  High-density DNA arrays, used to monitor gene expression at a genomic scale,
have produced vast amounts of information which require the development of
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 profiles
that were generated recently during a study of the yeast cell cycle. SPC was
used to organize genes into biologically relevant clusters that are suggestive
for their co-regulation. Some of the advantages of SPC are its robustness
against noise and initialization, a clear signature of cluster formation and
splitting, and an unsupervised self-organized determination of the number of
clusters at each resolution. Our analysis revealed interesting correlated
behavior of several groups of genes which has not been previously identified.
