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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...

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Autores principales: Rantalainen, Mattias, Cloarec, Olivier, Ebbels, Timothy MD, Lundstedt, Torbjörn, Nicholson, Jeremy K, Holmes, Elaine, Trygg, Johan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2373572/
https://www.ncbi.nlm.nih.gov/pubmed/18284665
http://dx.doi.org/10.1186/1471-2105-9-105
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author Rantalainen, Mattias
Cloarec, Olivier
Ebbels, Timothy MD
Lundstedt, Torbjörn
Nicholson, Jeremy K
Holmes, Elaine
Trygg, Johan
author_facet Rantalainen, Mattias
Cloarec, Olivier
Ebbels, Timothy MD
Lundstedt, Torbjörn
Nicholson, Jeremy K
Holmes, Elaine
Trygg, Johan
author_sort Rantalainen, Mattias
collection PubMed
description 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 time-related 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 non-linear 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 for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
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spelling pubmed-23735722008-05-08 Piecewise multivariate modelling of sequential metabolic profiling data Rantalainen, Mattias Cloarec, Olivier Ebbels, Timothy MD Lundstedt, Torbjörn Nicholson, Jeremy K Holmes, Elaine Trygg, Johan BMC Bioinformatics Methodology Article 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 time-related 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 non-linear 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 for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data. BioMed Central 2008-02-19 /pmc/articles/PMC2373572/ /pubmed/18284665 http://dx.doi.org/10.1186/1471-2105-9-105 Text en Copyright © 2008 Rantalainen et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Rantalainen, Mattias
Cloarec, Olivier
Ebbels, Timothy MD
Lundstedt, Torbjörn
Nicholson, Jeremy K
Holmes, Elaine
Trygg, Johan
Piecewise multivariate modelling of sequential metabolic profiling data
title Piecewise multivariate modelling of sequential metabolic profiling data
title_full Piecewise multivariate modelling of sequential metabolic profiling data
title_fullStr Piecewise multivariate modelling of sequential metabolic profiling data
title_full_unstemmed Piecewise multivariate modelling of sequential metabolic profiling data
title_short Piecewise multivariate modelling of sequential metabolic profiling data
title_sort piecewise multivariate modelling of sequential metabolic profiling data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2373572/
https://www.ncbi.nlm.nih.gov/pubmed/18284665
http://dx.doi.org/10.1186/1471-2105-9-105
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