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Bioinformatics Advance Access published online on November 16, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl581
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© 2006 The Author(s)
Received August 27, 2006
Revised November 14, 2006
Accepted November 14, 2006

Article

Network neighborhood analysis with the multi-node topological overlap measure

Ai Li 1 and Steve Horvath 2 *

1 Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095-1772, USA
2 Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095-1772, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095-7088, USA

* To whom correspondence should be addressed.
Steve Horvath, E-mail: shorvath{at}mednet.ucla.edu


  Abstract

Motivation: The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial ‘seed’ set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures.

Results: The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes.

Availability: A executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/.

Associate Editor: Golan Yona
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