使用LAML对具有混合型缺失数据的时间尺度细胞谱系树进行最大似然推断。

Maximum likelihood inference of time-scaled cell lineage trees with mixed-type missing data using LAML.

作者信息

Chu Gillian, Mai Uyen, Schmidt Henri, Raphael Benjamin J

机构信息

Department of Computer Science, Princeton University, 08540, Princeton, NJ, USA.

出版信息

Genome Biol. 2025 Jul 2;26(1):189. doi: 10.1186/s13059-025-03649-9.

Abstract

Dynamic lineage tracing technologies combine genome editing with single-cell sequencing to track cell divisions. We introduce Lineage Analysis via Maximum Likelihood (LAML) to infer a maximum likelihood time-resolved cell lineage tree under the Probabilistic Mixed-type Missing model, which we derive to describe key features of dynamic lineage tracing systems. LAML produces accurate tree topologies with branch lengths representing experimental time between ancestral cells. LAML outperforms existing methods in terms of accuracy and scalability on simulated data, and calculates the timing of cell migrations to metastatic sites in a mouse model of lung adenocarcinoma, revealing distinct epochs of metastasis progression.

摘要

动态谱系追踪技术将基因组编辑与单细胞测序相结合以追踪细胞分裂。我们引入了通过最大似然法进行谱系分析(LAML),以在概率混合型缺失模型下推断最大似然时间分辨细胞谱系树,我们推导该模型以描述动态谱系追踪系统的关键特征。LAML生成准确的树拓扑结构,其分支长度代表祖先细胞之间的实验时间。在模拟数据上,LAML在准确性和可扩展性方面优于现有方法,并计算了肺腺癌小鼠模型中细胞迁移到转移部位的时间,揭示了转移进展的不同时期。

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