An adaptive refinement for community detection methods for disease module identification in biological networks using novel metric based on connectivity, conductance & modularity

Disease processes are usually driven by several genes interacting in molecular modules or pathways leading to the disease. The identification of such modules in gene or protein networks is at the core of several analysis methods in biomedical research. However, there is still a need to develop a generic framework to uncover biologically relevant modules for different types of networks. With this pretext in mind, the Disease Module Identification DREAM Challenge was initiated as an effort to systematically assess module identification methods on a panel of 6 diverse state-of-the-art genomic networks. Methods: In this paper, we propose a generic refinement method based on ideas of merging and splitting the hierarchical tree obtained from any community detection technique for constrained disease module identification in biological networks. The only constraint for a module to be considered as a candidate disease module was size of the community to be: 3 ≤ community size ≤ 100. Here, we propose a novel quality metric, called F-score, computed from several unsupervised quality metrics like modularity, conductance and connectivity to determine the quality of a graph partition at a given level of hierarchy. We also propose a quality metric, namely Inverse Confidence, which ranks and prune insignificant modules to obtain a curated list of candidate disease modules for a given biological network. The predicted modules are then evaluated on the basis of the total number of unique candidate modules that are associated with complex traits and diseases from over 200 genome-wide association study (GWAS) datasets. Results: We stood 9
^th
out of a total of 42 teams in the competition at the offical FDR cut-off of 0.05 for identifying statistically significant disease associated modules in the 6 benchmark networks. Our proposed approach detected a total of 44 disease modules in the 6 benchmark networks in comparison to 60 for the winner of the DREAM Challenge. For several benchmark networks we were better or competitive with the winner.