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ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

Matthew D. Wilkerson; D. Neil Hayes · Bioinformatics · 2010

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Author / responsible party
Matthew D. Wilkerson; D. Neil Hayes
Publisher
Bioinformatics
Year
2010
Language
English

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UNLABELLED: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. AVAILABILITY: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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APA 7

Wilkerson, M. D. & Hayes, D. N. (2010). ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. https://doi.org/10.1093/bioinformatics/btq170

MLA

Wilkerson, Matthew D, and D. Neil Hayes. "ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking." 2010. https://doi.org/10.1093/bioinformatics/btq170.

Chicago

Wilkerson, Matthew D. and D. Neil Hayes. 2010. "ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.". https://doi.org/10.1093/bioinformatics/btq170.

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Wilkerson, M. D. and Hayes, D. N. 2010, ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking, Bioinformatics, available at: https://doi.org/10.1093/bioinformatics/btq170 [Accessed 5 Aug. 2026].

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Title
ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking
Author / contributors
Matthew D. Wilkerson; D. Neil Hayes
Publisher
Bioinformatics
Publication year
2010
Language
English

Subjects

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Bioconductor; Computer science; Class (philosophy); Cluster analysis; Software; Data mining; R package; False discovery rate; Machine learning; Artificial intelligence; Biology; Programming language
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