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From data processing to multivariate validation: essential steps in extracting interpretable information from metabolomics data
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2011 (English)In: Current Pharmaceutical Biotechnology, ISSN 1389-2010, E-ISSN 1873-4316, Vol. 12, no 7, 996-1004 p.Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Bentham Science Publishers , 2011. Vol. 12, no 7, 996-1004 p.
Keyword [en]
multivariate data analysis, data processing, chemometrics, metabolomics, statistical validation, validation procedures, chemometric and machine learning analysis, NMR, downstream data analysis, Filtration, non-linear regression method
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-44567DOI: 10.2174/138920111795909041ISI: 000292758600004PubMedID: 21466461OAI: oai:DiVA.org:umu-44567DiVA: diva2:421438
Available from: 2011-06-08 Created: 2011-06-08 Last updated: 2017-01-17Bibliographically approved

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Eliasson, MattiasTrygg, Johan

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