MiATDS: MiATDS

In XiaoyunHuang33/MiATDS: A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels

Description

This function "MiATDS" is an adaptive microbiome-based association test integrating the microbiome higher criticism analysis (MiHC) and adaptive weighted sum of powered score tests (aWSPU). MiATDS has a good performance for detecting microbial association signals with diverse sparsity levels.

Usage

MiATDS(y, otu.tab, tree=NULL, cov=NULL, model = c("gaussian", "binomial"), pow=c(1:5), comp=FALSE, CLR=FALSE, opt.ncl=30, n.perm=5000)

Arguments

y	Host phenotype of interest (Continuous or binary).
otu.tab	A matrix of OTU count table.
tree	A rooted phylogenetic tree.
cov	Covariate (e.g., age, gender).
model	"gaussian" for Gaussian outcomes, "binomial" for Binomial outcomes.
comp	An indicator if the OTU table contains absolute abundances or relative abundances. Default is comp=FALSE for absolute abundances.
CLR	The centered log-ratio (CLR) transformation. Default is CLR=FALSE for no CLR. transformation.
opt.ncl	A upper limit to find the optimal number of clusters. Default is opt.ncl=30.
n.perm	A number of permutations. Default is n.perm=5000.

Value

pd.rank: The ranking for probability degree.

awSPU.pvs: The p-value for the wSPU test and awSPU test.

aWSPU.pvs: The p-value for the WSPU test and aWSPU test.

omnibus.pvs: The p-value for the MiATDS test.

Examples

# Import requisite R packages
library(cluster)
library(compositions)
library(phyloseq)
library(permute)
library(MiHC)


# Import example microbiome data

load("D:/R_pro/MiATDS/data/obesity_data.rda")
otu.tab <- obesity_data@otu_table
tree <- obesity_data@phy_tree
y<- obesity_data@sam_data$label
cov <- as.matrix(obesity_data@sam_data$x1)


# Fit MiATDS
set.seed(123)
out <- MiATDS(y, cov=cov, otu.tab=otu.tab, tree=tree, model="binomial", n.perm=5000)
out

XiaoyunHuang33/MiATDS documentation built on Dec. 18, 2021, 7:23 p.m.