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Bioinformatics Advance Access published online on July 31, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl406
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received February 4, 2006
Revised May 23, 2006
Accepted July 21, 2006

Article

Evaluation and comparison of gene clustering methods in microarray analysis

Anbupalam Thalamuthu 1 $, Indranil Mukhopadhyay 1 $, Xiaojing Zheng 1, and George C. Tseng 2 *

1 Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
2 Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA


  Abstract

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 data sets. 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.

Associate Editor: Thomas Lengauer

$These authors contributed equally.


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