Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing.

Fonville, J; Bylesjö, M; Coen, M; Nicholson, J; Holmes, E; Lindon, J; Rantalainen, M

Journal article

Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing.

Abstract:: Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.

Publication status:: Published

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APA Style

Fonville, J., Bylesjö, M., Coen, M., Nicholson, J., Holmes, E., Lindon, J., & Rantalainen, M. (2011). Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing. Analytica Chimica Acta, 705(1-2), 72–80.

MLA Style

Fonville, J, et al. “Non-Linear Modeling of 1H NMR Metabonomic Data Using Kernel-Based Orthogonal Projections to Latent Structures Optimized by Simulated Annealing.” Analytica Chimica Acta, vol. 705, nos. 1-2, 2011, pp. 72–80.

Chicago Style

Fonville, J, M Bylesjö, M Coen, et al. 2011. “Non-Linear Modeling of 1H NMR Metabonomic Data Using Kernel-Based Orthogonal Projections to Latent Structures Optimized by Simulated Annealing.” Analytica Chimica Acta 705 (1-2): 72–80.
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