基于零模型的网络比较揭示了核心关联
Lisa Röttjers1, Doris Vandeputte1,2, Jeroen Raes1,2
1Laboratory of Molecular Bacteriology, Rega Institute, KU Leuven, Leuven, Belgium.
ISME communications
|November 8, 2023
概括
我们开发了anuran,一个使用零模型分析杂微生物协会网络的工具箱. 它识别了保存的核心关联网络 (CAN),在各种条件下揭示了微生物生态中的非随机模式.
科学领域:
- 微生物生态学 微生物生态学
- 生物信息学是一种生物信息学.
- 网络分析 网络分析
背景情况:
- 微生物网络的构建在微生物生态学中至关重要,但通常依赖于推断的关联,导致不准确.
- 微生物协会网络的现有方法容易出现错误,可能无法准确地代表真正的生态相互作用或社区结构.
研究的目的:
- 引入"anuran",这是一个新的工具箱,用于使用零模型调查杂的微生物网络.
- 为了能够在多个微生物网络中识别保存的子集 (核心关联网络,CAN) 和共享的网络属性.
主要方法:
- 开发"anuran"工具箱,结合零模型来根据随机关联假设生成数据.
- 应用零模型来识别协会网络组中的非随机模式.
- 对多个网络进行比较,以检测保存的核心关联网络 (CAN) 和共享属性.
主要成果:
- 从20名女性的便样本的时间序列中证明了CANs的存在,突出了个体中保存的微生物模式.
- 利用来自全球海绵项目的数据表明,海绵序列表现出大于随机的CAN,表明微生物协会得到保护.
- 验证了"anuran"在识别保存的微生物网络结构中的有效性.
结论:
- "anuran"工具箱提供了一种可靠的方法,用于在各种条件,时间序列,梯度或宿主中比较微生物网络.
- 基于零模型的分析显著提高了微生物网络解释的可靠性,超越了简单的统计学关联.
- 核心协会网络 (CAN) 的识别为稳定的微生物社区结构提供了有价值的见解.
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