Fuzzy J-Means and VNS methods for clustering genes from microarray data

  • Authors:

  • Nabil Belacel;Miroslava Čuperlović-Culf;Mark Laflamme;Rodney Ouellette

  • Affiliations:

  • National Research Council Canada, Institute for Information Technology-e-Health group, 127 Carleton Street, St-John, NB, Canada E2L2Z6;Institut de recherche médicale Beauséjour, Hotel-Dieu Pavilion 35 Providence, Moncton, NB, Canada E1C 8X3;Institut de recherche médicale Beauséjour, Hotel-Dieu Pavilion 35 Providence, Moncton, NB, Canada E1C 8X3;Institut de recherche médicale Beauséjour, Hotel-Dieu Pavilion 35 Providence, Moncton, NB, Canada E1C 8X3

  • Venue:
  • Bioinformatics
  • Year:
  • 2004

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Abstract

Motivation: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. Results: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. Availability: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca