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

      1 , * , 1 , 2

      Bioinformatics

      Oxford University Press

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          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: mwilkers@ 123456med.unc.edu

          Supplementary Information: Supplementary data are available at Bioinformatics online.

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          Most cited references 5

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          GenePattern 2.0.

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            Diversity of gene expression in adenocarcinoma of the lung.

            The global gene expression profiles for 67 human lung tumors representing 56 patients were examined by using 24,000-element cDNA microarrays. Subdivision of the tumors based on gene expression patterns faithfully recapitulated morphological classification of the tumors into squamous, large cell, small cell, and adenocarcinoma. The gene expression patterns made possible the subclassification of adenocarcinoma into subgroups that correlated with the degree of tumor differentiation as well as patient survival. Gene expression analysis thus promises to extend and refine standard pathologic analysis.
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              Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data.

               M. STEFANO,  T Pablo,  M Jill (2003)
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 June 2010
                28 April 2010
                28 April 2010
                : 26
                : 12
                : 1572-1573
                Affiliations
                1 Lineberger Comprehensive Cancer Center and 2 Department of Internal Medicine, Division of Medical Oncology, Multidisciplinary Thoracic Oncology Program, 450 West Drive, Campus Box 7295, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Trey Ideker

                Article
                btq170
                10.1093/bioinformatics/btq170
                2881355
                20427518
                © The Author 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Applications Note
                Gene Expression

                Bioinformatics & Computational biology

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