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Bioinformatics Advance Access first published online on October 27, 2004
This version published online on October 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti074
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 4, 2004
Revised August 31, 2004
Accepted September 20, 2004

Article

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 4

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

* To whom correspondence should be addressed.
F. R. Pinto, E-mail: frpinto{at}itqb.unl.pt


  Abstract

Motivation: Integrated analysis of expression data and gene ontology annotations is a prime example of biological data that needs 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 paper are available at (http://bioinformatics.musc.edu/~frpinto/).

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


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