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 considerably 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.
Highlights
Biological networks provide a comprehensive overview of biological systems
Building on prior studies of specific pairs of networks, we introduce and study the fundamental problem of constructing a summary map of two biological networks H and G, where the nodes of both are the same genes or proteins, and the edges in each represent a distinct type of relations
We apply ModMap to experimental data in three biological scenarios: (i) using yeast protein–protein interaction (PPI) and negative genetic interaction (GI), we find epistatic relations among protein complexes, (ii) using yeast PPIs and DNA damage-specific positive GIs, we detect emerging connections among protein complexes involved in DNA damage response and (iii) using differential correlation (DC) analysis of gene expression profiles of non–small-cell lung cancer (NSCLC) tissues, we identify disease-specific loss of correlation between immune activation processes and detect disease-specific microRNAs
Summary
Biological networks provide a comprehensive overview of biological systems They enable better understanding of the system and can shed light on the function of genes and other molecular compounds. Among other applications, they have been used for discovery and prediction of gene interactions, gene functions and disease–gene associations [1,2,3,4,5,6,7,8,9]. They have been used for discovery and prediction of gene interactions, gene functions and disease–gene associations [1,2,3,4,5,6,7,8,9] In these networks, the nodes represent molecular entities and the edges represent interdependencies. With the growing use and number of types of biological networks, computational methods that exploit these rich data are of great importance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
