh_terms: D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D025341:Principal Component Analysis; D012984:Software