Bioinformatics Advance Access originally published online on December 26, 2008
Bioinformatics 2009 25(4):526-532; doi:10.1093/bioinformatics/btn660
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Differential dependency network analysis to identify condition-specific topological changes in biological networks
1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, 2Bioinformatics Unit, RRB, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, 3Lombardi Comprehensive Cancer Center and Department of Oncology, Physiology and Biophysics, Georgetown University, Washington, DC 20057, 4Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231 and 5Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Significant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets.
Results: Here, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics.
Availability: The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.
Contact: yuewang{at}vt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Jonathan Wren
Received on November 3, 2008; revised on December 17, 2008; accepted on December 22, 2008
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