MiRKAT.iQ {MiRKAT}

MiRKAT-iQ

Description

Integrated quantile regression-based kernel association test.

Usage

MiRKAT.iQ(Y, X, K, weight = c(0.25, 0.25, 0.25, 0.25))

Arguments

Y

A numeric vector of the continuous outcome variable.

X

A numeric matrix for additional covariates that you want to adjust for.

K

A list of n by n kernel matrices at a single n by n kernel matrix, where n is the sample size.

weight

A length 4 vector specifying the weight for Cauchy combination, corresponding to wilcoxon/normal/inverselehmann/lehmann functions. The sum of the weight should be 1.

Value

Returns a list containing the p values for single kernels, or the omnibus p-value if multiple candidate kernel matrices are provided.

Author(s)

Tianying Wang, Xiang Zhan.

References

Wang T, et al. (2021) Testing microbiome association using integrated quantile regression models. Bioinformatics (to appear).

Examples

library(GUniFrac)
library(quantreg)
library(PearsonDS)
library(MiRKAT)

data(throat.tree)
data(throat.otu.tab)

## Create UniFrac and Bray-Curtis distance matrices 
unifracs = GUniFrac(throat.otu.tab, throat.tree, alpha = c(1))$unifracs
if (requireNamespace("vegan")) {
 library(vegan)
 BC= as.matrix(vegdist(throat.otu.tab, method="bray"))
 Ds = list(w = unifracs[,,"d_1"], uw = unifracs[,,"d_UW"], BC = BC)
} else {
 Ds = list(w = unifracs[,,"d_1"], uw = unifracs[,,"d_UW"])
}

## Convert to kernels           
Ks = lapply(Ds, FUN = function(d) D2K(d))
covar = cbind(throat.meta$Age, as.numeric(throat.meta$Sex == "Male"))
n = nrow(throat.meta)
y = rnorm(n)
result = MiRKAT.iQ(y, X = covar, K = Ks)