Journal Article

Matrix correlations for high-dimensional data: the modified RV-coefficient

A. K. Smilde, H. A. L. Kiers, S. Bijlsma, C. M. Rubingh and M. J. van Erk

in Bioinformatics

Volume 25, issue 3, pages 401-405
Published in print February 2009 | ISSN: 1367-4803
Published online December 2008 | e-ISSN: 1460-2059 | DOI:
Matrix correlations for high-dimensional data: the modified RV-coefficient

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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 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.

Availability: The Matlab m-files of the methods presented can be downloaded from


Journal Article.  3564 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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