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Last data update: 2014.03.03

R: Bayesian Multiple eQTL mapping using MCMC

eqtlMcmc

R Documentation

Bayesian Multiple eQTL mapping using MCMC

Description

Compute the MCMC algorithm to produce Posterior Probability of Association values for eQTL mapping.

Usage

eqtlMcmc(snp, expr, n.iter, burn.in, n.sweep, mc.cores,
 write.output = TRUE, RIS = TRUE)

Arguments

`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.

Details

The value of mc.cores may be ignored and set to one when the iBMQ installation does not support openMP.

Value

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.

References

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.

Examples

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> library(iBMQ)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: ggplot2
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/iBMQ/eqtlMcmc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: eqtlMcmc
> ### Title: Bayesian Multiple eQTL mapping using MCMC
> ### Aliases: eqtlMcmc
> 
> ### ** Examples
> 
> 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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>