Gene clustering using an evolutionary algorithm

Microarray technology enables the study of measuring gene expression levels for thousands of genes simultaneously. Cluster analysis of gene expression profiles has been applied for analyzing the function of gene because co-expressed genes are likely to share the same biological function. K-MEANS is one of well-known clustering methods. However, it requires a precise estimation of number of clusters and it has to assign all the genes into clusters. Other main problems are sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. We present a new clustering method for microarray gene data, called ppoCluster. It has two steps: 1) Estimate the number of clusters 2) Take sub-clusters resulting from the first step as input, and bridge a variation of traditional Particle Swarm Optimization (PSO) algorithm into K-MEANS for particles perform a parallel search for an optimal clustering. Our results indicate that ppoCluster is generally more accurate than K-MEANS and FKM. It also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. And it outperforms comparable methods with fast convergence rate and low computation load.