微生物组调查数据的变异推理与全球海洋数据的应用
Aditya Mishra1, Jesse McNichol2,3, Jed Fuhrman3
1Department of Statistics, University of Georgia, Athens, GA, 30606, United States.
ISME communications
|May 12, 2025
概括
我们开发了VI-MIDAS,这是分析微生物组数据的新统计框架. 这种工具将微生物种类与环境因素联系起来,揭示了不同的海洋微生物社区结构和相互作用.
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:
- 分析微生物群调查数据以将微生物种群的丰度与宿主生理或息地特征联系起来是一项挑战.
- 微生物组数据分析需要可复制和可解释的方法.
研究的目的:
- 介绍VI-MIDAS (微生物群调查数据分析的变量推理),一个灵活的概率模型框架.
- 能够共同估计上下文依赖的驱动因素和微生物类型的丰富性关联的广泛模式.
- 促进时空信息和分类种之间的相互作用的整合.
主要方法:
- 开发了一种概率模型框架,VI-MIDAS,其中包含了与共变量和特定类型的潜伏合直接合分类种丰度的机制.
- 使用平均场变化推理用于后置模型参数估计.
- 将框架应用于塔拉海洋探险队的调查数据,并结合了网络分析工具.
主要成果:
- 确定了五个主要的海洋微生物社区模块,包括SAR11,Nitrosopumilus和Alteromondales主导的社区.
- 将这些模块与特定的环境和时空特征联系起来.
- 揭示了SAR11/Rhodospirillales类内的大部分积极的分类-分类关联,以及与Alteromonadales/Flavobacteriales类的负面关联.
结论:
- VI-MIDAS为微生物组数据分析提供了一个强大的综合统计框架.
- 该框架成功地发现了微生物种类和共变量数据之间的广泛联系模式.
- 证明了VI-MIDAS在描述海洋微生物社区结构和相互作用方面的实用性.
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