Unsupervised clustering in mRNA expression profiles

The development of microarray technologies gives scientists the ability to examine, discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis. The most commonly used computational approach for analyzing microarray data is cluster analysis, since the number of genes is usually very high compared to the number of samples. In this paper, we investigate the application of the recently proposed k-windows clustering algorithm on gene expression microarray data. This algorithm apart from identifying the clusters present in a data set also calculates their number and thus requires no special knowledge about the data. To improve the quality of the clustering, we employ various dimension reduction techniques and propose a hybrid one. The results obtained by the application of the algorithm exhibit high classification success.