A common strategy to discovering cancer associated copy number aberrations (CNAs) from a cohort of cancer samples is to detect recurrent CNAs (RCNAs). Although the previous methods can successfully identify communal RCNAs shared by nearly all tumor samples, detecting subgroup-specific RCNAs and their related subgroup samples from cancer samples with heterogeneity is still invalid for these existing approaches. In this paper, we introduce a novel integrated method called HetRCNA, which can identify statistically significant subgroup-specific RCNAs and their related subgroup samples. Based on matrix decomposition framework with weight constraint, HetRCNA can successfully measure the subgroup samples by coefficients of left vectors with weight constraint and subgroup-specific RCNAs by coefficients of the right vectors and significance test. When we evaluate HetRCNA on simulated dataset, the results show that HetRCNA gives the best performances among the competing methods and is robust to the noise factors of the simulated data. When HetRCNA is applied on a real breast cancer dataset, our approach successfully identifies a bunch of RCNA regions and the result is highly correlated with the results of the other two investigated approaches. Notably, the genomic regions identified by HetRCNA harbor many breast cancer related genes reported by previous researches.

中文翻译：

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HetRCNA：一种基于矩阵分解框架从异类肿瘤样本中识别经常性拷贝数变化的新方法。

从一组癌症样本中发现与癌症相关的拷贝数异常（CNA）的常见策略是检测复发性CNA（RCNA）。尽管以前的方法可以成功地识别几乎所有肿瘤样本共享的公共RCNA，但是从具有异质性的癌症样本中检测亚组特异性RCNA及其相关的亚组样本对于这些现有方法仍然无效。在本文中，我们介绍了一种称为HetRCNA的新型集成方法，该方法可以识别具有统计意义的特定于亚组的RCNA及其相关的亚组样本。基于具有权重约束的矩阵分解框架，HetRCNA可以通过权重约束左向量的系数成功地测量子组样本，并通过权向量约束和显着性检验成功地测量子组特定的RCNA。当我们在模拟数据集上评估HetRCNA时，结果表明HetRCNA在竞争方法中具有最佳性能，并且对模拟数据的噪声因子具有鲁棒性。当将HetRCNA应用于真实的乳腺癌数据集时，我们的方法成功地识别出一堆RCNA区域，其结果与其他两种研究方法的结果高度相关。值得注意的是，HetRCNA鉴定的基因组区域包含许多先前研究报告的与乳腺癌相关的基因。我们的方法成功地识别出一堆RCNA区域，其结果与其他两种研究方法的结果高度相关。值得注意的是，HetRCNA鉴定的基因组区域包含许多先前研究报告的与乳腺癌相关的基因。我们的方法成功地识别出一堆RCNA区域，其结果与其他两种研究方法的结果高度相关。值得注意的是，HetRCNA鉴定的基因组区域包含许多先前研究报告的与乳腺癌相关的基因。