网GAM:使用通用添加模型来提高使用时间序列数据构建的生态网络分析的预测能力
Samantha J Gleich1, Jacob A Cram2, J L Weissman3
1Department of Biological Sciences, University of Southern California, 3616 Trousdale Parkway, AHF, Los Angeles, CA, 90089-0371, USA. gleich@usc.edu.
ISME communications
|November 8, 2023
概括
使用时间序列数据进行生态网络分析可能由于季节性模式而不准确. 一个新的通用添加模型 (GAM) 转换有效地消除了这些时间信号,提高了生态网络的准确性和预测能力.
科学领域:
- 生态生态学 生态生态学
- 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.
背景情况:
- 生态网络分析从物种丰度数据中推断出微生物相互作用.
- 时间序列数据带来了统计上的挑战,可能会导致由于非生物的关联导致不准确的网络预测.
研究的目的:
- 开发和验证一种数据转换方法,从微生物丰度数据中去除时间序列信号.
- 提高生态网络推断的准确性和可靠性.
主要方法:
- 应用了基于通用添加模型 (GAM) 的转换来从物种丰度数据中删除时间信号.
- 用已知协差结构的模拟时间序列数据集进行验证.
- 使用和不使用GAM转换进行了网络分析,将输出与已知的结构进行比较.
主要成果:
- 季节性丰度模式显著降低了推断生态网络的准确性.
- GAM转换增强了生态网络的预测能力 (F1评分).
- 这种转换提高了网络推断方法捕获关键网络结构的能力.
结论:
- 在生态网络分析中,必须考虑季节性模式等时间动态.
- 描述的GAM转换是一种简单而有效的工具,可以提高从时间序列数据推断生态网络推断的准确性.
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