Super-paramagnetic clustering of yeast gene expression profiles

Getz, G., Levine, E., Domany, E., Zhang, M. Q. (May 2000) Super-paramagnetic clustering of yeast gene expression profiles. Physica A: Statistical Mechanics and its Applications, 279 (1-4). pp. 457-464. ISSN 03784371 (ISSN)

DOI: 10.1016/S0378-4371(99)00524-5


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. (C) 2000 Elsevier Science B.V, All rights reserved.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > analysis and processing > microarray gene expression processing
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
organism description > yeast
CSHL Authors:
Communities: CSHL labs > Zhang lab
Depositing User: Matt Covey
Date: May 2000
Date Deposited: 28 Jan 2014 21:14
Last Modified: 28 Jan 2014 21:14

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