title: Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering.
abstract: Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability 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 and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.
authors.affiliation: Rush University Medical Center, Alzheimer's Disease Center, Chicago, IL.|Allen Institute for Brain Science, Modeling, Analysis and Theory Group, Seattle, WA.
authors.forename: Chris
authors.initials: C
authors.lastname: Gaiteri
authors.affiliation: Rennselaer Polytechnic Institute, Department of Computer Science, Troy, NY.
authors.forename: Mingming
authors.initials: M
authors.lastname: Chen
authors.affiliation: Rennselaer Polytechnic Institute, Department of Computer Science, Troy, NY.|Społeczna Akademia Nauk, Łódź, Poland.
authors.forename: Boleslaw
authors.initials: B
authors.lastname: Szymanski
authors.affiliation: Rennselaer Polytechnic Institute, Department of Computer Science, Troy, NY.
authors.forename: Konstantin
authors.initials: K
authors.lastname: Kuzmin
authors.affiliation: Rennselaer Polytechnic Institute, Department of Computer Science, Troy, NY.|Samsung Research America, San Jose, CA.
authors.forename: Jierui
authors.initials: J
authors.lastname: Xie
authors.affiliation: Allen Institute for Brain Science, Modeling, Analysis and Theory Group, Seattle, WA.
authors.forename: Changkyu
authors.initials: C
authors.lastname: Lee
authors.affiliation: Allen Institute for Brain Science, Modeling, Analysis and Theory Group, Seattle, WA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Blanche
authors.affiliation: Sage Bionetworks, Seattle, WA.
authors.forename: Elias
authors.initials: E
authors.lastname: Chaibub Neto
authors.affiliation: University of Washington, Department of Neurology, Seattle, WA.
authors.forename: Su-Chun
authors.initials: SC
authors.lastname: Huang
authors.affiliation: University of Washington, Department of Neurology, Seattle, WA.|University of Washington, Department of Radiology, Seattle, WA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Grabowski
authors.affiliation: University of Washington, Department of Radiology, Seattle, WA.
authors.forename: Tara
authors.initials: T
authors.lastname: Madhyastha
authors.affiliation: Trialomics, Seattle WA.
authors.forename: Vitalina
authors.initials: V
authors.lastname: Komashko
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical