Abstract

Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here, we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that consider-ably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein–protein interactions and negative genetic interactions in yeast, we discover epistatic relations among protein complexes. Second, we analyze protein–protein interactions and DNA damage-specific positive genetic interactions in yeast and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non–small-cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high-throughput omic data.