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Bioinformatics 2008 24(13):i390-i398; doi:10.1093/bioinformatics/btn142
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Differential variability analysis of gene expression and its application to human diseases

Joshua W.K. Ho 1,5,*, Maurizio Stefani 2, Cristobal G. dos Remedios 2 and Michael A. Charleston 1,3,4,*

1School of Information Technologies, 2Muscle Research Unit, Bosch Institute, 3Sydney Bioinformatics, 4Centre for Mathematical Biology, The University of Sydney, Sydney, NSW 2006 and 5National ICT Australia, Australian Technology Park, Eveleigh, NSW 1430, Australia

*To whom correspondence should be addressed.


  Abstract

Motivation: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significantincrease or decrease in expression variability (variance). Asthese observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting.

Results: Here we propose a novel analysis for changes in gene expression variability between groups of amples, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal.

Availability: The R source code for differential variability analysis is available from the contact authors upon request.

Contact: joshua{at}it.usyd.edu.au; mcharleston{at}it.usyd.edu.au


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