Cited by 77Complex Network Analysis TechniquesAdvanced Clustering Algorithms ResearchBioinformatics and Genomic Networks

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

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Gaiteri, Chris, Chen, Mingming, Szymanski, Boleslaw, Kuzmin, Konstantin, Xie, Jierui, Lee, Changkyu, Blanche, Timothy, Chaibub Neto, Elias +4 more·2015·Scientific Reports

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.

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  1. Gaiteri C., Ding Y., French B., Tseng G. C. & Sibille E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 13, 13– 24, 10.1111/gbb.12106 (2014). 24320616 PMC3896950 · DOI · Paper page

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Authors
Gaiteri, C., Chen, M., Szymanski, B., Kuzmin, K., Xie, J., Lee, C., Blanche, T., Chaibub Neto, E., Huang, S.-C., Grabowski, T., Madhyastha, T., & Komashko, V.
Affiliations
  • Rush University Medical Center (US)
  • Rensselaer Polytechnic Institute (US)
  • Allen Institute for Brain Science (US)
  • University of Washington (US)
Corresponding author
Gaiteri, Chris
Venue
Scientific Reports
Published
2015-11-09
Volume
5
Issue
1
Pages
16361-16361
ISSN
2045-2322 (ISSN-L)
OpenAlex
W2165123547
Cited by
76
References
77
Citations by year
2026: 1 · 2025: 3 · 2024: 3 · 2023: 6 · 2022: 3 · 2021: 9 · 2020: 10 · 2019: 10 · 2018: 15 · 2017: 3 · 2016: 11 · 2015: 1
FWCI
6.29
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Institutes of Health (1rc4ns073008-01)
  • Office of Naval Research (N00014-09-1-0607, N00014)
  • National Institute of Neurological Disorders and Stroke (RC4 NS073008, 1RC4NS073008-01)
  • Army Research Laboratory (W911NF, W911NF-09-2-0053)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
community detection, overlapping communities, network clustering, community stability, biological networks, protein-protein interaction network, gene expression networks, brain connectivity networks, speakeasies
MeSH terms
Algorithms, Models, Theoretical, Cluster Analysis

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