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Bioinformatics Advance Access originally published online on October 27, 2004
Bioinformatics 2005 21(7):1037-1045; doi:10.1093/bioinformatics/bti074
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Local correlation of expression profiles with gene annotations—proof of concept for a general conciliatory method

F. R. Pinto 1,*, L. Ashley Cowart 2, Yusuf A. Hannun 2, B. Rohrer 3 and J. S. Almeida 1,4

1Biomathematics Group, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa 2781-901 Oeiras, Portugal
2Department of Biochemistry and Molecular Biology, Medical University of South Carolina Charleston, SC 29425, USA
3Departments of Ophthalmology and Physiology & Neuroscience, Medical University of South Carolina Charleston, SC 29425, USA
4Department of Biostatistics Bioinformatics and Epidemiology, Medical University of South Carolina Charleston, SC 29425, USA

*To whom correspondence should be addressed.

Motivation: Integrated analysis of expression data and gene ontology annotations is a prime example of biological data that need co-explanatory interpretation. This particular application is used to validate a new method for integrated analysis of varied biological information.

Results: The proposed method consists of determining local correlation coefficients and the corresponding P-values calculated per biological entity. This measure considers the combined intensity and significance of the agreement or disagreement, between two data sources about the same biological entity. The method is applied to the integrated analysis of gene expression and annotation of two gene sets, one from yeast and other from mouse. The potential of the method to generate accurate mechanistic hypothesis is also demonstrated. Specially, negative correlation results pose a new kind of biological hypothesis. Method performance was compared with annotation enrichment methods, and optimal conditions for the superiority of local correlation results are discussed.

Availability: The matlab functions described in this article are available at http://bioinformatics.musc.edu/~frpinto/

Contact: almeidaj{at}musc.edu

Supplementary information: Further information, tables and figures are available at http://bioinformatics.musc.edu/~frpinto/


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