Title: Differential Shannon entropy and differential coefficient of variation: alternatives and augmentations to differential expression in the search for disease-related genes

Authors: Kai Wang; Charles A. Phillips; Gary L. Rogers; Fredrik Barrenas; Mikael Benson; Michael A. Langston

Addresses: Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee 37996, USA ' Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee 37996, USA ' National Institute for Computational Sciences, Oak Ridge, Tennessee 37831, USA ' The Center for Individualized Medication, Linköping University Hospital, 58185, Linköping, Sweden ' The Center for Individualized Medication, Linköping University Hospital, 58185, Linköping, Sweden ' Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee 37996, USA

Abstract: Differential expression has been a standard tool for analysing case-control transcriptomic data since the advent of microarray technology. It has proved invaluable in characterising the molecular mechanisms of disease. Nevertheless, the expression profile of a gene across samples can be perturbed in ways that leave the expression level unaltered, while a biological effect is nonetheless present. This paper describes and analyses differential Shannon entropy and differential coefficient of variation, two alternate techniques for identifying genes of interest. Ontological analysis across 16 human disease datasets demonstrates that these alternatives are effective at identifying disease-related genes not found by mere differential expression alone. Because the two alternate techniques are based on somewhat different mathematical formulations, they tend to produce somewhat different gene lists. Moreover, each may pinpoint genes completely overlooked by the other. Thus, measures of entropy and variation can be used to replace or better yet augment standard differential expression computations.

Keywords: coefficient of variation; differential entropy; differential expression; gene ontology; ACOXL; ERBB4; ESR1; IRF4; disease-related genes; Shannon entropy; gene expression.

DOI: 10.1504/IJCBDD.2014.061656

International Journal of Computational Biology and Drug Design, 2014 Vol.7 No.2/3, pp.183 - 194

Published online: 27 May 2014 *

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