Global, local and unique decompositions in OnPLS for multiblock data analysis
2013 (English)In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 791, 13-24 p.Article in journal (Other academic) Published
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.
OnPLS, OPLS, O2PLS, Orthogonal variation, PLS, Decomposition
Research subject Statistics; Analytical Chemistry; Genetics
IdentifiersURN: urn:nbn:se:umu:diva-55433DOI: 10.1016/j.aca.2013.06.026OAI: oai:DiVA.org:umu-55433DiVA: diva2:526719
FunderSwedish Research Council, 2011-6044eSSENCE - An eScience Collaboration