Super-paramagnetic clustering of yeast gene expression profiles

High-density DNA arrays, used to monitor gene expression at a genomic scale , have produced vast amounts of information which require the development o f efficient computational methods to analyze them. The important first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of a novel clustering algorithm, super-paramagnetic clustering (SPC) to analysis of gene expression profile s that were generated recently during a study of the yeast cell cycle. SPC was used to organize genes into biologically relevant clusters that are sug gestive for their co-regulation. Some of the advantages of SPC are its robu stness against noise and initialization, a clear signature of cluster forma tion and splitting, and an unsupervised self-organized determination of the number of clusters at each resolution. Our analysis revealed interesting c orrelated behavior of several groups of genes which has not been previously identified. (C) 2000 Elsevier Science B.V, All rights reserved.