title: A statistical framework for biomarker discovery in metabolomic time course data.
abstract: BACKGROUND
  Metabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two experimental conditions or groups (e.g. a control and drug-treated group) with the goal of identifying discriminatory metabolites or biomarkers that characterize each condition. A common study design consists of repeated measurements taken on each experimental unit thus producing time courses of all metabolites. We describe a statistical framework for estimating time-varying metabolic profiles and their within-group variability and for detecting between-group differences. Specifically, we propose (i) a smoothing splines mixed effects (SME) model that treats each longitudinal measurement as a smooth function of time and (ii) an associated functional test statistic. Statistical significance is assessed by a non-parametric bootstrap procedure.
  
  
  RESULTS
  The methodology has been extensively evaluated using simulated data and has been applied to real nuclear magnetic resonance spectroscopy data collected in a preclinical toxicology study as part of a larger project lead by the COMET (Consortium for Metabonomic Toxicology). Our findings are compatible with the previously published studies.
  
  
  BACKGROUND
  An R script is freely available for download at http://www2.imperial.ac.uk/~gmontana/sme.htm.
authors: Statistics Section, Department of Mathematics, Imperial College London, Huxley Building, South Kensington, London SW7 2AZ, UK.
authors: Maurice
authors: M
authors: Berk
authors: Timothy
authors: T
authors: Ebbels
authors: Giovanni
authors: G
authors: Montana
mesh_terms: D000818:Animals; D015415:Biomarkers / Q000378:metabolism; D001699:Biometry; D019992:Databases as Topic; D009682:Magnetic Resonance Spectroscopy; D055442:Metabolome; D055432:Metabolomics; D008954:Models, Biological; D051381:Rats; D012984:Software
chemical_list: D015415:Biomarkers