Super-paramagnetic clustering of yeast gene expression profiles

Abstract

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.

Cited by in corpus (6)

• Coupled Two-Way Clustering Analysis of Gene Microarray Data
• Algorithms of maximum likelihood data clustering with applications
• The Gibbs-plaid biclustering model
• Fast Super-Paramagnetic Clustering
• Including transcription factor information in the superparamagnetic clustering of microarray data
• Resampling Method For Unsupervised Estimation Of Cluster Validity