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Global, local and unique decompositions in OnPLS for multiblock data analysis
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2013 (English)In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 791, 13-24 p.Article in journal (Other academic) Published
Abstract [en]

Background OnPLS is an extension of O2PLS that decomposes a set of matrices, in either multiblock or path model analysis, such that each matrix consists of two parts: a globally joint part containing variation shared with all other connected matrices, and another containing unique or locally joint variation, i.e. variation that is specific to a particular matrix or shared with some, but not all, other connected matrices.

Results A further extension of OnPLS suggested here decomposes the non-globally joint parts into locally joint and unique parts, using the OnPLS method to first find and extract a globally joint model, and then applying OnPLS recursively to subsets of matrices containing the non-globally joint variation remaining after the globally joint variation has been extracted. This results in a set of locally joint models. The variation that is left after the globally joint and locally joint variation has been extracted is not related (by definition) to the other matrices and thus represents the strictly unique variation specific to each matrix. The method's utility is demonstrated by its application to both a simulated data set and a real data set acquired from metabolomic, proteomic and transcriptomic profiling of three genotypes of hybrid aspen.

Conclusions The results show that OnPLS can successfully decompose each matrix into global, local and unique models, resulting in lower numbers of globally joint components and higher intercorrelations of scores. OnPLS also increases the interpretability of models of connected matrices, because of the locally joint and unique models it generates.

Place, publisher, year, edition, pages
2013. Vol. 791, 13-24 p.
Keyword [en]
OnPLS, OPLS, O2PLS, Orthogonal variation, PLS, Decomposition
National Category
Chemical Sciences
Research subject
Statistics; Analytical Chemistry; Genetics
URN: urn:nbn:se:umu:diva-55433DOI: 10.1016/j.aca.2013.06.026OAI: diva2:526719
Swedish Research Council, 2011-6044eSSENCE - An eScience Collaboration
Available from: 2012-05-14 Created: 2012-05-14 Last updated: 2013-09-12Bibliographically 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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Löfstedt, TommyHoffman, DanielTrygg, Johan
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