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

Nabil Belacel; Miroslava Cuperlović-Culf; Mark Laflamme; Rodney Ouellette

doi:10.1093/bioinformatics/bth142

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

Nabil Belacel·Miroslava Cuperlović-Culf·Mark Laflamme·Rodney Ouellette

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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

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Algorithms Blood Proteins Breast Neoplasms Cluster Analysis Computer Simulation Fuzzy Logic Gene Expression Profiling Gene Expression Regulation, Neoplastic Genetic Testing Genetic Variation Humans Neoplasm Proteins Oligonucleotide Array Sequence Analysis Reproducibility of Results Sensitivity and Specificity Comparative Study Evaluation Study Journal Article Validation Study

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Nabil Belacel, Miroslava Cuperlović-Culf, Mark Laflamme, Rodney Ouellette. 2004-02-26. Fuzzy J-Means and VNS methods for clustering genes from microarray data.. https://doi.org/10.1093/bioinformatics%2Fbth142

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