Trend analysis of time-series data: A novel method for untargeted metabolite discovery.
A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical tools to distinguish such compounds from those showing only random variation. Two univariate methods, autocorrelation and curve-fitting, are used either as stand-alone methods or in combination to discover unknown metabolites in data sets originating from target-compound analysis. Both techniques reduce the long list of detected compounds in the kinetic sample set to include only those having a pre-defined interesting time profile. Thus, new metabolites may be discovered within data structures that are usually only used for target-compound analysis. The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 9000 potentially interesting features was reduced to less than 150, thus significantly reducing the expensive and time-consuming manual examination.
Peters
Sonja
S
Unilever Research and Development, Advanced Measurement and Data Modelling, P.O. Box 114, 3130 AC Vlaardingen, The Netherlands. sonja.peters@unilever.com
Janssen
Hans-Gerd
HG
Vivó-Truyols
Gabriel
G
D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism; D016208:Databases, Factual; D005419:Flavonoids / Q000032:analysis* / Q000378:metabolism; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D010636:Phenols / Q000032:analysis* / Q000378:metabolism; D059808:Polyphenols; D013997:Time Factors
D015415:Biomarkers; D005419:Flavonoids; D010636:Phenols; D059808:Polyphenols