title: Matrix correlations for high-dimensional data: the modified RV-coefficient.
abstract: 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.
authors.affiliation: Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. a.k.smilde@uva.nl
authors.forename: A K
authors.initials: AK
authors.lastname: Smilde
authors.forename: H A L
authors.initials: HA
authors.lastname: Kiers
authors.forename: S
authors.initials: S
authors.lastname: Bijlsma
authors.forename: C M
authors.initials: CM
authors.lastname: Rubingh
authors.forename: M J
authors.initials: MJ
authors.lastname: van Erk
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D023281:Genomics / Q000379:methods*; D055432:Metabolomics / Q000379:methods