Bioinformatics Vol. 19 no. 9 2003
Pages 1090-1099
© 2003 Oxford University Press
Bagging to improve the accuracy of a clustering procedure
1 Division of Biostatistics, School of
Public Health, University of California, Berkeley, 140 Earl Warren
Hall, #7360, Berkeley, CA 94720-7360, USA
2 Jain Lab, Comprehensive Cancer Center,
University of California, San Francisco, 2340 Sutter St., #N412,
San Francisco, CA 94143-0128, USA
Received on November 15, 2001
; revised on November 8, 2002
; accepted on November 11, 2002
Motivation: The microarray technology is increasingly being applied in biological and medical research to address a wide range of problems such as the classification of tumors. An important statistical question associated with tumor classification is the identification of new tumor classes using gene expression profiles. Essential aspects of this clustering problem include identifying accurate partitions of the tumor samples into clusters and assessing the confidence of cluster assignments for individual samples.
Results: Two new resampling methods, inspired from bagging in prediction, are proposed to improve and assess the accuracy of a given clustering procedure. In these ensemble methods, a partitioning clustering procedure is applied to bootstrap learning sets and the resulting multiple partitions are combined by voting or the creation of a new dissimilarity matrix. As in prediction, the motivation behind bagging is to reduce variability in the partitioning results via averaging. The performances of the new and existing methods were compared using simulated data and gene expression data from two recently published cancer microarray studies. The bagged clustering procedures were in general at least as accurate and often substantially more accurate than a single application of the partitioning clustering procedure. A valuable by-product of bagged clustering are the cluster votes which can be used to assess the confidence of cluster assignments for individual observations
Contact: sandrine{at}stat.berkeley.edu
Supplementary information: For supplementary information on datasets, analyses, and software, consult http://www.stat.berkeley.edu/~sandrine and http://www.bioconductor.org.
* To whom correspondence should be addressed.
The authors wish it to be known that, in their opinion, both authors should be regarded as joint First Authors.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  I. Ozden, H. M. Lee, M. R. Sullivan, and S. S.-H. Wang Identification and Clustering of Event Patterns From In Vivo Multiphoton Optical Recordings of Neuronal Ensembles J Neurophysiol, July 1, 2008; 100(1): 495 - 503. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Amaratunga, J. Cabrera, and V. Kovtun Microarray learning with ABC Biostat., January 1, 2008; 9(1): 128 - 136. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Seo Young Kim and J. Won Lee Ensemble clustering method based on the resampling similarity measure for gene expression data Statistical Methods in Medical Research, December 1, 2007; 16(6): 539 - 564. [Abstract] [PDF]
|
|
|
|
 |
|
 Z. Yu, H.-S. Wong, and H. Wang Graph-based consensus clustering for class discovery from gene expression data Bioinformatics, November 1, 2007; 23(21): 2888 - 2896. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Guo, Y. Li, X. Gong, C. Yao, W. Ma, D. Wang, Y. Li, J. Zhu, M. Zhang, D. Yang, et al. Edge-based scoring and searching method for identifying condition-responsive protein protein interaction sub-network Bioinformatics, August 15, 2007; 23(16): 2121 - 2128. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D.-W. Kim, K.-Y. Lee, K. H. Lee, and D. Lee Towards clustering of incomplete microarray data without the use of imputation Bioinformatics, January 1, 2007; 23(1): 107 - 113. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Valentini Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data Bioinformatics, February 1, 2006; 22(3): 369 - 370. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. W. Mount and R. Pandey Using bioinformatics and genome analysis for new therapeutic interventions Mol. Cancer Ther., October 1, 2005; 4(10): 1636 - 1643. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D.-W. Kim, K. H. Lee, and D. Lee Detecting clusters of different geometrical shapes in microarray gene expression data Bioinformatics, May 1, 2005; 21(9): 1927 - 1934. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Holleman, M. H. Cheok, M. L. den Boer, W. Yang, A. J.P. Veerman, K. M. Kazemier, D. Pei, C. Cheng, C.-H. Pui, M. V. Relling, et al. Gene-Expression Patterns in Drug-Resistant Acute Lymphoblastic Leukemia Cells and Response to Treatment N. Engl. J. Med., August 5, 2004; 351(6): 533 - 542. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. D. Rasmussen, M. S. Deshpande, G. Karypis, J. Johnson, J. A. Crow, and E. F. Retzel wCLUTO: A Web-Enabled Clustering Toolkit Plant Physiology, October 1, 2003; 133(2): 510 - 516. [Abstract] [Full Text] [PDF]
|
|