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Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry pp 177–201Cite as

Statistical Approach for Biomarker Discovery Using Label-Free LC-MS Data: An Overview

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Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

The identification of new diagnostic, prognostic, or theranostics biomarkers is one of the main aims of clinical research. Technologies like mass spectrometry (MS) focus on the discovery of proteins as biomarkers and are commonly being used for this purpose. Mass spectrometry consists in the separation by gas of charged molecules, based on their mass-over-charge. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) first involves a separation by liquid chromatography (LC) followed by mass spectrometry in the MS and MS/MS modes.

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Acknowledgements

We wish to thank the members of the CLIPP Platform (University of Burgundy) for their contribution, and most particularly Géraldine Lucchi and Delphine Pecqueur, who read the article thoroughly. We also wish to thank the “Centre de Langues” (University of Burgundy) for editing the manuscript.

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Authors and Affiliations

  1. CLIPP (Clinical Innovation Proteomic Platform), Pôle de Recherche Université de Bourgogne, F- 21000, Dijon, France

    Caroline Truntzer & Patrick Ducoroy

Authors
  1. Caroline Truntzer
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  2. Patrick Ducoroy
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Corresponding author

Correspondence to Caroline Truntzer .

Editor information

Editors and Affiliations

  1. Department of Biostatistics, University of Florida, Gainesville, Florida, USA

    Susmita Datta

  2. Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, RC Leiden, The Netherlands

    Bart J. A. Mertens

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Truntzer, C., Ducoroy, P. (2017). Statistical Approach for Biomarker Discovery Using Label-Free LC-MS Data: An Overview. In: Datta, S., Mertens, B. (eds) Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-45809-0_10

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