ComparePvalHist: p-value Histogram

In NormalizeMets: Analysis of Metabolomics Data

Description

Make a p-value Histogram of results

Usage

ComparePvalHist(lpvals = NULL, normmeth = NULL, saveplot = FALSE,
  savetype = c("png", "bmp", "jpeg", "tiff", "pdf"), xlab = "P-Values",
  ylab = "Frequency", ylim = NULL, xlim = c(0, 1), col = "grey",
  plotname = "PvalHistComp", ...)

Arguments

lpvals	A list with vectors of p-values
normmeth	A vector with the normalization method used corresponding in order to the data supplied to be displayed on the plot.
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.
xlab	x-axis label
ylab	y-axis label
ylim	y-axis limit
xlim	x-axis limit
col	a colour to be used to fill the bars. The default of NULL yields unfilled bars.
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.
...	Other parameters for the hist function.

Author(s)

Alysha M De Livera, Gavriel Olshansky

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

#Exploring metabolites associated with age
lpvals_age<-list(unadjusted=unadjustedFit$p.value[,"Age"],
                is=isFit$p.value[,"Age"],
                ruv2=ruv2Fit$p.value[,"Age"])

ComparePvalHist(lpvals = lpvals_age,ylim=c(0,40), 
     normmeth = c("unadjusted","is","ruv2"))

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