image {IMAGE} | R Documentation |
Perform high-powered detection of genetic effects on DNA methylation using integrated methylation QTL (methylation quantitative-trait locus) mapping and allele-specific analysis.
image(geno, data, K, Covariates = NULL, numCore = 1, fit.maxiter = 500, fit.tol = 1e-05, verbose = TRUE)
geno |
a data list containing the genotype data |
data |
a data list containing the methylation data |
K |
a known kinship matrix. This matrix should be a positive semi-definite matrix with dimensions equal to the sample size |
Covariates |
a matrix containing the covariates subject to adjustment (Default = NULL) |
numCore |
a positive integer specifying the number of cores for parallel computing (default = 1) |
fit.maxiter |
a positive integer specifying the maximum number of iterations when fitting the generalized linear mixed model (default = 500) |
fit.tol |
a positive number specifying tolerance, the difference threshold for parameter estimates below which iterations should be stopped (default = 1e-5) |
verbose |
include verbose output |
A data.frame
containing the following named elements:
loc:
ordinal number of SNP-CpG pair being analyzed
numIDV:
number of observations of SNP-CpG pair being analyzed
beta:
the fixed effect parameter estimate for the predictor of interest
se_beta:
the standard deviation of fixed effect
pvalue:
P value for the fixed effect, based on the Wald test
h2:
heritability of the transformed rate
sigma2:
total variance component
converged:
a logical indicator for convergence
time:
time to converge
Fan, Y., Vilgalys, T.P., Sun, S., Peng, Q., Tung, J. and Zhou, X., 2019. High-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis. bioRxiv, p.615039.
# This example demonstrates IMAGE: data(ExampleData) geno <- ExampleData$geno K <- ExampleData$K data <- ExampleData$data res=image(geno,data,K) # We've saved the results of the example above to show an example of # the outputs IMAGE produces: data(example_results) # Toy example for testing purposes only: geno <- list() geno$hap1 = matrix(sample(c(0,1),25, replace = TRUE, prob = c(0.6,0.4)), nrow = 5, ncol = 5) geno$hap2 = matrix(sample(c(0,1),25, replace = TRUE, prob = c(0.6,0.4)), nrow = 5, ncol = 5) data <- list() data$r = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5) data$y = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5) data$r1 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5) data$r2 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5) data$y1 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5) data$y2 = matrix(sample(0:50,25, replace = TRUE), nrow = 5, ncol = 5) K = matrix(runif(25,-0.1,0.1), nrow = 5, ncol = 5) res=image(geno,data,K)