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巴特多元:通过将单细胞数据中的RNA和染色体可访问性信息结合起来,推断出拷贝数的变化

Ruitong Li^1,2, Jean-Baptiste Alberge^1,2,3, Tina Keshavarzian^4,5

¹Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States.

Briefings in bioinformatics

|October 17, 2025

概括

纳姆巴特多元体使用单细胞RNA和ATAC测序数据推断癌症拷贝数的变化. 这个新工具整合了多种体的资料,以更深入地了解癌症的演变和表观遗传变化.

科学领域:

基因组学就是基因组学.
癌症生物学癌症生物学
计算生物学计算生物学

背景情况:

异常的基因组变化,包括副本数变异 (CNVs),在癌症发展中至关重要.
建立了Numbat算法,用于从单细胞RNA测序 (scRNA-seq) 中推断CNV.
整合多原子数据可以更全面地了解癌症异质性.

研究的目的:

介绍Numbat-multiome,这是Numbat的扩展,用于从scRNA-seq和单细胞转移酶[Tn5]-可访问染色体测序 (scATAC-seq) 数据的CNV推断.
为了使scRNA-seq和scATAC-seq数据用于CNV检测的单独或综合分析.
为分析癌症多模单细胞数据提供统一的计算框架.

主要方法:

开发了Numbat-multiome,通过共同的基因组坐标系统和捆绑策略统一scRNA-seq和scATAC-seq数据.
在四种模式中评估性能:RNA基因,RNA bin,ATAC bin和组合 bin.
使用基准瘤队列 (多发性骨髓瘤,里希特综合征) 对全基因组测序进行验证.

主要成果:

在不同类型的CNV事件中,Numbat-multiome表现出强大的性能,实现了高精度和回忆 (中位数F1>0.9).
在各种样本类型和CNV事件长度中观察到一致的性能.
该工具成功追踪了克隆进化,并在连续样本中识别了罕见的亚克隆.

结论:

努姆巴特多元组有效地从多模式单细胞数据中推断CNV,增强癌症基因组分析.
在亚克隆水平上整合表观基因组资料,为癌症进展提供了新的见解.
这种工具有助于更深入地了解癌症表型变化的遗传和表观遗传驱动因素.

关键词:

在 CNV 推断中推断出 CNV 的结论.癌症的演化癌症的演化单细胞多基因单细胞多基因

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