在复杂的生物系统中使用细胞特异性因果网络的关键状态的识别
Jiayuan Zhong1, Ziyi Huang2, Jianqiang Qiu1
1School of Mathematics, Foshan University, Foshan 528000, China.
Research (Washington, D.C.)
|August 28, 2025
概括
我们开发了一种新的方法,即细胞特异性因果网络 (CCNE), 用单细胞数据识别生物系统中的关键状态. 它有效地检测到关键转变的早期预警信号, 提供对复杂生物过程的洞察力.
科学领域:
- 复杂的生物系统
- 系统生物学
- 计算生物学
背景情况:
- 复杂的生物系统经常经历关键的转变,在稳定状态之间发生突然的定性变化.
- 识别这些关键状态及其信号分子对于理解生物机制至关重要.
- 传统的方法难以处理高维度,杂的单细胞数据,
研究的目的:
- 提出一种新的定量方法,用于在单细胞水平上识别复杂生物过程中的关键状态.
- 能够及早发现和描述生物系统中的关键转变.
- 解决无模型,高维单细胞数据分析现有方法的局限性.
主要方法:
- 开发了细胞特异性因果网络 (CCNE),是一种新的定量方法.
- 推断细胞特异性因果网络和量化动态因果变化.
- 使用数值模拟和五个不同的真实单细胞数据集验证CCNE.
主要成果:
- CCNE准确地识别了关键状态和关键转变的早期预警信号.
- 在检测关键过渡信号方面,CCNE的性能优于现有的方法.
- CCNE 作为分析细胞异质性和细胞聚类时间变化的计算工具.
结论:
- 在复杂的生物过程中使用单细胞数据识别关键状态的有效方法.
- 通过CCNE得分,可以获得有关动态因果变化和细胞异质性的宝贵见解.
- 通过功能丰富和信号分子路径分析来支持CCNE的可靠性.
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