DNA拷贝数分割方法的性能评价
Performance evaluation of DNA copy number segmentation methods
摘要：
许多生物信息学或生物统计学方法可用于分析从微阵列或测序技术测量的DNA拷贝数谱。在缺乏足够丰富的黄金标准数据集的情况下，这些方法的性能通常使用不切实际的模拟研究或基于小的真实数据分析来评估。我们已经设计并实现了一个框架，以生成具有已知真相的癌症样本的真实DNA拷贝数谱。这些谱是通过来自具有已知拷贝数状态的基因组区域的真实SNP微阵列数据产生的。原始的真实数据是从肿瘤细胞系的稀释系列中提取的，具有几个浓度的匹配血液样品。因此，可以通过样品中肿瘤细胞的（已知）百分比来控制生成的谱的信噪比。在本文中，我们描述了这个框架，并说明了一些建议的数据生成方法的实际用例的好处：从SNP微阵列分割DNA拷贝数分布图的方法之间的比较研究。这项研究表明，没有一种方法是一致优于所有其他方法。它还有助于确定比较方法的利弊，作为生物信息参数的函数，例如样品中肿瘤细胞的分数和杂合标记物的比例。可用性：R包jointSeg：http://r-forge.r-project.org/R/?组ID=1562
英文摘要：
A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. We have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling real SNP microarray data from genomic regions with known copy-number state. The original real data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. In this paper, we describe this framework and illustrate some of the benefits of the proposed data generation approach on a practical use case: a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons for the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. Availability: R package jointSeg: http://r-forge.r-project.org/R/?group\_id=1562
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