title: Modelling short time series in metabolomics: a functional data analysis approach.
abstract: 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 or more experimental conditions (e.g. a control and drug-treated group), thus producing time course data. Models from traditional time series analysis are often unsuitable because, by design, only very few time points are available and there are a high number of missing values. We propose a functional data analysis approach for modelling short time series arising in metabolomic studies which overcomes these obstacles. Our model assumes that each observed time series is a smooth random curve, and we propose a statistical approach for inferring this curve from repeated measurements taken on the experimental units. A test statistic for detecting differences between temporal profiles associated with two experimental conditions is then presented. The methodology has been applied to NMR spectroscopy data collected in a pre-clinical toxicology study.
authors: Mathematics, Imperial College, London, UK. giovanni.montana@imperial.ac.uk
authors: Giovanni
authors: G
authors: Montana
authors: Maurice
authors: M
authors: Berk
authors: Tim
authors: T
authors: Ebbels
mesh_terms: D000818:Animals; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D006834:Hydrazines / Q000008:administration & dosage / Q000378:metabolism / Q000633:toxicity; D009682:Magnetic Resonance Spectroscopy / Q000706:statistics & numerical data; D055432:Metabolomics / Q000706:statistics & numerical data*; D008954:Models, Biological; D015233:Models, Statistical; D051381:Rats; D017207:Rats, Sprague-Dawley; D012984:Software; D013997:Time Factors; D014116:Toxicology / Q000706:statistics & numerical data
chemical_list: D006834:Hydrazines; C029424:hydrazine