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.