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Bi-modal OnPLS
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC),)
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2012 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 26, no 6, 236-245 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents an extension to the recently published OnPLS data analysis method. Bi-modal OnPLS allows for arbitrary block relationships in both columns and rows and is able to extract orthogonal variation in both columns and rows without bias towards any particular direction or matrix: the method is fully symmetric with regard to both rows and columns.

Bi-modal OnPLS extracts a minimal number of globally predictive score vectors that exhibit maximal covariance and correlation in the column space and a corresponding set of predictive loading vectors that exhibit maximal correlation in the row space. The method also extracts orthogonal variation (i.e. variation that is not related to all other matrices) in both columns and rows. The method was applied to two synthetic datasets and one real data set regarding sensory information and consumer likings of dairy products. It was shown that Bi-modal OnPLS greatly improves the intercorrelations between both loadings and scores while still finding the correct variation. This facilitates interpretation of the predictive components and makes it possible to study the orthogonal variation in the data.

Place, publisher, year, edition, pages
John Wiley & Sons, 2012. Vol. 26, no 6, 236-245 p.
Keyword [en]
PLS, OnPLS, bi-modal analysis, OPLS
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-54278DOI: 10.1002/cem.2448ISI: 000305510100006OAI: diva2:517440
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2012-07-17Bibliographically 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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