VennPlot: Venn Diagram

In NormalizeMets: Analysis of Metabolomics Data

Description

Produces a Venn diagram showing the number of common metabolites.

Usage

VennPlot(lnames, group.labels = c("A", "B", "C"), saveplot = FALSE,
  savetype = c("png", "bmp", "jpeg", "tiff", "pdf"), plotname = "VennPlot",
  main = "Venn Diagram", cexval = 1, asp = 1, ...)

Arguments

lnames	A list of up to three vectors, e.g. metabolite names.
group.labels	A vector of reference values to be plotted, such as an internal standard or sample weights.
saveplot	A logical indication whether to save the plot produced.
savetype	The required format for the plot to be saved in. Threre is a choice of "png","bmp","jpeg","tiff","pdf" type files.
plotname	Name of the output file if the file is to be saved. This is the general name for all the graphs and the specific type prefix will be added automatically.
main	A title for the plot.
cexval	The font size of the text labels.
asp	The aspect ratio of the plot. A value of 1 produces a square plot region.
...	Other graphical parameters. See par.

Author(s)

Alysha M De Livera

Examples

 
data("alldata_eg")
featuredata_eg<-alldata_eg$featuredata
dataview(featuredata_eg)
sampledata_eg<-alldata_eg$sampledata
dataview(sampledata_eg)
metabolitedata_eg<-alldata_eg$metabolitedata
dataview(metabolitedata_eg)

logdata <- LogTransform(featuredata_eg)
dataview(logdata$featuredata)
imp <-  MissingValues(logdata$featuredata,sampledata_eg,metabolitedata_eg,
                     feature.cutof=0.8, sample.cutoff=0.8, method="knn")
dataview(imp$featuredata)

#Linear model fit using unadjusted data
factormat<-model.matrix(~gender +Age +bmi, sampledata_eg)
unadjustedFit<-LinearModelFit(featuredata=imp$featuredata,
                             factormat=factormat,
                             ruv2=FALSE)
unadjustedFit

#Linear model fit using `is' normalized data 
Norm_is <-NormQcmets(imp$featuredata, method = "is", 
                    isvec = imp$featuredata[,which(metabolitedata_eg$IS ==1)[1]])
isFit<-LinearModelFit(featuredata=Norm_is$featuredata,
                     factormat=factormat,
                     ruv2=FALSE)
isFit

#Linear model fit with ruv-2 normalization
ruv2Fit<-LinearModelFit(featuredata=imp$featuredata,
                       factormat=factormat,
                       ruv2=TRUE,k=2,
                       qcmets = which(metabolitedata_eg$IS ==1))
ruv2Fit

lnames<- list(names(ruv2Fit$coef[,"Age"])[which(ruv2Fit$p.value[,"Age"]<0.05)],
             names(unadjustedFit$coef[,"Age"])[which(unadjustedFit$p.value[,"Age"]<0.05)],
             names(isFit$coef[,"Age"])[which(isFit$p.value[,"Age"]<0.05)])

VennPlot(lnames, group.labels=c("ruv2","unadjusted","is"))

NormalizeMets documentation built on May 1, 2019, 10:26 p.m.