R/Data_correction.R
In MASHUOA/MassOmics: Data extraction pipeline for large-scale GC-MS metabolomics datasets
Defines functions
Data_scaling_impute
DataCorrection
######################################### DataCorrection #########################################
DataCorrection <- function(){
## normalize data by mutiple internal stadnards
NormMultIS <-function(cutoff= 0.3, IS.index = c(1,2,3,4)){
library(tcltk)
print(cutoff)
print(IS.index)
folder <- tk_choose.dir(caption = "Select working directory")
setwd(folder)
dir()
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
info <- tk_choose.files(caption = "Select subject informatiom in .csv format (e.g. info.csv)")
data.df <- read.csv(dataR)
data.df1 <- t(data.df[,-c(1:which(names(data.df)=="Name"))])
data.df1[is.na(data.df1)] <- 0
info.df <- read.csv(info)
info.df$Name <-make.names(info.df$Name)
merge.df <- merge(info.df, data.df1, by.x = "Name", by.y = "row.names" , all= TRUE)
nTrue <- length(merge.df[is.na(merge.df[,ncol(merge.df)])==TRUE])
if(nTrue> 0) { tkmessageBox(message = paste("ERROR:",nTrue, "Sample names differ between spreadsheets"), icon = "warning", type = "ok"); stop("ERROR: Sample names differ")}
#Batch <- merge.df$Batch
Type <- merge.df$Type
Data <-merge.df[,-c(1:length(names(info.df)))]
IS.names<-as.character(data.df$Name[IS.index])
AA<-gsub(",", "",toString(paste(IS.names,'\n',sep="")))
res <- tkmessageBox(title = "Your select internal standards",
message = AA, icon = "question", type = "yesno", default = "yes")
if(as.character(res)=="no"){stop("select internal standards again")}
HairMetab <-Data
IS<-Data[,IS.index]
nMetab<-dim(HairMetab)[2]
cormat<-matrix(NA,nMetab,length(IS.index))
for(i in 1:nMetab){
for(j in 1:length(IS.index)){
cormat[i,j]<-cor(HairMetab[Type=='QC',i],IS[Type=='QC',j], use='pairwise.complete.obs')
}
}
maxcors<-apply(cormat,1,max)
#abline(v=0.7, col=2)
norm.index<-apply(cormat,1,which.max)
# show which internal standard are used
under<-maxcors< cutoff
colnames(cormat) <- IS.names
MetabolteName<-as.character(data.df$Name)
ChosenIS <- norm.index
ChosenIS[under]<-NA
hist(maxcors, main ="Correlation")
corMatrix <- cbind(MetabolteName, cormat, ChosenIS)
write.csv(corMatrix, file= "Chosen IS for each metabolites.csv", row.names = FALSE)
dev.new.OS()
boxplot(data.df$Ret.Time~norm.index, main='Chosen Internal Standards', ylab='Retention time')
HairMetabReplace<- HairMetab
for(i in 1:nMetab){
if(under[i]==TRUE) next()
HairMetabReplace[,i]<-HairMetab[,i]/IS[,norm.index[i]]*median(IS[,norm.index[i]])
}
final.df <- cbind(data.df[,c(1:which(names(data.df)=="Name"))],t(HairMetabReplace))
names(final.df) <-c( names(data.df[,c(1:which(names(data.df)=="Name"))]), merge.df$Name)
do.call(data.frame,lapply(final.df, function(x) replace(x, is.infinite(x),NA)))
final.df[is.na(final.df)]<-0
store<- "2_GC-MS Result_NormIS.csv"
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(final.df, file=FileName , row.names = FALSE)
}
## interphase
library(tcltk2)
tclRequire("BWidget")
# parameters
#function
submitA <- function() {
a <- as.numeric(strsplit(tclvalue(avar), ",")[[1]])
b <- as.numeric(tclvalue(bvar))
NormMultIS(IS.index=a, cutoff=b)
}
#Button
win1 <- tktoplevel()
tkwm.title(win1,"Data correction")
win1$env$nb <- tk2notebook(win1, tabs = c(" Intro ", " Scaling "," Contaminants ", " Multiple IS ", " Batch effect "))
tkpack(win1$env$nb, fill = "both", expand = TRUE)
win1$env$tb1 <- tk2notetab(win1$env$nb, " Multiple IS ")
submitA.but <- tkbutton(win1, text="Run Analysis", command=submitA, width= 10)
quit.but <- tkbutton(win1 , text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
open.but <- tkbutton(win1 ,text="Open Folder",
width= 10,
command= function(){
dir = getwd()
shell.exec(dir)
}
)
avar <- tclVar(c("31, 50, 98, 159"))
bvar <- tclVar("0")
a.entry <- tkentry(background="white",win1, textvariable=avar, width=20)
b.entry <- tkentry(background="white",win1, textvariable=bvar, width=20)
frameNM<- tkframe(win1$env$tb1,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameNM,text="Positions for IS"), a.entry, pady= 8, padx= 10, sticky="w")
tkgrid(tklabel(frameNM,text="Correlation cut-off"), b.entry, pady= 8, padx= 10, sticky="w")
frameButton<- tkframe(win1$env$tb1)
tkgrid(tklabel(frameButton,text=""), submitA.but, open.but , pady= 10, padx= 10, sticky="w")
#tkgrid(tklabel(kk,image=logo_1))
tkgrid(tklabel(win1$env$tb1,text=""), pady= 5, padx= 20, sticky="w")
tkgrid(tklabel(win1$env$tb1,text="Settings for multiple internal standard (IS) normalization"), pady = 10, padx= 20, sticky="w")
tkgrid(frameNM, pady= 0, padx= 30)
tkgrid(tklabel(win1$env$tb1,text=""), pady= 5, padx= 20, sticky="w")
tkgrid(frameButton, pady= 10, padx= 30)
######################################### Remove contamint #########################
RemoveContaminant <- function(A=0.4,B=2){
#Setup workding directory
folder <- tk_choose.dir(caption = "Select working directory")
setwd(folder)
dir()
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
info <- tk_choose.files(caption = "Select subject informatiom in .csv format (e.g. info.csv)")
data.df <<- read.csv(dataR)
data.df1 <- t(data.df[,-c(1:which(names(data.df)=="Name"))])
data.df1[is.na(data.df1)] <- 0
info.df <- read.csv(info)
info.df$Name <-make.names(info.df$Name)
merge.df <- merge(info.df, data.df1, by.x = "Name", by.y = "row.names" , all= TRUE)
nTrue <- length(merge.df[is.na(merge.df[,ncol(merge.df)])==TRUE])
merge.df[,1:5]
if(nTrue> 0) { tkmessageBox(message = paste("ERROR:",nTrue, "Sample names differ between spreadsheets"), icon = "warning", type = "ok"); stop("ERROR: Sample names differ")}
Batch <<- merge.df$Batch
Type <<- merge.df$Type
Data <<-merge.df[,-c(1:length(names(info.df)))]
# Beatrix codes
IdentifyContaminants<-function(Data, Batch, Type, A, B){
nMetab<-dim(Data)[[2]]
Batch<-factor(Batch)
nBatch<-length(unique(Batch))
outcome<-data.frame(Crit1=rep(NA,nMetab),Crit2=rep(NA, nMetab), PropBelow=rep(NA, nMetab), RatioAbove=rep(NA, nMetab))
# print(c(A, B))
for(i in 1:nMetab){
blankmeans<-sapply(split(Data[Type=='Blank',i],Batch[Type=='Blank']),mean)
samples<-split(Data[Type=='Sample',i], Batch[Type=='Sample'])
BatchLength<-sapply(samples, length)
TotBelow<-rep(NA, nBatch)
RatioAbove<-rep(NA, nBatch)
for(j in 1:nBatch){
TotBelow[j]<-sum(samples[[j]]< blankmeans[[j]])
RatioAbove[j]<-mean((samples[[j]][samples[[j]]>blankmeans[[j]]])/blankmeans[[j]])
}
outcome$PropBelow[i]<-sum(TotBelow, na.rm=T)/sum(Type=='Sample')
outcome$RatioAbove[i]<-mean(RatioAbove[blankmeans>0], na.rm=T)
if(outcome$PropBelow[i]<A & mean(samples$`1`)!=0){outcome$Crit1[i]<-'PASS' } else{outcome$Crit1[i]<-'FAIL'}
if(outcome$PropBelow[i]>0 |mean(samples$`1`)==0){
if(!is.na(outcome$RatioAbove[i]) & outcome$RatioAbove[i]> B & (1-outcome$PropBelow[i])> (1-A)/2 & mean(samples$`1`)!=0){outcome$Crit2[i]<-'PASS'}
else{outcome$Crit2[i]<-'FAIL'}
}}
outcome[,2][is.na(outcome[,2])]<-'PASS'
return(outcome)
}
Finaloutcome<-IdentifyContaminants(Data, Batch, Type, A, B)
x<-(1-A)*100
names(Finaloutcome) <- c(paste( x ,"PercenAboveBlank"), paste( B, "RatioAboveBlank"),"PercenAboveBlank","RatioAboveBlank" )
subtract<-sapply(Finaloutcome[3],function(x) (1-x)*100)
Finaloutcome[,3] <-c(round(subtract,1))
Finaloutcome[,4] <-c(round(Finaloutcome[4],1))
findResult.df <- cbind(Finaloutcome, data.df)
store<-"1_GC-MS Result_RemoveContaminants.csv"
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(findResult.df, file=FileName )
}
plotfigure <-function(metaNo=30){
Batch1 <- Batch[Type=="Blank"|Type=="Sample"]
Type1 <- Type[Type=="Blank"|Type=="Sample"]
MetaName<-data.df$Name[metaNo]
Data1 <- Data[Type=="Blank"|Type=="Sample",]
plot(Batch1,Data1[,metaNo], col=factor(Type1), ylab="level", main= MetaName ,xlab ="Batch")
points(Batch1[Type=='Blank'], Data1[,metaNo][Type1=='Blank'],pch=19)
legend('topright',pch=c(19,1), col=c(1:2),legend=c('Blank','Sample'),cex=0.8)
}
## Interphase for Remove contaminat
xvar <- tclVar("60")
vvar <- tclVar("2")
yvar <- tclVar("1")
# Run function
submit <- function() {
x <- (100-as.numeric(tclvalue(xvar)))/100
v <- as.numeric(tclvalue(vvar))
RemoveContaminant(A=x,B=v)
}
# Plot function
plotBatchfigure <- function() {
yfigure <- as.numeric(tclvalue(yvar))
plotfigure(metaNo= yfigure)
}
#Tip
Tips<-function () {
require(tcltk)
kk <- tktoplevel()
tktitle(kk) <- "Excel format for the subject information"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/SubjectInfo.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "MaxC2.image", file=image.path)
tkgrid(tklabel(kk,image=FrontImage))
}
# Reset function
reset <- function() {
tclvalue(xvar)<-"60"
tclvalue(vvar)<-"2"
tclvalue(yvar)<-"1"
}
reset.but <- tkbutton(win1, text="Reset", command=reset, width=9)
submit.but <- tkbutton(win1, text="Run Analysis", command=submit, width= 10)
quit.but <- tkbutton(win1, text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
open.but <- tkbutton(win1,text="Open Folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
plot.but <- tkbutton(win1, text = "Plot Figure",
command = plotBatchfigure, width=9)
Tips.but<-tkbutton(win1, text="Example", command=Tips,width=9)
## Userinterphase structure
win1$env$tb2 <- tk2notetab(win1$env$nb, " Contaminants ")
frameMiddle<- tkframe(win1$env$tb2 ,borderwidth=2, relief="groove",padx=5,pady=5)
x.entry <- tkentry(background="white",frameMiddle, textvariable=xvar, width=15)
v.entry <- tkentry(background="white",frameMiddle, textvariable=vvar, width=15)
y.entry <- tkentry(background="white",frameMiddle, textvariable=yvar, width=15)
tkgrid(tklabel(frameMiddle,text="% samples above blanks"), x.entry,pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Fold changes"),v.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Plot metabolite using ID"), y.entry, pady= 10, padx= 10, sticky="w")
#tkgrid(tklabel(frameMiddle,text="Retention Time (min,max)"), y.entry, z.entry, tklabel(frameMiddle,text="Sec"), pady= 8, padx= 10, sticky="w")
# Button
frameButton<- tkframe(win1$env$tb2)
tkgrid(tklabel(frameButton,text=""), open.but, submit.but, plot.but, pady= 10, padx= 10, sticky="w")
#reset.but, open.but, quit.but,
tkgrid(tklabel(win1$env$tb2,text=""), pady= 5, padx= 20, sticky="w")
# Interphase structure
#tkgrid(tklabel(kk,image=logo_1))
tkgrid(tklabel(win1$env$tb2 ,text="Fitler Criteria to remove contaminants"), pady = 10, padx= 20, sticky="w")
#tkgrid(frameAll)
tkgrid(frameMiddle, pady= 0, padx= 30)
tkgrid(frameButton, pady= 10, padx= 30)
################################################Batch correction######################
BatchCorrection <- function(funMeanMedian= "median", SampleType = "QC", datatype="original", Log_trans=F, vis_norm_result=F){
## Batch normalization ##
library(tcltk)
library("rfviz")
message(paste("Applying",paste(SampleType,collapse = "+"),"'s",funMeanMedian,"on samples"))
folder <- tk_choose.dir(caption = "Select working directory")
setwd(folder)
#dir()
if (datatype=="original"){
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
info <- tk_choose.files(caption = "Select subject informatiom in .csv format (e.g. info.csv)")
data.df <- read.csv(dataR)
data.df1 <- t(data.df[,-c(1:which(names(data.df)=="Name"))])
data.df1[is.na(data.df1)] <- 0
data.df1[(data.df1==-Inf)] <- 0
if (Log_trans) {
data.df1<-log(data.df1)
data.df1[(data.df1==-Inf)] <- 0
}
info.df <- read.csv(info)
info.df <- info.df[,c("Name", "Type", "Batch")]
info.df$Name <-make.names(info.df$Name)
merge.df <- merge(info.df[,c("Name", "Type", "Batch")], data.df1, by.x = "Name", by.y = "row.names" , all.y= TRUE)
merge.df <- merge.df[!is.na(merge.df$Batch),]
nTrue <- nrow(merge.df[is.na(merge.df[,ncol(merge.df)]),])
if(nTrue> 0) { tkmessageBox(message = paste("ERROR:",nTrue, "Sample names differ between spreadsheets"), icon = "warning", type = "ok"); stop("ERROR: Sample names differ")}
Type <- merge.df$Type
Data <- merge.df[,-c(1:length(names(info.df)))]
Batch <-merge.df$Batch
SampleNorm <- Data
nMetab<-dim(SampleNorm)[[2]]
nBatch <- length(table(Batch))
}
if (datatype=="Customized"){
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
}
if(funMeanMedian=="median"){funM <-median}
if(funMeanMedian=="mean"){funM <-mean}
if(funMeanMedian=="SERRF"){funM <-SERRF}
if((funMeanMedian=="median")|(funMeanMedian=="mean")){
for( i in 1:nMetab){
M<-median(Data[Type==SampleType,i], na.rm=T)
for(j in 1:nBatch){
m<-funM(Data[Type==SampleType & Batch==j,i], na.rm=T) # change here for mean or median
if(m==0) m<-mean(SampleNorm[Type==SampleType & Batch==j,i], na.rm=T)
if(m>0){SampleNorm[Batch==j,i] <- Data[Batch==j,i]*M/m } else {print(c(round(i,digits = 0),round(j,digits = 0),m, M/m))}
}
}
}else if(funMeanMedian=="SERRF"){
SERRF(input=input,Predict_level=SampleT)
}
# Write results
final.df <- cbind(data.df[,c(1:which(names(data.df)=="Name"))],t(SampleNorm))
names(final.df) <-c( names(data.df[,c(1:which(names(data.df)=="Name"))]), merge.df$Name)
final.df[is.na(final.df)]<-0
store<- "3_GC-MS Result_NormBatch.csv"
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(final.df, file=FileName , row.names = FALSE)
if(vis_norm_result){
if(SampleType=="QC"){
## Plot QC
means.before<-apply(Data[Type=="QC",],2,mean, na.rm=T)
sds.before<-apply(Data[Type=="QC",],2,sd, na.rm=T)
cv.s.before<-sds.before/means.before
means.post<-apply(SampleNorm[Type=="QC",],2,mean, na.rm=T)
sds.post<-apply(SampleNorm[Type=="QC",],2,sd, na.rm=T)
cv.s.post<-sds.post/means.post
dev.new.OS()
reg1 <- lm(cv.s.before~cv.s.post)
plot(cv.s.post, cv.s.before, ylab='Coefficent of variation (CV) before batch correction',
xlab='Coefficent of variation (CV) after batch correction' ,main='QC',col="red")
abline(reg1)
abline(a=0,b=1, col = "lightgray", lty = 3)
mtext(paste("Number of imrpoved metabolites:", sum(cv.s.before > cv.s.post,na.rm = T)))
}
## Plot sample
means.s.before<-apply(Data[Type=="Sample",],2,mean, na.rm=T)
sds.s.before<-apply(Data[Type=="Sample",],2,sd, na.rm=T)
cv.s.before<-sds.s.before/means.s.before
means.s.post<-apply(SampleNorm[Type=="Sample",],2,mean, na.rm=T)
sds.s.post<-apply(SampleNorm[Type=="Sample",],2,sd, na.rm=T)
cv.s.post<-sds.s.post/means.s.post
dev.new.OS()
reg1 <- lm(cv.s.before~cv.s.post)
plot(cv.s.post, cv.s.before, ylab='Coefficent of variation (CV) before batch correction',
xlab='Coefficent of variation (CV) after batch correction' ,main='Samples',col="red")
abline(reg1)
abline(a=0,b=1, col = "lightgray", lty = 3)
mtext(paste("Number of imrpoved metabolites:", sum(cv.s.before > cv.s.post,na.rm = T)))
}
}
## interphase for BatchCorrection
#library(tcltk2)
#tclRequire("BWidget")
#win1 <- tktoplevel()
#tkwm.title(win1,"Data correction")
#win1$env$nb <- tk2notebook(win1, tabs = c(" Contaminants ", " Multiple IS ", " Batch effect "))
#tkpack(win1$env$nb, fill = "both", expand = TRUE)
win1$env$tb3 <- tk2notetab(win1$env$nb, " Batch effect ")
## Setup
SampleType <- c("QC","Sample","QC+Sample")
#StatType <- c("median","mean","polynomial")
StatType <- c("SERRF","median","mean")
comboBox1 <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=SampleType ,textvariable=tclVar("QC"),width=9)
comboBox2 <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=StatType ,textvariable=tclVar("SERRF"),width=9)
zero_imputaion_check <- tkcheckbutton(win1)
zero_imputaion<- tclVar("1")
tkconfigure(zero_imputaion_check,variable=zero_imputaion)
vis_norm_result_check <- tkcheckbutton(win1)
vis_norm_result<- tclVar("1")
tkconfigure(vis_norm_result_check,variable=vis_norm_result)
## Function
submitC <- function() {
zero_imputaion=tclvalue(zero_imputaion)
if (zero_imputaion=="0"){zero_imputaion<-FALSE}
if (zero_imputaion=="1"){zero_imputaion<-TRUE}
vis_norm_result=tclvalue(vis_norm_result)
if (vis_norm_result=="0"){vis_norm_result<-FALSE}
if (vis_norm_result=="1"){vis_norm_result<-TRUE}
SampleT <- strsplit(SampleType[[as.numeric(tclvalue(tcl(comboBox1,"getvalue")))+1]],"\\+")[[1]]
StatT <- StatType[[as.numeric(tclvalue(tcl(comboBox2,"getvalue")))+1]]
if (StatT %in% c("median","mean")){
BatchCorrection(funMeanMedian= StatT , SampleType = SampleT,vis_norm_result=vis_norm_result)
}else if (StatT %in% c("SERRF")){
SERRF(Predict_level = SampleT,datatype="MASSOMICS",zero_imputaion=zero_imputaion,vis_norm_result=vis_norm_result)
}
}
submitd <- function() {
zero_imputaion=tclvalue(zero_imputaion)
if (zero_imputaion=="0"){zero_imputaion<-FALSE}
if (zero_imputaion=="1"){zero_imputaion<-TRUE}
SampleT <- base::strsplit(SampleType[[as.numeric(tclvalue(tcl(comboBox1,"getvalue")))+1]],"\\+")[[1]]
StatT <- StatType[[as.numeric(tclvalue(tcl(comboBox2,"getvalue")))+1]]
input=Data_prep_area_SERRF()
SERRF(input=input,Predict_level=SampleT,zero_imputaion=zero_imputaion)
}
submitC.but <- tkbutton(win1, text="MassOmics", command=submitC , width= 12)
submitd.but <- tkbutton(win1, text="MSDIAL", command=submitd , width= 12)
open.but <- tkbutton(win1,text="Open Folder", width= 10,
command= function(){dir = getwd()
shell.exec(dir)}
)
frameCorrection<- tkframe(win1$env$tb3 ,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameCorrection,text="Centralised batches based on"), comboBox1,pady= 10, padx= 10, sticky="W")
tkgrid(tklabel(frameCorrection,text="Normalization method"),comboBox2, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameCorrection,text="Perform Zero value imputation (1/2*min)"), zero_imputaion_check, sticky="w")
tkgrid(tklabel(frameCorrection,text="Check normalization result"), vis_norm_result_check, sticky="w")
frameButtonC<- tkframe(win1$env$tb3)
tkgrid(tklabel(frameButtonC,text=""), submitC.but,submitd.but, open.but , pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(win1$env$tb3,text=""), pady= 5, padx= 20, sticky="w")
tkgrid(tklabel(win1$env$tb3 ,text="Methods for minimise the batch effect"), pady = 10, padx= 20, sticky="w")
tkgrid(frameCorrection, pady= 0, padx= 30)
tkgrid(tklabel(win1$env$tb3,text="Select the source of file to be normalized"), pady= 5, padx= 20, sticky="w")
tkgrid(frameButtonC, pady= 10, padx= 40)
#################### Intro ###########################
win1$env$tb0 <- tk2notetab(win1$env$nb, " Intro ")
frameButtonE<- tkframe(win1$env$tb0)
tkgrid(tklabel(frameButtonE,text=""), Tips.but , pady= 5, padx= 5, sticky="w")
tkgrid(tklabel(win1$env$tb0 ,text="Suggest GC-MS analysis setup (each batch)"), pady= 5, padx= 20, sticky="w")
image.path1c<-c(paste(file.path(path.package(package="MassOmics")),"/R/Intro.gif", sep=""))
logo_3<-tcl("image", "create", "photo", "MaxC34.image", file=image.path1c)
tkgrid(tklabel(win1$env$tb0,image=logo_3))
tkgrid(tklabel(win1$env$tb0 ,text="Requirements:"), pady= 5, padx= 5, sticky="w")
tkgrid(tklabel(win1$env$tb0,text="QCs, negative controls, and samples are organised in the\n subject information spreadsheet as shown by the example"), pady= 5, padx= 5, sticky="w")
tkgrid( frameButtonE, pady= 5, padx= 5)
#################### Scaling ###########################
win1$env$tb5 <- tk2notetab(win1$env$nb, " Scaling ")
Scaling<-function(){
ScalingT <- ScaleType[[as.numeric(tclvalue(tcl(comboBox_scaling,"getvalue")))+1]]
ImputeT <- ImputeType[[as.numeric(tclvalue(tcl(comboBox_Impute,"getvalue")))+1]]
Data_scaling_impute(ScalingT=ScalingT,Impute=ImputeT)
}
ImputeType <- c("NULL","KNN","Minimum", "Zero")
ScaleType <- c("NULL","Log","Mean","Pareto")
comboBox_Impute <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=ImputeType ,textvariable=tclVar("KNN"),width=9)
comboBox_scaling <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=ScaleType ,textvariable=tclVar("Log"),width=9)
frameButtonF<- tkframe(win1$env$tb5)
Scaling.but<-tkbutton(win1, text="Choose file", command=Scaling,width=9)
tkgrid(tklabel(frameButtonF,text="Imputation method"),comboBox_Impute, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameButtonF,text="scaling method"),comboBox_scaling, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameButtonF,text="Run"), Scaling.but , pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(win1$env$tb5 ,text="Select Massomics integration result to have a data scaling"), pady= 5, padx= 20, sticky="w")
tkgrid(frameButtonF, pady= 5, padx= 5)
}
Data_scaling_impute<-function(path=NULL,ScalingT=NULL,Impute=NULL){
library(tcltk)
library(tcltk2)
library(data.table)
library(impute)
if (is.null(path)){path=tk_choose.files(filter = matrix(c( "CSV", ".csv","excel file", ".xls", "excel file", ".xlsx"),3, 2, byrow = TRUE),
caption = "Choose integration result to have a data scaling")}
data=fread(path)
datacol=colnames(data)[!(colnames(data) %in% c("CAS","Ref.Ion" , "Total.ID(n=3)","Library.match","Ret.Time" ,"RT.shfit.Lower","Warnings" ,"Name"))]
datafcol=colnames(data)[(colnames(data) %in% c("CAS","Ref.Ion" , "Total.ID(n=3)","Library.match","Ret.Time" ,"RT.shfit.Lower","Warnings" ,"Name"))]
data=as.data.frame(data)
for (col in datacol){
data[,col]=as.numeric(as.character(data[,col]))
}
# impute missing value.
e=data[,datacol]
e[e==0]<-NA
e[e=="Inf"]<-NA
e[e=="-Inf"]<-NA
e[e==""]<-NA
e[e=="NaN"]<-NA
e=as.matrix(e)
if((sum(is.na(e)))>0 && !is.null(Impute)){
switch(Impute,
Minimum= {e[is.na(e)] = min(e[!is.na(e)])},
Zero= {e[is.na(e)] = 0},
KNN= {e=try(impute.knn(e)$data,silent = T)}
)
}
if(!is.null(ScalingT)){
switch(ScalingT,
Log= {
e<-log(e)
e[(e==-Inf)] <- 0
},
Mean= {e=e/mean(na.omit(e))},
Pareto = {
for (row in nrow(e)){
e[row,]=e[row,]/sqrt(sd(e[row,]))
}
}
)
}
data=cbind(data[,datafcol],e)
write.csv(data,file = paste0(tools::file_path_sans_ext(path)," Impute_",ifelse(is.null(Impute),"None",Impute)," Scaling_",ifelse(is.null(ScalingT),"None",ScalingT),".csv"),row.names = F)
message("Done")
}
MASHUOA/MassOmics documentation built on Nov. 3, 2023, 10:48 p.m.
R Package Documentation
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Defines functions Data_scaling_impute DataCorrection
######################################### DataCorrection #########################################
DataCorrection <- function(){
## normalize data by mutiple internal stadnards
NormMultIS <-function(cutoff= 0.3, IS.index = c(1,2,3,4)){
library(tcltk)
print(cutoff)
print(IS.index)
folder <- tk_choose.dir(caption = "Select working directory")
setwd(folder)
dir()
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
info <- tk_choose.files(caption = "Select subject informatiom in .csv format (e.g. info.csv)")
data.df <- read.csv(dataR)
data.df1 <- t(data.df[,-c(1:which(names(data.df)=="Name"))])
data.df1[is.na(data.df1)] <- 0
info.df <- read.csv(info)
info.df$Name <-make.names(info.df$Name)
merge.df <- merge(info.df, data.df1, by.x = "Name", by.y = "row.names" , all= TRUE)
nTrue <- length(merge.df[is.na(merge.df[,ncol(merge.df)])==TRUE])
if(nTrue> 0) { tkmessageBox(message = paste("ERROR:",nTrue, "Sample names differ between spreadsheets"), icon = "warning", type = "ok"); stop("ERROR: Sample names differ")}
#Batch <- merge.df$Batch
Type <- merge.df$Type
Data <-merge.df[,-c(1:length(names(info.df)))]
IS.names<-as.character(data.df$Name[IS.index])
AA<-gsub(",", "",toString(paste(IS.names,'\n',sep="")))
res <- tkmessageBox(title = "Your select internal standards",
message = AA, icon = "question", type = "yesno", default = "yes")
if(as.character(res)=="no"){stop("select internal standards again")}
HairMetab <-Data
IS<-Data[,IS.index]
nMetab<-dim(HairMetab)[2]
cormat<-matrix(NA,nMetab,length(IS.index))
for(i in 1:nMetab){
for(j in 1:length(IS.index)){
cormat[i,j]<-cor(HairMetab[Type=='QC',i],IS[Type=='QC',j], use='pairwise.complete.obs')
}
}
maxcors<-apply(cormat,1,max)
#abline(v=0.7, col=2)
norm.index<-apply(cormat,1,which.max)
# show which internal standard are used
under<-maxcors< cutoff
colnames(cormat) <- IS.names
MetabolteName<-as.character(data.df$Name)
ChosenIS <- norm.index
ChosenIS[under]<-NA
hist(maxcors, main ="Correlation")
corMatrix <- cbind(MetabolteName, cormat, ChosenIS)
write.csv(corMatrix, file= "Chosen IS for each metabolites.csv", row.names = FALSE)
dev.new.OS()
boxplot(data.df$Ret.Time~norm.index, main='Chosen Internal Standards', ylab='Retention time')
HairMetabReplace<- HairMetab
for(i in 1:nMetab){
if(under[i]==TRUE) next()
HairMetabReplace[,i]<-HairMetab[,i]/IS[,norm.index[i]]*median(IS[,norm.index[i]])
}
final.df <- cbind(data.df[,c(1:which(names(data.df)=="Name"))],t(HairMetabReplace))
names(final.df) <-c( names(data.df[,c(1:which(names(data.df)=="Name"))]), merge.df$Name)
do.call(data.frame,lapply(final.df, function(x) replace(x, is.infinite(x),NA)))
final.df[is.na(final.df)]<-0
store<- "2_GC-MS Result_NormIS.csv"
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(final.df, file=FileName , row.names = FALSE)
}
## interphase
library(tcltk2)
tclRequire("BWidget")
# parameters
#function
submitA <- function() {
a <- as.numeric(strsplit(tclvalue(avar), ",")[[1]])
b <- as.numeric(tclvalue(bvar))
NormMultIS(IS.index=a, cutoff=b)
}
#Button
win1 <- tktoplevel()
tkwm.title(win1,"Data correction")
win1$env$nb <- tk2notebook(win1, tabs = c(" Intro ", " Scaling "," Contaminants ", " Multiple IS ", " Batch effect "))
tkpack(win1$env$nb, fill = "both", expand = TRUE)
win1$env$tb1 <- tk2notetab(win1$env$nb, " Multiple IS ")
submitA.but <- tkbutton(win1, text="Run Analysis", command=submitA, width= 10)
quit.but <- tkbutton(win1 , text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
open.but <- tkbutton(win1 ,text="Open Folder",
width= 10,
command= function(){
dir = getwd()
shell.exec(dir)
}
)
avar <- tclVar(c("31, 50, 98, 159"))
bvar <- tclVar("0")
a.entry <- tkentry(background="white",win1, textvariable=avar, width=20)
b.entry <- tkentry(background="white",win1, textvariable=bvar, width=20)
frameNM<- tkframe(win1$env$tb1,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameNM,text="Positions for IS"), a.entry, pady= 8, padx= 10, sticky="w")
tkgrid(tklabel(frameNM,text="Correlation cut-off"), b.entry, pady= 8, padx= 10, sticky="w")
frameButton<- tkframe(win1$env$tb1)
tkgrid(tklabel(frameButton,text=""), submitA.but, open.but , pady= 10, padx= 10, sticky="w")
#tkgrid(tklabel(kk,image=logo_1))
tkgrid(tklabel(win1$env$tb1,text=""), pady= 5, padx= 20, sticky="w")
tkgrid(tklabel(win1$env$tb1,text="Settings for multiple internal standard (IS) normalization"), pady = 10, padx= 20, sticky="w")
tkgrid(frameNM, pady= 0, padx= 30)
tkgrid(tklabel(win1$env$tb1,text=""), pady= 5, padx= 20, sticky="w")
tkgrid(frameButton, pady= 10, padx= 30)
######################################### Remove contamint #########################
RemoveContaminant <- function(A=0.4,B=2){
#Setup workding directory
folder <- tk_choose.dir(caption = "Select working directory")
setwd(folder)
dir()
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
info <- tk_choose.files(caption = "Select subject informatiom in .csv format (e.g. info.csv)")
data.df <<- read.csv(dataR)
data.df1 <- t(data.df[,-c(1:which(names(data.df)=="Name"))])
data.df1[is.na(data.df1)] <- 0
info.df <- read.csv(info)
info.df$Name <-make.names(info.df$Name)
merge.df <- merge(info.df, data.df1, by.x = "Name", by.y = "row.names" , all= TRUE)
nTrue <- length(merge.df[is.na(merge.df[,ncol(merge.df)])==TRUE])
merge.df[,1:5]
if(nTrue> 0) { tkmessageBox(message = paste("ERROR:",nTrue, "Sample names differ between spreadsheets"), icon = "warning", type = "ok"); stop("ERROR: Sample names differ")}
Batch <<- merge.df$Batch
Type <<- merge.df$Type
Data <<-merge.df[,-c(1:length(names(info.df)))]
# Beatrix codes
IdentifyContaminants<-function(Data, Batch, Type, A, B){
nMetab<-dim(Data)[[2]]
Batch<-factor(Batch)
nBatch<-length(unique(Batch))
outcome<-data.frame(Crit1=rep(NA,nMetab),Crit2=rep(NA, nMetab), PropBelow=rep(NA, nMetab), RatioAbove=rep(NA, nMetab))
# print(c(A, B))
for(i in 1:nMetab){
blankmeans<-sapply(split(Data[Type=='Blank',i],Batch[Type=='Blank']),mean)
samples<-split(Data[Type=='Sample',i], Batch[Type=='Sample'])
BatchLength<-sapply(samples, length)
TotBelow<-rep(NA, nBatch)
RatioAbove<-rep(NA, nBatch)
for(j in 1:nBatch){
TotBelow[j]<-sum(samples[[j]]< blankmeans[[j]])
RatioAbove[j]<-mean((samples[[j]][samples[[j]]>blankmeans[[j]]])/blankmeans[[j]])
}
outcome$PropBelow[i]<-sum(TotBelow, na.rm=T)/sum(Type=='Sample')
outcome$RatioAbove[i]<-mean(RatioAbove[blankmeans>0], na.rm=T)
if(outcome$PropBelow[i]<A & mean(samples$`1`)!=0){outcome$Crit1[i]<-'PASS' } else{outcome$Crit1[i]<-'FAIL'}
if(outcome$PropBelow[i]>0 |mean(samples$`1`)==0){
if(!is.na(outcome$RatioAbove[i]) & outcome$RatioAbove[i]> B & (1-outcome$PropBelow[i])> (1-A)/2 & mean(samples$`1`)!=0){outcome$Crit2[i]<-'PASS'}
else{outcome$Crit2[i]<-'FAIL'}
}}
outcome[,2][is.na(outcome[,2])]<-'PASS'
return(outcome)
}
Finaloutcome<-IdentifyContaminants(Data, Batch, Type, A, B)
x<-(1-A)*100
names(Finaloutcome) <- c(paste( x ,"PercenAboveBlank"), paste( B, "RatioAboveBlank"),"PercenAboveBlank","RatioAboveBlank" )
subtract<-sapply(Finaloutcome[3],function(x) (1-x)*100)
Finaloutcome[,3] <-c(round(subtract,1))
Finaloutcome[,4] <-c(round(Finaloutcome[4],1))
findResult.df <- cbind(Finaloutcome, data.df)
store<-"1_GC-MS Result_RemoveContaminants.csv"
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(findResult.df, file=FileName )
}
plotfigure <-function(metaNo=30){
Batch1 <- Batch[Type=="Blank"|Type=="Sample"]
Type1 <- Type[Type=="Blank"|Type=="Sample"]
MetaName<-data.df$Name[metaNo]
Data1 <- Data[Type=="Blank"|Type=="Sample",]
plot(Batch1,Data1[,metaNo], col=factor(Type1), ylab="level", main= MetaName ,xlab ="Batch")
points(Batch1[Type=='Blank'], Data1[,metaNo][Type1=='Blank'],pch=19)
legend('topright',pch=c(19,1), col=c(1:2),legend=c('Blank','Sample'),cex=0.8)
}
## Interphase for Remove contaminat
xvar <- tclVar("60")
vvar <- tclVar("2")
yvar <- tclVar("1")
# Run function
submit <- function() {
x <- (100-as.numeric(tclvalue(xvar)))/100
v <- as.numeric(tclvalue(vvar))
RemoveContaminant(A=x,B=v)
}
# Plot function
plotBatchfigure <- function() {
yfigure <- as.numeric(tclvalue(yvar))
plotfigure(metaNo= yfigure)
}
#Tip
Tips<-function () {
require(tcltk)
kk <- tktoplevel()
tktitle(kk) <- "Excel format for the subject information"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/SubjectInfo.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "MaxC2.image", file=image.path)
tkgrid(tklabel(kk,image=FrontImage))
}
# Reset function
reset <- function() {
tclvalue(xvar)<-"60"
tclvalue(vvar)<-"2"
tclvalue(yvar)<-"1"
}
reset.but <- tkbutton(win1, text="Reset", command=reset, width=9)
submit.but <- tkbutton(win1, text="Run Analysis", command=submit, width= 10)
quit.but <- tkbutton(win1, text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
open.but <- tkbutton(win1,text="Open Folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
plot.but <- tkbutton(win1, text = "Plot Figure",
command = plotBatchfigure, width=9)
Tips.but<-tkbutton(win1, text="Example", command=Tips,width=9)
## Userinterphase structure
win1$env$tb2 <- tk2notetab(win1$env$nb, " Contaminants ")
frameMiddle<- tkframe(win1$env$tb2 ,borderwidth=2, relief="groove",padx=5,pady=5)
x.entry <- tkentry(background="white",frameMiddle, textvariable=xvar, width=15)
v.entry <- tkentry(background="white",frameMiddle, textvariable=vvar, width=15)
y.entry <- tkentry(background="white",frameMiddle, textvariable=yvar, width=15)
tkgrid(tklabel(frameMiddle,text="% samples above blanks"), x.entry,pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Fold changes"),v.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Plot metabolite using ID"), y.entry, pady= 10, padx= 10, sticky="w")
#tkgrid(tklabel(frameMiddle,text="Retention Time (min,max)"), y.entry, z.entry, tklabel(frameMiddle,text="Sec"), pady= 8, padx= 10, sticky="w")
# Button
frameButton<- tkframe(win1$env$tb2)
tkgrid(tklabel(frameButton,text=""), open.but, submit.but, plot.but, pady= 10, padx= 10, sticky="w")
#reset.but, open.but, quit.but,
tkgrid(tklabel(win1$env$tb2,text=""), pady= 5, padx= 20, sticky="w")
# Interphase structure
#tkgrid(tklabel(kk,image=logo_1))
tkgrid(tklabel(win1$env$tb2 ,text="Fitler Criteria to remove contaminants"), pady = 10, padx= 20, sticky="w")
#tkgrid(frameAll)
tkgrid(frameMiddle, pady= 0, padx= 30)
tkgrid(frameButton, pady= 10, padx= 30)
################################################Batch correction######################
BatchCorrection <- function(funMeanMedian= "median", SampleType = "QC", datatype="original", Log_trans=F, vis_norm_result=F){
## Batch normalization ##
library(tcltk)
library("rfviz")
message(paste("Applying",paste(SampleType,collapse = "+"),"'s",funMeanMedian,"on samples"))
folder <- tk_choose.dir(caption = "Select working directory")
setwd(folder)
#dir()
if (datatype=="original"){
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
info <- tk_choose.files(caption = "Select subject informatiom in .csv format (e.g. info.csv)")
data.df <- read.csv(dataR)
data.df1 <- t(data.df[,-c(1:which(names(data.df)=="Name"))])
data.df1[is.na(data.df1)] <- 0
data.df1[(data.df1==-Inf)] <- 0
if (Log_trans) {
data.df1<-log(data.df1)
data.df1[(data.df1==-Inf)] <- 0
}
info.df <- read.csv(info)
info.df <- info.df[,c("Name", "Type", "Batch")]
info.df$Name <-make.names(info.df$Name)
merge.df <- merge(info.df[,c("Name", "Type", "Batch")], data.df1, by.x = "Name", by.y = "row.names" , all.y= TRUE)
merge.df <- merge.df[!is.na(merge.df$Batch),]
nTrue <- nrow(merge.df[is.na(merge.df[,ncol(merge.df)]),])
if(nTrue> 0) { tkmessageBox(message = paste("ERROR:",nTrue, "Sample names differ between spreadsheets"), icon = "warning", type = "ok"); stop("ERROR: Sample names differ")}
Type <- merge.df$Type
Data <- merge.df[,-c(1:length(names(info.df)))]
Batch <-merge.df$Batch
SampleNorm <- Data
nMetab<-dim(SampleNorm)[[2]]
nBatch <- length(table(Batch))
}
if (datatype=="Customized"){
dataR <- tk_choose.files(caption = "Select metabolite profile in .csv format (e.g. GC-MS Result.csv)")
}
if(funMeanMedian=="median"){funM <-median}
if(funMeanMedian=="mean"){funM <-mean}
if(funMeanMedian=="SERRF"){funM <-SERRF}
if((funMeanMedian=="median")|(funMeanMedian=="mean")){
for( i in 1:nMetab){
M<-median(Data[Type==SampleType,i], na.rm=T)
for(j in 1:nBatch){
m<-funM(Data[Type==SampleType & Batch==j,i], na.rm=T) # change here for mean or median
if(m==0) m<-mean(SampleNorm[Type==SampleType & Batch==j,i], na.rm=T)
if(m>0){SampleNorm[Batch==j,i] <- Data[Batch==j,i]*M/m } else {print(c(round(i,digits = 0),round(j,digits = 0),m, M/m))}
}
}
}else if(funMeanMedian=="SERRF"){
SERRF(input=input,Predict_level=SampleT)
}
# Write results
final.df <- cbind(data.df[,c(1:which(names(data.df)=="Name"))],t(SampleNorm))
names(final.df) <-c( names(data.df[,c(1:which(names(data.df)=="Name"))]), merge.df$Name)
final.df[is.na(final.df)]<-0
store<- "3_GC-MS Result_NormBatch.csv"
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(final.df, file=FileName , row.names = FALSE)
if(vis_norm_result){
if(SampleType=="QC"){
## Plot QC
means.before<-apply(Data[Type=="QC",],2,mean, na.rm=T)
sds.before<-apply(Data[Type=="QC",],2,sd, na.rm=T)
cv.s.before<-sds.before/means.before
means.post<-apply(SampleNorm[Type=="QC",],2,mean, na.rm=T)
sds.post<-apply(SampleNorm[Type=="QC",],2,sd, na.rm=T)
cv.s.post<-sds.post/means.post
dev.new.OS()
reg1 <- lm(cv.s.before~cv.s.post)
plot(cv.s.post, cv.s.before, ylab='Coefficent of variation (CV) before batch correction',
xlab='Coefficent of variation (CV) after batch correction' ,main='QC',col="red")
abline(reg1)
abline(a=0,b=1, col = "lightgray", lty = 3)
mtext(paste("Number of imrpoved metabolites:", sum(cv.s.before > cv.s.post,na.rm = T)))
}
## Plot sample
means.s.before<-apply(Data[Type=="Sample",],2,mean, na.rm=T)
sds.s.before<-apply(Data[Type=="Sample",],2,sd, na.rm=T)
cv.s.before<-sds.s.before/means.s.before
means.s.post<-apply(SampleNorm[Type=="Sample",],2,mean, na.rm=T)
sds.s.post<-apply(SampleNorm[Type=="Sample",],2,sd, na.rm=T)
cv.s.post<-sds.s.post/means.s.post
dev.new.OS()
reg1 <- lm(cv.s.before~cv.s.post)
plot(cv.s.post, cv.s.before, ylab='Coefficent of variation (CV) before batch correction',
xlab='Coefficent of variation (CV) after batch correction' ,main='Samples',col="red")
abline(reg1)
abline(a=0,b=1, col = "lightgray", lty = 3)
mtext(paste("Number of imrpoved metabolites:", sum(cv.s.before > cv.s.post,na.rm = T)))
}
}
## interphase for BatchCorrection
#library(tcltk2)
#tclRequire("BWidget")
#win1 <- tktoplevel()
#tkwm.title(win1,"Data correction")
#win1$env$nb <- tk2notebook(win1, tabs = c(" Contaminants ", " Multiple IS ", " Batch effect "))
#tkpack(win1$env$nb, fill = "both", expand = TRUE)
win1$env$tb3 <- tk2notetab(win1$env$nb, " Batch effect ")
## Setup
SampleType <- c("QC","Sample","QC+Sample")
#StatType <- c("median","mean","polynomial")
StatType <- c("SERRF","median","mean")
comboBox1 <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=SampleType ,textvariable=tclVar("QC"),width=9)
comboBox2 <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=StatType ,textvariable=tclVar("SERRF"),width=9)
zero_imputaion_check <- tkcheckbutton(win1)
zero_imputaion<- tclVar("1")
tkconfigure(zero_imputaion_check,variable=zero_imputaion)
vis_norm_result_check <- tkcheckbutton(win1)
vis_norm_result<- tclVar("1")
tkconfigure(vis_norm_result_check,variable=vis_norm_result)
## Function
submitC <- function() {
zero_imputaion=tclvalue(zero_imputaion)
if (zero_imputaion=="0"){zero_imputaion<-FALSE}
if (zero_imputaion=="1"){zero_imputaion<-TRUE}
vis_norm_result=tclvalue(vis_norm_result)
if (vis_norm_result=="0"){vis_norm_result<-FALSE}
if (vis_norm_result=="1"){vis_norm_result<-TRUE}
SampleT <- strsplit(SampleType[[as.numeric(tclvalue(tcl(comboBox1,"getvalue")))+1]],"\\+")[[1]]
StatT <- StatType[[as.numeric(tclvalue(tcl(comboBox2,"getvalue")))+1]]
if (StatT %in% c("median","mean")){
BatchCorrection(funMeanMedian= StatT , SampleType = SampleT,vis_norm_result=vis_norm_result)
}else if (StatT %in% c("SERRF")){
SERRF(Predict_level = SampleT,datatype="MASSOMICS",zero_imputaion=zero_imputaion,vis_norm_result=vis_norm_result)
}
}
submitd <- function() {
zero_imputaion=tclvalue(zero_imputaion)
if (zero_imputaion=="0"){zero_imputaion<-FALSE}
if (zero_imputaion=="1"){zero_imputaion<-TRUE}
SampleT <- base::strsplit(SampleType[[as.numeric(tclvalue(tcl(comboBox1,"getvalue")))+1]],"\\+")[[1]]
StatT <- StatType[[as.numeric(tclvalue(tcl(comboBox2,"getvalue")))+1]]
input=Data_prep_area_SERRF()
SERRF(input=input,Predict_level=SampleT,zero_imputaion=zero_imputaion)
}
submitC.but <- tkbutton(win1, text="MassOmics", command=submitC , width= 12)
submitd.but <- tkbutton(win1, text="MSDIAL", command=submitd , width= 12)
open.but <- tkbutton(win1,text="Open Folder", width= 10,
command= function(){dir = getwd()
shell.exec(dir)}
)
frameCorrection<- tkframe(win1$env$tb3 ,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameCorrection,text="Centralised batches based on"), comboBox1,pady= 10, padx= 10, sticky="W")
tkgrid(tklabel(frameCorrection,text="Normalization method"),comboBox2, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameCorrection,text="Perform Zero value imputation (1/2*min)"), zero_imputaion_check, sticky="w")
tkgrid(tklabel(frameCorrection,text="Check normalization result"), vis_norm_result_check, sticky="w")
frameButtonC<- tkframe(win1$env$tb3)
tkgrid(tklabel(frameButtonC,text=""), submitC.but,submitd.but, open.but , pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(win1$env$tb3,text=""), pady= 5, padx= 20, sticky="w")
tkgrid(tklabel(win1$env$tb3 ,text="Methods for minimise the batch effect"), pady = 10, padx= 20, sticky="w")
tkgrid(frameCorrection, pady= 0, padx= 30)
tkgrid(tklabel(win1$env$tb3,text="Select the source of file to be normalized"), pady= 5, padx= 20, sticky="w")
tkgrid(frameButtonC, pady= 10, padx= 40)
#################### Intro ###########################
win1$env$tb0 <- tk2notetab(win1$env$nb, " Intro ")
frameButtonE<- tkframe(win1$env$tb0)
tkgrid(tklabel(frameButtonE,text=""), Tips.but , pady= 5, padx= 5, sticky="w")
tkgrid(tklabel(win1$env$tb0 ,text="Suggest GC-MS analysis setup (each batch)"), pady= 5, padx= 20, sticky="w")
image.path1c<-c(paste(file.path(path.package(package="MassOmics")),"/R/Intro.gif", sep=""))
logo_3<-tcl("image", "create", "photo", "MaxC34.image", file=image.path1c)
tkgrid(tklabel(win1$env$tb0,image=logo_3))
tkgrid(tklabel(win1$env$tb0 ,text="Requirements:"), pady= 5, padx= 5, sticky="w")
tkgrid(tklabel(win1$env$tb0,text="QCs, negative controls, and samples are organised in the\n subject information spreadsheet as shown by the example"), pady= 5, padx= 5, sticky="w")
tkgrid( frameButtonE, pady= 5, padx= 5)
#################### Scaling ###########################
win1$env$tb5 <- tk2notetab(win1$env$nb, " Scaling ")
Scaling<-function(){
ScalingT <- ScaleType[[as.numeric(tclvalue(tcl(comboBox_scaling,"getvalue")))+1]]
ImputeT <- ImputeType[[as.numeric(tclvalue(tcl(comboBox_Impute,"getvalue")))+1]]
Data_scaling_impute(ScalingT=ScalingT,Impute=ImputeT)
}
ImputeType <- c("NULL","KNN","Minimum", "Zero")
ScaleType <- c("NULL","Log","Mean","Pareto")
comboBox_Impute <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=ImputeType ,textvariable=tclVar("KNN"),width=9)
comboBox_scaling <- tkwidget(background="white",win1 ,"ComboBox",editable=FALSE,values=ScaleType ,textvariable=tclVar("Log"),width=9)
frameButtonF<- tkframe(win1$env$tb5)
Scaling.but<-tkbutton(win1, text="Choose file", command=Scaling,width=9)
tkgrid(tklabel(frameButtonF,text="Imputation method"),comboBox_Impute, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameButtonF,text="scaling method"),comboBox_scaling, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameButtonF,text="Run"), Scaling.but , pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(win1$env$tb5 ,text="Select Massomics integration result to have a data scaling"), pady= 5, padx= 20, sticky="w")
tkgrid(frameButtonF, pady= 5, padx= 5)
}
Data_scaling_impute<-function(path=NULL,ScalingT=NULL,Impute=NULL){
library(tcltk)
library(tcltk2)
library(data.table)
library(impute)
if (is.null(path)){path=tk_choose.files(filter = matrix(c( "CSV", ".csv","excel file", ".xls", "excel file", ".xlsx"),3, 2, byrow = TRUE),
caption = "Choose integration result to have a data scaling")}
data=fread(path)
datacol=colnames(data)[!(colnames(data) %in% c("CAS","Ref.Ion" , "Total.ID(n=3)","Library.match","Ret.Time" ,"RT.shfit.Lower","Warnings" ,"Name"))]
datafcol=colnames(data)[(colnames(data) %in% c("CAS","Ref.Ion" , "Total.ID(n=3)","Library.match","Ret.Time" ,"RT.shfit.Lower","Warnings" ,"Name"))]
data=as.data.frame(data)
for (col in datacol){
data[,col]=as.numeric(as.character(data[,col]))
}
# impute missing value.
e=data[,datacol]
e[e==0]<-NA
e[e=="Inf"]<-NA
e[e=="-Inf"]<-NA
e[e==""]<-NA
e[e=="NaN"]<-NA
e=as.matrix(e)
if((sum(is.na(e)))>0 && !is.null(Impute)){
switch(Impute,
Minimum= {e[is.na(e)] = min(e[!is.na(e)])},
Zero= {e[is.na(e)] = 0},
KNN= {e=try(impute.knn(e)$data,silent = T)}
)
}
if(!is.null(ScalingT)){
switch(ScalingT,
Log= {
e<-log(e)
e[(e==-Inf)] <- 0
},
Mean= {e=e/mean(na.omit(e))},
Pareto = {
for (row in nrow(e)){
e[row,]=e[row,]/sqrt(sd(e[row,]))
}
}
)
}
data=cbind(data[,datafcol],e)
write.csv(data,file = paste0(tools::file_path_sans_ext(path)," Impute_",ifelse(is.null(Impute),"None",Impute)," Scaling_",ifelse(is.null(ScalingT),"None",ScalingT),".csv"),row.names = F)
message("Done")
}
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