Description Usage Arguments Value References Examples
Perform high-powered detection of genetic effects on DNA methylation using integrated methylation QTL (methylation quantitative-trait locus) mapping and allele-specific analysis.
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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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # 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)
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