Open Access

The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis

EURASIP Journal on Advances in Signal Processing20042004:823191

https://doi.org/10.1155/S1110865704309145

Received: 28 February 2003

Published: 21 January 2004

Abstract

An unsupervised data clustering method, called the local maximum clustering (LMC) method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999).

Keywords

data clusterclustering methodmicroarraygene expressionclassificationmodel data sets

Authors’ Affiliations

(1)
Laboratory of Biophysical Chemistry, National Heart, Lung, and Blood Institute, National Institutes of Health
(2)
National Human Genome Research Institute, National Institutes of Health
(3)
Department of Pathology, Loyola University Medical Center

Copyright

© Wu et al. 2004

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.