Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

Genetic weighted k-means algorithm for clustering large-scale gene expression data

Fang-Xiang Wu

Author Affiliations

Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada

Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada

BMC Bioinformatics 2008, 9(Suppl 6):S12  doi:10.1186/1471-2105-9-S6-S12

Published: 28 May 2008

Abstract

Background

The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It is sensitive to initial partitions, its result is prone to the local minima, and it is only applicable to data with spherical-shape clusters. The last shortcoming means that we must assume that gene expression data at the different conditions follow the independent distribution with the same variances. Nevertheless, this assumption is not true in practice.

Results

In this paper, we propose a genetic weighted K-means algorithm (denoted by GWKMA), which solves the first two problems and partially remedies the third one. GWKMA is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). In GWKMA, each individual is encoded by a partitioning table which uniquely determines a clustering, and three genetic operators (selection, crossover, mutation) and a WKM operator derived from WKMA are employed. The superiority of the GWKMA over the k-means is illustrated on a synthetic and two real-life gene expression datasets.

Conclusion

The proposed algorithm has general application to clustering large-scale biological data such as gene expression data and peptide mass spectral data.