Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
Bioinformatics. 2012 Jun 15;28(12):i115-20. doi: 10.1093/bioinformatics/bts203.
In cancer genomes, chromosomal regions harboring cancer genes are often subjected to genomic aberrations like copy number alteration and loss of heterozygosity. Given this, finding recurrent genomic aberrations is considered an apt approach for screening cancer genes. Although several permutation-based tests have been proposed for this purpose, none of them are designed to find recurrent aberrations from the genomic dataset without paired normal sample controls. Their application to unpaired genomic data may lead to false discoveries, because they retrieve pseudo-aberrations that exist in normal genomes as polymorphisms.
We develop a new parametric method named parametric aberration recurrence test (PART) to test for the recurrence of genomic aberrations. The introduction of Poisson-binomial statistics allow us to compute small P-values more efficiently and precisely than the previously proposed permutation-based approach. Moreover, we extended PART to cover unpaired data (PART-up) so that there is a statistical basis for analyzing unpaired genomic data. PART-up uses information from unpaired normal sample controls to remove pseudo-aberrations in unpaired genomic data. Using PART-up, we successfully predict recurrent genomic aberrations in cancer cell line samples whose paired normal sample controls are unavailable. This article thus proposes a powerful statistical framework for the identification of driver aberrations, which would be applicable to ever-increasing amounts of cancer genomic data seen in the era of next generation sequencing.
Our implementations of PART and PART-up are available from http://www.hgc.jp/~niiyan/PART/manual.html.
在癌症基因组中，包含癌症基因的染色体区域经常受到基因组异常的影响，如拷贝数改变和杂合性丢失。考虑到这一点，寻找复发性基因组异常被认为是筛选癌症基因的一种合适方法。尽管已经提出了几种基于排列的测试方法，但它们都没有被设计用于从没有配对正常样本对照的基因组数据集中发现复发性异常。将它们应用于非配对的基因组数据可能会导致错误的发现，因为它们会检索到正常基因组中作为多态性存在的伪异常。
我们开发了一种新的参数方法，名为参数异常复发测试（PART），用于测试基因组异常的复发。泊松二项式统计的引入使我们能够比以前提出的基于排列的方法更有效地和精确地计算小 P 值。此外，我们将 PART 扩展到涵盖非配对数据（PART-up），以便为分析非配对基因组数据提供统计基础。PART-up 使用非配对正常样本对照中的信息来去除非配对基因组数据中的伪异常。使用 PART-up，我们成功地预测了其配对正常样本对照不可用的癌细胞系样本中的复发性基因组异常。因此，本文提出了一种强大的统计框架，用于识别驱动异常，这将适用于下一代测序时代不断增加的癌症基因组数据。
我们的 PART 和 PART-up 的实现可从 http://www.hgc.jp/~niiyan/PART/manual.html 获得。
