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Bioinformatics Advance Access published online on May 30, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm276
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Mining co-regulated gene profiles for the detection of functional associations in gene expression data

Attila Gyenesei 1,#, Ulrich Wagner 2,#, Simon Barkow-Oesterreicher 2, Etzard Stolte 1 and Ralph Schlapbach 2

1Knowledge and Data Analysis, Unilever Research Vlaardingen, 3130 AC Vlaardingen, The Netherlands
2Functional Genomics Center Zürich, Uni ETH Zürich, CH-8057 Zürich, Switzerland

To whom correspondence should be addressed. Dr. Ulrich Wagner, E-mail: ulrich.wagner{at}fgcz.ethz.ch, gyenesei{at}gmail.com


  Abstract

Motivation: Association pattern discovery (APD) methods have been successfully applied to gene expression data. They find groups of co-regulated genes in which the genes are either up- or down-regulated throughout the identified conditions. These methods, however, fail to identify similarly expressed genes whose expressions change between up- and down-regulation from one condition to another. In order to discover these hidden patterns, we propose the concept of mining co-regulated gene profiles. Co-regulated gene profiles contain two gene sets such that genes within the same set behave identically (up or down) while genes from different sets display contrary behavior. To reduce and group the large number of similar resulting patterns, we propose a new similarity measure that can be applied together with hierarchical clustering methods.

Results: We tested our proposed method on two well-known yeast microarray datasets. Our implementation mined the data effectively and discovered patterns of co-regulated genes that are hidden to traditional APD methods. The high content of biologically relevant information in these patterns is demonstrated by the significant enrichment of co-regulated genes with similar functions. Our experimental results show that the MAP method is an efficient tool for the analysis of gene expression data and competitive with bi-clustering techniques.

Supplementary information: Supplementary data and an executable demo program of the MAP implementation are freely available at http://www.fgcz.ethz.ch/publications/map

Associate Editor: Dr. Olga Troyanskaya

# These authors contributed equally to this work.

Received on February 16, 2007; revised on April 25, 2007; accepted on May 16, 2007

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