Assessment of Cluster Overlaps to Improve Accuracy of Module Detection from PPI Networks

Recent computational techniques have facilitated analyzing genome-wide protein-protein interactions. Various graph-clustering algorithms have been applied to the protein interaction networks for identifying protein complexes and functional modules. Since each protein performs multiple functions, a clustering algorithm should be able to produce overlapping clusters. In this paper, we use the seed-refinement algorithm to generate a set of preliminary overlapping clusters. Next, for further refining the preliminary clusters, we carry out a systematic analysis of their overlaps by novel metrics: overlap coverage and overlapping consistency. We propose the cluster- merging algorithm to yield final clusters by parameterizing the metrics. In the test with the yeast protein-protein interaction network, we demonstrate the proposed approach improves accuracy on detecting protein complexes and functional modules by optimizing the parameter values.