文章目录
- 介绍
- 使用
- 安装
- 教程
- 案例
- 参考
介绍
许多微生物组研究揭示了微生物组与人类健康和疾病之间的重要联系。这些发现促使研究人员探索微生物组在人类复杂特征和疾病中的因果作用。然而，微生物组数据的复杂性给统计分析和因果关系的解释带来了挑战。 引入了一个新的统计框架，CRAmed，用于推断微生物组在治疗和结果之间的中介作用。CRAmed通过将自然间接效应分解为两部分，分别对应于微生物的存在-缺失和丰度，提高了中介分析的可解释性。综合仿真表明，与现有的中介分析方法相比，CRAmed在召回率、精度和F1得分方面表现优异，具有显著的鲁棒性。此外，两个实际数据应用说明了该方法的有效性和可解释性。我们的研究表明，CRAmed有望揭示微生物组的介导作用，并了解影响宿主健康的因素。
Abstract
Motivation
Numerous microbiome studies have revealed significant associations between the microbiome and human health and disease. These findings have motivated researchers to explore the causal role of the microbiome in human complex traits and diseases. However, the complexities of microbiome data pose challenges for statistical analysis and interpretation of causal effects.Results
We introduced a novel statistical framework, CRAmed, for inferring the mediating role of the microbiome between treatment and outcome. CRAmed improved the interpretability of the mediation analysis by decomposing the natural indirect effect into two parts, corresponding to the presence–absence and abundance of a microbe, respectively. Comprehensive simulations demonstrated the superior performance of CRAmed in Recall, precision, and F1 score, with a notable level of robustness, compared to existing mediation analysis methods. Furthermore, two real data applications illustrated the effectiveness and interpretability of CRAmed. Our research revealed that CRAmed holds promise for uncovering the mediating role of the microbiome and understanding of the factors influencing host health.
使用
安装
Before you install the CRAmed, some other libraries are required to install, see the requirement.R:
library(MASS)
library(plyr)
library(glmnet)
library(pscl)
Then you can install CRAmed from github with:
install.packages("devtools")
devtools::install_github("liudoubletian/CRAmed")
library(CRAmed)
教程
The details of the manual and the code used to generate each figure in the manuscript are provided in the fold ../vignettes.
Here, we show a brief example.
案例
library(CRAmed); packageVersion("CRAmed")#Simulate the ZINB data
otu_n <- 50;num <- 50
set.seed(1)
sim_zinb.mat <- sim_zinb(otu_n, num, alpha=-2, beta=2, gamma=-2)#Detect the mediators by CRAmed
cramed.res <- CRAmed(M_mat=sim_zinb.mat$M_mat, Y=sim_zinb.mat$Y, Exposure=sim_zinb.mat$trt, n.perm=10, CI=TRUE)
cramed.res
参考
- CRAmed: a conditional randomization test for high-dimensional mediation analysis in sparse microbiome data
- https://github.com/liudoubletian/CRAmed
