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

摘要

Biological functions are carried out by groups of interacting molecules,cells or tissues, known as communities. Membership in these communities mayoverlap when biological components are involved in multiple functions. However,traditional clustering methods detect non-overlapping communities. Thesedetected communities may also be unstable and difficult to replicate, becausetraditional methods are sensitive to noise and parameter settings. Theseaspects of traditional clustering methods limit our ability to detectbiological communities, and therefore our ability to understand biologicalfunctions. To address these limitations and detect robust overlapping biologicalcommunities, we propose an unorthodox clustering method called SpeakEasy whichidentifies communities using top-down and bottom-up approaches simultaneously.Specifically, nodes join communities based on their local connections, as wellas global information about the network structure. This method can quantify thestability of each community, automatically identify the number of communities,and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks andaccurately identifies meaningful biological communities in a range of datasets,including: gene microarrays, protein interactions, sorted cell populations,electrophysiology and fMRI brain imaging.