Change search
ReferencesLink to record
Permanent link

Direct link
Piecewise multivariate modelling of sequential metabolic profiling data
Show others and affiliations
2008 (English)In: BMC Bioinformatics, ISSN 1471-2105, Vol. 9, no 1, 105- p.Article in journal (Refereed) Published
Abstract [en]


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.


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.


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.

Place, publisher, year, edition, pages
2008. Vol. 9, no 1, 105- p.
National Category
Biological Sciences
URN: urn:nbn:se:umu:diva-9643DOI: doi:10.1186/1471-2105-9-105PubMedID: 18284665OAI: diva2:149314
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2012-09-05

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Trygg, Johan
By organisation
Department of Chemistry
Biological Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 45 hits
ReferencesLink to record
Permanent link

Direct link