Detangling PPI networks to uncover functionally meaningful clusters

Abstract

Background: Decomposing a protein-protein interaction network (PPI network) into non-overlapping clusters or communities, sometimes called "network modules," is an important way to explore functional roles of sets of genes. When the method to accomplish this decomposition is solely based on purely graph-theoretic measures of the interconnection structure of the network, this is often called unsupervised clustering or community detection. In this study, we compare unsupervised computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO). Results: We compare the performance of three popular community detection algorithms with the same algorithms run after the network is pre-processed by removing and reweighting based on the diffusion state distance (DSD) between pairs of nodes in the network. We call this "detangling" the network. In almost all cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters. Conclusions: Re-embedding using the DSD distance metric, before applying standard community detection algorithms, can assist in uncovering GO functionally enriched clusters in the yeast PPI network.