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Bioinformatics Advance Access originally published online on September 7, 2007
Bioinformatics 2007 23(20):2733-2740; doi:10.1093/bioinformatics/btm441
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Exploiting sample variability to enhance multivariate analysis of microarray data

Carla S. Möller-Levet 1,2,*, Catharine M. West 2 and Crispin J. Miller 1

1Paterson Institute for Cancer Research, Cancer Research UK and 2Academic Radiation Oncology, The University of Manchester, Christie Hospital, Manchester, M20 4BX, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses.

Results: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data.

Availability: Matlab script files are available from the author.

Contact: cmoller{at}picr.man.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Trey Ideker

Received on June 12, 2007; revised on July 31, 2007; accepted on August 20, 2007

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