Estimating mutual information using B-spline functions--an improved similarity measure for analysing gene expression data

BMC Bioinformatics
Carsten O DaubSebastian Kloska

Abstract

The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. In the context of the clustering of genes with similar patterns of expression it has been suggested as a general quantity of similarity to extend commonly used linear measures. Since mutual information is defined in terms of discrete variables, its application to continuous data requires the use of binning procedures, which can lead to significant numerical errors for datasets of small or moderate size. In this work, we propose a method for the numerical estimation of mutual information from continuous data. We investigate the characteristic properties arising from the application of our algorithm and show that our approach outperforms commonly used algorithms: The significance, as a measure of the power of distinction from random correlation, is significantly increased. This concept is subsequently illustrated on two large-scale gene expression datasets and the results are compared to those obtained using other similarity measures.A C++ source code of our algorithm is available for non-commercial use from kloska@scienion.de upon request. The utilisation of mutual information as similarity measure ena...Continue Reading

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Citations

Feb 15, 2013·Briefings in Functional Genomics·Liliana López-KleineCamilo López
Jul 13, 2011·Briefings in Bioinformatics·Yaqun WangRongling Wu
Oct 24, 2009·Bioinformatics·Joshua BucknerFan Meng
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