Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering
Open accessAbstract
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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Using the π1 statistic, 55-90% of associated genes are shared among correlated Alzheimer's disease-related traits.
“Overall, using the π1 statistic20, we estimated that 55–90% of the associated genes are shared amongst these correlated AD-related (Figure S2).”
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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
- ORCID
- Venue
- Scientific Reports
- Published
- 2015-11-09
- Volume
- 5
- Issue
- 1
- Pages
- 16361-16361
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4637843
- 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
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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