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Super-paramagnetic clustering of yeast gene expression profiles

Author

Listed:
  • Getz, G.
  • Levine, E.
  • Domany, E.
  • Zhang, M.Q.

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.

Suggested Citation

  • Getz, G. & Levine, E. & Domany, E. & Zhang, M.Q., 2000. "Super-paramagnetic clustering of yeast gene expression profiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 279(1), pages 457-464.
    • Handle: RePEc:eee:phsmap:v:279:y:2000:i:1:p:457-464
    DOI: 10.1016/S0378-4371(99)00524-5
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    Cited by:

    1. de Arruda, Guilherme F. & Costa, Luciano da Fontoura & Rodrigues, Francisco A., 2012. "A complex networks approach for data clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6174-6183.
    2. Yelibi, Lionel & Gebbie, Tim, 2020. "Fast Super-Paramagnetic Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

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