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A statistical framework for biomarker discovery in metabolomic time course data

Metabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. This paper presents a novel statistical framework for detecting metabolomic biomarkers using time-varying metabolomics profiles observed in two populations (e.g. a control and a drug-treated group). The key observation of the paper models short time series as smooth functions of time, which improves upon existing methods that ignore the chronological ordering of the observations. The methodology has been successfully applied to nuclear magnetic resonance spectroscopy data collected in a preclinical toxicology study as part of a larger project lead by Consortium for Metabonomic Toxicology.