plotRVS: Image Plots for top Variant and Region Inclusions

In BVS: Bayesian Variant Selection: Bayesian Model Uncertainty Techniques for Genetic Association Studies

Description

This function allows the user to create image plots of the top variants and top Regions (any user specified set of variants such as pathways or genes) included in the top models. Variants and Regions are ordered based on marginal BF and regional BF which are plotted on the right axis. The width of the inclusion blocks are proportional to the posterior model probability that the variant or region is included in.

Usage

plotBVS(results, num.models=100, num.snps=20, num.regions=20, plot.coef=FALSE, 
        true.coef=NULL,main=NULL, regions=NULL, type="s",prop.cases=NULL,...)

Arguments

results	output list from summaryBVS.
num.models	the number of the top models to place on the x-axis.
num.snps	If type="s", the number of the top variants to place on the y-axis.
num.regions	If type="r", the number of the top regions to place on the y-axis.
plot.coef	Only to be used for rare variant analysis when rare=TRUE and mult.regions = FALSE. When plot.coef=TRUE, the log(OR) of the risk indices specified by each of the top models are plotted on the x axis
type	specifies if we want to plot the variant inclusion ("s") or region inclusion ("r")
true.coef	optional vector giving the true odds ratios of each of the variants (if results are from a simulation)
main	optional vector giving the title of the plot
regions	an optional vector of character strings giving the names of the regions for each of the variants in data set needed when plotting type is "r" or can be added to include the region names of each variant on the y axis when plotting type is "s".
prop.cases	an optional (p x 2) dimensional matrix giving the number of cases that have the variant in column 1 and the number of controls with the variant in column 2. If specified, these counts will be reported on the right axis under each variants marginal BF
...	General parameters for plotting functions

Author(s)

Melanie Quintana <[e-mail]>

Examples

## RARE VARIANT BRI EXAMPLE
  ## Load the data for Rare variant example
  data(RareData)

  ## Load the results from running sampleBVS on rare variant data for 100K iterations
  data(RareBVS.out)

  ## Load summary results 
  data(RareResults)
  
  ## Plot the variant inclusions in the top 100 models for the top 10 variants 
  plotBVS(RareResults,num.models=100,num.snps=10)

  ##Include the estimated log(OR) of the risk indices for the top models
  plotBVS(RareResults,num.models=100,num.snps=10,plot.coef=TRUE)


## INFORMATIVE iBMU EXAMPLE
  ##Load the data for the informative example
  data(InformData)

  ## Load the results from running sampleBVS with inform=FALSE for 100K iteration
  data(InformBVS.NI.out)

  ## Load summary results
  data(Informresults.NI)

  ## Make SNP and Gene inclusion plots
  plotBVS(Informresults.NI,num.models=50,num.snps=10,regions=InformData$genes)
  plotBVS(Informresults.NI,num.models=50,num.regions=10,regions=InformData$genes,type="r")

  ## Load the results from running sampleBVS with inform=TRUE for 100K iterations
  data(InformBVS.I.out)

  ## load summary results
  data(Informresults.I)

  ## Make SNP and Gene inclusion plots
  plotBVS(Informresults.I,num.models=50,num.snps=10,regions=InformData$genes)
  plotBVS(Informresults.I,num.models=50,num.regions=10,regions=InformData$genes,type="r")

BVS documentation built on May 1, 2019, 10:16 p.m.