IEEE/ACM Trans Comput Biol Bioinform. 2020 Mar-Apr;17(2):422-434. doi: 10.1109/TCBB.2018.2846599. Epub 2018 Jun 12.
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.
从癌症样本队列中发现与癌症相关的拷贝数异常（CNA）的常用策略是检测重现性 CNA（RCNAs）。虽然以前的方法可以成功识别几乎所有肿瘤样本共有的公共 RCNAs，但对于这些现有的方法来说，从具有异质性的癌症样本中检测亚组特异性 RCNAs 及其相关亚组样本仍然无效。在本文中，我们介绍了一种称为 HetRCNA 的新颖集成方法，它可以识别具有统计学意义的亚组特异性 RCNAs 及其相关的亚组样本。基于带有权重约束的矩阵分解框架，HetRCNA 可以通过带有权重约束的左向量的系数成功地测量亚组样本，通过右向量的系数和显著性检验来测量亚组特异性 RCNAs。当我们在模拟数据集上评估 HetRCNA 时，结果表明，HetRCNA 在竞争方法中表现最好，并且对模拟数据的噪声因素具有鲁棒性。当 HetRCNA 应用于真实的乳腺癌数据集时，我们的方法成功地识别了一堆 RCNAs 区域，并且结果与另外两种研究方法的结果高度相关。值得注意的是，HetRCNA 识别的基因组区域包含了许多先前研究报道的与乳腺癌相关的基因。
