title: Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis.
abstract: We introduce a statistical approach for integrating data from several analytical platforms. We illustrate this approach using (1)H-(13)C Heteronuclear Multiple Bond Connectivity nuclear magnetic resonance spectroscopy ((1)H-(13)C HMBC NMR) and Pyrolysis Metastable Atom Bombardment Time-of-Flight mass spectrometry (Py-MAB-TOF-MS) to perform metabolic fingerprinting on cattle treated with anabolic steroids. Multiple factor analysis (MFA) integrates complementary aspects from NMR and MS data into a unique metabolic signature describing the biomarkers related to the dose-response. This work also indicates that, from a practical point of view, metabonomics and other "-omics" biotechnologies can benefit significantly from a generalized multi-platform integrative approach using multiple factor analysis.
authors.affiliation: Biological Chemistry Section, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom. m.dumas@imperial.ac.uk
authors.forename: Marc-Emmanuel
authors.initials: ME
authors.lastname: Dumas
authors.forename: Cécile
authors.initials: C
authors.lastname: Canlet
authors.forename: Laurent
authors.initials: L
authors.lastname: Debrauwer
authors.forename: Pascal
authors.initials: P
authors.lastname: Martin
authors.forename: Alain
authors.initials: A
authors.lastname: Paris
mesh_terms: D000818:Animals; D015415:Biomarkers / Q000378:metabolism*; D002417:Cattle; D009682:Magnetic Resonance Spectroscopy; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D015999:Multivariate Analysis; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D013997:Time Factors
chemical_list: D015415:Biomarkers; D020543:Proteome