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Fuzzy C-means method for clustering microarray data.

Doulaye Dembélé·Philippe Kastner
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Abstract

MOTIVATION: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. RESULTS: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. AVAILABILITY: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

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AlgorithmsCluster AnalysisDatabases, GeneticFuzzy LogicGene Expression ProfilingGene Expression RegulationHumansNeoplasmsOligonucleotide Array Sequence AnalysisQuality ControlSequence Analysis, DNATumor Cells, CulturedYeastsComparative StudyEvaluation StudyJournal ArticleValidation Study

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BibTeXRIS

Doulaye Dembélé, Philippe Kastner. 2003-05-22. Fuzzy C-means method for clustering microarray data.. https://doi.org/10.1093/bioinformatics%2Fbtg119

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