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Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies

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Clinical Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1730))

Abstract

Assessing potential alterations of metabolic pathways using large-scale approaches today plays a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies should be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter considering their application to clinical research.

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Authors and Affiliations

  1. School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva 4, Switzerland

    Julien Boccard & Serge Rudaz

  2. Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Basel, Switzerland

    Julien Boccard & Serge Rudaz

Authors
  1. Julien Boccard
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  2. Serge Rudaz
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Corresponding author

Correspondence to Serge Rudaz .

Editor information

Editors and Affiliations

  1. Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands

    Martin Giera

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Boccard, J., Rudaz, S. (2018). Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies. In: Giera, M. (eds) Clinical Metabolomics. Methods in Molecular Biology, vol 1730. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7592-1_28

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  • DOI: https://doi.org/10.1007/978-1-4939-7592-1_28

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7591-4

  • Online ISBN: 978-1-4939-7592-1

  • eBook Packages: Springer Protocols

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