MiRKAT (version 1.2.3)

MiRKAT.Q: Robust MiRKAT (quantile regression)

Description

A more robust version of MiRKAT utlizing a linear model that uses quantile regression.

Usage

MiRKAT.Q(y, X, Ks, omnibus = "kernel_om", returnKRV = FALSE, returnR2 = FALSE)

Value

Returns p-values for each individual kernel matrix, an omnibus p-value if multiple kernels were provided, and measures of effect size KRV and R2.

p_values

labeled individual p-values for each kernel

omnibus_p

omnibus p_value, calculated as for the KRV test

KRV

A vector of kernel RV statistics (a measure of effect size), one for each candidate kernel matrix. Only returned if returnKRV = TRUE

R2

A vector of R-squared statistics, one for each candidate kernel matrix. Only returned if returnR2 = TRUE

Arguments

y

A numeric vector of the a continuous or dichotomous outcome variable.

X

A numerical matrix or data frame, containing additional covariates that you want to adjust for. Mustn't be NULL.

Ks

A list of n by n kernel matrices (or a single n by n kernel matrix), where n is the sample size. If you have distance metric from metagenomic data, each kernel can be constructed through function D2K. Each kernel can also be constructed through other mathematical approaches, such as linear or Gaussian kernels.

omnibus

A string equal to either "Cauchy" or "kernel_om" (or nonambiguous abbreviations thereof), specifying whether to use the Cauchy combination test or an omnibus kernel to generate the omnibus p-value.

returnKRV

A logical indicating whether to return the KRV statistic. Defaults to FALSE.

returnR2

A logical indicating whether to return the R-squared coefficient. Defaults to FALSE.

Author

Weija Fu

Details

MiRKAT.Q creates a kernel matrix using the linear model created with the function rq, a quantile regression function, then does the KRV analysis on Ks and the newly formed kernel matrix representing the outome traits.

Missing data is not permitted. Please remove all individuals with missing y, X, Ks prior to analysis

Examples

    
library(GUniFrac)
library(quantreg)

# Generate data
data(throat.tree)
data(throat.otu.tab)
data(throat.meta)

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"])
}
Ks = lapply(Ds, FUN = function(d) D2K(d))

covar = scale(cbind(throat.meta$Age, as.numeric(throat.meta$Sex == "Male")))

# Continuous phenotype
n = nrow(throat.meta)
y = rchisq(n, 2) + apply(covar, 1, sum) 
MiRKAT.Q(y, X = covar, Ks = Ks)