Network neighborhood analysis with the multi-node topological overlap measure.
BACKGROUND
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.


BACKGROUND
An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).
Li
Ai
A
Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095-1772, USA.
Horvath
Steve
S
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins