title: ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.
abstract: 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.
  
  
  BACKGROUND
  ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/).
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. mwilkers@med.unc.edu
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Wilkerson
authors.forename: D Neil
authors.initials: DN
authors.lastname: Hayes
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D025341:Principal Component Analysis; D012984:Software