癌症单细胞数据拷贝数变异检测方法

随着单细胞测序数据的异质性优势在癌症研究中的逐渐体现，现有拷贝数变异检测方法在检测单细胞数据时效果差的问题亟待解决。提出一种新的单细胞数据拷贝数变异检测方法（FL-CNV），通过动态窗口划分及数据估算对变异区间进行范围估计和断点确定，以明确拷贝数变异的断点位置和变异类型。所提方法突破了现有检测方法在单细胞数据上的局限性，对其检测效果在模拟数据和真实数据上进行了实验验证。结果表明：与现有方法相比，本文所提方法能显著提高拷贝数变异检测的精度和敏感度，且所得结果与比较基因组杂交（array-based comparative genomic hybridization，aCGH）的拷贝数变异进行了相关性验证，具有更高的可信度。

A method for discovering copy number variants in single-cell sequencing data

With the gradual manifestation of the heterogeneity of single-cell sequencing data in cancer research, the problem of the poor effectiveness of existing copy number variation detection methods in detecting single-cell data needs to be resolved urgently. In this work, a new single-cell data copy number variation detection method (FL-CNV) is proposed, which uses dynamic window division and data estimation to estimate the range of variation and find the location of the breakpoint and the type of copy number variation. The proposed method overcomes the limitations of existing detection methods for analyzing single-cell data, and the detection effect of the method is verified by experiments using both simulated data and real data. The results show that: compared with existing methods, the method proposed in this paper can significantly improve the accuracy and sensitivity of copy number variation detection. The results have been verified by correlation with the copy number variation of array-based comparative genomic hybridization, which has high credibility.