Undergraduate Senior Honors Thesis Defense: Detangling PPI Networks to Uncover Functionally Meaningful Clusters

Abstract

We compare computational methods for decomposing a PPI network intoa 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). We compare the performance of three popular community detection algorithms that produce non-overlapping clusters 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 some cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters. We look at extending to methods that produce overlapping clusters.