微生物共变网络的推断使用具有混合边缘的模模型
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
Bioinformatics (Oxford, England)
|June 28, 2023
概括
从测序数据中量化微生物关系是具有挑战性的. 这项研究引入了混合零-β边缘的模型,以准确估计分类种-分类种共变性,并建立强大的微生物网络.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 统计建模 统计建模
背景情况:
- 使用16S rRNA和元基因组测序数据进行微生物社区分析,由于数据稀疏,在量化分类-分类共变方面存在挑战.
- 准确估计这些共变对于理解微生物生态和构建有意义的相互作用网络至关重要.
研究的目的:
- 从稀疏的测序数据开发一种新的统计框架,用于估计微生物分类-分类共变.
- 引入混合零-β边缘的模,作为此估计的可靠方法.
- 为了使生物相关的微生物网络的建设.
主要方法:
- 使用混合零-β边缘的模来估计从正常化微生物相对丰度的分类-分类共变量.
- 为了准确的参数估计,采用了两阶段的最大概率方法.
- 开发了对依赖参数的两阶段概率比测试,以构建共变网络.
主要成果:
- 拟议的方法使用两阶段最大概率方法准确估计模型参数.
- 推导的概率比测试是有效的,强大的,并证明与皮尔森和等级相关性相比更高的力量.
- 通过使用美国肠道项目的数据,成功构建了具有生物意义的微生物网络.
结论:
- 混合零-β边缘的模型提供了一种强大而准确的方法来量化微生物共变.
- 开发的统计测试和网络构建方法推进了微生物社区结构的分析.
- R包"CoMiCoN"为研究人员促进了这种方法的实施.
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