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GEDA-C : Gene Expression Data Analysis-Clustering based-on seed method

With the advances in genomics and microarray technologies and large amounts of microarray data produced, clustering has been applied to identifying groups of genes. For example, Eisen et al. applied a hierarchical clustering algorithm to identify groups of co-regulated yeast genes [1]. Tamayo et al. used SOM (self-organizing maps) to identify clusters of genes with similar expression patterns [2]. Tavazoie et al. applied K-means method to identify clusters of genes [3]. However, major problem of conventional clustering methods is that the clustering results are dependent on which clustering method is used. Users could be confused by obtaining various results according to different clustering methods.