在未配对和配对的单细胞多omics数据中进行指导协集群传输
Hongyao Li1, Yunrui Liu1, Pengcheng Zeng1
1Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, 201210, China.
Bioinformatics (Oxford, England)
|December 1, 2025
概括
我们开发了Guided Co-clustering Transfer (GuidedCoC) 来整合单细胞RNA测序 (scRNA-seq) 和单细胞测试用于转化酶可访问的染色体测序 (scATAC-seq) 数据. 这种方法利用未配对的scRNA-seq数据来增强配对的多omics数据集中的聚类和对齐.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞多基因技术同时描述基因表达和染色质可访问性.
- 整合配对单细胞RNA测序 (scRNA-seq) 和单细胞测定用于转化酶可访问的染色体测序 (scATAC-seq) 数据的数据是具有挑战性的,因为数据稀疏和噪声.
- 大量的未配对scRNA-seq数据集具有强大的细胞聚类结构.
研究的目的:
- 引入指导协集群转移 (GuidedCoC),这是一个整合未配对和配对单细胞多omics数据的框架.
- 通过从未配对的scRNA-seq数据转移知识来改进配对scRNA-seq/scATAC-seq数据中的细胞聚类和特征对齐.
- 为了使细胞种群在数据集之间自动对齐,而无需明确的注释.
主要方法:
- GuidedCoC采用统一的信息理论目标,用于跨模式和领域的细胞和特征的联合协集.
- 该框架将基因表达模块与监管元素对齐,并执行隐性交叉模式维度减少.
- 它有助于在未配对和配对数据集之间自动对齐细胞群.
主要成果:
- 与现有方法相比,GuidedCoC在基准数据集上展示了优越的集群准确性和生物解释性.
- 该框架有效地将结构知识从未配对的scRNA-seq数据转移到配对的多omics数据.
- 它成功地将细胞种群与数据集对齐,而不需要先前的注释.
结论:
- 结构引导的转移学习为单细胞多omics数据的强大和可扩展的集成提供了一个有希望的方法.
- GuidedCoC提供了一种可解释的方法来分析复杂的单细胞多omics数据集.
- 由于GuidedCoC的开源可用性,使其在研究界的应用更加容易.
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