Fang-Xiang Wu1. 1. Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada. faw341@mail.usask.ca
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.
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.
Authors: G Sherlock; T Hernandez-Boussard; A Kasarskis; G Binkley; J C Matese; S S Dwight; M Kaloper; S Weng; H Jin; C A Ball; M B Eisen; P T Spellman; P O Brown; D Botstein; J M Cherry Journal: Nucleic Acids Res Date: 2001-01-01 Impact factor: 16.971
Authors: Chang F Quo; Chanchala Kaddi; John H Phan; Amin Zollanvari; Mingqing Xu; May D Wang; Gil Alterovitz Journal: Brief Bioinform Date: 2012-07 Impact factor: 11.622