Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis.
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.
Dumas
Marc-Emmanuel
ME
Biological Chemistry Section, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom. m.dumas@imperial.ac.uk
Canlet
Cécile
C
Debrauwer
Laurent
L
Martin
Pascal
P
Paris
Alain
A
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
D015415:Biomarkers; D020543:Proteome