Advances in Cluster Analysis of Microarray Data

Abstract

Clustering genes into biological meaningful groups according to their pattern of expression is a main technique of microarray data analysis, based on the assumption that similarity in gene expression implies some form of regulatory or functional similarity. We give an overview of various clustering techniques, including conventional clustering methods (such as hierarchical clustering, k-means clustering, and self-organizing maps), as well as several clustering methods specifically developed for gene expression analysis.