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

      research-article
      Author(s): 1 , * , 1 , 2
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: 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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          Diversity of gene expression in adenocarcinoma of the lung.

          M. Garber, O. Troyanskaya, K Schluens … (2001)
          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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            Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts.

            Richard D. Hayes, Katsuhiko Naoki, Giovanni Parmigiani … (2006)
            Published reports suggest that DNA microarrays identify clinically meaningful subtypes of lung adenocarcinomas not recognizable by other routine tests. This report is an investigation of the reproducibility of the reported tumor subtypes. Three independent cohorts of patients with lung cancer were evaluated using a variety of DNA microarray assays. Using the integrative correlations method, a subset of genes was selected, the reliability of which was acceptable across the different DNA microarray platforms. Tumor subtypes were selected using consensus clustering and genes distinguishing subtypes were identified using the weighted difference statistic. Gene lists were compared across cohorts using centroids and gene set enrichment analysis. Cohorts of 31, 72, and 128 adenocarcinomas were generated for a total of 231 microarrays, each with 2,553 reliable genes. Three adenocarcinoma subtypes were identified in each cohort. These were named bronchioid, squamoid, and magnoid according to their respective correlations with gene expression patterns from histologically defined bronchioalveolar carcinoma, squamous cell carcinoma, and large-cell carcinoma. Tumor subtypes were distinguishable by many hundreds of genes, and lists generated in one cohort were predictive of tumor subtypes in the two other cohorts. Tumor subtypes correlated with clinically relevant covariates, including stage-specific survival and metastatic pattern. Most notably, bronchioid tumors were correlated with improved survival in early-stage disease, whereas squamoid tumors were associated with better survival in advanced disease. DNA microarray analysis of lung adenocarcinomas identified reproducible tumor subtypes which differ significantly in clinically important behaviors such as stage-specific survival.
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              Optimized alignment and visualization of clustering results

              M Hoffmann (2007)
              Article 0 comments Cited 1 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 June 2010
                Publication date (Electronic): 28 April 2010
                Publication date PMC-release: 28 April 2010
                Volume: 26
                Issue: 12
                Pages: 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
                Publisher ID: btq170
                DOI: 10.1093/bioinformatics/btq170
                PMC ID: 2881355
                PubMed ID: 20427518
                SO-VID: 9db45713-a762-4afc-a1fd-2fe91d5e6285
                Copyright © © The Author 2010. Published by Oxford University Press.
                License:

                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.

                History
                Date received : 5 March 2010
                Date revision received : 5 March 2010
                Date accepted : 13 April 2010
                Categories
                Subject: Applications Note
                Subject: Gene Expression

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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