The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

中文翻译：

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利用单细胞测序数据同时进行拷贝数估计和亚克隆聚类的统计学习方法

单细胞测序（SCS）的可用性使我们能够评估肿瘤内异质性并识别细胞亚克隆，而不会受到混合细胞的混杂影响。拷贝数畸变 (CNA) 通常用于使用各种聚类方法来识别 SCS 数据中的亚克隆，因为组成亚群的细胞被发现共享遗传图谱。然而，目前可用的方法可能在CNA检测过程中产生虚假结果（例如，错误识别的变体），从而降低了大型复杂细胞群中亚克隆识别的准确性。在本研究中，我们开发了一种基于融合套索模型的亚克隆聚类方法，简称FLCNA，该方法可以同时检测单细胞DNA测序（scDNA-seq）数据中的CNA。通过 Spike-in 模拟来评估 FLCNA 的聚类和 CNA 检测性能，结合常用的聚类方法与现有的拷贝数估计方法（SCOPE、HMMcopy）进行基准测试。将FLCNA应用于乳腺癌的scDNA-seq数据集揭示了新辅助化疗处理的样本和预处理样本中不同的基因组变异模式。我们证明 FLCNA 是一种实用且强大的方法，可用于利用 scDNA-seq 数据进行亚克隆鉴定和 CNA 检测。