MacDonald James W, Ghosh Debashis
University of Michigan Cancer Center, Ann Arbor, MI, USA.
Bioinformatics. 2006 Dec 1;22(23):2950-1. doi: 10.1093/bioinformatics/btl433. Epub 2006 Aug 7.
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.
染色体易位在癌症中很常见，在某些情况下可能是疾病进展的病因。使用能够同时测量数千个基因表达的微阵列，有可能使人们检测出特定癌症类型中的复发性易位。标准统计检验，如t检验，并不适合检测这些易位，但基于数据的稳健中心化和缩放以帮助检测异常样本，随后搜索具有相互排斥异常值的样本对的简单检验，可用于找到参与复发性易位的基因。我们已在一个R包（我们称之为copa包）中实现了这种称为癌症异常谱分析（COPA）的方法，并展示了其在一个公开可用数据集上的适用性。
