DataSheet1_RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals.docx

Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive models. However, its use as a statistical testing method has not been explored. In this study, we propose “Random Forest Test” (RFtest), a global (community-level) test based on random forest for high-dimensional and phylogenetically structured microbiome data. RFtest is a permutation test using the generalization error of random forest as the test statistic. Our simulations demonstrate that RFtest has controlled type I error rates, that its power is superior to competing methods for phylogenetically clustered signals, and that it is robust to outliers and adaptive to interaction effects and non-linear associations. Finally, we apply RFtest to two real microbiome datasets to ascertain whether microbial communities are associated or not with the outcome variables.

随机森林（Random Forest）被视作当前最成功的机器学习算法之一，已被广泛用于构建基于微生物组的预测模型。然而，其作为统计检验方法的应用价值尚未得到充分探索。本研究提出「随机森林检验（Random Forest Test，RFtest）」，一种基于随机森林的全局（群落水平）检验方法，适用于高维且具有系统发育结构的微生物组数据。RFtest以随机森林的泛化误差作为检验统计量，属于置换检验范畴。本研究通过模拟实验证实，RFtest可有效控制一类错误率，针对系统发育聚集信号的检验效能优于同类竞争方法，且对异常值具备鲁棒性，同时能够适配交互效应与非线性关联场景。最后，本研究将RFtest应用于两个真实微生物组数据集，以验证微生物群落是否与结局变量存在关联。