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MuDCoD:来自单细胞RNA测序的个性化动态基因网络中的多主体社区检测
Ali Osman Berk Şapcı1,2, Shan Lu3, Shuchen Yan3
1Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, United States.
Bioinformatics (Oxford, England)
|September 23, 2023
概括
我们开发了多主体动态社区检测 (MuDCoD) 来分析来自单细胞RNA测序数据的个性化基因网络,跨多个主体和时间点,揭示动态生物过程.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 种群规模单细胞RNA测序 (scRNA-seq) 数据可以提供新的生物学见解.
- 现有的基因共同表达网络分析方法无法同时考虑多个受试者和时间点.
- 了解基因网络中的特定对象和特定时间的变异对于解释表型差异至关重要.
研究的目的:
- 从scRNA-seq数据中开发一种新的计算框架,用于从scRNA-seq数据中在个性化的动态基因网络中进行多主体社区检测.
- 为了确定基因社区,这些基因社区在时间和受试者之间变化或共享.
主要方法:
- 开发了基于光谱聚类的多主体动态社区检测 (MuDCoD) 方法.
- MuDCoD促进跨网络的信息共享,来自不同主题和时间点.
- 应用MuDCoD来分析人类诱导的多能干细胞和CD4+T细胞的scRNA-seq数据集.
主要成果:
- MuDCoD有效地利用主题特定网络之间的共享信号,并且在有限的信息共享方面表现强大.
- 该方法成功地在现实世界scRNA-seq数据集中识别了时间变化的个性化基因模块.
- 证明了个性化动态社区检测对于探索特定生物过程的实用性.
结论:
- MuDCoD提供了一种强大的方法来分析复杂的,多主体的,纵向的scRNA-seq数据.
- 该框架能够发现动态的,个性化的基因调控模式.
- 个性化动态社区检测可以显著提高对生物系统中个体变异性的理解.
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