Title
Matrix correlations for high-dimensional data: The modified RV-coefficient
Author
Smilde, A.K.
Kiers, H.A.L.
Bijlsma, S.
Rubingh, C.M.
van Erk, M.J.
TNO Kwaliteit van Leven
Publication year
2009
Abstract
Motivation: Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations familiar to biologists. The high-dimensionality of functional genomics data is, however, problematic for existing matrix correlations. The motivation of this article is 2-fold: (i) we introduce the idea of matrix correlations to the bioinformatics community and (ii) we give an improvement of the most promising matrix correlation coefficient (the RV-coefficient) circumventing the problems of high-dimensional data. Results: The modified RV-coefficient can be usedin high-dimensional data analysis studies as an easy measure of common information of two datasets. This is shown by theoretical arguments, simulations and applications to two real-life examples from functional genomics, i.e. a transcriptomics and metabolomics example. © The Author 2008. Published by Oxford University Press. All rights reserved.
Subject
Biomedical Innovation
Biology
Healthy Living
Analytical research
Biomedical research
Bioinformatics
Controlled study
Correlation coefficient
Data analysis
Functional genomics
Matrix correlation
Metabolomics
Nonhuman
Priority journal
RV coefficient
Simulation
Statistical analysis
Theory
Transcriptomics
Algorithms
Computer Simulation
Genomics
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http://resolver.tudelft.nl/uuid:a9a4b7bd-cc0c-4fa6-a5f4-96361a91e23d
DOI
https://doi.org/10.1093/bioinformatics/btn634
TNO identifier
241416
ISSN
1367-4803
Source
Bioinformatics, 25 (3), 401-405
Document type
article