基因组网络的概率模型框架,包括样本异质性.
Liying Chen1, Satwik Acharyya2, Chunyu Luo3
1Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Cell reports methods
|February 15, 2025
概括
图形回归 (GraphR) 通过考虑样本异质性来解决生物网络分析的局限性. 这种贝叶斯式方法可以对特定样本的网络进行估计,并揭示了其他方法遗漏的生物见解.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 概率图形模型对于分析复杂的生物网络在高通量欧米克数据中至关重要.
- 现有的模型通常假设样本的均性,限制它们的应用到异质的生物系统.
研究的目的:
- 引入图形回归 (GraphR),一种灵活的贝叶斯方法,用于在异质生物数据中进行网络分析.
- 为了使稀疏,样本特定的网络估计,并量化异质性对网络结构的影响.
主要方法:
- 开发了一个基于回归的贝叶斯框架 (GraphR),以在多个尺度上纳入样本异质性.
- 利用变量贝叶斯算法在网络估计中的计算效率.
- 将GraphR的性能与用于网络结构恢复和计算成本的最先进方法进行比较.
主要成果:
- 在各种设置中,GraphR在网络结构恢复和计算成本方面表现出卓越的效率.
- 对多组和空间转录组数据集的分析揭示了新的样本间和样本内分子网络洞察力.
- 确定了现有网络分析方法无法实现的生物发现.
结论:
- GraphR提供了一种强大而灵活的方法来分析具有样本异质性的复杂生物网络.
- 开发的 GraphR R 软件包和 Shiny App 促进了对生物网络的全面分析和动态可视化.
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