通过多模态高阶邻域拉普拉斯矩阵优化,对多omics单细胞数据进行强大的联合聚类
Hao Jiang1, Senwen Zhan1, Wai-Ki Ching2
1School of Mathematics, Renmin University of China, Beijing 100872, China.
Bioinformatics (Oxford, England)
|June 29, 2023
概括
我们开发了scHoML,这是一个集成多omics单细胞数据的新框架. 这种方法可对复杂的细胞异质性进行强有力的分析,并促进生物发现.
科学领域:
- 单细胞多组组学分析
- 计算生物学是一种计算生物学.
- 基因组学和表观基因组学
背景情况:
- 同时对多组的单细胞数据进行分析,为细胞状态和异质性提供了先进的见解.
- 诸如CITE-seq和单细胞甲基组测序等技术使单个细胞内不同分子层的并行分析成为可能.
- 需要有效的集成方法来处理多模式单细胞数据集固有的复杂性,噪声和稀疏性.
研究的目的:
- 引入scHoML,这是一个用于整合多omics单细胞数据的新型框架.
- 为分析不同分子形状的细胞异质性提供一个强大的方法.
- 通过系统的多学科数据集成,促进更深层次的生物发现.
主要方法:
- 开发一个多模态高阶社区拉普拉斯矩阵优化框架 (scHoML).
- 应用层次聚类来分析最佳嵌入表示.
- 使用强大的,综合的方法识别细胞群.
主要成果:
- scHoML有效地集成多omics单细胞数据,稳定地表示复杂的数据结构.
- 该框架允许在单细胞水平上进行系统分析.
- 从综合数据中识别细胞群的层次分类辅助.
结论:
- scHoML提供了一个强大的新工具,用于整合和分析多omics单细胞数据.
- 该方法通过结合高阶和多模式拉普拉斯矩阵来增强对细胞异质性的理解.
- 预计这种方法将推动单细胞生物学领域的进一步进步和发现.
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