Matrix correlations for high-dimensional data: the modified RV-coefficient.
BACKGROUND
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 used in 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.


BACKGROUND
The Matlab m-files of the methods presented can be downloaded from http://www.bdagroup.nl.
Smilde
A K
AK
Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. a.k.smilde@uva.nl
Kiers
H A L
HA
Bijlsma
S
S
Rubingh
C M
CM
van Erk
M J
MJ
D000465:Algorithms; D003198:Computer Simulation; D023281:Genomics / Q000379:methods*; D055432:Metabolomics / Q000379:methods