BVS (version 4.12.0)

plotBVS: Image Plots for top Variant and Gene Inclusions

Description

This function allows the user to create image plots of the top variants and top Genes included in the top models. Variants and Genes are ordered based on marginal 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 Gene is included in.

Usage

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

Arguments

results

output list from summaryBVS.

num.models

the number of the top models to place on the x-axis.

num.snps

If inc.type="s", the number of the top variants to place on the y-axis.

plot.coef

Only to be used for rare variant analysis when rare=TRUE. 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 gene inclusion ("g")

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

genes

an optional vector of character strings giving the names of the genes for each of the variants in data set needed when plotting type is "g" or can be added to include the gene names of each variant on the y axis.

...

General parameters for plotting functions

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,genes=InformData$genes)
  plotBVS(Informresults.NI,num.models=50,num.snps=10,genes=InformData$genes,type="g")

  ## 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,genes=InformData$genes)
  plotBVS(Informresults.I,num.models=50,num.snps=10,genes=InformData$genes,type="g")