肿瘤进化的研究正通过单细胞测序技术的革新而发生革命，该技术可以调查肿瘤细胞体内的体细胞变异。在这些努力中，可靠地推断单细胞的进化关系是一个关键步骤。然而，单细胞序列中含有许多错误和缺失碱基，这需要改进标准的分子系统发育方法以分析这些数据集。我们开发了一种计算方法，通过综合应用标准系统发育最优性原则和序列变异共现模式，以在单细胞序列数据集中生成更广泛和准确的细胞系统发育关系。我们发现这种新方法在CRISPR/Cas9基因组编辑数据集中表现良好，表明它可以用于各种应用。我们将这种新方法应用于一些实证数据集，以展示其在重复突变和突变逆转的恢复重建以及使用演化动力学分析推断肿瘤之间的转移细胞迁移方面的用途。 © 2023 作者们

The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.© 2023 The Authors.