Application of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data
نویسندگان
چکیده
منابع مشابه
Application of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data
Researchers are frequently faced with the analysis of microarray data of a relatively large number of genes using a small number of tissue samples. We examine the application of two statistical methods for clustering such microarray expression data: EMMIX-GENE and GeneClust. EMMIX-GENE is a mixture-model based clustering approach, designed primarily to cluster tissue samples on the basis of the...
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About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex-linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the...
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MOTIVATION Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present nov...
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The analysis of microarray data remains a challenge as one wish to investigate the possibility of the expression of thousands of genes across multiple samples. Naturally the issue of multiplicity arises as one examines the significance of large numbers of genes. Recently, one of the coauthors, DBA, and colleagues developed a mixed model approach to this very problem with successful application ...
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ژورنال
عنوان ژورنال: Cancer Informatics
سال: 2007
ISSN: 1176-9351,1176-9351
DOI: 10.1177/117693510700500002