Cluster Analysis of Breast Cancer Microarray Data High-throughput genomic te chnology has ra pidly become a major tool for the study of breast ca ncer. The re cent development of gene expression microarray and related technologies provides an opportunity to pe rform more deta iled profiling of the disease. However, whole-genome approaches are still relatively new a nd critics have bee n quick to highlight non-overlapping results from groups testing similar hypotheses. In this canopy, cluster analysis helps to reduce complex multivariate data and may be used to devise in the development of classifica tion systems or taxonomies by gene ontology. Cluster software developed by the Eisen Lab: http://rana.lbl.gov/EisenSoftware.htm Java Treeview software developed by the Eisen Lab: http://rana.lbl.gov/EisenSoftware.htm Princeton GO Term Finder: http://go.princeton.edu/cgi-bin/GOTermFinder Cancer gene chip database tool and David bioinformatics resource. Data set (.txt file) obtained from Perou et al., (2000) including gene expression profiles obtained on a collection of 65 breast tissue samples (n=42) was taken as raw data. Filtration of the data was carried out to find out the gene of interest>=75% present. Further, for clustering of genes, complete linkage was used. The cluster analysis results (.cdt file) was analyzed by the java tree view software. The cluster analysis showed over expression of 33 genes. The genes list were converted into gene symbols list by using cancer genes Chip database tool and David bioinformatics resource. Gene ontology of the 33 genes were carried out in order to search for the functional groups. Cluster of breast cancer data Process ontology tree Functional ontology tree
Cluster Analysis of Breast Cancer Microarray DataHigh-throughput genomic te chnology has ra pidly become a major tool for the study of breast ca ncer. The re cent development of gene expression microarray and related technologies provides an opportunity to pe rform more deta iled profiling of the disease. However, whole-genome approaches are still relatively new a nd critics have bee n quick to highlight non-overlapping results from groups testing similar hypotheses.In this canopy, cluster analysis helps to reduce complex multivariate data and may be used to devise in the development of classifica tion systems or taxonomies by gene ontology. Data set (.txt file) obtained from Perou et al., (2000) including gene expression profiles obtained on a collection of 65 breast tissue samples (n=42) was taken as raw data. Filtration of the data was carried out to find out the gene of interest>=75% present. Further, for clustering of genes, complete linkage was used. The cluster analysis results (.cdt file) was analyzed by the java tree view software. The cluster analysis showed over expression of 33 genes. The genes list were converted into gene symbols list by using cancer genes Chip database tool and David bioinformatics resource. Gene ontology of the 33 genes were carried out in order to search for the functional groups. Cluster of breast cancer data Process ontology treeFunctional ontology tree
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