MOTIVATION

Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we focus on several shortcomings of conventional clustering algorithms and propose a new one that is able to produce better clustering solution than that produced by some others.

RESULTS

We present the Divisive Correlation Clustering Algorithm (DCCA) that is suitable for finding a group of genes having similar pattern of variation in their expression values. To detect clusters with high correlation and biological significance, we use the correlation clustering concept introduced by Bansal et al. Our proposed algorithm DCCA produces a clustering solution without taking number of clusters to be created as an input. DCCA uses the correlation matrix in such a way that all genes in a cluster have highest average correlation with genes in that cluster. To test the performance of the DCCA, we have applied DCCA and some well-known conventional methods to an artificial dataset, and nine gene-expression datasets, and compared the performance of the algorithms. The clustering results of the DCCA are found to be more significantly relevant to the biological annotations than those of the other methods. All these facts show the superiority of the DCCA over some others for the clustering of gene-expression data.

AVAILABILITY

The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.