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mbtransfer:使用转移函数和镜像统计数据进行微生物群干预分析
Kris Sankaran1, Pratheepa Jeganathan2
1Department of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, United States of America.
PLoS computational biology
|June 14, 2024
概括
我们开发了微生物社区动态的新型转移函数模型,改善了时间记忆和预测干预效应的准确性. 这种方法有助于识别受环境变化影响的关键微生物种类.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 微生物组时间序列研究对于理解微生物生态系统结构至关重要.
- 目前的微生物社区动态模型在时间记忆和表达性方面存在局限性,通常依赖于马尔科夫或线性假设.
- 这些局限性阻碍了对干预措施微生物反应的准确预测.
研究的目的:
- 引入基于转移函数的微生物社区动态模型的新类型.
- 为了捕捉环境变化对微生物群落的延迟影响.
- 为了能够在假设干预下模拟轨迹,并识别显著扰乱的种类.
主要方法:
- 开发了一种使用转移函数来学习冲动反应的新型模型类.
- 模拟了环境变化对微生物社区结构的潜在延迟影响.
- 实施错误发现率 (FDR) 保障,以选择明显扰乱的种类.
- 利用模拟来比较新的方法与现有的基线.
主要成果:
- 与强大的基线相比,转移函数模型显示预测错误减少.
- 该方法准确地确定了感兴趣的种群,表明有效识别扰乱的微生物.
- 案例研究展示了从模型中得出的差异反应轨迹的可解释性.
结论:
- 转移函数模型为微生物社区动态提供了增强的时间记忆和表达能力.
- 这种新的方法提高了预测干预效应和识别关键微生物种群的准确性.
- 开发的R包,mbtransfer,促进了这些方法的应用和复制.
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