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OnPLS—a novel multiblock method for the modelling of predictive and orthogonal variation
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2011 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 25, no 8, 441-455 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a new multiblock analysis method called OnPLS, a general extension of O2PLS to the multiblock case. The proposed method is equivalent to O2PLS in cases involving only two matrices, but generalises to cases involving more than two matrices without giving preference to any particular matrix: the method is fully symmetric. OnPLS extracts a minimal number of globally predictive components that exhibit maximal covariance and correlation. Furthermore, the method can be used to study orthogonal variation, i.e. local phenomena captured in the data that are specific to individual combinations of matrices or to individual matrices. The method's utility was demonstrated by its application to three synthetic data sets. It was shown that OnPLS affords a reduced number of globally predictive components and increased intercorrelations of scores, and that it greatly facilitates interpretation of the predictive model.

Place, publisher, year, edition, pages
John Wiley & Sons, Ltd , 2011. Vol. 25, no 8, 441-455 p.
Keyword [en]
OnPLS, multiblock analysis, O2PLS, orthogonal projections to latent structures, PLS
National Category
Analytical Chemistry
URN: urn:nbn:se:umu:diva-46406DOI: 10.1002/cem.1388OAI: diva2:438146

Article first published online: 25 APR 2011

Available from: 2011-09-01 Created: 2011-09-01 Last updated: 2012-09-05
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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