Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
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Consensus clustering identified five major proteomic subtypes in the tumor cohort, with 15, 9, 25, 11, and 19 cases in subtypes A through E.
“Using the Consensus Clustering24 method (Supplementary Methods 9.1–9.2, Extended Data Fig. 9), we identified five major proteomic subtypes in this tumor cohort, with 15, 9, 25, 11, and 19 cases in subtypes A through E, respectively (Fig. 4a–b).”
From Proteogenomic characterization of human colon and rectal cancer (2014) · cites this paper as [24]
We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering to RNASeq data from 68 tumors from TCGA.
“We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering81–83 to RNASeq data from 68 tumors from The Cancer Genome Atlas (TCGA) dataset67.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [83]
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- Authors
- Monti, S., Tamayo, P., Mesirov, J., & Golub, T.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Machine Learning
- Published
- 2003-07
- Volume
- 52
- Issue
- 1-2
- Pages
- 91-118
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1548779692
- Cited by
- 2,116
- References
- 46
- Citations by year
- 2026: 134 · 2025: 113 · 2024: 155 · 2023: 133 · 2022: 139 · 2021: 192 · 2020: 123 · 2019: 112 · 2018: 120 · 2017: 100 · 2016: 85 · 2015: 94 · 2014: 78 · 2013: 77 · 2012: 80
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- consensus clustering, gene expression microarrays, cluster validation, cluster stability, resampling, cluster visualization, optimal number of clusters, K-means clustering, self-organizing maps, Bayesian clustering, sensitivity to initial conditions
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