全基因组单细胞 DNA 测序 (scDNA-seq) 的进步促进了多种检测拷贝数畸变 (CNA) 的方法的发展，拷贝数畸变是癌症遗传异质性的关键驱动因素。虽然大多数这些方法仅限于推断总拷贝数，但最近的一些方法现在使用创新技术来推断等位基因特异性 CNA，以估计低覆盖度 scDNA-seq 数据中的等位基因频率。然而，这些现有的等位基因特异性方法在其分割策略方面受到限制，而分割策略是 CNA 检测流程中的关键步骤。我们提出了 SEACON（等位基因特异性 COpy 数的单细胞估计），这是一种用于 scDNA 的等位基因特异性拷贝数分析器序列数据。 SEACON 采用高斯混合模型 (GMM) 来识别跨细胞的连续片段之间的潜在拷贝数状态和断点，使用集成技术过滤片段以获得高质量断点，并采用多种策略来容忍噪声读取深度和等位基因频率测量。使用大量真实和模拟数据集，我们证明 SEACON 可以在大量实验条件下得出准确的拷贝数并超越现有方法，并确定其优点和缺点。SEACON 用 Python 实现，可以从 https 免费获得开源： //github.com/NabaviLab/SEACON 和 https://doi.org/10.5281/zenodo.12727008。补充材料可在 XX 获取。© 作者 2024 年。由牛津大学出版社出版。

Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline.We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON employs a Gaussian Mixture Model (GMM) to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses.SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.Supplementary material is available at XX.© The Author(s) 2024. Published by Oxford University Press.