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Analyzing Microarray Data with Homogeneity Analysis
Microarray technology has been widely used in biological and medical research. It enables us to get a global picture of the gene expression profiles of the cells. On the other hand, large amount of gene function knowledge have been obtained by traditional biochemical or genetic methods during a long period of time. With the rapid development of information technology, these knowledge have been organized in well designed formats, such as MIPS functional Categories, Proteome's BioKownledge Library, etc. Especially, a dynamic controlled vocabulary called Gene Ontology has been designed as a unified description of the knowledge about genes of all eukaryotes. These functional categories represent well-organized knowledge of gene function. This information has been used to check if a gene cluster is enriched by genes that belong to a certain functional category after the clustering analysis is done. Such analysis does not address the full potential of the previously accumulated gene function knowledge for understanding the microarray experiment results. Homogeneity Analysis is a graphical multivariate method for analyzing categorical data. It makes the complicated data more accessible by displaying their main structures and regularities. It is a flexible framework for integrating the analysis of microarray gene expression data, functional category data and other categorical data based on any classification of the genes. (Software)
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