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COPA---cancer outlier profile analysis

Published: 10 November 2006 Publication History

Abstract

Summary: Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type. Standard statistical tests, such as the t -test are not suited for detecting these translocations, but a simple test based on robust centering and scaling of the data to help detect outlier samples, followed by a search for pairs of samples with mutually exclusive outliers, may be used to find genes involved in recurrent translocations. We have implemented this method, termed Cancer Outlier Profile Analysis (COPA) in an R package (that we call the copa package), and show its applicability on a publicly available dataset.
Availability: http://www.bioconductor.org

Cited By

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  • (2018)GSEHIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.261892715:1(129-146)Online publication date: 1-Jan-2018
  • (2014)Outlier analysis and top scoring pair for integrated data analysis and biomarker discoveryIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2013.15311:3(520-532)Online publication date: 1-May-2014
  • (2014)CSAXProceedings of the 18th Annual International Conference on Research in Computational Molecular Biology - Volume 839410.1007/978-3-319-05269-4_18(222-236)Online publication date: 2-Apr-2014
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cover image Bioinformatics
Bioinformatics  Volume 22, Issue 23
November 2006
132 pages

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Oxford University Press, Inc.

United States

Publication History

Published: 10 November 2006

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Cited By

View all
  • (2018)GSEHIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.261892715:1(129-146)Online publication date: 1-Jan-2018
  • (2014)Outlier analysis and top scoring pair for integrated data analysis and biomarker discoveryIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2013.15311:3(520-532)Online publication date: 1-May-2014
  • (2014)CSAXProceedings of the 18th Annual International Conference on Research in Computational Molecular Biology - Volume 839410.1007/978-3-319-05269-4_18(222-236)Online publication date: 2-Apr-2014
  • (2013)Outlier gene set analysis combined with top scoring pair provides robust biomarkers of pathway activityProceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics10.1007/978-3-642-39159-0_5(47-58)Online publication date: 17-Jun-2013
  • (2012)Tri-mean-based statistical differential gene expression detectionInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2012.0492456:3(255-271)Online publication date: 1-Sep-2012
  • (2011)Identifying novel prostate cancer associated pathways based on integrative microarray data analysisComputational Biology and Chemistry10.1016/j.compbiolchem.2011.04.00335:3(151-158)Online publication date: 1-Jun-2011
  • (2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009

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