Motivation: The identification of condition specific sub-networks from gene expression profiles has important biological applications, ranging from the selection of disease-related biomarkers to the discovery of pathway alterations across different phenotypes. Although many methods exist for extracting these sub-networks, very few existing approaches simultaneously consider both the differential expression of individual genes and the differential correlation of gene pairs, losing potentially valuable information in the data.#R##N##R##N#Results: In this article, we propose a new method, COSINE (COndition SpecIfic sub-NEtwork), which employs a scoring function that jointly measures the condition-specific changes of both ‘nodes’ (individual genes) and ‘edges’ (gene–gene co-expression). It uses the genetic algorithm to search for the single optimal sub-network which maximizes the scoring function. We applied COSINE to both simulated datasets with various differential expression patterns, and three real datasets, one prostate cancer dataset, a second one from the across-tissue comparison of morbidly obese patients and the other from the across-population comparison of the HapMap samples. Compared with previous methods, COSINE is more powerful in identifying truly significant sub-networks of appropriate size and meaningful biological relevance.#R##N##R##N#Availability: The R code is available as the COSINE package on CRAN: http://cran.r-project.org/web/packages/COSINE/index.html.#R##N##R##N#Contact: ude.elay@oahz.uygnoh#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Haisu Ma', 'Eric E. Schadt', 'Lee M. Kaplan', 'Hongyu Zhao']
COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method