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Bioinformatics Advance Access originally published online on June 6, 2007
Bioinformatics 2007 23(16):2096-2103; doi:10.1093/bioinformatics/btm309
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

Laura L. Elo 1,2,*, Henna Järvenpää 2, Matej Oresic 2,3, Riitta Lahesmaa 2 and Tero Aittokallio 1,2,4

1Department of Mathematics, FI-20014 University of Turku, 2Turku Centre for Biotechnology, PO Box 123, FI-20521 Turku, 3VTT Biotechnology, PO Box 1500, FI-02044 Espoo, Finland and 4Systems Biology Unit, Institut Pasteur, FR-75724 Paris, France

*To whom correspondence should be addressed.


  Abstract

Motivation: Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene–gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.

Results: We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human–mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process.

Contact: laliel{at}utu.fi

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on April 5, 2007; revised on May 21, 2007; accepted on June 3, 2007

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