Statistical significance analysis of nuclear magnetic resonance-based metabonomics data.
Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.
Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
Aaron M
AM
Goodpaster
Lindsey E
LE
Romick-Rosendale
Michael A
MA
Kennedy
D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000032:analysis / Q000737:chemistry; D003627:Data Interpretation, Statistical; D005243:Feces / Q000737:chemistry; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D051379:Mice; D025341:Principal Component Analysis
D015415:Biomarkers