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A Road Map for Computational Surgery: Challenges and Opportunities Read chapter Read first chapter

2014 | OriginalPaper | Chapter

6. A Statistical Framework for Biomarker Analysis and HR-MAS 2D Metabolite Identification

Authors : Akram Belghith, Christophe Collet, Jean-Paul Armspach

Published in: Computational Surgery and Dual Training

Publisher: Springer New York

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Abstract

Metabolomics is an exponentially growing field of “omics” research concerned with the high throughout comparison, identification and quantification of large numbers of metabolites in biological system. This emergent science of metabolomics brings increasing promise to identify biomarker diseases that integrate biochemical changes in disease and predict human reaction to treatment. In this context, the 2D High Resolution Magic Angle Spinning (HR-MAS) Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as an ideal platform for studying metabolites of biopsies. In this study, we particularly focus on the 2D Heteronuclear Single Quantum Coherence (HSQC) NMR spectrum analysis. The metabolomic analysis requires comparison of metabolite profiles obtained from multiple replicates of samples exposed to different experimental conditions. What adds difficulty to automating this analysis process is that each peak of a given metabolite (a set of peaks with specified locations) can be shifted slightly from one sample to the next. In this study, we propose a new framework to detect and align simultaneously peaks representing different metabolites within a biopsy for metabonomic analysis. The method was validated on synthetic and real HSQC spectra.

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Metadata
Title
A Statistical Framework for Biomarker Analysis and HR-MAS 2D Metabolite Identification
Authors
Akram Belghith
Christophe Collet
Jean-Paul Armspach
Copyright Year
2014
Publisher
Springer New York
Book
Computational Surgery and Dual Training

Print ISBN: 978-1-4614-8647-3

Electronic ISBN: 978-1-4614-8648-0

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-1-4614-8648-0_6