title: COPA--cancer outlier profile analysis.
abstract: 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.
  
  
  BACKGROUND
  http://www.bioconductor.org
authors.affiliation: University of Michigan Cancer Center, Ann Arbor, MI, USA. jmacdon@med.umich.edu
authors.forename: James W
authors.initials: JW
authors.lastname: MacDonald
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins