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

Matthew D. Wilkerson*, D. Neil Hayes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3181 Scopus citations

Abstract

Summary: 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/). Contact: [email protected]. Supplementary Information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Article numberbtq170
Pages (from-to)1572-1573
Number of pages2
JournalBioinformatics
Volume26
Issue number12
DOIs
StatePublished - 28 Apr 2010
Externally publishedYes

Fingerprint

Dive into the research topics of 'ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking'. Together they form a unique fingerprint.

Cite this