Piecewise multivariate modelling of sequential metabolic profiling data

Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results: A supervised multivariate modelling approach with the objective to model the timerelated variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are nonlinear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion: The proposed method is effective...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Mattias Rantalainen, Olivier Cloarec, Timothy M. D. Ebbels, Torbjörn Lundstedt, Jeremy K. Nicholson, Elaine Holmes, Johan Trygg
• Keywords: BMCBI 2008, Metabolic Profiling, Modelling, Supervised Multivariate Modelling