Clustering and analyzing microarray data of lymphoma using singular value decomposition (SVD) and hybrid clustering

Abstract

DNA microarray technology is used to analyze thousands of gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Clustering algorithm has been used as an alternative approach to identify structures from gene expression data. In this paper, we introduce a transform technique based on Singular Value Decomposition (SVD) to identify normalized matrix of gene expression data followed by Partitioning Around Medoids (PAM) to cluster and then displaying the best cluster according to Davis Bouldin Index (DBI) based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard dataset demonstrated the effectiveness of the algorithm in gene expression data.