Kevin Lim1, Limsoon Wong. 1. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417.
Abstract
MOTIVATION: Microarray data analysis is often applied to characterize disease populations by identifying individual genes linked to the disease. In recent years, efforts have shifted to focus on sets of genes known to perform related biological functions (i.e. in the same pathways). Evaluating gene sets reduces the need to correct for false positives in multiple hypothesis testing. However, pathways are often large, and genes in the same pathway that do not contribute to the disease can cause a method to miss the pathway. In addition, large pathways may not give much insight to the cause of the disease. Moreover, when such a method is applied independently to two datasets of the same disease phenotypes, the two resulting lists of significant pathways often have low agreement. RESULTS: We present a powerful method, PFSNet, that identifies smaller parts of pathways (which we call subnetworks), and show that significant subnetworks (and the genes therein) discovered by PFSNet are up to 51% (64%) more consistent across independent datasets of the same disease phenotypes, even for datasets based on different platforms, than previously published methods. We further show that those methods which initially declared some large pathways to be insignificant would declare subnetworks detected by PFSNet in those large pathways to be significant, if they were given those subnetworks as input instead of the entire large pathways. AVAILABILITY: http://compbio.ddns.comp.nus.edu.sg:8080/pfsnet/
MOTIVATION: Microarray data analysis is often applied to characterize disease populations by identifying individual genes linked to the disease. In recent years, efforts have shifted to focus on sets of genes known to perform related biological functions (i.e. in the same pathways). Evaluating gene sets reduces the need to correct for false positives in multiple hypothesis testing. However, pathways are often large, and genes in the same pathway that do not contribute to the disease can cause a method to miss the pathway. In addition, large pathways may not give much insight to the cause of the disease. Moreover, when such a method is applied independently to two datasets of the same disease phenotypes, the two resulting lists of significant pathways often have low agreement. RESULTS: We present a powerful method, PFSNet, that identifies smaller parts of pathways (which we call subnetworks), and show that significant subnetworks (and the genes therein) discovered by PFSNet are up to 51% (64%) more consistent across independent datasets of the same disease phenotypes, even for datasets based on different platforms, than previously published methods. We further show that those methods which initially declared some large pathways to be insignificant would declare subnetworks detected by PFSNet in those large pathways to be significant, if they were given those subnetworks as input instead of the entire large pathways. AVAILABILITY: http://compbio.ddns.comp.nus.edu.sg:8080/pfsnet/