title: From data processing to multivariate validation--essential steps in extracting interpretable information from metabolomics data.
abstract: In metabolomics studies there is a clear increase of data. This indicates the necessity of both having a battery of suitable analysis methods and validation procedures able to handle large amounts of data. In this review, an overview of the metabolomics data processing pipeline is presented. A selection of recently developed and most cited data processing methods is discussed. In addition, commonly used chemometric and machine learning analysis methods as well as validation approaches are described.
authors.affiliation: Computational Life Science Cluster, Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.
authors.forename: Mattias
authors.initials: M
authors.lastname: Eliasson
authors.forename: Stefan
authors.initials: S
authors.lastname: Rännar
authors.forename: Johan
authors.initials: J
authors.lastname: Trygg
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D055442:Metabolome / Q000502:physiology*; D055432:Metabolomics / Q000379:methods*; D015999:Multivariate Analysis