Bioinformatics Advance Access originally published online on July 31, 2006
Bioinformatics 2006 22(19):2405-2412; doi:10.1093/bioinformatics/btl406
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Evaluation and comparison of gene clustering methods in microarray analysis
1 Department of Human Genetics, University of Pittsburgh Pittsburgh, PA, USA
2 Department of Biostatistics, University of Pittsburgh Pittsburgh, PA, USA
*To whom correspondence should be addressed.
Motivation: Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including hierarchical clustering, K-means, PAM, SOM, mixture model-based clustering and tight clustering have been widely used in the literature. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods.
Results: In this paper, six gene clustering methods are evaluated by simulated data from a hierarchical log-normal model with various degrees of perturbation as well as four real datasets. A weighted Rand index is proposed for measuring similarity of two clustering results with possible scattered genes (i.e. a set of noise genes not being clustered). Performance of the methods in the real data is assessed by a predictive accuracy analysis through verified gene annotations. Our results show that tight clustering and model-based clustering consistently outperform other clustering methods both in simulated and real data while hierarchical clustering and SOM perform among the worst. Our analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis.
Contact: ctseng{at}pitt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on February 4, 2006; revised on May 23, 2006; accepted on July 21, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. J. Kiddle, O. P. F. Windram, S. McHattie, A. Mead, J. Beynon, V. Buchanan-Wollaston, K. J. Denby, and S. Mukherjee Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana Bioinformatics, February 1, 2010; 26(3): 355 - 362. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Shen, W. Sun, and K.-C. Li Dynamically weighted clustering with noise set Bioinformatics, February 1, 2010; 26(3): 341 - 347. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Scharl, B. Gru, and F. Leisch Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects Bioinformatics, February 1, 2010; 26(3): 370 - 377. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. P. Brass, M. A. Peters, K. W. Hinchcliff, Y. D. He, and R. G. Ulrich Temporal pattern of skeletal muscle gene expression following endurance exercise in Alaskan sled dogs J Appl Physiol, August 1, 2009; 107(2): 605 - 612. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y.-S. Lee, C.-H. Chen, C.-N. Tsai, C.-L. Tsai, A. Chao, and T.-H. Wang Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips Nucleic Acids Res., May 1, 2009; 37(8): e61 - e61. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Sharma, R. Podolsky, J. Zhao, and R. A. McIndoe A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets Bioinformatics, May 1, 2009; 25(9): 1152 - 1157. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Rogers, M. Girolami, W. Kolch, K. M. Waters, T. Liu, B. Thrall, and H. S. Wiley Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models Bioinformatics, December 15, 2008; 24(24): 2894 - 2900. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Krishnan and A. Pereira Integrative approaches for mining transcriptional regulatory programs in Arabidopsis Briefings in Functional Genomics, July 16, 2008; (2008) eln035v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Langfelder, B. Zhang, and S. Horvath Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R Bioinformatics, March 1, 2008; 24(5): 719 - 720. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. C. Tseng Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data Bioinformatics, September 1, 2007; 23(17): 2247 - 2255. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. L. Elo, H. Jarvenpaa, M. Oresic, R. Lahesmaa, and T. Aittokallio Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process Bioinformatics, August 15, 2007; 23(16): 2096 - 2103. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Lu, X. He, and S. Zhong Cross-species microarray analysis with the OSCAR system suggests an INSR->Pax6->NQO1 neuro-protective pathway in aging and Alzheimer's disease Nucleic Acids Res., July 13, 2007; 35(suppl_2): W105 - W114. [Abstract] [Full Text] [PDF]
|
|