Diskin Sharon J, Eck Thomas, Greshock Joel, Mosse Yael P, Naylor Tara, Stoeckert Christian J, Weber Barbara L, Maris John M, Grant Gregory R
Division of Oncology, Children's Hospital of Philadelphia, Pennsylvania 19104, USA.
Genome Res. 2006 Sep;16(9):1149-58. doi: 10.1101/gr.5076506. Epub 2006 Aug 9.
Regions of gain and loss of genomic DNA occur in many cancers and can drive the genesis and progression of disease. These copy number aberrations (CNAs) can be detected at high resolution by using microarray-based techniques. However, robust statistical approaches are needed to identify nonrandom gains and losses across multiple experiments/samples. We have developed a method called Significance Testing for Aberrant Copy number (STAC) to address this need. STAC utilizes two complementary statistics in combination with a novel search strategy. The significance of both statistics is assessed, and P-values are assigned to each location on the genome by using a multiple testing corrected permutation approach. We validate our method by using two published cancer data sets. STAC identifies genomic alterations known to be of clinical and biological significance and provides statistical support for 85% of previously reported regions. Moreover, STAC identifies numerous additional regions of significant gain/loss in these data that warrant further investigation. The P-values provided by STAC can be used to prioritize regions for follow-up study in an unbiased fashion. We conclude that STAC is a powerful tool for identifying nonrandom genomic amplifications and deletions across multiple experiments. A Java version of STAC is freely available for download at http://cbil.upenn.edu/STAC.
基因组DNA的增减区域在许多癌症中都会出现，并可推动疾病的发生和发展。这些拷贝数变异（CNA）可以通过基于微阵列的技术进行高分辨率检测。然而，需要强大的统计方法来识别多个实验/样本中的非随机增减。我们开发了一种名为异常拷贝数显著性检验（STAC）的方法来满足这一需求。STAC结合了两种互补统计量并采用了一种新颖的搜索策略。对这两种统计量的显著性进行评估，并通过使用多重检验校正的置换方法为基因组上的每个位置分配P值。我们通过使用两个已发表的癌症数据集来验证我们的方法。STAC识别出已知具有临床和生物学意义的基因组改变，并为之前报道区域的85%提供了统计支持。此外，STAC在这些数据中识别出许多额外的显著增减区域，值得进一步研究。STAC提供的P值可用于以无偏的方式对后续研究区域进行优先级排序。我们得出结论，STAC是一种用于识别多个实验中非随机基因组扩增和缺失的强大工具。STAC的Java版本可在http://cbil.upenn.edu/STAC免费下载。
