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Updated: Sep 9, 2025

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从单细胞RNA测序数据对拷贝数的瘤内异质性的贝叶斯推断

PuXue Qiao¹, Chun Fung Kwok^1,2, Guoqi Qian²

¹Bioinformatics & Cellular Genomics, St. Vincent's Institute of Medical Research, Melbourne, 3065, Australia.

|September 1, 2025

概括

这项研究引入了一种新的贝叶斯模型,用于分析使用单细胞RNA测序数据的瘤克隆结构. 该模型自动分组细胞并识别副本数量变化 (CNA),从而提高了癌症治疗的洞察力.

科学领域:

基因组学
计算生物学
癌症研究

背景情况:

副本数量变化 (CNA) 是瘤进化和异质性的关键驱动因素.
了解单细胞分辨率的瘤内克隆结构对于有效的癌症治疗至关重要.
现有的克隆分析方法通常需要手动输入参数,并与CNA检测分离细胞聚类.

研究的目的:

在瘤中开发自动化的贝叶斯模型,同时进行细胞聚类和CNA分析.
确定不同瘤克隆的数量及其相关的CNA事件.
利用单细胞RNA测序 (scRNA-seq) 数据进行全面的瘤内克隆分析.

主要方法:

开发了将基因表达和生殖系单核酸多态数据集成的贝叶斯模型.
实现了基布斯抽样算法用于模型推断.
创建了一个名为Chloris的R包,用于方法实施和可访问性.

主要成果:

开发的模型准确地将单个细胞聚集成不同的瘤克隆.
它同时推断每个已识别的克隆的CNA概况.
与现有工具相比,在细胞聚类和CNA识别准确性方面表现出优异的性能.
已成功应用于人类转移性黑色素瘤和甲状腺瘤数据集.

结论:

贝叶斯模型提供了一种自动化和准确的方法来剖析瘤的克隆结构.
它揭示了与瘤克隆相关的功能基因表达差异.
这种方法有助于更好地了解瘤内异质性,并对个性化癌症治疗策略产生影响.

关键词:

贝叶斯模型吉布斯采样副本编号的更改一个单元一个瘤

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