Autotuned Multilevel Clustering of Gene Expression Data

Abstract

DNA microarray technology has revolutionized biological and med ical research by enabling bio logists to measure expression levels of thousands of genes in a single experiment. Different computational techniques have been proposed to extract important biological information fro m the massive amount of gene expression data generated by DNA microarray technology. This paper presents a top down hierarchical clustering algorithm that produces a tree of genes called GERC tree (GERC stands for Gene Exp ression Recursive Clustering) along with the generated clusters. GERC tree is an amp le resource of biolog ical information about the genes in an expression dataset. Unlike dendrogram, a GERC tree is not a binary tree. Genes in a leaf node of GERC t ree represent a cluster. The clustering method was used with real-life datasets and the proposed method has been found satisfactory in terms of ho mogeneity, p value and z-score.