eqtlMcmc {iBMQ} | R Documentation |
Compute the MCMC algorithm to produce Posterior Probability of Association values for eQTL mapping.
eqtlMcmc(snp, expr, n.iter, burn.in, n.sweep, mc.cores, write.output = TRUE, RIS = TRUE)
snp |
SnpSet class object |
expr |
ExpressionSet class object |
n.iter |
Number of samples to be saved from the Markov Chain |
burn.in |
Number of burn-in iterations for the Markov Chain |
n.sweep |
Number of iterations between samples of the Markov Chain (AKA thinning interval) |
mc.cores |
The number of cores you would like to use for parallel processing. Can be set be set via ‘options(cores=4)’, if not set, the code will automatically detect the number of cores. |
write.output |
Write chain iterations to file. If TRUE, output for variables will be written to files created in the working directory. |
RIS |
If TRUE, the genotype needs to be either 0 and 1. If FALSE the genotype need to be either 1,2 and 3. |
The value of mc.cores
may be ignored and set to one when the iBMQ installation does not support openMP.
A matrix with Posterior Probability of Association values. Rows correspond to snps from original snp data objects, columns correspond to genes from expr data objects.
Scott-Boyer, MP., Tayeb, G., Imholte, Labbe, A., Deschepper C., and Gottardo R. An integrated Bayesian hierarchical model for multivariate eQTL mapping (iBMQ). Statistical Applications in Genetics and Molecular Biology Vol. 11, 2012.
data(phenotype.liver) data(genotype.liver) #PPA.liver <- eqtlMcmc(genotype.liver, phenotype.liver, n.iter=100,burn.in=100,n.sweep=20,mc.cores=6, RIS=FALSE)