Statistical Methods for Handling Unwanted Variation in Metabolomics Data

Alysha M. De Livera (Corresponding Author), Marko Sysi-Aho, Laurent Jacob, Johann A. Gagnon-Bartsch, Sandra Castillo, Julie A. Simpson, Terence P. Speed

    Research output: Contribution to journalArticleScientificpeer-review

    80 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)3606-3615
    JournalAnalytical Chemistry
    Volume87
    Issue number7
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Keywords

    • chemical analysis
    • batch effect
    • biological variation
    • metabolomics
    • metabolomics data
    • normalization methods
    • statistical approach

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