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OnPLS path modelling
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
Unité de Recherches "Sensometrics and Chemometrics", ONIRIS, Site de la Géraudière, BP 82 225 Nantes 44322 Cedex 03, France.
INRA-UMR 1083 SPO, INRA, 2 Place Viala, 34060 Montpellier, France.
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2012 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 118, 139-149 p.Article in journal (Refereed) Published
Abstract [en]

OnPLS was recently presented as a general extension of O2PLS to the multiblock case. OnPLS is equivalent to O2PLS in the case of two matrices, but generalises symmetrically to cases with more than two matrices, i.e. without giving preference to any one of the matrices.

This article presents a straight-forward extension to this method and thereby also introduces the OPLS framework to the field of PLS path modelling. Path modelling links a number of data blocks to each other, thereby establishing a set of paths along which information is considered to flow between blocks, representing for instance a known time sequence, an assumed causality order, or some other chosen organising principle. Compared to existing methods for path analysis, OnPLS path modelling extracts a minimum number of predictive components that are maximally covarying with maximised correlation. This is a significant contribution to path modelling, because other methods may yield score vectors with variation that obstructs the interpretation. The method achieves this by extracting a set of "orthogonal" components that capture local phenomena orthogonal to the variation shared with all the connected blocks.

Two applications will be used to illustrate the method. The first is based on a simulated dataset that show how the interpretation is improved by removing orthogonal variation and the second on a real data process for monitoring of protein structure changes during cheese ripening by analysing infrared data.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 118, 139-149 p.
Keyword [en]
OnPLS, OPLS, Orthogonal variation, PLS, PLS path model
National Category
Chemical Sciences
Research subject
Statistics; Analytical Chemistry
URN: urn:nbn:se:umu:diva-55431DOI: 10.1016/j.chemolab.2012.08.009OAI: diva2:526711
Swedish Research Council, 2008-3588
Available from: 2012-05-14 Created: 2012-05-14 Last updated: 2013-02-01Bibliographically approved
In thesis
1. OnPLS: Orthogonal projections to latent structures in multiblock and path model data analysis
Open this publication in new window or tab >>OnPLS: Orthogonal projections to latent structures in multiblock and path model data analysis
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The amounts of data collected from each sample of e.g. chemical or biological materials have increased by orders of magnitude since the beginning of the 20th century. Furthermore, the number of ways to collect data from observations is also increasing. Such configurations with several massive data sets increase the demands on the methods used to analyse them. Methods that handle such data are called multiblock methods and they are the topic of this thesis.

Data collected from advanced analytical instruments often contain variation from diverse mutually independent sources, which may confound observed patterns and hinder interpretation of latent variable models. For this reason, new methods have been developed that decompose the data matrices, placing variation from different sources of variation into separate parts. Such procedures are no longer merely pre-processing filters, as they initially were, but have become integral elements of model building and interpretation. One strain of such methods, called OPLS, has been particularly successful since it is easy to use, understand and interpret.

This thesis describes the development of a new multiblock data analysis method called OnPLS, which extends the OPLS framework to the analysis of multiblock and path models with very general relationships between blocks in both rows and columns. OnPLS utilises OPLS to decompose sets of matrices, dividing each matrix into a globally joint part (a part shared with all the matrices it is connected to), several locally joint parts (parts shared with some, but not all, of the connected matrices) and a unique part that no other matrix shares.

The OnPLS method was applied to several synthetic data sets and data sets of “real” measurements. For the synthetic data sets, where the results could be compared to known, true parameters, the method generated global multiblock (and path) models that were more similar to the true underlying structures compared to models without such decompositions. I.e. the globally joint, locally joint and unique models more closely resembled the corresponding true data. When applied to the real data sets, the OnPLS models revealed chemically or biologically relevant information in all kinds of variation, effectively increasing the interpretability since different kinds of variation are distinguished and separately analysed.

OnPLS thus improves the quality of the models and facilitates better understanding of the data since it separates and separately analyses different kinds of variation. Each kind of variation is purer and less tainted by other kinds. OnPLS is therefore highly recommended to anyone engaged in multiblock or path model data analysis.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2012. 76 p.
OnPLS, OPLS, O2PLS, PLS, Multivariate analysis, Multiblock and path modelling, Chemometrics
National Category
Chemical Sciences
urn:nbn:se:umu:diva-55438 (URN)978-91-7459-442-3 (ISBN)
Public defence
2012-06-15, KBC-huset, KB3A9, Umeå universitet, Umeå, 10:00 (English)
Available from: 2012-05-16 Created: 2012-05-15 Last updated: 2012-05-15Bibliographically approved

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