Clustering analysis of microarray gene expression data by splitting algorithm

Journal of Parallel and Distributed Computing(2003)

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摘要
A clustering method based on recursive bisection is introduced for analyzing microarray gene expression data. Either or both dimensions for the genes and the samples of a given microarray dataset can be classified in an unsupervised fashion. Alternatively, if certain prior knowledge of the genes or samples is available, a supervised version of the clustering analysis can also be carried out. Either approach may be used to generate a partial or complete binary hierarchy, the dendrogram, showing the underlying structure of the dataset. Compared to other existing clustering methods used for microarray data analysis (such as hierarchical and K-means), the method presented here has the advantage of much improved computational efficiency while retaining effective separation of data clusters under a distance metric, a straightforward parallel implementation, and useful extraction and presentation of biological information. Clustering results of both synthesized and experimental microarray data are presented to demonstrate the performance of the algorithm.
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关键词
clustering analysis,data cluster,existing clustering method,clustering method,microarray gene expression data,clustering result,splitting algorithm,microarray data analysis,experimental microarray data,biological information,microarray dataset,cluster analysis
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