Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Bioinformatics. 2011 Mar 1;27(5):678-85. doi: 10.1093/bioinformatics/btq717. Epub 2010 Dec 23.
DNA copy number gains and losses are commonly found in tumor tissue, and some of these aberrations play a role in tumor genesis and development. Although high resolution DNA copy number data can be obtained using array-based techniques, no single method is widely used to distinguish between recurrent and sporadic copy number aberrations.
Here we introduce Discovering Copy Number Aberrations Manifested In Cancer (DiNAMIC), a novel method for assessing the statistical significance of recurrent copy number aberrations. In contrast to competing procedures, the testing procedure underlying DiNAMIC is carefully motivated, and employs a novel cyclic permutation scheme. Extensive simulation studies show that DiNAMIC controls false positive discoveries in a variety of realistic scenarios. We use DiNAMIC to analyze two publicly available tumor datasets, and our results show that DiNAMIC detects multiple loci that have biological relevance.
Source code implemented in R, as well as text files containing examples and sample datasets are available at http://www.bios.unc.edu/research/genomic_software/DiNAMIC.
肿瘤组织中常发现 DNA 拷贝数的增益和缺失，其中一些异常与肿瘤的发生和发展有关。虽然可以使用基于阵列的技术获得高分辨率的 DNA 拷贝数数据，但没有一种方法被广泛用于区分复发性和散发性拷贝数异常。
在这里，我们介绍了用于评估癌症中表现出的复发性拷贝数异常的统计显著性的新方法，即发现拷贝数异常的方法（DiNAMIC）。与竞争方法相比，DiNAMIC 的测试过程经过精心设计，并采用了新颖的循环置换方案。广泛的模拟研究表明，DiNAMIC 在各种现实场景中控制了假阳性发现。我们使用 DiNAMIC 分析了两个公开可用的肿瘤数据集，我们的结果表明 DiNAMIC 检测到了多个具有生物学相关性的位点。
在 http://www.bios.unc.edu/research/genomic_software/DiNAMIC 上提供了用 R 实现的源代码，以及包含示例和样本数据集的文本文件。
