Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 418445
Title Canonical correlation analysis of multiple sensory directed metabolomics data blocks reveals corresponding parts between data blocs
Author(s) Doeswijk, T.G.; Hageman, J.A.; Westerhuis, J.A.; Tikunov, Y.M.; Bovy, A.G.; Eeuwijk, F.A. van
Source Chemometrics and Intelligent Laboratory Systems 107 (2011)2. - ISSN 0169-7439 - p. 371 - 376.
DOI https://doi.org/10.1016/j.chemolab.2011.05.010
Department(s) Biometris (WU MAT)
WUR Plant Breeding
PRI BIOS Applied Metabolic Systems
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) variable selection - component analysis - multiblock - quality - fusion - models - pls
Abstract Multiple analytical platforms are frequently used in metabolomics studies. The resulting multiple data blocks contain, in general, similar parts of information which can be disclosed by chemometric methods. The metabolites of interest, however, are usually just a minor part of the complete data block and are related to a response of interest such as quality traits. Concatenation of data matrices is frequently used to simultaneously analyze multiple data blocks. Two main problems may occur with this approach: 1) the number of variables becomes very large in relation to the number of observations which may deteriorate model performance, and 2) scaling issues between the data blocks need to be resolved. Therefore, a method is proposed that circumvents direct concatenation of two data matrices but does uncover the shared and distinct parts of the data sets in relation to quality traits. The relevant part of the data blocks with respect to the quality trait of interest is revealed by partial least squares regression on each of the data blocks. The score vectors of both models that are predictive for the quality trait are then used in a canonicalcorrelationanalysis. Highly correlating score vectors indicate parts of the data blocks that are closely related. By inspecting the relevant loading vectors, the metabolites of interest are revealed
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