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ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.
Matthew D Wilkerson1, D Neil Hayes
1Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. mwilkers@med.unc.edu
Bioinformatics (Oxford, England)
|April 30, 2010
Summary
Unsupervised class discovery aids cancer research by identifying unknown biological groups. Consensus clustering (CC) and the ConsensusClusterPlus tool offer enhanced visualizations for more precise class identification.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Research
Background:
- Unsupervised class discovery is crucial in cancer research for identifying intrinsic biological subgroups.
- Consensus clustering (CC) is a method for estimating the number of classes in a dataset, providing stability evidence.
- ConsensusClusterPlus is an R package implementing CC with enhanced functionality.
Purpose of the Study:
- To introduce ConsensusClusterPlus, an R package for consensus clustering.
- To extend CC with new visualizations and features for improved unsupervised class discovery.
- To provide users with detailed information for making specific decisions in class discovery.
Main Methods:
- Implementation of the consensus clustering (CC) method in R.
- Development of new visualizations including item tracking, item-consensus, and cluster-consensus plots.
- Extension of CC functionality for enhanced data analysis.
Main Results:
- ConsensusClusterPlus provides quantitative and visual evidence for class stability.
- New features enable more specific decisions in unsupervised class discovery.
- The software facilitates detailed analysis of intrinsic groups within datasets.
Conclusions:
- ConsensusClusterPlus enhances unsupervised class discovery in cancer research.
- The tool offers advanced visualizations and detailed information for robust analysis.
- It supports precise identification of biological subgroups through consensus clustering.
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