A Cluster Identification Method without Using a Threshold of Correlation Coefficient in Hierarchical Cluster Analysis
نویسندگان
چکیده
One of the microarray data analyses using Gene Ontology (GO) [1] is grouping of genes by hierarchical cluster analysis (HCA) followed by extraction of common functions from the grouped genes. When some genes display similar expression changes in response to a situation change, it is thought that those have a similar function or role in a biological process. Therefore, genetic classifications from HCA allow us to estimate the functions and roles of genes in organism. The result of HCA can be shown by a dendrogram, and cluster identification from the dendrogram is generally performed by a break of cluster mergers using a threshold of correlation coefficient among genes. However the setting of the threshold prevents us from comprehensively estimating the functions of genes from HCA. We therefore propose a cluster identification method using only a statistical significance of GO without setting a threshold of correlation coefficient.
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