Identifying robust clusters and multi-community nodes by combining top-down and bottom-up approaches to clustering

Biological functions are often realized by groups of interacting molecules or cells. Membership in these groups may overlap when molecules or cells are reused in multiple functions. Traditional clustering methods assign components to no more than one group, and cannot identify multi-community nodes. Technical noise is common in high-throughput biological datasets and further blurs distinctions between clusters. Together, overlapping nodes and high levels of noise reduce our ability to accurately define clusters in biological datasets and to interpret their biological functions To address these limitations, we designed an algorithm called SpeakEasy, which detects overlapping or non-overlapping communities in commonly studied biological networks. Input to SpeakEasy can be physical networks, such as molecular interactions, or inferred networks, such as gene coexpression networks. The networks can be directed or undirected, and may contain negative links. SpeakEasy combines traditional bottom-up and top-down approaches to clustering, by creating competition between clusters. Nodes that oscillate between multiple clusters in this competition are classified as multi-community nodes. SpeakEasy can quickly process networks with tens of thousands of nodes, quantify the stability of each cluster and select an optimal number of clusters automatically without requiring manual “parameter tuning” for classleading results. Clustering networks derived from gene microarrays, protein affinity, sorted cell populations, electrophysiology, functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights. For instance, we can identify overlapping co-regulated genes sets, multi-complex proteins and robust changes to the community structure of co-active brain regions in Parkinson disease. These insights rely on a robust overlapping clustering approach that enables a more realistic interpretation of common highthroughput datasets.