R/MassOmics.r
In MASHUOA/MassOmics: Data extraction pipeline for large-scale GC-MS metabolomics datasets
Defines functions
MassOmics.GUI
TMS_analysis
runFalsePostive
run
GCMS_analysis
LcOrbitalTRap
IonExtractor_Window
Documented in
MassOmics.GUI
#' \tabular{ll}{
#' Package: \tab MassOmics \cr
#' License: \tab GPL (>= 2)\cr
#' URL: \tab
#' }
#'
#' @docType package
#' @name MassOmics
#' @author George GUO \email{George.GUO@@auckland.ac.nz}
#' @references TBD
#' @keywords package
#' @import tcltk
#' @import tcltk2
#' @import BiocManager
#' @import pacman
#' @import BiocParallel
#' @import pander
#' @importFrom stats na.omit runif
#' @importFrom utils download.file modifyList packageVersion read.table tail
######################################### clean.fix #########################################
clean.fix <- function (main.folder = tk_choose.dir(caption = "Select working directory"),
amdis.report = read.csv(tk_choose.files(default = "Select the AMDIS report in .TXT",
multi = FALSE, caption = "Select the AMDIS report in .TXT"),
sep = "\t", colClass = "character"), ion.lib = read.csv(tk_choose.files(default = "Select the Idenditification report in .CSV",
multi = FALSE, caption = "Select the Idenditification report in .CSV"),
sep = ","), save = TRUE, output = "Fianl Metab result")
{
require(xcms)
old.wd <- getwd()
main.folder <- main.folder
setwd(main.folder)
names(ion.lib)[1]<-"Name"
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
largest <- 1
for (i in 1:length(conditions)) {
largest <- c(largest, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
}
largest <- max(largest)
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions[1], full.names = TRUE)),
ignore.case = TRUE, value = TRUE)
replicates <- matrix(c(1:length(samples)), nrow = 1, ncol = largest)
if (length(conditions) > 1) {
for (i in 2:length(conditions)) {
num.rep <- c(num.rep, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
samples <- c(samples, grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
up.num <- length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
if (i < 3) {
low.num <- (replicates[length(replicates)] +
1)
}
else {
low.num <- max(replicates[i - 1, !duplicated(replicates[i -
1, ])]) + 1
}
replicates <- rbind(replicates, c((low.num):(low.num +
(up.num - 1))))
}
}
if (is.data.frame(ion.lib) == FALSE) {
ion.lib = read.csv(ion.lib, sep = ",")
}
library_file <- ion.lib
library_file$Name <- gsub(" ", "", library_file$Name)
if (is.data.frame(amdis.report) == TRUE) {
amdis.report <- amdis.report
}
else {
amdis.report = read.csv(amdis.report, sep = "\t")
}
for (i in 1:nrow(amdis.report)) {
original <- as.character(amdis.report[i, 1])
new <- unlist(strsplit(original, "\\", fixed = TRUE))
new <- new[length(new)]
amdis.report[i, 1] <- new
}
total_report <- amdis.report
final.df <- 1
for (c in 1:length(conditions)) {
filenames <- dir(conditions[c])
for (q in 1:length(filenames)) {
file <- filenames[q]
name.file <- file
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
file <- gsub("CDF", "FIN", file, ignore.case=TRUE)
file <- which(total_report$FileName == file)
if (length(file) > 0) {
data.df <- total_report[file, ]
Keep <- c("Name", "RT", "Scan", "Expec..RT",
"Base.Peak")
data.df <- data.df[, Keep]
data.df[2] <- as.numeric(data.df[, 2])
data.df[3] <- as.numeric(data.df[, 3])
data.df[4] <- as.numeric(data.df[, 4])
data.df$deff <- data.df$RT - data.df$Expec..RT
data.df <- subset(data.df, data.df$deff < 2.5 &
data.df$deff > -2.5)
sure <- data.df[1, ]
while (nrow(data.df) >= 1) {
line <- data.df[1, ]
sameRT <- subset(data.df, data.df$RT == line$RT)
if (nrow(sameRT) > 1) {
isoleucine <- grep("isoleucine", sameRT$Name,
ignore.case = TRUE)
if (length(isoleucine) > 0) {
isoleucine2 <- grep("isoleucine", sure$Name,
ignore.case = TRUE)
if (length(isoleucine2) > 0) {
sameRT2 <- sameRT[-isoleucine, ]
sure <- rbind(sure, sameRT2[which.min(which.min(abs(sameRT2$deff))),
])
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
else {
sure <- rbind(sure, sameRT[isoleucine,
])
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
}
else {
sure <- rbind(sure, sameRT[which.min(which.min(abs(sameRT$deff))),
])
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
}
else {
sure <- rbind(sure, sameRT)
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
}
surefinal <- sure
surefinal$Name <- gsub("? ", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("?? ", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("??? ", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("?", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("??", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("???", "", surefinal$Name,
fixed = TRUE)
raw_data <- xcmsRaw(filename = paste(conditions[c],
filenames[q], sep = "\\"))
dev.new.OS()
plotChrom(raw_data)
for (h in 1:nrow(surefinal)) {
metabolite <- surefinal[h, ]
amdis_scan <- (surefinal$Scan[h])
ion.ref <- grep(gsub(" ", "", metabolite$Name),
library_file$Name, fixed = TRUE)
if (length(ion.ref) == 0) {
error.lib <- paste("There is no ion defined for metabolite",
metabolite$Name, "in the reference ion library",
sep = " ")
print(error.lib)
}
metabolite <- library_file[ion.ref, ]
if (is.na(metabolite$ref_ion)) {
error.lib2 <- paste("The metabolite", metabolite$Name,
"was detected in the reference ion library, however, there is no ion fragment defined for that.",
sep = " ")
print(error.lib2)
}
scan <- getScan(raw_data, scan = surefinal$Scan[h])
scan <- data.frame(scan)
scan$mz <- trunc(scan$mz)
scan_ion <- (metabolite$ref_ion) - 2
for (k in 1) {
abundance <- subset(scan, scan$mz == (scan_ion +
k))
maxabundance <- max(abundance$intensity)
ion_set <- c(maxabundance)
}
for (k in 2:3) {
abundance <- subset(scan, scan$mz == (scan_ion +
k))
maxabundance <- max(abundance$intensity)
ion_set <- c(ion_set, maxabundance)
}
correct_abundance <- max(ion_set)
surefinal$Base.Peak[h] <- max(correct_abundance)
}
surefinal$RT <- NULL
surefinal$Expec..RT <- NULL
surefinal$deff <- NULL
surefinal$Scan <- NULL
surefinal <- surefinal[!duplicated(surefinal$Name),
]
names(surefinal)[2] <- name.file
if (final.df == 1) {
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
else {
final.df <- merge(final.df, surefinal, by.x = "Name",
by.y = "Name", all = TRUE)
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
else {
confirmation <- paste("File", q, "(", name.file,
")", "not analyzed! Probably there is no metabolites detected for this sample in the AMDIS report",
sep = " ")
print(confirmation)
if (final.df != 1) {
final.df$error <- "Error"
names(final.df)[ncol(final.df)] <- name.file
}
}
}
}
for (r in 1:nrow(replicates)) {
rep.row <- replicates[r, ]
rep.row <- rep.row[!duplicated(rep.row)]
rep.name <- rep(conditions[r], length(rep.row))
if (r == 1) {
rep.name.final <- c(rep.name)
rep.name.final <- c("Replicates", rep.name.final)
}
else {
rep.name.final <- c(rep.name.final, rep.name)
}
}
final.df <- rbind(rep.name.final, final.df)
final.df <- final.df[order(final.df[, 2], decreasing = T),
]
row.names(final.df) <- 1:nrow(final.df)
if (save == TRUE) {
sheet <- output
store <- paste(main.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
}
setwd(old.wd)
return(final.df)
}
######################################### clean.fix3 #########################################
clean.fix3 <- function (main.folder = tk_choose.dir(caption = "Select working directory"),
amdis.report = read.csv(tk_choose.files(default = "Select the AMDIS report in .TXT",
multi = FALSE, caption = "Select the AMDIS report in .TXT"),
sep = "\t", stringsAsFactors=FALSE),
save = TRUE,
output = "Final Metab3 Results",
Ion.bin= 0.5,
RT.bin = 5,
File.name = "Identification Report.csv",
MS.L= tk_choose.files(caption="Select MS library (e.g. SVB_Totoal) in .msl")) {
require(xcms)
require(lattice)
require(plyr)
main.folder <- main.folder
setwd(main.folder)
################# Step 1. Function transforms AMDIS library to spreadsheet of data for each metabolite
getIonLib<-function(lib.fn = MS.L){
lib.txt<-readLines(lib.fn)
lib.txt<-lib.txt[-grep("^[ ]*$",lib.txt)]
att.nms<-unique(sapply(strsplit(lib.txt[grep(":",lib.txt)],":"),function(x) x[1]))
entry.nms<-sapply(strsplit(lib.txt[grep("NAME:",lib.txt)],":"), function(x) x[2])
rez<-matrix(NA, nrow=length(entry.nms), ncol=length(att.nms), dimnames=list(NULL,att.nms ))
starts=grep("NAME:", lib.txt)
stops=c(starts[1:(length(starts)-1)]+diff(starts)-1,length(lib.txt))
for (i in 1:length(starts)){
#i=1
tmp<-lib.txt[starts[i]:stops[i]]
sapply(strsplit(tmp[grep(":",tmp)],":"), function(x) rez[i,x[1]]<<-x[2])
}
rez<-data.frame(rez,stringsAsFactors=FALSE)
rez=within(rez, {
RT=as.numeric(RT)
RSN=as.numeric(RSN)
NUM.PEAKS=as.numeric(NUM.PEAKS)
})
return(rez)
}
libr<-getIonLib()## returns list of peaks
############ Step 2. Generate the list of metabolites and clean up
AmRep<-amdis.report
AmRep<-AmRep[!AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)+1.5 & AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)-1.5,]
AmRep$Name <- gsub("?", "", AmRep$Name, fixed = TRUE)
AmRep$Name <- gsub("^ ", "", AmRep$Name, perl=T)
## RT statistics from the AReport
RT.stats<-t(sapply(split(AmRep$RT, AmRep$Name),function(x) c(RT.mean=round(mean(x,na.rm=TRUE),3),
RT.median=round(median(x,na.rm=T),3),
RT.sd=sd(x,na.rm=T),
Number.of.ID=length(x[!is.na(x)]))))
AmRep.RT.stats <- as.data.frame(cbind(RT.stats,Exp.RT=0, ref_ion=0,Mol.mass=0, Diff.RT=0, CAS=0))##data frame for list of metabolites
AmRep.RT.stats$ref_ion <- libr$RSN[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Mol.mass <- libr$FORM[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Exp.RT <- libr$RT[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Diff.RT <- AmRep.RT.stats$Exp.RT- AmRep.RT.stats$RT.median
AmRep.RT.stats$CAS <- libr$CAS[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats <- AmRep.RT.stats[order(AmRep.RT.stats$RT.median, decreasing = F),]
write.csv(AmRep.RT.stats, file = File.name)
check <- tk_select.list(c("Good Identification Report (continue)", "Bad Identificaiton Reprot (stop)","Load a corrected report"), title = "Check Identification Report")
if (check == "Bad Identificaiton Reprot (stop)"){
stop("Data process terminated")
}else if(check== "Load a corrected report" ){
final.check.data = read.csv(tk_choose.files(caption = "Load a corrected report", multi = FALSE),stringsAsFactors=FALSE)
}else{
final.check.data<-read.csv(File.name,stringsAsFactors=FALSE)
}
GGReport <- tk_select.list(c("Yes (Slower analysis speed)", "No (Faster - but cann't perform the Diagnosis of Auto GC-Peak Integration )"), title = "Turn on the Diagnosis function")
names(final.check.data)[1]<-"Name"
ion.lib<-final.check.data
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
largest <- 1
for (i in 1:length(conditions)) {
largest <- c(largest, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
}
largest <- max(largest)
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions[1], full.names = TRUE)), ignore.case = TRUE, value = TRUE)
replicates <- matrix(c(1:length(samples)), nrow = 1, ncol = largest)
if (length(conditions) > 1) {
for (i in 2:length(conditions)) {
num.rep <- c(num.rep, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
samples <- c(samples, grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
up.num <- length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
if (i < 3) {
low.num <- (replicates[length(replicates)] +
1)
}
else {
low.num <- max(replicates[i - 1, !duplicated(replicates[i -
1, ])]) + 1
}
replicates <- rbind(replicates, c((low.num):(low.num +
(up.num - 1))))
}
}
library_file <- ion.lib[,c("Name","ref_ion","RT.median")]
final.df<-NULL
remove(final.df)
Graphic.df<-data.frame()
if(length(conditions)==1) {
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=ion.lib[,"Name"],Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions,
filenames[q], sep = "\\"))
if (GGReport=="Yes (Slower analysis speed)"){
dev.new.OS()
plotChrom(raw_data) # plot TIC
}
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$ref_ion
R.Time<-metabolite$RT.median*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(R.Time-RT.bin,R.Time+RT.bin))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
maxabundance <- max(abundance$intensity)
surefinal$Base.Peak[h] <- maxabundance
if (GGReport=="Yes (Slower analysis speed)"){
Graphic.df<-rbind.fill(Graphic.df,data.frame(Metabolite.Names=paste(metabolite$Name," (m/z:",R.Ion,")",sep="") ,Retention.Time=(abundance$rt)/60,
Intensity=abundance$intensity ,Sample.Names=name.file ))
}
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
final.df<-cbind(CAS=ion.lib$CAS, Num.ID=ion.lib$Number.of.ID, Ref.Ion=ion.lib$ref_ion, Ret.Time=ion.lib$RT.median, final.df)
final.df <- final.df[order(final.df$Ret.Time , decreasing = F),]
row.names(final.df) <- 1:nrow(final.df)
} else{
for (c in 1:length(conditions)) {
filenames <- dir(conditions[c])
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)] # remove the effect of fin and leu
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=ion.lib[,"Name"],Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions[c],
filenames[q], sep = "\\"))
if (GGReport=="Yes (Slower analysis speed)"){
dev.new.OS()
plotChrom(raw_data) # plot TIC
}
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$ref_ion
R.Time<-metabolite$RT.median*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(R.Time-RT.bin,R.Time+RT.bin))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
maxabundance <- max(abundance$intensity)
surefinal$Base.Peak[h] <- maxabundance
if (GGReport=="Yes (Slower analysis speed)"){
Graphic.df<-rbind.fill(Graphic.df,data.frame(Metabolite.Names=paste(metabolite$Name," (m/z:",R.Ion,")",sep="") ,Retention.Time=(abundance$rt)/60,
Intensity=abundance$intensity ,Sample.Names=name.file ))
}
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
}
for (r in 1:nrow(replicates)) {
rep.row <- replicates[r, ]
rep.row <- rep.row[!duplicated(rep.row)]
rep.name <- rep(conditions[r], length(rep.row))
if (r == 1) {
rep.name.final <- c(rep.name)
rep.name.final <- c(rep("Replicates",4), rep.name.final)
}
else {
rep.name.final <- c(rep.name.final, rep.name)
}
}
final.df$Name<-as.character(final.df$Name)
final.df<-cbind(CAS=ion.lib$CAS, Num.ID=ion.lib$Number.of.ID, Ref.Ion=ion.lib$ref_ion, Ret.Time=ion.lib$RT.median, final.df)
final.df$CAS<-as.character(final.df$CAS)
final.df <- final.df[order(final.df$Ret.Time, decreasing = F),]
final.df <- rbind(rep.name.final, final.df)
row.names(final.df) <- 1:nrow(final.df)
}
if (save == TRUE) {
sheet <- output
store <- paste(main.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
if (GGReport=="Yes (Slower analysis speed)"){
write.csv(Graphic.df,file="GC-Peak Diagnosis Report.csv")
cat(paste("Data process is completed and called",sheet, sep=" "))
}
}
return(final.df)
}
######################################### IonExtractor_Window #########################################
IonExtractor_Window <- function(){
require(RColorBrewer)
require(flux)
IonExtractor<- function(folder = tk_choose.dir(caption = "Select working directory"),
mz = 106,
RT.Time1 = 10.5,
RT.Time2 = 11,
Ion.bin = 0.5,
heightMAX= 50000,
Metabolite.Names = "D4-Methanol",
save = TRUE,
peak="Peak Height"
){
require(xcms)
old.wd <- getwd()
main.folder <- folder
folderRE <<- folder
setwd(main.folder)
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre <- c(files[isDir == TRUE])
conditions <- tk_select.list(conditions_pre, multiple = FALSE,
title = paste("Folder conatins all cdf files"))
condtions1<<-conditions
final.df<-NULL
plot_1<-NULL
rm(final.df)
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
colors<-rainbow(length(filenames))
# dev.new.OS()
for (q in 1:length(filenames)) {
raw_data <- xcmsRaw(filename = paste(conditions, filenames[q], sep = "\\"))
name.file <- filenames[q]
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
IonExtract <-getEIC(raw_data,mzrange=cbind(mz-Ion.bin,mz+Ion.bin),rtrange=cbind(RT.Time1*60,RT.Time2*60))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
ifelse(peak=="Peak Height", Peakvalue <- max(abundance$intensity) ,Peakvalue<-auc(abundance$rt, abundance$intensity*10))
if (!exists("final.df")){
final.df <- data.frame(t(c(name.file,Peakvalue)))
} else {
final.df <- rbind(final.df, data.frame(t(c(name.file,Peakvalue))))
}
print(paste("File", q, name.file,"done!"))
## Plotting
Intensity=abundance$intensity
Retention.Time<-abundance$rt/60
#CombinRT <- as.data.frame(cbind(Retention.Time,Intensity))
if(q==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= colors[q],bty="n", type="l",main=paste(Metabolite.Names," (",mz," m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""),
xlab="Retention Time (min)",ylab="Intensity", ylim=c(0,heightMAX))
## test new scrip
#plot_1 <- ggplot(CombinRT , aes(x=Retention.Time, y=Intensity)) + geom_line(col= colors[q]) + labs(title=paste(Metabolite.Names," (",mz," m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""), x = "Retention Time (min)", y ="Intensity")
}else{
lines(Intensity~Retention.Time,col= colors[q])
#plot_1 <- plot_1 + geom_line( aes(x=Retention.Time, y=Intensity*q), col=colors[q])
#print(plot_1)
}
}
final.df2<-NULL
final.df2<- data.frame(t(final.df[2]))
names(final.df2) <- t(final.df[1])
row.names(final.df2) <- Metabolite.Names
if (save == TRUE) {
ifelse(peak=="Peak Height",ValueType<-"PeakHeight",ValueType<-"PeakArea")
store <- paste(main.folder, "\\", Metabolite.Names,"(",mz,")RT",RT.Time1,"-",RT.Time2,"_",ValueType,".csv", sep = "")
write.csv(final.df2, file = store, row.names = TRUE)
print("Complete!")
print(paste("The file ",Metabolite.Names, ".csv", " was saved in the folder ", folder, sep=""))
} else {
print("No file was saved because the argument save was set as FALSE")
}
return(final.df2)
}
ReIonExtractor<- function( folder = tk_choose.dir(caption = "Select working directory"),
mz = 106,
RT.Time1 = 10.5,
RT.Time2 = 10.7,
Ion.bin = 0.5,
heightMAX= 50000,
Metabolite.Names = "D4-alanine",
conditions=1,
peak="Peak Height",
save = TRUE){
require(xcms)
old.wd <- getwd()
main.folder <- folder
setwd(main.folder)
if(conditions==1){
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre <- c(files[isDir == TRUE])
conditions <- tk_select.list(conditions_pre, multiple = FALSE, title = paste("Folder conatins all cdf files"))
}
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
final.df<-NULL
rm(final.df)
colors<-rainbow(length(filenames))
for (q in 1:length(filenames)) {
raw_data <- xcmsRaw(filename = paste(conditions, filenames[q], sep = "\\"))
name.file <- filenames[q]
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
IonExtract <-getEIC(raw_data,mzrange=cbind(mz-Ion.bin,mz+Ion.bin),rtrange=cbind(RT.Time1*60,RT.Time2*60))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
ifelse(peak=="Peak Height", Peakvalue <- max(abundance$intensity) ,Peakvalue<-auc(abundance$rt, abundance$intensity*10))
if (!exists("final.df")){
final.df <- data.frame(t(c(name.file, Peakvalue)))
} else {
final.df <- rbind(final.df, data.frame(t(c(name.file, Peakvalue))))
}
print(paste("File",q, name.file,"done!"))
## Plotting
Intensity=abundance$intensity
Retention.Time<-abundance$rt/60
if(q==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= colors[q],bty="n", type="l",main=paste(Metabolite.Names," (",mz," m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""),
xlab="Retention Time (min)",ylab="Intensity", ylim=c(0,heightMAX))
}else{
lines(Intensity~Retention.Time,col= colors[q])
}
}
final.df2<-NULL
final.df2<- data.frame(t(final.df[2]))
names(final.df2) <- t(final.df[1])
row.names(final.df2) <- Metabolite.Names
if (save == TRUE) {
ifelse(peak=="Peak Height",ValueType<-"PeakHeight",ValueType<-"PeakArea")
store <- paste(main.folder, "\\", Metabolite.Names,"(",mz,")RT",RT.Time1,"-",RT.Time2,"_",ValueType,".csv", sep = "")
write.csv(final.df2, file = store, row.names = TRUE)
print("Complete!")
print(paste("The file ",Metabolite.Names, ".csv", " was saved in the folder ", folder, sep=""))
} else {
print("No file was saved because the argument save was set as FALSE")
}
return(final.df2)
}
### interphase
require(tcltk)
tclRequire("BWidget")
kk <- tktoplevel()
frame_All <- tkframe(kk)
tkwm.title(kk,"GC-MS IonExtractor")
xvar <- tclVar("106")
yvar <- tclVar("10.5")
zvar <- tclVar("10.9")
wvar <- tclVar("D4-alanine")
hvar <- tclVar("300000")
Peakvar <- tclVar("Peak Height")
x.entry <- tkentry(background="white",kk, textvariable=xvar, width=17)
y.entry <- tkentry(background="white",kk, textvariable=yvar, width=17)
z.entry <- tkentry(background="white",kk, textvariable=zvar, width=17)
w.entry <- tkentry(background="white",kk, textvariable=wvar, width=17)
h.entry <- tkentry(background="white",kk, textvariable=hvar, width=17)
## choose between peakheight or area
Peak <- c("Peak Height","Peak Area")
comboBox <- tkwidget(kk,"ComboBox",editable=FALSE,values=Peak,textvariable=tclVar("Peak Height"),width=15)
####################
Re_analysis <-function () {
w <- as.character(tclvalue(wvar))
x <- as.numeric(tclvalue(xvar))
y <- as.numeric(tclvalue(yvar))
z <- as.numeric(tclvalue(zvar))
h <- as.numeric(tclvalue(hvar))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
if (!exists("folderRE")){c(folder<- tk_choose.dir(caption = "Select working directory"),conditions<-1)
}else{c(folder<-folderRE, conditions <- condtions1)}
ReIonExtractor(Metabolite.Names=w,mz=x,RT.Time1=y,RT.Time2=z, heightMAX=h, folder = folder, conditions =conditions ,peak= Peakvar)
}
re_analysis.but <- tkbutton(kk, text="Re-analysis", command=Re_analysis)
reset <- function() {
tclvalue(xvar)<-""
tclvalue(yvar)<-""
tclvalue(zvar)<-""
tclvalue(wvar)<-""
tclvalue(hvar)<-"300000"
}
reset.but <- tkbutton(kk, text="Reset", command=reset)
submit <- function() {
w <- as.character(tclvalue(wvar))
x <- as.numeric(tclvalue(xvar))
y <- as.numeric(tclvalue(yvar))
z <- as.numeric(tclvalue(zvar))
h <- as.numeric(tclvalue(hvar))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
IonExtractor(Metabolite.Names=w,mz=x,RT.Time1=y,RT.Time2=z, heightMAX=h ,peak= Peakvar)
}
submit.but <- tkbutton(kk, text="Submit", command=submit)
quit.but <- tkbutton(kk, text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
# outline
frameL<- tkframe(frame_All,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameL,text="Metabolite Name"), w.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Reference Ion"), x.entry, tklabel(frameL, text="m/z"), pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Initial Retention Time"), y.entry, tklabel(frameL, text="minutes"), pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="End Retention Time"), z.entry, tklabel(frameL, text="minutes"), pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Min Intensitiy for y-axis"), h.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Integration Types"),comboBox, sticky="w",pady= 10, padx= 10)
frameButton<- tkframe(frame_All)
tkgrid(tklabel(frameButton,text=""), submit.but, re_analysis.but, reset.but, quit.but, pady= 10, padx= 10)
## load images
image.path_GC_IonExtractor<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCMS_IonExtractor_logo.gif", sep=""))
GC_IonExtractor_logo <-tcl("image", "create", "photo", "IonExtract_GC.image", file=image.path_GC_IonExtractor)
# Interphase
tkgrid(tklabel(frame_All,image=GC_IonExtractor_logo))
tkgrid(tklabel(frame_All,text="ENTER PARAMETERS"),columnspan=4, pady = 10, sticky="w")
tkgrid(frame_All)
tkgrid(frameL,pady= 0, padx= 10)
tkgrid(frameButton)
}
######################################### LcOrbitalTRap #########################################
LcOrbitalTRap <- function(){
# Load all require library
require(multtest)
require(xcms)
require(faahKO)
require(CAMERA)
require(flux)
require(tcltk)
if (!require(mzmatch.R)){
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS=T)
devtools::install_github("andzajan/mzmatch.R",upgrade ="never",quiet = T)
require(mzmatch.R)
}
mzmatch.init(version.1=FALSE)
#require(RColorBrewer)
################# Setup working directory ######################
WorkingDir <-function(){
Main.Folder <- choose.dir(caption = "Select working directory")
all.Main.Folder<<-Main.Folder
setwd(all.Main.Folder)
}
################# RUN LC Orbital Trap peak area integration ####
Run.LCMS.Analysis<-function(ppm_b=2.5, peakwidth_b=c(5,20), AllScangRange=TRUE, RtRangeM=c(0,13), SignalNoiseR_b=50, Prefilter_b=c(3,5000), mzwid_b=0.015, bw_b=2, modeP="positive"){
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
cdfpath<-all.Main.Folder
## Load all raw files
print("STEP 1a: Upload all LC-orbitral raw data in .mzXML formate")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
cdffiles <- cdffiles[grep(c(".cdf$|.mzXML$"),cdffiles, ignore.case=TRUE)]
print(cdffiles)
## Print parameters
print("-----XCMS Parameter Settings----")
print(paste("Mass Resolution:", ppm_b, "ppm"))
print(paste("Peak width:", toString(peakwidth_b), "s"))
print(paste("RT bin (bw):", bw_b, "s"))
print(paste("mz varation (mzwid):", mzwid_b))
print(paste("Signal-to-noise ratio:", SignalNoiseR_b))
print(paste("Pre-filter:", toString(Prefilter_b)))
print(paste("Polarity:", modeP))
## Peak identitication
print("STEP 1b: Peak Identications (long waiting time: ~2 min per sample)")
RtRangeS<-round(RtRangeM*60,0) # convert minute to second and no decemal place
if(AllScangRange==TRUE) {xset <- xcmsSet(cdffiles, ppm = ppm_b, peakwidth = peakwidth_b, method = "centWave", nSlaves=4, snthresh=SignalNoiseR_b, prefilter= Prefilter_b, integrate = 1, fitgauss= TRUE, verbose.columns=TRUE) } else {
xset <-xcmsSet(cdffiles, ppm = ppm_b, peakwidth = peakwidth_b, method = "centWave" , scanrange=RtRangeS, nSlaves=4, snthresh=SignalNoiseR_b, prefilter= Prefilter_b, integrate = 1, fitgauss= TRUE, verbose.columns=TRUE) }
## save the file
# xset_RT<-xset
#save(xset_RT,file="BackUp_ForRtFail.rda")
## Peak Match 1
# print("STEP 3: Matching Peaks Across Samples")
# xset <- group(xset, bw = 2, mzwid = 0.015)
## RT correction
print("STEP 1c: Retention Time Correction")
xset2 <- retcor(xset, method ="obiwarp", plottype = c("deviation"), profStep=0.5, center=3)
## Peak Match 2
print("STEP 1d: Matching Peaks Across Samples")
xset2 <- group(xset2, bw = bw_b , mzwid = mzwid_b)
print(xset2)
## Fill up the missing values
print("STEP 1e: Filling in Missing Peak Data")
xset3 <- fillPeaks(xset2)
## Save the file in the working folder
# peakTable(xset3, filebase="Final LC-MS result")
# print("Data process is completed and save Final LC-MS result.tsv")
## save the process LC-MS data in the working folder and can be load for futher downstream analysis
if(AllScangRange==TRUE) {RtRangFinal=" AllScan"} else { RtRangFinal=toString(RtRangeM) }
LongFileName <-paste("Processed raw data(ppm_",ppm_b,")(peakwidth_",toString(peakwidth_b),")(RtRange_",RtRangFinal,")(bw_",bw_b,")(mzwid_",
mzwid_b,")(SignalNoise_",SignalNoiseR_b,")(Pre-filter_",toString(Prefilter_b),")(Polarity_",modeP,").rda",sep="")
#rawdata_FileName <- tclvalue(tkgetSaveFile(initialfile= LongFileName))
save(xset3,file= LongFileName)
## Annotation by CAMERA packages
xset3_annotation <- xset3
print("STEP 1f: Annotation of adduct fragments and isotopes in progress....")
if(length(levels(xset3_annotation@phenoData$class))==2){
peaklist<- annotateDiffreport(xset3_annotation,polarity=modeP)
write.csv(peaklist, file=paste("Final LC-MS result(XCMS)_TTtest","_",modeP,".csv",sep=""))
} else {
an <- xsAnnotate(xset3_annotation)
an <- groupFWHM(an)
an <- findIsotopes(an) # optional but recommended.
an <- groupCorr(an) # optional but very recommended step
an <- findAdducts(an,polarity=modeP)
peaklist <- getPeaklist(an)
write.csv(peaklist, file=paste("LC-MS result(XCMS)","_",modeP,".csv",sep=""))
}
print("LC-MS data extraction is completed and saved as LC-MS result(XCMS).csv")
tkmessageBox(title = "MassOmics-LCMS processing", message = paste("LC-MS data extraction is completed and saved as LC-MS result(XCMS).csv"))
}
########################## If the RT doesn't work
# RT_correct_error <- function(){
# Save_xsetRT<-choose.files(caption = "Select a processed raw data (e.g.BackUp_ForRtFail.rda")
# load(Save_xsetRT)
# print("STEP 3: Matching Peaks Across Samples")
# xset_RT <- group(xset_RT)
# xset_RT <- fillPeaks(xset_RT)
# xset3 <- xset_RT
# peakTable(xset_RT, filebase="Final LC-MS result(No RT correction)")
## need to save the files
# rawdata_FileName <- tclvalue(tkgetSaveFile(initialfile="Processed raw data(ppm)(peakwidth)(RrRange)_ForRTFail.rda"))
# save(xset3,file=rawdata_FileName)
# }
########################## mzMatch ####################################
mzMatch<-function(mode_b = "positive", ppm_b = 5, rtwindow_b = 20, BlankName= "Blank", BlankFilter= TRUE, javaMemorySizeG=4){
print("-----mzMatch Parameter Settings----")
print(paste("Mass Resolution:", ppm_b, "ppm"))
print(paste("Retention time window:", rtwindow_b, "s"))
print(paste("Blank filter:", BlankFilter))
print(paste("Name for blank class:", BlankName))
print(paste("Polarity:", mode_b))
print(paste("Memory usuage:", javaMemorySizeG, "GB"))
javaMemorySize<-javaMemorySizeG*1000
mzmatch.init(memorysize= javaMemorySize, version.1=FALSE)
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
Save_xset3<-choose.files(caption = "Select a XCMS processed raw data (e.g.Processed raw data(ppm)(peakwidth)....rda)")
load(Save_xset3)
## Convert xcms process file to .PeakML
print("STEP 2a: Convert xcms process data to .peakML")
PeakML.xcms.write(xset3, filename= "1_LCMS.peakML", ionisation= mode_b)
## Remove noise using blank samples
print("STEP 2b:Remove noise using blank")
if(BlankFilter==TRUE){
PeakML.BlankFilter(filename="1_LCMS.peakML",
ionisation= mode_b,
outputfile="1b_LCMS_BlankFilter.peakml",
BlankSample=BlankName)
}
## fill the gap
print("STEP 2c: Gap Filling the missing values")
if(BlankFilter==TRUE){
PeakML.GapFiller(filename = "1b_LCMS_BlankFilter.peakml", ionisation = mode_b, outputfile = "2_LCMS_GapFilling.peakml", ppm = 0, rtwin = 0, fillAll=FALSE, Rawpath=NULL, nSlaves = 4)
} else {
PeakML.GapFiller(filename = "1_LCMS.peakML", ionisation = mode_b, outputfile = "2_LCMS_GapFilling.peakml", ppm = 0, rtwin = 0, fillAll=FALSE, Rawpath=NULL, nSlaves = 4)
}
## Group peaks
print('STEP 3c: Group realtive peak together')
mzmatch.ipeak.sort.RelatedPeaks(i="2_LCMS_GapFilling.peakml", v=T,
o="3_LCMS_GapFilling_GroupPeak.peakml", ppm=ppm_b, rtwindow=rtwindow_b, JHeapSize= javaMemorySize)
## Identify peaks from databases
print("STEP 4c: Peak annoatation by databases")
DBS <- dir(paste(find.package("mzmatch.R"), "/dbs", sep=""), full.names=TRUE)
DBS # print a list of library
DBS <- paste(DBS[c(1:15)],collapse=",")
if(mode_b=="positive"){
mzmatch.ipeak.util.Identify(i="3_LCMS_GapFilling_GroupPeak.peakml", v=T,
o="4_LCMS_GapFilling_GroupPeak_Annotation(Positive).peakml", ppm=ppm_b, databases=DBS, polarity= mode_b,
adducts="M+H,M+ACN+Na,M+Na,M+K,M+ACN+H", JHeapSize= javaMemorySize) ## need to fix
mzmatch.ipeak.convert.ConvertToText (
i="4_LCMS_GapFilling_GroupPeak_Annotation(Positive).peakml",
o="Final_annoated_LCMS_data(positive).txt", databases=DBS,
annotations="identification,moleculeName,npeaks,ppm,adduct,relation.ship,charge", JHeapSize= javaMemorySize)
} else {
mzmatch.ipeak.util.Identify(i="3_LCMS_GapFilling_GroupPeak.peakml", v=T,
o="4_LCMS_GapFilling_GroupPeak_Annotation(Negative).peakml", ppm=ppm_b, databases=DBS, polarity= mode_b,
adducts="M-H", JHeapSize= javaMemorySize) ## need to fix
mzmatch.ipeak.convert.ConvertToText (
i="4_LCMS_GapFilling_GroupPeak_Annotation(Negative).peakml",
o= "Final_annoated_LCMS_data(Negative).txt", databases=DBS,
annotations="identification,moleculeName,npeaks,ppm,adduct,relation.ship,charge", JHeapSize= javaMemorySize)
}
tkmessageBox(title = "MassOmics-LCMS processing", message = paste("LC-MS data filter and annotation is completed and saved as Final_annoated_LCMS_data.txt"))
print("Analysis is completed and saved as - Final_annoated_LCMS_data.txt")
## end if mzmatch
}
## manually check data
ManualCheck <-function(){
require (tcltk)
require (mzmatch.R)
mzmatch.init(version.1=FALSE)
PeakML.Viewer (JHeapSize=5000)
tkmessageBox(title = "PeakML.Viewer", message = paste("May take long time to open PeakML.Viewer and open a file in .peakml format (e.g.4_LCMS_GapFilling_GroupPeak_Annotation.peakml"))
}
ExportToTXT <- function(javaMemorySizeG=4){
require (mzmatch.R)
require (tcltk)
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
javaMemorySize<-javaMemorySizeG*1000
mzmatch.init(memorysize= javaMemorySize, version.1=FALSE)
DBS <- dir(paste(find.package("mzmatch.R"), "/dbs", sep=""), full.names=TRUE)
DBS <- paste(DBS[c(1:15)],collapse=",")
InputName <- basename(choose.files(caption = "Select a .peakmL file (e.g.manual check.peakml)"))
OutputName <- basename(tclvalue(tkgetSaveFile(initialfile= "LC-MS_manual_check.txt")))
mzmatch.ipeak.convert.ConvertToText (
i=InputName,
o=OutputName, databases=DBS,
annotations="identification,moleculeName,npeaks,ppm,adduct,relation.ship,charge",
JHeapSize= javaMemorySize
)
print ("Done!")
}
################# stataiscally analysis for 2 conditions ####
LCMS_TTest<-function(nplot=100){
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
#Auotmatically load the proces data if xset3 not present
#if(!exists("xset3")){
# Save_xset3<-choose.files(caption = "Select Processed raw data")
# load(Save_xset3)
#}
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
condition_1<- as.character(unique(xset3@ phenoData $class)[[1]])
condition_2<- as.character(unique(xset3@ phenoData $class)[[2]])
print("Statistical analysis in progress....")
reporttab <- diffreport(xset3, condition_1, condition_2, "Final LC-MS result(T-Test)", nplot, metlin = 0.15, h=480, w=640)
print("The TTest report is completed and save as Final LC-MS result(T-Test.tsv")
}
################# Plot ion extracted chromatogram base on ID
choose.xset3<-function(){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_ID<<-xset3
}
plot.ID<-function(ID.NO=712){
if(!exists("xset3_ID")){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_ID<<-xset3
}
eiccor <- getEIC(xset3_ID, groupidx = ID.NO)
# Mass range
mzmin<-round(as.numeric(eiccor@ mzrange[1,1]),5)
mzmax<-round(as.numeric(eiccor@ mzrange[1,2]),5)
# RT range
RTmin<-round(as.numeric(eiccor@ rtrange[1,1]),1)
RTmax<-round(as.numeric(eiccor@ rtrange[1,2]),1)
# plot and save image
fig.names<-paste("ID(",ID.NO,")_Ion(",mzmin,"-",mzmax, "mz)_RT(", RTmin,"-",RTmax,"s).png", sep="")
png(fig.names)
plot(eiccor, xset3_ID)
dev.off() ## Need to make it plot the figure.....>_<
}
################# LC-MS Single ion extraction ##############
RUN.LCMS.Ion.Extractor<-function(){
require(xcms)
require(flux)
# Setup working dirctory
WorkingDir <-function(){
Main.Folder <- choose.dir(caption = "Select working directory")
all.Main.Folder<<-Main.Folder
setwd(all.Main.Folder)
}
# Located the process file
choose.xset3_Ion<-function(){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_Ion<<-xset3
}
# LC-MS IonExtractor
LCMS.Ion.Extractor<-function(mz.1 = 210.1281,
mz.2 = 210.1289,
RT.Time1 = 190,
RT.Time2 = 210,
heightMAX= 1500000000,
Metabolite.Names = "D5-tryptophan",
peak="Peak Area"){
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
if(!exists("xset3_Ion")){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_Ion<<-xset3
}
# Extract specific ion and rentension time
IonExtract <-getEIC(xset3_Ion,mzrange=cbind(mz.1,mz.2),rtrange=cbind(RT.Time1,RT.Time2))
# Determine colour of figure
#mycolors<-rainbow(length(t(unique(xset3@ phenoData))))
#mycolors<-brewer.pal(length(t(unique(xset3@ phenoData))),"Set1")
#names(mycolors) <- levels(xset3@ phenoData$class)
#list.df<-t(xset3@ phenoData)
#list.color<-mycolors[match(list.df,names(mycolors))]
list.color<-rainbow(length(IonExtract@eic))
IonExctor.df<-NULL
for (i in 1:length(IonExtract@eic)){
#Extraction
Extra.Data<-data.frame(IonExtract@eic[i])
ifelse(peak=="Peak Height", Peakvalue <- max(Extra.Data[,2]) ,Peakvalue<-auc(Extra.Data[,1], Extra.Data[,2]))
name.file<-names(IonExtract@eic)[i]
if (!exists("IonExctor.df")){
IonExctor.df <- data.frame(t(c(name.file,Peakvalue)))
} else {
IonExctor.df <- rbind(IonExctor.df, data.frame(t(c(name.file,Peakvalue))))
}
print(paste("File", i, name.file,"done!"))
# Plot
Intensity<-Extra.Data[,2]
Retention.Time<-Extra.Data[,1]
if(i==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= list.color[i],bty="n", type="l",main=paste(Metabolite.Names," (",mz.1,"-",mz.2, " m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""),
xlab="Retention Time (Sec)",ylab="Intensity", ylim=c(0,heightMAX))
}else{
lines(Intensity~Retention.Time,col= list.color[i])
}
}
final.df2<- data.frame(t(IonExctor.df[2]))
names(final.df2) <- t(IonExctor.df[1])
row.names(final.df2) <- Metabolite.Names
# Save files
ifelse(peak=="Peak Height",ValueType<-"PeakHeight",ValueType<-"PeakArea")
store <- paste(all.Main.Folder, "\\", Metabolite.Names,"(",mz.1,"-",mz.2," m.z",")RT",RT.Time1,"-",RT.Time2,"_",ValueType,".csv", sep = "")
write.csv(final.df2, file =store, row.names = TRUE)
print("Complete!")
return(final.df2)
print(paste("The file ",Metabolite.Names, ".csv", " was saved in ", all.Main.Folder, sep=""))
}
## User interphse specific for LC-MS IonExtractor
require(tcltk)
tclRequire("BWidget")
# Setup frame
kk <- tktoplevel()
frameAll <- tkframe(kk)
tkwm.title(kk,"LCMS-IonExtractor")
# Setup parameters
xvar <- tclVar("210.1281")
vvar <- tclVar("210.1289")
yvar <- tclVar("190")
zvar <- tclVar("210")
wvar <- tclVar("D5-tryptophan")
hvar <- tclVar("5000000")
Peakvar <- tclVar("Peak Area")
x.entry <- tkentry(background="white",kk, textvariable=xvar, width=17)
v.entry <- tkentry(background="white",kk, textvariable=vvar, width=17)
y.entry <- tkentry(background="white",kk, textvariable=yvar, width=17)
z.entry <- tkentry(background="white",kk, textvariable=zvar, width=17)
w.entry <- tkentry(background="white",kk, textvariable=wvar, width=17)
h.entry <- tkentry(background="white",kk, textvariable=hvar, width=17)
# Choose between peakheight or area
Peak <- c("Peak Area","Peak Height")
comboBox <- tkwidget(kk,"ComboBox",editable=FALSE,values=Peak,textvariable=tclVar("Peak Area"),width=15)
# Reset function
reset <- function() {
tclvalue(xvar)<-""
tclvalue(vvar)<-""
tclvalue(yvar)<-""
tclvalue(zvar)<-""
tclvalue(wvar)<-""
tclvalue(hvar)<-"5000000"
Peakvar <- tclVar("Peak Area")
}
reset.but <- tkbutton(kk, text="Reset", command=reset, width=9)
# Run function
submit <- function() {
w <- as.character(tclvalue(wvar))
x <- as.numeric(tclvalue(xvar))
v <- as.numeric(tclvalue(vvar))
y <- as.numeric(tclvalue(yvar))
z <- as.numeric(tclvalue(zvar))
h <- as.numeric(tclvalue(hvar))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
LCMS.Ion.Extractor(Metabolite.Names=w, mz.1=x, mz.2=v, RT.Time1=y, RT.Time2=z, heightMAX=h ,peak= Peakvar)
}
submit.but <- tkbutton(kk, text="Run", command=submit, width= 9)
# Quit function
quit.but <- tkbutton(kk, text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
Wdir.but <- tkbutton(kk, text="Choose", command= WorkingDir ,width=9)
Process.but <- tkbutton(kk, text="Choose", command= choose.xset3_Ion ,width=9)
open.but <- tkbutton(kk,text="Open folder",width= 9,
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path2<-c(paste(file.path(path.package(package="MassOmics")),"/R/LCMS_IonExtractor.gif", sep=""))
logo_1<-tcl("image", "create", "photo", "MaxC4.image", file=image.path2)
# Upper frame
frameUpper<- tkframe(frameAll,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Setup Working Directory "), Wdir.but, pady = 2, padx= 10, sticky="w") #columnspan=4
tkgrid(tklabel(frameUpper,text="Select processed LC-MS file "), Process.but, pady = 2, padx= 10, sticky="w")
#middle frame
frameMiddle<- tkframe(frameAll,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameMiddle,text="Metabolite Name"), w.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Reference Ion (min,max)"), x.entry, v.entry, tklabel(frameMiddle,text="m/z"), pady= 8, 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")
tkgrid(tklabel(frameMiddle,text="Min Intensitiy for y-axis"), h.entry, pady= 8, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Integration type"),comboBox, pady= 8, padx= 10, sticky="w")
# Button
frameButton<- tkframe(frameAll)
tkgrid(tklabel(frameButton,text=""), reset.but, submit.but, open.but, quit.but, pady= 10, padx= 10, sticky="w")
# Interphase structure
tkgrid(tklabel(kk,image=logo_1))
tkgrid(tklabel(frameAll,text="1. SETUP"), pady = 10, padx= 20, sticky="w")
tkgrid(frameAll)
tkgrid(frameUpper, pady= 0, padx= 30, sticky="w")
tkgrid(tklabel(frameAll,text="2. ENTER PARAMETERS"), pady = 10, padx= 20, sticky="w") #columnspan=4
tkgrid(frameMiddle, pady= 0, padx= 30)
tkgrid(frameButton, pady= 10, padx= 30)
}
## Annotation of Adduct and isotopes
# LCMS_Adduct_isotpes<-function(mode="positive"){
# if(exists("all.Main.Folder")){
# setwd(all.Main.Folder)
# } else {
# all.Main.Folder<<-choose.dir(caption = "Select working directory")
# setwd(all.Main.Folder)
# }
#
# Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
# load(Save_xset3)
# xset3_annotation <- xset3
#
# print("Annotation of adduct fragments and isotopes in progress....")
#
# if(length(levels(xset3_annotation@phenoData$class))==2){
# peaklist<- annotateDiffreport(xset3_annotation,polarity=mode)
# write.csv(peaklist, file=paste("Final LC-MS result(Adduct_Isotope)_TTtest","_",mode,".csv",sep=""))
# } else {
#
# an <- xsAnnotate(xset3_annotation)
# an <- groupFWHM(an)
# an <- findIsotopes(an) # optional but recommended.
# an <- groupCorr(an) # optional but very recommended step
# an <- findAdducts(an,polarity=mode)
# peaklist <- getPeaklist(an)
# write.csv(peaklist, file=paste("Final LC-MS result(Adduct_Isotope)","_",mode,".csv",sep=""))
# }
### Visilization ##
#plotEICs(an,1)
#plotPsSpectrum(an,1,maxlabel=10)
## find nature lost
#xs.pseudo <- findNeutralLoss(an,mzdiff=18.01,mzabs=0.01)
#xs.pseudo@peaks #WATER LOST
## Calcualte moleuclar composition
#library(Rdisop)
#isolist <- getIsotopeCluster(an)
# test<-isolist[5][[1]]$peaks
# molecules<- decomposeMass(488.158)
# molecules<- decomposeIsotopes(test[,1],test[,2],ppm=10)
# errors = MFerrors(molecules,test[,1],test[,2])
# cbind(getFormula(molecules), getScore(molecules), getValid(molecules))
# print("Annotation of adduct fragments and isotopes are completed and save as Final LC-MS result(Adduct_Isotope).csv")
## save the process LC-MS data in the working folder and can be load for futher downstream analysis
# rawdata_FileName <- tclvalue(tkgetSaveFile(initialfile=paste("Annotation raw data","(",mode, ").rda",sep="")))
# save(an,file=rawdata_FileName)
# }
################# User interphase ####################
## Load library
require(tcltk)
require(rgl)
tclRequire("BWidget")
## Setup tk enviroment ##
LC_Orbital <- tktoplevel()
frameOverall0 <- tkframe(LC_Orbital)
frameOverall <- tkframe(LC_Orbital)
frameOverall2 <- tkframe(LC_Orbital)
frameOverall3 <- tkframe(LC_Orbital)
tkwm.title(LC_Orbital ,"LC-MS data analysis")
## Functions in the buttons
workingdirctory<-function(){WorkingDir()
}
LC_MS_analysis<-function(){
ppm_a <- as.numeric(tclvalue(ppm.var))
peakwidth_a <- as.numeric(strsplit(tclvalue(peakwidth.var), ",",)[[1]])
RtRangeMa <- as.numeric(strsplit(tclvalue(RtRangeM.var), ",",)[[1]])
SignalNoiseR_a <-as.numeric(tclvalue(SignalNoiseR.var))
mzwid_a <- as.numeric(tclvalue(mzwid.var))
bw_a <- as.numeric(tclvalue(bw.var))
Prefilter_a <- as.numeric(strsplit(tclvalue(Prefilter.var), ",",)[[1]])
modeB <- modeA[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
rtVal <- as.character(tclvalue(rtValue))
if (rtVal=="1")
AllScangRangeA<-TRUE
if (rtVal=="0")
AllScangRangeA<-FALSE
Run.LCMS.Analysis(ppm_b=ppm_a,peakwidth_b=peakwidth_a, AllScangRange=AllScangRangeA, RtRangeM = RtRangeMa, SignalNoiseR_b=SignalNoiseR_a, mzwid_b=mzwid_a, Prefilter_b= Prefilter_a, bw_b= bw_a, modeP=modeB)
}
LCMS.hlep.page<-function(){
TMS.help <- tktoplevel()
tkwm.title(TMS.help,"Help")
scr <- tkscrollbar(TMS.help, repeatinterval=5,
command=function(...)tkyview(txt,...))
txt <- tktext(TMS.help,bg="white",font="courier",yscrollcommand=function(...)tkset(scr,...))
tkgrid(txt,scr)
tkgrid.configure(scr,sticky="ns")
## Insert text here
tkinsert(txt,"end","Under construction\n")
tkconfigure(txt, state="disabled")
tkfocus(txt)
}
TT.Test<-function(){
nplot_a <- as.numeric(tclvalue(nPlot.var))
LCMS_TTest(nplot=nplot_a)
}
workingdirctoryID<-function(){choose.xset3()
}
ID.Ion.Extraction<-function(){
ID.NO_a <- as.numeric(tclvalue(ID.NO.var))
plot.ID(ID.NO=ID.NO_a)
}
# adduct_isotope<-function(){
# modeB <- modeA[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
# LCMS_Adduct_isotpes(mode=modeB)
# }
# RT_Error<-function(){
# RT_correct_error()
# }
xcms.Tips<-function () {
require(tcltk)
kk <- tktoplevel()
tktitle(kk) <- "Suggested XCMS parameter settings"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/XCMS_parameter_tips.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "setting.image", file=image.path)
tkgrid(tklabel(kk,image=FrontImage))
}
MzMatch.ini <- function() {
ppm_a <- as.numeric(tclvalue(ppm_mz.var))
memory_a <-as.numeric(tclvalue( memory.var))
rtwindow_a <- as.numeric(tclvalue(rtwindow_mz.var))
BlankName_a <- as.character(tclvalue(BlankName_mz))
BnaVal <- as.character(tclvalue(BlankName_apply_Value))
if (BnaVal=="1")
BlankFilter_a<-TRUE
if (BnaVal=="0")
BlankFilter_a<-FALSE
mode_a <- mode_mz.var[[as.numeric(tclvalue(tcl(comboBox_MzMatch,"getvalue")))+1]]
mzMatch(mode_b = mode_a, ppm_b = ppm_a, rtwindow_b = rtwindow_a, BlankName= BlankName_a, BlankFilter= BlankFilter_a, javaMemorySizeG= memory_a)
}
manualCheck.ini <- function() {
ManualCheck()
}
exportText.ini <- function() {
memory_a <-as.numeric(tclvalue(memory_b.var))
ExportToTXT(javaMemorySizeG= memory_a)
}
## Creat buttons
Wdir.but <- tkbutton(LC_Orbital, text="Choose", command=workingdirctory,width=6)
integrate.but <- tkbutton(LC_Orbital, text="Run", command=LC_MS_analysis,width=6)
help.but <-tkbutton(LC_Orbital, text="Help", command= LCMS.hlep.page,width=6)
TT.Test.but <- tkbutton(LC_Orbital, text="Run", command=TT.Test, width=6)
WdirID.but <- tkbutton(LC_Orbital, text="Choose", command=workingdirctoryID,width=6)
ID.Ion.Extraction.but <- tkbutton(LC_Orbital, text="Run", command=ID.Ion.Extraction, width=6)
Ion.Extraction.but <- tkbutton(LC_Orbital, text="Run", command=RUN.LCMS.Ion.Extractor, width=6)
# adduct_isotope.but <- tkbutton(LC_Orbital, text="Run", command=adduct_isotope, width=6)
# RT.Error.but <- tkbutton(LC_Orbital, text="Run", command=RT_Error, width=6)
xcms.setting.but <- tkbutton(LC_Orbital, text="Tip", command= xcms.Tips, width=6)
MzMatch.but <- tkbutton(LC_Orbital, text="Run", command= MzMatch.ini, width=6)
ManualCheck.but <- tkbutton(LC_Orbital, text="Run", command= manualCheck.ini, width=6)
exportText.but <- tkbutton(LC_Orbital, text="Run", command= exportText.ini, width=6)
## create entry box and define their default values
# PPM
ppm.var <- tclVar("2.5")
ppm.entry <- tkentry(background="white",LC_Orbital, textvariable=ppm.var, width=8)
# +6Width
peakwidth.var <- tclVar("5,20")
peakwidth.entry <- tkentry(background="white",LC_Orbital, textvariable=peakwidth.var, width=8)
# ScangeRange
RtRangeM.var <- tclVar("0,13")
RtRangeM.entry <- tkentry(background="white",LC_Orbital, textvariable=RtRangeM.var, width=8)
rtx <- tkcheckbutton(LC_Orbital)
rtValue <- tclVar("1")
tkconfigure(rtx,variable=rtValue)
# Signal noise ratio
SignalNoiseR.var <- tclVar("50")
SignalNoiseR.entry <- tkentry(background="white",LC_Orbital, textvariable=SignalNoiseR.var, width=8)
# Prefilter
Prefilter.var <- tclVar("3,5000")
Prefilter.entry <- tkentry(background="white",LC_Orbital, textvariable= Prefilter.var, width=8)
# mzwid
mzwid.var <- tclVar("0.015")
mzwid.entry <- tkentry(background="white",LC_Orbital, textvariable= mzwid.var, width=8)
#bw
bw.var <- tclVar("2")
bw.entry <- tkentry(background="white",LC_Orbital, textvariable= bw.var, width=8)
# Number of plot
nPlot.var<- tclVar("10")
nPlot.entry <- tkentry(background="white",LC_Orbital, textvariable=nPlot.var, width=8)
# Chramtogram ID
ID.NO.var<- tclVar("1")
ID.NO.entry <- tkentry(background="white",LC_Orbital, textvariable=ID.NO.var, width=8)
# Positive or negative for addcut determination
modeA <- c("positive","negative")
comboBox <- tkwidget(LC_Orbital,"ComboBox",editable=FALSE,values=modeA,textvariable=tclVar("positive"),width=7)
## variable for mzMatch
ppm_mz.var <- tclVar("5")
ppm_mz.entry <- tkentry(background="white",LC_Orbital, textvariable= ppm_mz.var, width=8)
rtwindow_mz.var <- tclVar("10")
rtwindow_mz.entry <- tkentry(background="white",LC_Orbital, textvariable= rtwindow_mz.var, width=8)
BlankName_mz <- tclVar("Blank")
BlankName_mz.entry <- tkentry(background="white",LC_Orbital, textvariable= BlankName_mz, width=8)
BlankName_apply <- tkcheckbutton(LC_Orbital)
BlankName_apply_Value <- tclVar("1")
tkconfigure(BlankName_apply,variable=BlankName_apply_Value)
mode_mz.var <- c("positive","negative")
comboBox_MzMatch <- tkwidget(LC_Orbital,"ComboBox", editable=FALSE, values= mode_mz.var, textvariable=tclVar("positive"), width=7)
## memory
memory.var <-tclVar("4")
memory.entry <- tkentry(background="white",LC_Orbital, textvariable= memory.var, width=8)
memory_b.var <-tclVar("4")
memory_b.entry <- tkentry(background="white",LC_Orbital, textvariable= memory_b.var, width=8)
### Frame structure of user interphase
## UpperFrame
frameUpper <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Setup Working Directory "),Wdir.but, tklabel(frameUpper,text=" "), sticky="w")
## MiddleFrame
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameMid,text="Mass Resolution"), ppm.entry, tklabel(frameMid,text="ppm"),sticky="w")
tkgrid(tklabel(frameMid,text="Peak width (min,max)"), peakwidth.entry,tklabel(frameMid,text="second"),sticky="w")
tkgrid(tklabel(frameMid,text="RT bin (bw)"), bw.entry, tklabel(frameMid,text="second"), sticky="w")
tkgrid(tklabel(frameMid,text="mz varation (mzwid)"), mzwid.entry, sticky="w")
tkgrid(tklabel(frameMid,text="Signal-to-noise ratio"), SignalNoiseR.entry, sticky="w")
tkgrid(tklabel(frameMid,text="Pre-filter "), Prefilter.entry, sticky="w")
tkgrid(tklabel(frameMid,text="All RT range (Ignore below)"), rtx, sticky="w")
tkgrid(tklabel(frameMid,text="RT range (min,max)"), RtRangeM.entry, tklabel(frameMid,text="minute"),sticky="w")
tkgrid(tklabel(frameMid,text="MS modes"), comboBox, tklabel(frameMid,text=" "),sticky="w")
tkgrid(tklabel(frameMid,text="Suggested XCMS settings"),xcms.setting.but, sticky="w")
tkgrid(tklabel(frameMid,text="Automatic LC-peak integration"),integrate.but,sticky="w")
## MiddleFrame_b
# frameRtError <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameRtError,text="No retention correction "),RT.Error.but, tklabel(frameRtError,text=" "),sticky="w")
## Lower Frame_adduct&Isotope
# frameLowerAdductIsotope <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameLowerAdductIsotope,text="MS modes"), comboBox, tklabel(frameLowerAdductIsotope,text=" "),sticky="w")
# tkgrid(tklabel(frameLowerAdductIsotope,text="Annotate Adducts and Isotopes"), adduct_isotope.but ,sticky="w")
# Mzmatch
framLowerMzMatch <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
#tkgrid(tklabel(framLowerMzMatch,text="Select processed XCMS raw data "), MzMatch.but, tklabel(framLowerMzMatch,text=" "), sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Mass Resolution"), ppm_mz.entry, tklabel(framLowerMzMatch,text="ppm"),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="RT bin to group related peak "), rtwindow_mz.entry, tklabel(framLowerMzMatch,text="second"),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="RAM memory reserve for java"), memory.entry, tklabel(framLowerMzMatch,text="GB"),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Use blank filter"), BlankName_apply, sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Name of blank folder"), BlankName_mz.entry, sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="MS modes"), comboBox_MzMatch, tklabel(framLowerMzMatch,text=" "),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Peak annotations"), MzMatch.but,sticky="w")
## Manually check data
framLowerCheck <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(framLowerCheck, text="Manually check peaks "), ManualCheck.but, tklabel(framLowerCheck, text=" "), sticky="w")
tkgrid(tklabel(framLowerCheck,text="RAM memory reserve for java"), memory_b.entry, tklabel(framLowerCheck,text="GB"),sticky="w")
tkgrid(tklabel(framLowerCheck, text="Export .peakml to .txt "), exportText.but, tklabel(framLowerCheck, text=" "), sticky="w")
## lowerFrame_ID
# frameLowerID <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameLowerID,text="Select processed raw data"),WdirID.but, tklabel(frameLowerID,text=" "), sticky="w")
# tkgrid(tklabel(frameLowerID ,text="Chromatrogram ID #"), ID.NO.entry, tklabel(frameLowerID ,text=" "),sticky="w")
# tkgrid(tklabel(frameLowerID ,text="Ion Extraction based on ID "), ID.Ion.Extraction.but ,sticky="w")
## lowerFrame_ionExtractor
frameLowerIon <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameLowerIon, text="Re-integrate a single peak "), Ion.Extraction.but, tklabel(frameLowerIon, text=" "),sticky="w")
## lowerFrame_1
frameLower1 <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameLower1,text="Dispaly most significant ion "), nPlot.entry, tklabel(frameLower1,text=" plot "),sticky="w")
tkgrid(tklabel(frameLower1,text="TT-Test"), TT.Test.but ,sticky="w")
# Quit button
quit.but <- tkbutton(LC_Orbital, text = "Close Session",
command = function() {
tkdestroy(LC_Orbital)
rgl.close()
}
)
# Open working directory
open.but <- tkbutton(LC_Orbital,text="Open folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path1<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_SIlas_Gravida_logo.gif", sep=""))
logo<-tcl("image", "create", "photo", "LCMS.image", file=image.path1)
## 3 bottom buttons
#button <- tkframe(frameOverall3,relief="groove", borderwidth=0,padx=5,pady=5)
#tkgrid(tklabel(button ,text=" "),quit.but,tklabel(button ,text=" "),open.but, tklabel(button ,text=" "),help.but)
## change to right
button <- tkframe(frameOverall2,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=""),quit.but,tklabel(button ,text=""),open.but, tklabel(button ,text=""),help.but)
## Add window scroll
## Userinterphase
tkgrid(frameOverall0,columnspan=2)
tkgrid(frameOverall,frameOverall2)
# tkgrid(frameOverall3,columnspan=2)
tkgrid(tklabel(frameOverall0,image=logo),pady= 10, padx= 10)
# tkgrid(tklabel(frameOverall,text=""),sticky="w")
tkgrid(tklabel(frameOverall,text=" STEP 1. Setup"),sticky="w")
tkgrid(frameUpper,pady= 10, padx= 10)
#tkgrid(tklabel(frameOverall,text=""),sticky="w")
tkgrid(tklabel(frameOverall,text=" STEP 2. Peak extraction & RT correction (XCMS)"),sticky="w")
tkgrid(frameMid,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall ,text=" STEP 2b. Statsitcal anaylsis for 2 conditions (OPTION for XCMS) "),sticky="w")
tkgrid(frameLower1 ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall,text=""),sticky="w")
#tkgrid(tklabel(frameOverall,text=" STEP 2b. Use When RT Correction Fail"),sticky="w")
#tkgrid(tklabel(frameOverall,text=""),sticky="w")
#tkgrid(frameRtError,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall,text=" STEP 3. Annotation of Adducts and Isotopes"),sticky="w")
#tkgrid(frameLowerAdductIsotope ,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall2,text=""),sticky="w")
#tkgrid(tklabel(frameOverall2,text=" STEP 4. Singel ion chromatogram view based on ID"),sticky="w")
#tkgrid(frameLowerID ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=" STEP 3. Peak filter, filling, annotation, and identifications"),sticky="w")
tkgrid(framLowerMzMatch ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=" STEP 4. Manully check peaks using PeakMLViewer"),sticky="w")
tkgrid(framLowerCheck ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=" STEP #. IonExtractor For LC-MS (OPTION)"),sticky="w")
tkgrid(frameLowerIon ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=""),sticky="w")
#tkgrid(tklabel(frameOverall,text=""),sticky="w")
#tkgrid(tklabel(frameOverall,text=" STEP #. Check LC-MS results"),sticky="w")
#tkgrid(frameLower,pady= 10, padx= 10)
tkgrid(button, pady= 10, padx= 10)
}
######################################### GCMS_analysis #########################################
GCMS_analysis <- function(workdir=tk_choose.dir(caption = "Select working directory")){
require(xcms)
require(lattice)
require(plyr)
require(flux)
library(tcltk)
library(stringr)
WorkingDir <- function(workdir = tk_choose.dir(caption = "Select working directory")){
workdir <<- workdir
setwd(workdir)
}
# Peak integration
## Help page
TMS.hlep.page<-function(){
TMS.help <- tktoplevel()
tkwm.title(TMS.help,"Help")
scr <- tkscrollbar(TMS.help, repeatinterval=5,
command=function(...)tkyview(txt,...))
txt <- tktext(TMS.help,bg="white",font="courier",yscrollcommand=function(...)tkset(scr,...))
tkgrid(txt,scr)
tkgrid.configure(scr,sticky="ns")
tkinsert(txt,"end","Step 1\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Prepare a working folder containing:\n")
tkinsert(txt,"end"," AMDIS Batch report in .txt formate\n")
tkinsert(txt,"end"," A folder contains all GC-MS raw data in .cdf formate\n")
tkinsert(txt,"end"," A MS library in .msl formate\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Suggested AMIDS setting\n")
tkinsert(txt,"end"," Minimum match factor : 80\n")
tkinsert(txt,"end"," Deconv. : one, High, Very High, Low\n")
tkinsert(txt,"end"," Batch Job : include only first 1 hit\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 2\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 3\n")
tkconfigure(txt, state="disabled")
tkfocus(txt)
}
## Building an interfance for lIZ mcf anlaysis
library(tcltk)
require(rgl)
tclRequire("BWidget")
#Setup
TMS <- tktoplevel()
frameOverall <- tkframe(TMS)
tkwm.title(TMS,"GC-MS data processing")
rb3 <- tkradiobutton(TMS,text=" AMDIS ",indicatoron=F)
rb4 <- tkradiobutton(TMS,text="MassHunter",indicatoron=F)
lib_search_mode <- tclVar("AMDIS")
tkconfigure(rb3,variable=lib_search_mode,value="AMDIS")
tkconfigure(rb4,variable=lib_search_mode,value="MassHunter")
#All the functions
workingdirctory<-function(){WorkingDir()}
Id_report1<-function(){
Ret.Time.Filter_s <- as.numeric(tclvalue(RT.filter.var))
NbVal <- as.character(tclvalue(NbValue))
generate_graph<-F
generate_rt_shift_graph_s=as.numeric(tclvalue(generate_rt_shift_graph))
rtchecks=tclvalue(RTcorrection)
if (rtchecks=="0"){rtchecks<-FALSE}
if (rtchecks=="1"){rtchecks<-TRUE}
if (NbVal=="0"){MsLibrary_s<-"InHouse"}
if (NbVal=="1"){MsLibrary_s<-"NIST"}
if (generate_rt_shift_graph_s=="0"){generate_graph<-F}
if (generate_rt_shift_graph_s=="1"){generate_graph<-T}
RT.shift.limt_s <- as.numeric(tclvalue(multimode.var))
mz_L_s=as.numeric(tclGetValue("mz_L"))
mz_U_s=as.numeric(tclGetValue("mz_U"))
if (as.character(tclvalue(lib_search_mode))=="AMDIS"){
amdis_id_Summary(workdir=workdir,
MsLibrary=MsLibrary_s,
Ret.Time.Filter=Ret.Time.Filter_s,
RT.shift.limt=RT.shift.limt_s,
mz_L=mz_L_s,
mz_U=mz_U_s,
generate_rt_shift_graph=generate_graph,
RTcorrection=rtchecks
)
} else if (as.character(tclvalue(lib_search_mode))=="MassHunter"){
MassHunter_id_Summary(workdir=workdir,
MsLibrary=MsLibrary_s,
Ret.Time.Filter=Ret.Time.Filter_s,
RT.shift.limt=RT.shift.limt_s,
mz_L=mz_L_s,
mz_U=mz_U_s,
generate_rt_shift_graph=generate_graph,
RTcorrection=rtchecks
)
}
message(paste("Summary report generated:",as.character(tclvalue(lib_search_mode)), MsLibrary_s,"library","\nRetention time:",Ret.Time.Filter_s,"Retention time shift:",RT.shift.limt_s,"\nmz range:",mz_L_s,"-",mz_U_s))
}
Tips1<-function(){Tips()}
GCMS_integration1<-function(){
# RT.bin_s <-as.numeric(tclvalue(SliderValue))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
cbVal <- as.character(tclvalue(cbValue))
if (cbVal=="0"){GGReport_s<-"Slow"}
if (cbVal=="1"){GGReport_s<-"Fast"}
Mzbin<-as.numeric(tclvalue(MzBinValue))
GCMS_integration(workdir=workdir, intensity_type=Peakvar,GGReport=GGReport_s,Ion.bin=Mzbin)
}
merage_RT_standard1<-function(){merage_RT_standard(workdir=workdir)}
TMS.hlep.page1<-function(){TMS.hlep.page()}
MZrange_autodetect1<-function(){
mzrange=MZrange_autodetect()
try(tclSetValue("mz_L", mzrange[1]))
try(tclSetValue("mz_U", mzrange[2]))
}
### all the button function
Wdir.but <- tkbutton(TMS, text="Choose", command=workingdirctory,width=6)
ID_report.but <- tkbutton(TMS, text="Run", command=Id_report1,width=6)
#Tips.but<-tkbutton(TMS, text="Tips", command=Tips1,width=6)
integrate.but<-tkbutton(TMS, text="Run", command= GCMS_integration1,width=6)
#mergeSM.but<-tkbutton(TMS, text="Run", command= merage_RT_standard1,width=6)
help.but<-tkbutton(TMS, text="Help", command= TMS.hlep.page1,width=6)
## fast or slow mode
cb <- tkcheckbutton(TMS)
cbValue <- tclVar("1")
tkconfigure(cb,variable=cbValue)
## NIST library mode
Nb <- tkcheckbutton(TMS)
NbValue <- tclVar("1")
tkconfigure(Nb,variable=NbValue)
## generate_rt_shift_graph_check
generate_rt_shift_graph_check <- tkcheckbutton(TMS)
generate_rt_shift_graph<- tclVar("0")
tkconfigure(generate_rt_shift_graph_check,variable=generate_rt_shift_graph)
## RTcorrection
RTcorrection_check <- tkcheckbutton(TMS)
RTcorrection<- tclVar("0")
tkconfigure(RTcorrection_check,variable=RTcorrection)
## NIST library mode
#MZrange_L <- tclVar(TMS)
# MZrange_U <- tclVar(TMS)
mz_L <- 0
mz_U <- 1000
#tkconfigure(MZrange_L,variable=mz_L)
#tkconfigure(MZrange_U,variable=mz_U)
#MZrange_L <- tkentry(background="white",TMS, textvariable=mz_L ,width=6)
#MZrange_L <- tkentry(background="white",TMS,width="6",textvariable=mz_L,validate="key",validatecommand="string is double %P")
tclSetValue("mz_L",mz_L)
tclSetValue("mz_U",mz_U)
MZrange_L <- tkentry(background="white",TMS,width="6",textvariable="mz_L",validate="key",validatecommand="string is double %P")
MZrange_U <- tkentry(background="white",TMS,width="6",textvariable="mz_U",validate="key",validatecommand="string is double %P")
MZrange_autodetect.but <- tkbutton(TMS, text="Detect", command=MZrange_autodetect1,width=6)
## RT filter
RT.filter.var <- tclVar("2.5")
RT.entry <- tkentry(background="white",TMS, textvariable=RT.filter.var,width=8)
## MZ bin
MzBinValue <- tclVar("0.5")
MzBin.entry <- tkentry(background="white",TMS, textvariable=MzBinValue ,width=8)
## find mutiple peak
multimode.var <- tclVar("60")
multimode.entry <- tkentry(background="white",TMS, textvariable=multimode.var ,width=8)
## Upper
frameUpper <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Select library search mode"),rb3,rb4, sticky="w")
tkgrid(tklabel(frameUpper,text="Setup Working Directory"),Wdir.but, sticky="w")
tkgrid(tklabel(frameUpper,text="AutoSelect Reference Ions (NIST)"),Nb,sticky="w")
tkgrid(tklabel(frameUpper,text="Specify the MZ range"),MZrange_L,MZrange_U,sticky="w")
tkgrid(tklabel(frameUpper,text="Detect the MZ range"),MZrange_autodetect.but,sticky="w")
#tkgrid(tklabel(frameUpper,text="Add StandardMix RT (option)"),mergeSM.but,sticky="w ")
tkgrid(tklabel(frameUpper,text="Perform RT correction"), RTcorrection_check, sticky="w")
tkgrid(tklabel(frameUpper,text="Detect Multiple Peaks - RT Range above"), multimode.entry,tklabel(frameUpper,text="s"),sticky="w")
tkgrid(tklabel(frameUpper,text="Retention Time filter by library"), RT.entry,tklabel(frameUpper,text="min "),sticky="w")
tkgrid(tklabel(frameUpper,text="Generate plots for RT shift"),generate_rt_shift_graph_check,sticky="w")
tkgrid(tklabel(frameUpper,text="Create a Summary Report"),ID_report.but,sticky="w")
## Middle
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
## Slider
# SliderValue <- tclVar("2")
# SliderValueLabel <- tklabel(TMS,text=as.character(tclvalue(SliderValue)),widt=2)
# tkconfigure(SliderValueLabel,textvariable=SliderValue)
# slider <- tkscale(frameMid, from=1, to=5,
# showvalue=F, variable=SliderValue,
# resolution=0.5, orient="horizontal")
## choose between peakheight or area
Peak <- c("Peak Area","Peak Height")
comboBox <- tkwidget(TMS,"ComboBox",editable=FALSE,values=Peak,textvariable=tclVar("Peak Area"),width=12)
# tkgrid(tklabel(frameMid,text="Manually correct the Identification Report"),Tips.but,sticky="w")
tkgrid(tklabel(frameMid,text="Fast Mode (No Chromatograms)"),cb,sticky="w")
tkgrid(tklabel(frameMid,text="Peak Height or Peak Area "),comboBox, tklabel(frameMid,text=" "), sticky="w")
# tkgrid(tklabel(frameMid,text="Minimum RT Bin (Default = 2 s) "),slider,SliderValueLabel,tklabel(frameMid,text="s"),sticky="w")
tkgrid(tklabel(frameMid,text="Ion Mass Bin (m/z)"), MzBin.entry,sticky="w")
tkgrid(tklabel(frameMid,text="Automatic GC-Peak Integration "),integrate.but,sticky="w")
## lower
font.size.var <- tclVar("0.5")
font.size.entry <- tkentry(background="white",TMS, textvariable=font.size.var,width=4)
rb1 <- tkradiobutton(TMS)
rb2 <- tkradiobutton(TMS)
rbValue <- tclVar("PeakDiagnosis")
tkconfigure(rb1,variable=rbValue,value="PeakDiagnosis")
tkconfigure(rb2,variable=rbValue,value="IonExtractor_Window")
OnOK <- function(){
rbVal <- as.character(tclvalue(rbValue))
if (rbVal=="PeakDiagnosis"){
font.size<- as.numeric(tclvalue(font.size.var))
ifelse(sum(dir()=="GC-Peak Diagnosis Report.csv")==1,PeakDiagnosis(font.size=font.size),stop("Please select correct working directory"))
}
if (rbVal=="IonExtractor_Window"){
IonExtractor_Window()}
}
OK.but <- tkbutton(TMS, text="Run", command=OnOK, width=6)
frameLower <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
#tkgrid(tklabel(frameLower,text="Check TMS results"))
tkgrid(tklabel(frameLower,text="Overlay All Chromatograms "),rb1,tklabel(frameLower,text="Font Size "),font.size.entry, sticky="w")
tkgrid(tklabel(frameLower,text="IonExtractor "), rb2,OK.but,sticky="w")
quit.but <- tkbutton(TMS, text = "Close Session",
command = function() {
tkdestroy(TMS)
rgl.close()
}
)
## Open working directory
open.but <- tkbutton(TMS,text="Open Folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path1a<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_SIlas_logo.gif", sep=""))
logo_2<-tcl("image", "create", "photo", "MaxC3.image", file=image.path1a)
button <- tkframe(frameOverall,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=" "),quit.but,open.but, tklabel(button ,text=" "),help.but)
## Userinterphase
tkgrid(tklabel(frameOverall,image=logo_2))
tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" STEP 1. Setup"),sticky="w")
tkgrid(frameUpper,pady= 10, padx= 10)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" STEP 2. Peak Integration"),sticky="w")
tkgrid(frameMid,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" STEP 3. Check GC-MS results"),sticky="w")
tkgrid(frameLower,pady= 10, padx= 10)
tkgrid(frameOverall)
#tkgrid(quit.but,help.but, pady= 10, padx= 10)
tkgrid(button,pady= 10, padx= 10)
tkgrid(tklabel(frameOverall,text=" "))
}
######################################### raw.DIC #########################################
raw.DIC <- function (main.folder = choose.dir(caption = "Select Working Directory"),
save = TRUE, output = "DI findal data",ppm = 3)
{
require(xcms)
old.wd <- getwd()
main.folder <- main.folder
setwd(main.folder)
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
top<-100
final.df<-NULL
remove(final.df)
if (length(conditions)==1){
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
file.name <- paste(conditions,name.file, sep = "\\")
spectrum<-xcmsRaw(file.name,includeMSn=F)
scans<-lapply(1:length(spectrum@scantime), function(i) getScan(spectrum,i))
lens<-sapply(scans, nrow)
#scans<-scans[lens < median(lens)*1.2 ]
tops<-lapply(scans, function(x) {
tmp=x[order(x[,2],decreasing=T)[1:top],]
tmp[order(tmp[,1]),]
})
topss<-matrix(unlist(tops), nrow=top)
all.reads<-data.frame(
mz=as.vector(topss[,ii<-seq(1,ncol(topss),by=2)]),
I=as.vector(topss[,ii+1]),
scN=rep(1:(ncol(topss)/2), each=top)
)
all.reads<-all.reads[order(all.reads$mz),]
clusters<-vector()
clusters<-unlist(c((all.reads[1,]), clust=1))
curClust<-1
for (i in 2:nrow(all.reads)){
# i<-2
# print(i)
temp<-all.reads[i,]
if((temp$mz - all.reads[i-1, "mz"]) >=0.07){
curClust<-curClust+1
print(i)
}
clusters<-rbind(clusters, data.frame((temp), clust=curClust))
}
#######show cluster number "k". Show all.reads
############
all.reads[120:150,]
k<-5 ################
clusters[clusters$clus==k,]
#plot(clusters[clusters$clus==k,][,c(1,3)], pch="o", cex=1)
############################################################
singleClust<-as.numeric(names(table(clusters$clust)[table(clusters$clust) < round(0.1*length(spectrum@scanindex),0)]))# removes clusters which have less than 10 per cent of elements
clusters.clean<-clusters[!clusters$clust %in% singleClust,]
length(unique(clusters$clust))
length(unique(clusters.clean$clust))
means<-vector()
for(i in levels(factor(as.character((clusters.clean$clust))))){
print(i)
means<-rbind(means, c(i, colMeans(clusters.clean[clusters.clean$clust == i, c("mz", "I")])))
}
rez<-data.frame(means)[,-1]
names(rez)[2] <- name.file
rez[1]<-round(as.numeric(as.character(rez$mz)),ppm)
if (!exists("final.df")){
final.df <- rez
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- merge(final.df, rez, by.x = "mz",
by.y = "mz", all = TRUE)
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
}else {
stop("place all the cdf files in a folder")
}
final.df <- final.df[order(final.df$mz, decreasing = F),]
if (save == TRUE) {
sheet <- output
store <- paste(main.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
}
return(final.df)
}
######################################### raw.peaks #########################################
raw.peaks <- function (main.folder = choose.dir(caption = "Select Working Directory"),
correct.RT = TRUE, method = "loess", save = TRUE, output = "Total MassFragments Result")
{
require(xcms)
old.wd <- getwd()
main.folder <- main.folder
setwd(main.folder)
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
if (length(conditions)==1){
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions, full.names = TRUE)),
ignore.case = TRUE, value = TRUE)
replicates <- (c(1:length(samples)))
xset <- xcmsSet(samples)
xset <- group(xset)
if (correct.RT == TRUE) {
xset <- retcor(xset, method = method)
xset <- group(xset)
}
mat.raw <- groupval(xset, "medret", "into")
mat.raw <- mat.raw[!duplicated(row.names(mat.raw)), ]
Names <- as.character(row.names(mat.raw))
mat.raw <- cbind(Names, mat.raw)
}else {
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions[1], full.names = TRUE)),
ignore.case = TRUE, value = TRUE)
replicates <- (c(1:length(samples)))
for (i in 2:length(conditions)) {
num.rep <- c(num.rep, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
samples <- c(samples, grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
up.num <- length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
if (i < 3) {
low.num <- (replicates[length(replicates)] + 1)
}
else {
low.num <- max(replicates[i - 1, !duplicated(replicates[i -
1, ])]) + 1
}
replicates <- rbind(replicates, c((low.num):(low.num +
(up.num - 1))))
}
xset <- xcmsSet(samples)
xset <- group(xset)
if (correct.RT == TRUE) {
xset <- retcor(xset, method = method)
xset <- group(xset)
}
mat.raw <- groupval(xset, "medret", "into")
mat.raw <- mat.raw[!duplicated(row.names(mat.raw)), ]
Names <- as.character(row.names(mat.raw))
mat.raw <- cbind(Names, mat.raw)
for (r in 1:nrow(replicates)) {
rep.row <- replicates[r, ]
rep.row <- rep.row[!duplicated(rep.row)]
rep.name <- rep(conditions[r], length(rep.row))
if (r == 1) {
rep.name.final <- c(rep.name)
rep.name.final <- c("Replicates", rep.name.final)
}
else {
rep.name.final <- c(rep.name.final, rep.name)
}
}
mat.raw <- rbind(rep.name.final, mat.raw)
}
row.names(mat.raw) <- 1:nrow(mat.raw)
mat.raw <- data.frame(mat.raw)
if (save == TRUE) {
store <- paste(main.folder, "\\", output,".csv", sep = "")
write.csv(mat.raw, file = store, row.names = FALSE)
cat(paste("The file", output, "was saved in the folder",
main.folder, "\n"))
}
setwd(old.wd)
return(mat.raw)
print("Data processing is completed and saved as Meta Result!")
}
######################################### run #########################################
run <- function(){
library("pacman")
p_load(tcltk2)
tt <- tktoplevel(width=600, height=200)
tktitle(tt) <- "MassOmics"
topMenu <- tk2menu(tt)
tkconfigure(tt, menu = topMenu)
fileMenu <- tk2menu(topMenu, tearoff = FALSE)
software <- tk2menu(topMenu, tearoff = FALSE) # Our cascaded menu
installation <- tk2menu(topMenu, tearoff = FALSE)
Update<-tkmenu(topMenu, tearoff = FALSE)
Statistics <- tk2menu(topMenu, tearoff = FALSE)
PAPi<- tkmenu(topMenu, tearoff = FALSE)
Graph<-tkmenu(topMenu, tearoff = FALSE)
tkadd(fileMenu, "cascade", label = "Installation", menu = installation)
tkadd(fileMenu, "command", label = "Quit", command = function() tkdestroy(tt))
tkadd(topMenu, "cascade", label = "File", menu = fileMenu)
tkadd(topMenu, "cascade", label = "Softwares", menu = software)
tkadd(topMenu, "cascade", label = "Statistics",menu=Statistics)
tkadd(topMenu, "cascade", label = "Graph",menu=Graph)
tkadd(topMenu, "cascade", label = "Update", menu=Update)
#tkadd(software, "command", label = "Run IonExtractor (Batch)",command = function() IonE())
#tkadd(software, "command", label = "Run Metab",command = function() clean.fix())
# tkadd(software, "command", label = "Run Metab 3",command = function() clean.fix3())
# tkadd(software, "command", label = "Run GC-MS data anlaysis (Liz version)",command = function() Liz_analysis())
tkadd(software, "command", label = "GC-MS data processing",command = function() GCMS_analysis())
# tkadd(software, "command", label = "Run TMS data anlaysis",command = function() TMS_analysis())
#tkadd(software, "command", label = "IonExtractor Single Mode",command = function() IonExtractor_Window())
# tkadd(software, "command", label = "IonExtractor Single Mode_5ppm",command = function() IonExtractor_Window_5ppm())
# tkadd(software, "command", label = "Run Total MassFragment Analysis",command = function() raw.peaks())
# tkadd(software, "command", label = "Run DI-TOF data analysis",command = function() raw.DIC())
# tkadd(software, "command", label = "Remove false positive results",command = function() runFalsePostive())
# tkadd(software, "command", label = "ChemStation data analysis I (OLD)",command = function() Chemstation_DataMining())
# tkadd(software, "command", label = "ChemStation data analysis II (Advance)",command = function() Liz_analysis_chemstation())
tkadd(software, "command", label = "LC-MS data processing",command = function() LcOrbitalTRap())
tkadd(software, "command", label = "Create a sub-NIST library",command = function() create_sub_library())
tkadd(software, "command", label = "Data clean-up and normalization",command = function() DataCorrection())
tkadd(software, "command", label = "Accurate mass extraction",command = function() RunGangliosides())
tkadd(installation, "command", label = "Required R packages",command = function() install.MassOmics_dependency())
tkadd(installation, "command", label = "Required R packages for MzMatch",command = function() install.mzmatch())
tkadd(Update, "cascade", label = "Version 2.5 (11June2017)")
## Statistics
tkadd(Statistics, "command", label = "Run ANOVA & T-test",command = function() Omics_htest())
tkadd(Statistics, "command", label = "Run PCA analysis",command = function() Omics_PCA())
#tkadd(Statistics, "command", label = "Run sparse PCA analysis",command = function() Omics_sPCA())
tkadd(Statistics, "command", label = "Run PLSDA analysis",command = function() Omics_PLSDA())
# Graph
tkadd(Graph, "command", label = "Diagnose GC-Peak Auto Integration",command = function() PeakDiagnosis())
##PAPi
tkadd(software, "cascade", label = "Pathway analysis", menu = PAPi)
tkadd(PAPi ,"command", label = "Format massomics data for pathway analysis", command = function() PAPi_formatting())
tkadd(PAPi ,"command", label = "Building KEGG database in your PC", command = function() PAPi::buildDatabase())
tkadd(PAPi ,"command", label = "Replace name by KeggCodes", command = function() convert_PAPi())
tkadd(PAPi ,"command", label = "Run pathway analysis", command = function() run_PAPi())
#image
status_text<-"Maintainer: George GUO (George.GUO@auckland.ac.nz)"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/LabLogo.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "MaxC1.image", file=image.path)
tkgrid(tklabel(tt,image=FrontImage))
tkgrid(tklabel(tt,text=status_text))
tkfocus(tt)
##Update 29April2016
}
######################################### runFalsePostive #########################################
runFalsePostive <- function(){
FalsePostive<-function (true = 0.2, conditions=1:3, save = TRUE,
output = "Remove false positive results")
{
report_folder <- choose.dir(caption = "Select the Working directory")
setwd(report_folder)
final.df<- read.csv(choose.files(default = "Select the CSV file containing the input data",
multi = FALSE, caption = "Select the CSV file containing the input data"))
final.stat <- final.df
abc <- 1:100
selected.samples <- numeric()
for (q in 1:length(conditions)) {
cat(paste("Select samples from condition:", q, "\n"))
samples <- tk_select.list(names(final.stat), multiple = TRUE,
title = paste("Samples in condition", q))
columns <- names(final.stat)
equal <- 0
equal_list <- 0
for (t in 1:length(samples)) {
equal <- which(columns == samples[t])
if (length(equal) != 0) {
equal_list <- rbind(equal_list, equal)
}
}
equal_list <- (equal_list[-1])
names(final.stat)[equal_list] <- abc[q]
if (length(selected.samples) == 0) {
selected.samples <- equal_list
}
else {
selected.samples <- c(selected.samples, equal_list)
}
}
group <- names(final.stat)[selected.samples]
data.df<- final.stat[,selected.samples]
names(data.df)<-names(final.df[,selected.samples])
data.df1<-rbind(group, data.df)
group<-as.numeric(group)
reps <- factor(group)
cat(paste("Data clean up is in progress..", "\n"))
for (z in 1:length(levels(reps))) {
column <- which(as.character(levels(reps)[z]) ==
as.character( data.df1[1, ]))
yMat <- data.df1[column]
pb <- txtProgressBar(min = 0, max = nrow(yMat), style = 3, width = 50)
for (i in 1:nrow(yMat)) {
setTxtProgressBar(pb, i)
if (sum(!is.na(yMat[i, ]))/ncol(yMat) < true) {
yMat[i, ] <- NA
}
}
cat(paste(" Finish Condition",z, "\n"))
data.df1[column] <- yMat
}
yMat <- data.df1[-1,]
missing <- t(apply(yMat, 1, function(y) tapply(as.numeric(y),
reps, function(x) sum(!is.na(x)))))
missing2 <- apply(t(apply(missing, 1, function(x) x == 0)),
1, sum)
missing2 <- as.data.frame(missing2)
data.df2<-cbind(final.df[,-selected.samples],yMat)
data.df2 <- merge( data.df2, missing2, by = 0)
data.df2 <- subset( data.df2, data.df2$missing2 < ((length(levels(reps)))-(length(levels(reps)))*true)) ## Change the
data.df2$missing2 <- NULL
data.df2$Row.names <- NULL
if (save == TRUE) {
sheet <- output
store <- paste(report_folder, "\\", sheet,"(",true*100,"%",")", ".csv", sep = "")
write.csv( data.df2, file = store, row.names = FALSE)
cat(paste("Process Complete!",sheet ,"was saved in the folder",
report_folder, "\n"))
}
return( data.df2)
}
####### user interphase
require(tcltk)
ConditionN <- tclVar("")
true <- tclVar("50")
gg <- tktoplevel()
tkwm.title(gg,"Remove false postive data")
c.entry <- tkentry(background="white",gg, textvariable=ConditionN)
p.entry <- tkentry(background="white",gg, textvariable=true)
reset <- function() {
tclvalue(ConditionN)<-""
tclvalue(true)<-""
}
reset.but <- tkbutton(gg, text="Reset", command=reset)
submit <- function() {
c <- as.numeric(tclvalue(ConditionN))
conditions<-1:c
True<-as.numeric(tclvalue(true))
FalsePostive(conditions=conditions,true=True/100)
}
submit.but <- tkbutton(gg, text="submit", command=submit)
quit.but <- tkbutton(gg, text = "Close Session",
command = function() {
tkdestroy(gg)
}
)
tkgrid(tklabel(gg,text="Enter Parameters"),columnspan=3, pady = 10)
tkgrid(tklabel(gg,text="Number of conditions"), c.entry, pady= 10, padx= 10)
tkgrid(tklabel(gg,text="Exclude compounds less than"), p.entry, tklabel(gg,text="%"), pady= 10, padx= 10)
tkgrid(submit.but, reset.but, quit.but, pady= 10, padx= 10)
}
######################################### RunGangliosides #########################################
RunGangliosides <- function (){
runG <-function( output = "Peak area for gangliosides",
Ion.bin= 0.01,
WorkingFolder= tk_choose.dir(caption = "Select working directory"),
heightMAX= 300000,
info = read.csv(tk_choose.files(caption = "Accurate mass list for gangliosides.csv", multi = FALSE), stringsAsFactors=FALSE),
RTrange=c(8,14)
) {
require(xcms)
require(lattice)
require(plyr)
require(flux)
library(tcltk)
print(paste("Mass Resolution:", Ion.bin))
print(paste("heightMAX:", heightMAX))
print(paste("RT range:", RTrange))
setwd(WorkingFolder)
final.check.data <- info
library_file <-final.check.data
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre1 <- c(files[isDir == TRUE])
conditions_pre<-c(conditions_pre1 ,"Manually choose folder")
conditions <- tk_select.list(conditions_pre, multiple = FALSE,
title = paste("Folder contains all cdf files"))
if (conditions=="Manually choose folder"){ conditions<-tk_choose.dir(default = "", caption = "A folder contains all cdf files")}
###
final.df<-NULL
remove(final.df)
Graphic.df<-data.frame()
filenames <- dir(conditions)
filenames <- filenames[grep(c(".cdf$|.mzXML$"),filenames, ignore.case=TRUE)]
colors<-rainbow(length(library_file[,1]))
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=paste(library_file[,"Gangliosides"],"_", library_file[,"Ion"],sep=""),Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions,
filenames[q], sep = "\\"))
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$Ion
R.Time<-metabolite$RT.min.*60
RT.lowerset<- R.Time-metabolite$bin.min.*60
RT.upperset<- R.Time+metabolite$bin.min.*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(RT.lowerset, RT.upperset))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
Peakvalue<-auc(abundance$rt, abundance$intensity*10)
surefinal$Base.Peak[h] <- Peakvalue
## Plotting
Intensity <- abundance$intensity
Retention.Time<-abundance$rt/60
if(h==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= colors[h],bty="n", type="l",main=filenames[q],
xlab="Retention Time (min)",ylab="Intensity", ylim=c(0,heightMAX),xlim=RTrange)
}else{
lines(Intensity~Retention.Time,col= colors[h])
}
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
finalF.df <- cbind( Ref.Ion=library_file$Ion, Ret.Time=library_file$RT.min., RT.bin=library_file$bin.min., final.df)
finalF.df <- finalF.df[order(finalF.df$Ret.Time , decreasing = FALSE),]
## Save Files
print("Save file")
store <- paste(WorkingFolder, "\\", output, ".csv", sep = "")
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(finalF.df, file = FileName, row.names = FALSE)
print(paste("Ganglioside data extraction is completed and saved as: ",output,".csv", sep=""))
}
################################################################################################################
## Building an interfance
library(tcltk)
require(rgl)
tclRequire("BWidget")
#Setup
TMS <- tktoplevel()
frameOverall <- tkframe(TMS)
tkwm.title(TMS,"Accurate mass extraction for gangliosides")
# Parameters
## MZ bin
MzBinValue <- tclVar("0.01")
MzBin.entry <- tkentry(background="white",TMS, textvariable=MzBinValue ,width=8)
heightValue <- tclVar("300000")
height.entry <- tkentry(background="white",TMS, textvariable=heightValue ,width=8)
RangeM.var <- tclVar("8,14")
RangeM.entry <- tkentry(background="white",TMS, textvariable=RangeM.var, width=8)
# All the functions
GCMS_integration1<-function(){
Mzbin<-as.numeric(tclvalue(MzBinValue))
Range <- as.numeric(strsplit(tclvalue(RangeM.var),",")[[1]])
height <- as.numeric(tclvalue(heightValue))
runG(RTrange=Range, heightMAX=height ,Ion.bin=Mzbin)
}
## the button function
integrate.but<-tkbutton(TMS, text="Run", command= GCMS_integration1,width=6)
quit.but <- tkbutton(TMS, text = "Close Session",
command = function() {
tkdestroy(TMS)
rgl.close()
}
)
## Interphase
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameMid,text="Minimum RT Bin (Default = 2 s) "),slider,SliderValueLabel,tklabel(frameMid,text="s"),sticky="w")
tkgrid(tklabel(frameMid,text="Mass resolution"), MzBin.entry,tklabel(frameMid,text="(m/z)"), sticky="w")
tkgrid(tklabel(frameMid,text="RT range (min,max)"), RangeM.entry, tklabel(frameMid,text="min"),sticky="w")
tkgrid(tklabel(frameMid,text="Min Intensitiy for y-axis"), height.entry, sticky="w")
tkgrid(tklabel(frameMid,text="Automatic accurate mass extraction "),integrate.but,sticky="w")
## Open working directory
open.but <- tkbutton(TMS,text="Open Folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path1b<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_ganglioside_logogif.gif", sep=""))
logo_3<-tcl("image", "create", "photo", "MaxC3X.image", file=image.path1b)
button <- tkframe(frameOverall,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=" "),quit.but,open.but)
## Userinterphase
tkgrid(tklabel(frameOverall,image=logo_3))
tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" Accurate mass extraction"),sticky="w")
tkgrid(frameMid,pady= 10, padx= 30)
tkgrid(frameOverall)
#tkgrid(quit.but,help.but, pady= 10, padx= 10)
tkgrid(button,pady= 10, padx= 10)
tkgrid(tklabel(frameOverall,text=" "))
}
######################################### TMS_analysis #########################################
TMS_analysis <- function(main.TMS.folder=tk_choose.dir(caption = "Select working directory")){
require(xcms)
require(lattice)
require(plyr)
require(MassOmics)
WorkingDir <-function(main.TMS.folder = tk_choose.dir(caption = "Select working directory")){
main.TMS.folder <<- main.TMS.folder
setwd(main.TMS.folder)
}
Id_report<-function( main.TMS.folder=main.TMS.folder,
MS.L= tk_choose.files(caption="Select MS library (e.g.AgilentGCMS_TMS_Library) in .msl"),
amdis.report = read.csv(tk_choose.files(default = "Select the AMDIS report in .TXT",
multi = FALSE, caption = "Select the AMDIS report in .TXT"),
sep = "\t", stringsAsFactors=FALSE),
File.name = "Identification Report (TMS).csv"){
main.TMS.folder <- main.TMS.folder
setwd(main.TMS.folder)
getIonLib<-function(lib.fn = MS.L){
lib.txt<-readLines(lib.fn)
lib.txt<-lib.txt[-grep("^[ ]*$",lib.txt)]
att.nms<-unique(sapply(strsplit(lib.txt[grep(":",lib.txt)],":"),function(x) x[1]))
entry.nms<-sapply(strsplit(lib.txt[grep("NAME:",lib.txt)],":"), function(x) x[2])
rez<-matrix(NA, nrow=length(entry.nms), ncol=length(att.nms), dimnames=list(NULL,att.nms ))
starts=grep("NAME:", lib.txt)
stops=c(starts[1:(length(starts)-1)]+diff(starts)-1,length(lib.txt))
for (i in 1:length(starts)){
#i=1
tmp<-lib.txt[starts[i]:stops[i]]
sapply(strsplit(tmp[grep(":",tmp)],":"), function(x) rez[i,x[1]]<<-x[2])
}
rez<-data.frame(rez,stringsAsFactors=FALSE)
rez=within(rez, {
RT=as.numeric(RT)
RSN=as.numeric(RSN)
NUM.PEAKS=as.numeric(NUM.PEAKS)
})
return(rez)
}
libr<-getIonLib()## returns list of peaks
############ Step 2. Generate the list of metabolites and clean up
AmRep<-amdis.report
AmRep<-AmRep[!AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)+2 & AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)-2,]## check this one ##
AmRep$Name <- gsub("?", "", AmRep$Name, fixed = TRUE)
AmRep$Name <- gsub("^ ", "", AmRep$Name, perl=T)
## RT statistics from the AReport
RT.stats<-t(sapply(split(AmRep$RT, AmRep$Name),function(x) c(RT.median=round(median(x,na.rm=T),3),
RT.mean=round(mean(x,na.rm=TRUE),3),
RT.sd=sd(x,na.rm=T),
Number.of.ID=length(x[!is.na(x)]))))
AmRep.RT.stats <- as.data.frame(cbind(Name=0,RT.stats,Quality=0, Exp.RT=0,Diff.RT=0,Diff.RT.by.Ribitol=0,ref_ion=0))##data frame for list of metabolites
AmRep.RT.stats$ref_ion <- libr$RSN[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Exp.RT <- libr$RT[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Diff.RT <- AmRep.RT.stats$Exp.RT- AmRep.RT.stats$RT.median
AmRep.RT.stats$Diff.RT.by.Ribitol <-AmRep.RT.stats$Diff.RT- AmRep.RT.stats["16.1473 min RIBITOL","Diff.RT"]
AmRep.RT.stats$Quality <- "Good/Bad"
AmRep.RT.stats$Name <- rownames(RT.stats)
AmRep.RT.stats <- AmRep.RT.stats[order(AmRep.RT.stats$RT.median, decreasing = F),]
write.csv(AmRep.RT.stats, file = File.name,row.names = FALSE)
print(paste("Identificaiton report was created and save in",main.TMS.folder, sep=" "))
}
### MERAGE TWO FILE TOGETHER
merage_RT_standard<-function(main.TMS.folder=main.TMS.folder,
File.name = "Identification Report (TMS) with StandardMix_RT.csv"
){
main.TMS.folder <- main.TMS.folder
setwd(main.TMS.folder)
data.df=read.csv(tk_choose.files(caption = "Load Samples Identification Report.csv", multi = FALSE),stringsAsFactors=FALSE)
standard.df=read.csv(tk_choose.files(caption = "Load Standard Mixture Identification Report.csv", multi = FALSE),stringsAsFactors=FALSE)
RT_standard_names <- tk_select.list(names(standard.df), multiple = FALSE, title = paste("Select Rentention time"))
SelectStandard<-standard.df[,RT_standard_names]
standard.df.final<-data.frame(cbind(Name=standard.df$Name,StandardMixture_RT=SelectStandard))
Merge.data <- merge(data.df, standard.df.final, by.x = "Name",
by.y = "Name", all = TRUE)
Merge.data <- Merge.data [order(Merge.data $RT.median, decreasing = F),]
write.csv(Merge.data, file = File.name,row.names = FALSE)
print(paste("Identificaiton report with StandardMix RT was created and save in",main.TMS.folder))
}
Tips<-function () {
require(tcltk)
kk <- tktoplevel()
tktitle(kk) <- "GC-Chromatogram of TMS standard mixtures"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/TMSstandardMix.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "MaxC2.image", file=image.path)
tkgrid(tklabel(kk,image=FrontImage))
}
RunTMS<-function(save = TRUE,
output = "Final TMS Results",
Ion.bin= 0.5,
main.TMS.folder=main.TMS.folder,
RT.bin = 5) {
require(xcms)
require(lattice)
require(plyr)
setwd(main.TMS.folder)
final.check.data <- read.csv(tk_choose.files(caption = "Load a corrected Identification Report (TMS).csv", multi = FALSE),stringsAsFactors=FALSE)
ion.lib<-final.check.data
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre <- c(files[isDir == TRUE])
conditions <- tk_select.list(conditions_pre, multiple = FALSE,
title = paste("Folder contains all cdf files"))
###
library_file <- ion.lib[,c("Name","ref_ion","RT.median")]
final.df<-NULL
remove(final.df)
Graphic.df<-data.frame()
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=ion.lib[,"Name"],Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions,
filenames[q], sep = "\\"))
dev.new.OS()
plotChrom(raw_data) # plot TIC
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$ref_ion
R.Time<-metabolite$RT.median*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(R.Time-RT.bin,R.Time+RT.bin))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
maxabundance <- max(abundance$intensity)
surefinal$Base.Peak[h] <- maxabundance
Graphic.df<-rbind.fill(Graphic.df,data.frame(Metabolite.Names=paste(metabolite$Name," (m/z:",R.Ion,")",sep="") ,Retention.Time=(abundance$rt)/60,
Intensity=abundance$intensity ,Sample.Names=name.file ))
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
final.df<-cbind( Num.ID=ion.lib$Number.of.ID, Ref.Ion=ion.lib$ref_ion, Ret.Time=ion.lib$RT.median, Quality =ion.lib$Quality ,final.df)
final.df <- final.df[order(final.df$Ret.Time , decreasing = F),]
row.names(final.df) <- 1:nrow(final.df)
if (save == TRUE) {
sheet <- paste(output," (RT bin=", RT.bin,"s)", sep="")
store <- paste(main.TMS.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
write.csv(Graphic.df,file="GC-Peak Diagnosis Report.csv")
print(paste("Data process is completed and saved as: ",sheet,".csv", sep=""))
}
}
## Help page
TMS.hlep.page<-function(){
TMS.help <- tktoplevel()
tkwm.title(TMS.help,"Help")
scr <- tkscrollbar(TMS.help, repeatinterval=5,
command=function(...)tkyview(txt,...))
txt <- tktext(TMS.help,bg="white",font="courier",yscrollcommand=function(...)tkset(scr,...))
tkgrid(txt,scr)
tkgrid.configure(scr,sticky="ns")
tkinsert(txt,"end","Step 1\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Prepare a working folder containing:\n")
tkinsert(txt,"end"," AMDIS Batch report in .txt formate\n")
tkinsert(txt,"end"," A folder contains all GC-MS raw data in .cdf formate\n")
tkinsert(txt,"end"," A TMS MS library in .msl formate\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Suggested AMIDS setting\n")
tkinsert(txt,"end"," Minimum match factor : 80\n")
tkinsert(txt,"end"," Deconv. : one, High, Very High, Low\n")
tkinsert(txt,"end"," Batch Job : include only first 1 hit\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 2\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 3\n")
tkconfigure(txt, state="disabled")
tkfocus(txt)
}
## Building an interfance for TMS anlaysis
library(tcltk)
require(rgl)
TMS <- tktoplevel()
frameOverall <- tkframe(TMS)
tkwm.title(TMS,"TMS analysis")
workingdirctory<-function(){WorkingDir()}
Id_report1<-function(){Id_report(main.TMS.folder=main.TMS.folder)}
Tips1<-function(){Tips()}
RunTMS1<-function(){
RT.bin_s <-as.numeric(tclvalue(SliderValue))
RunTMS(RT.bin = RT.bin_s,main.TMS.folder=main.TMS.folder)
}
merage_RT_standard1<-function(){merage_RT_standard(main.TMS.folder=main.TMS.folder)}
TMS.hlep.page1<-function(){TMS.hlep.page()}
### all the button function
Wdir.but <- tkbutton(TMS, text="Choose", command=workingdirctory,width=6)
ID_report.but <- tkbutton(TMS, text="Run", command=Id_report1,width=6)
Tips.but<-tkbutton(TMS, text="Tips", command=Tips1,width=6)
integrate.but<-tkbutton(TMS, text="Run", command= RunTMS1,width=6)
mergeSM.but<-tkbutton(TMS, text="Run", command= merage_RT_standard1,width=6)
help.but<-tkbutton(TMS, text="Help", command= TMS.hlep.page1,width=6)
## Upper
frameUpper <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Setup Working Directory"),Wdir.but, sticky="w")
tkgrid(tklabel(frameUpper,text="Create an Identification Report "),ID_report.but,sticky="w")
tkgrid(tklabel(frameUpper,text="Add StandardMix RT (option)"),mergeSM.but,sticky="w")
## Middle
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
## Slider
SliderValue <- tclVar("5")
SliderValueLabel <- tklabel(TMS,text=as.character(tclvalue(SliderValue)),width=2)
tkconfigure(SliderValueLabel,textvariable=SliderValue)
slider <- tkscale(frameMid, from=1, to=10,
showvalue=F, variable=SliderValue,
resolution=1, orient="horizontal")
tkgrid(tklabel(frameMid,text="Manually correct the Identification Report"),Tips.but,sticky="w")
tkgrid(tklabel(frameMid,text="Automatic GC-peak integration"),integrate.but,sticky="w")
tkgrid(tklabel(frameMid,text="Rentention time bin (Default = 5 s) "),slider,SliderValueLabel,tklabel(frameMid,text="s"),sticky="w")
## lower
rb1 <- tkradiobutton(TMS)
rb2 <- tkradiobutton(TMS)
rbValue <- tclVar("PeakDiagnosis")
tkconfigure(rb1,variable=rbValue,value="PeakDiagnosis")
tkconfigure(rb2,variable=rbValue,value="IonExtractor_Window")
OnOK <- function(){
rbVal <- as.character(tclvalue(rbValue))
if (rbVal=="PeakDiagnosis")
ifelse(sum(dir()=="GC-Peak Diagnosis Report.csv")==1,PeakDiagnosis(),stop("Please select correct working directory"))
if (rbVal=="IonExtractor_Window")
IonExtractor_Window()
}
OK.but <- tkbutton(TMS,text="RUN",command=OnOK,width=6)
frameLower <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
#tkgrid(tklabel(frameLower,text="Check TMS results"))
tkgrid(tklabel(frameLower,text="Diagnose GC-Peak Auto Integration "),rb1, OK.but,sticky="w")
tkgrid(tklabel(frameLower,text="IonExtractor Single Mode"),rb2,sticky="w")
quit.but <- tkbutton(TMS, text = "Close Session",
command = function() {
tkdestroy(TMS)
rgl.close()
}
)
image.path1<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_SIlas_logo.gif", sep=""))
logo<-tcl("image", "create", "photo", "MaxC3.image", file=image.path1)
button <- tkframe(frameOverall,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=" "),quit.but,tklabel(button ,text=" "),help.but)
## Userinterphase
tkgrid(tklabel(frameOverall,image=logo))
tkgrid(tklabel(frameOverall,text="Step 1: Setup"))
tkgrid(frameUpper,pady= 10, padx= 10)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text="Step 2: Integration"))
tkgrid(frameMid,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text="Step 3: Check TMS results"))
tkgrid(frameLower,pady= 10, padx= 10)
tkgrid(frameOverall)
#tkgrid(quit.but,help.but, pady= 10, padx= 10)
tkgrid(button,pady= 10, padx= 10)
}
###############################MassOmics.GUI##################
#' MassOmics.GUI
#'
#' This function initiate a graphical user interface for MassOmics. To initiate the GG-MS data processing software, open the R console and type the codes in the example to initate the user interface.
#' @return None
#'
#' @examples
#' MassOmics.GUI()
#'
#' @export
MassOmics.GUI<-function(){
library(tcltk2)
run()
}
MASHUOA/MassOmics documentation built on Nov. 3, 2023, 10:48 p.m.
R Package Documentation
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Defines functions MassOmics.GUI TMS_analysis runFalsePostive run GCMS_analysis LcOrbitalTRap IonExtractor_Window
Documented in MassOmics.GUI
#' \tabular{ll}{
#' Package: \tab MassOmics \cr
#' License: \tab GPL (>= 2)\cr
#' URL: \tab
#' }
#'
#' @docType package
#' @name MassOmics
#' @author George GUO \email{George.GUO@@auckland.ac.nz}
#' @references TBD
#' @keywords package
#' @import tcltk
#' @import tcltk2
#' @import BiocManager
#' @import pacman
#' @import BiocParallel
#' @import pander
#' @importFrom stats na.omit runif
#' @importFrom utils download.file modifyList packageVersion read.table tail
######################################### clean.fix #########################################
clean.fix <- function (main.folder = tk_choose.dir(caption = "Select working directory"),
amdis.report = read.csv(tk_choose.files(default = "Select the AMDIS report in .TXT",
multi = FALSE, caption = "Select the AMDIS report in .TXT"),
sep = "\t", colClass = "character"), ion.lib = read.csv(tk_choose.files(default = "Select the Idenditification report in .CSV",
multi = FALSE, caption = "Select the Idenditification report in .CSV"),
sep = ","), save = TRUE, output = "Fianl Metab result")
{
require(xcms)
old.wd <- getwd()
main.folder <- main.folder
setwd(main.folder)
names(ion.lib)[1]<-"Name"
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
largest <- 1
for (i in 1:length(conditions)) {
largest <- c(largest, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
}
largest <- max(largest)
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions[1], full.names = TRUE)),
ignore.case = TRUE, value = TRUE)
replicates <- matrix(c(1:length(samples)), nrow = 1, ncol = largest)
if (length(conditions) > 1) {
for (i in 2:length(conditions)) {
num.rep <- c(num.rep, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
samples <- c(samples, grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
up.num <- length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
if (i < 3) {
low.num <- (replicates[length(replicates)] +
1)
}
else {
low.num <- max(replicates[i - 1, !duplicated(replicates[i -
1, ])]) + 1
}
replicates <- rbind(replicates, c((low.num):(low.num +
(up.num - 1))))
}
}
if (is.data.frame(ion.lib) == FALSE) {
ion.lib = read.csv(ion.lib, sep = ",")
}
library_file <- ion.lib
library_file$Name <- gsub(" ", "", library_file$Name)
if (is.data.frame(amdis.report) == TRUE) {
amdis.report <- amdis.report
}
else {
amdis.report = read.csv(amdis.report, sep = "\t")
}
for (i in 1:nrow(amdis.report)) {
original <- as.character(amdis.report[i, 1])
new <- unlist(strsplit(original, "\\", fixed = TRUE))
new <- new[length(new)]
amdis.report[i, 1] <- new
}
total_report <- amdis.report
final.df <- 1
for (c in 1:length(conditions)) {
filenames <- dir(conditions[c])
for (q in 1:length(filenames)) {
file <- filenames[q]
name.file <- file
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
file <- gsub("CDF", "FIN", file, ignore.case=TRUE)
file <- which(total_report$FileName == file)
if (length(file) > 0) {
data.df <- total_report[file, ]
Keep <- c("Name", "RT", "Scan", "Expec..RT",
"Base.Peak")
data.df <- data.df[, Keep]
data.df[2] <- as.numeric(data.df[, 2])
data.df[3] <- as.numeric(data.df[, 3])
data.df[4] <- as.numeric(data.df[, 4])
data.df$deff <- data.df$RT - data.df$Expec..RT
data.df <- subset(data.df, data.df$deff < 2.5 &
data.df$deff > -2.5)
sure <- data.df[1, ]
while (nrow(data.df) >= 1) {
line <- data.df[1, ]
sameRT <- subset(data.df, data.df$RT == line$RT)
if (nrow(sameRT) > 1) {
isoleucine <- grep("isoleucine", sameRT$Name,
ignore.case = TRUE)
if (length(isoleucine) > 0) {
isoleucine2 <- grep("isoleucine", sure$Name,
ignore.case = TRUE)
if (length(isoleucine2) > 0) {
sameRT2 <- sameRT[-isoleucine, ]
sure <- rbind(sure, sameRT2[which.min(which.min(abs(sameRT2$deff))),
])
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
else {
sure <- rbind(sure, sameRT[isoleucine,
])
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
}
else {
sure <- rbind(sure, sameRT[which.min(which.min(abs(sameRT$deff))),
])
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
}
else {
sure <- rbind(sure, sameRT)
sure <- sure[!duplicated(sure$RT), ]
data.df <- subset(data.df, row.names(data.df) !=
row.names(sameRT))
}
}
surefinal <- sure
surefinal$Name <- gsub("? ", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("?? ", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("??? ", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("?", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("??", "", surefinal$Name,
fixed = TRUE)
surefinal$Name <- gsub("???", "", surefinal$Name,
fixed = TRUE)
raw_data <- xcmsRaw(filename = paste(conditions[c],
filenames[q], sep = "\\"))
dev.new.OS()
plotChrom(raw_data)
for (h in 1:nrow(surefinal)) {
metabolite <- surefinal[h, ]
amdis_scan <- (surefinal$Scan[h])
ion.ref <- grep(gsub(" ", "", metabolite$Name),
library_file$Name, fixed = TRUE)
if (length(ion.ref) == 0) {
error.lib <- paste("There is no ion defined for metabolite",
metabolite$Name, "in the reference ion library",
sep = " ")
print(error.lib)
}
metabolite <- library_file[ion.ref, ]
if (is.na(metabolite$ref_ion)) {
error.lib2 <- paste("The metabolite", metabolite$Name,
"was detected in the reference ion library, however, there is no ion fragment defined for that.",
sep = " ")
print(error.lib2)
}
scan <- getScan(raw_data, scan = surefinal$Scan[h])
scan <- data.frame(scan)
scan$mz <- trunc(scan$mz)
scan_ion <- (metabolite$ref_ion) - 2
for (k in 1) {
abundance <- subset(scan, scan$mz == (scan_ion +
k))
maxabundance <- max(abundance$intensity)
ion_set <- c(maxabundance)
}
for (k in 2:3) {
abundance <- subset(scan, scan$mz == (scan_ion +
k))
maxabundance <- max(abundance$intensity)
ion_set <- c(ion_set, maxabundance)
}
correct_abundance <- max(ion_set)
surefinal$Base.Peak[h] <- max(correct_abundance)
}
surefinal$RT <- NULL
surefinal$Expec..RT <- NULL
surefinal$deff <- NULL
surefinal$Scan <- NULL
surefinal <- surefinal[!duplicated(surefinal$Name),
]
names(surefinal)[2] <- name.file
if (final.df == 1) {
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
else {
final.df <- merge(final.df, surefinal, by.x = "Name",
by.y = "Name", all = TRUE)
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
else {
confirmation <- paste("File", q, "(", name.file,
")", "not analyzed! Probably there is no metabolites detected for this sample in the AMDIS report",
sep = " ")
print(confirmation)
if (final.df != 1) {
final.df$error <- "Error"
names(final.df)[ncol(final.df)] <- name.file
}
}
}
}
for (r in 1:nrow(replicates)) {
rep.row <- replicates[r, ]
rep.row <- rep.row[!duplicated(rep.row)]
rep.name <- rep(conditions[r], length(rep.row))
if (r == 1) {
rep.name.final <- c(rep.name)
rep.name.final <- c("Replicates", rep.name.final)
}
else {
rep.name.final <- c(rep.name.final, rep.name)
}
}
final.df <- rbind(rep.name.final, final.df)
final.df <- final.df[order(final.df[, 2], decreasing = T),
]
row.names(final.df) <- 1:nrow(final.df)
if (save == TRUE) {
sheet <- output
store <- paste(main.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
}
setwd(old.wd)
return(final.df)
}
######################################### clean.fix3 #########################################
clean.fix3 <- function (main.folder = tk_choose.dir(caption = "Select working directory"),
amdis.report = read.csv(tk_choose.files(default = "Select the AMDIS report in .TXT",
multi = FALSE, caption = "Select the AMDIS report in .TXT"),
sep = "\t", stringsAsFactors=FALSE),
save = TRUE,
output = "Final Metab3 Results",
Ion.bin= 0.5,
RT.bin = 5,
File.name = "Identification Report.csv",
MS.L= tk_choose.files(caption="Select MS library (e.g. SVB_Totoal) in .msl")) {
require(xcms)
require(lattice)
require(plyr)
main.folder <- main.folder
setwd(main.folder)
################# Step 1. Function transforms AMDIS library to spreadsheet of data for each metabolite
getIonLib<-function(lib.fn = MS.L){
lib.txt<-readLines(lib.fn)
lib.txt<-lib.txt[-grep("^[ ]*$",lib.txt)]
att.nms<-unique(sapply(strsplit(lib.txt[grep(":",lib.txt)],":"),function(x) x[1]))
entry.nms<-sapply(strsplit(lib.txt[grep("NAME:",lib.txt)],":"), function(x) x[2])
rez<-matrix(NA, nrow=length(entry.nms), ncol=length(att.nms), dimnames=list(NULL,att.nms ))
starts=grep("NAME:", lib.txt)
stops=c(starts[1:(length(starts)-1)]+diff(starts)-1,length(lib.txt))
for (i in 1:length(starts)){
#i=1
tmp<-lib.txt[starts[i]:stops[i]]
sapply(strsplit(tmp[grep(":",tmp)],":"), function(x) rez[i,x[1]]<<-x[2])
}
rez<-data.frame(rez,stringsAsFactors=FALSE)
rez=within(rez, {
RT=as.numeric(RT)
RSN=as.numeric(RSN)
NUM.PEAKS=as.numeric(NUM.PEAKS)
})
return(rez)
}
libr<-getIonLib()## returns list of peaks
############ Step 2. Generate the list of metabolites and clean up
AmRep<-amdis.report
AmRep<-AmRep[!AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)+1.5 & AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)-1.5,]
AmRep$Name <- gsub("?", "", AmRep$Name, fixed = TRUE)
AmRep$Name <- gsub("^ ", "", AmRep$Name, perl=T)
## RT statistics from the AReport
RT.stats<-t(sapply(split(AmRep$RT, AmRep$Name),function(x) c(RT.mean=round(mean(x,na.rm=TRUE),3),
RT.median=round(median(x,na.rm=T),3),
RT.sd=sd(x,na.rm=T),
Number.of.ID=length(x[!is.na(x)]))))
AmRep.RT.stats <- as.data.frame(cbind(RT.stats,Exp.RT=0, ref_ion=0,Mol.mass=0, Diff.RT=0, CAS=0))##data frame for list of metabolites
AmRep.RT.stats$ref_ion <- libr$RSN[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Mol.mass <- libr$FORM[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Exp.RT <- libr$RT[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Diff.RT <- AmRep.RT.stats$Exp.RT- AmRep.RT.stats$RT.median
AmRep.RT.stats$CAS <- libr$CAS[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats <- AmRep.RT.stats[order(AmRep.RT.stats$RT.median, decreasing = F),]
write.csv(AmRep.RT.stats, file = File.name)
check <- tk_select.list(c("Good Identification Report (continue)", "Bad Identificaiton Reprot (stop)","Load a corrected report"), title = "Check Identification Report")
if (check == "Bad Identificaiton Reprot (stop)"){
stop("Data process terminated")
}else if(check== "Load a corrected report" ){
final.check.data = read.csv(tk_choose.files(caption = "Load a corrected report", multi = FALSE),stringsAsFactors=FALSE)
}else{
final.check.data<-read.csv(File.name,stringsAsFactors=FALSE)
}
GGReport <- tk_select.list(c("Yes (Slower analysis speed)", "No (Faster - but cann't perform the Diagnosis of Auto GC-Peak Integration )"), title = "Turn on the Diagnosis function")
names(final.check.data)[1]<-"Name"
ion.lib<-final.check.data
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
largest <- 1
for (i in 1:length(conditions)) {
largest <- c(largest, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
}
largest <- max(largest)
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions[1], full.names = TRUE)), ignore.case = TRUE, value = TRUE)
replicates <- matrix(c(1:length(samples)), nrow = 1, ncol = largest)
if (length(conditions) > 1) {
for (i in 2:length(conditions)) {
num.rep <- c(num.rep, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
samples <- c(samples, grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
up.num <- length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
if (i < 3) {
low.num <- (replicates[length(replicates)] +
1)
}
else {
low.num <- max(replicates[i - 1, !duplicated(replicates[i -
1, ])]) + 1
}
replicates <- rbind(replicates, c((low.num):(low.num +
(up.num - 1))))
}
}
library_file <- ion.lib[,c("Name","ref_ion","RT.median")]
final.df<-NULL
remove(final.df)
Graphic.df<-data.frame()
if(length(conditions)==1) {
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=ion.lib[,"Name"],Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions,
filenames[q], sep = "\\"))
if (GGReport=="Yes (Slower analysis speed)"){
dev.new.OS()
plotChrom(raw_data) # plot TIC
}
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$ref_ion
R.Time<-metabolite$RT.median*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(R.Time-RT.bin,R.Time+RT.bin))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
maxabundance <- max(abundance$intensity)
surefinal$Base.Peak[h] <- maxabundance
if (GGReport=="Yes (Slower analysis speed)"){
Graphic.df<-rbind.fill(Graphic.df,data.frame(Metabolite.Names=paste(metabolite$Name," (m/z:",R.Ion,")",sep="") ,Retention.Time=(abundance$rt)/60,
Intensity=abundance$intensity ,Sample.Names=name.file ))
}
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
final.df<-cbind(CAS=ion.lib$CAS, Num.ID=ion.lib$Number.of.ID, Ref.Ion=ion.lib$ref_ion, Ret.Time=ion.lib$RT.median, final.df)
final.df <- final.df[order(final.df$Ret.Time , decreasing = F),]
row.names(final.df) <- 1:nrow(final.df)
} else{
for (c in 1:length(conditions)) {
filenames <- dir(conditions[c])
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)] # remove the effect of fin and leu
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=ion.lib[,"Name"],Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions[c],
filenames[q], sep = "\\"))
if (GGReport=="Yes (Slower analysis speed)"){
dev.new.OS()
plotChrom(raw_data) # plot TIC
}
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$ref_ion
R.Time<-metabolite$RT.median*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(R.Time-RT.bin,R.Time+RT.bin))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
maxabundance <- max(abundance$intensity)
surefinal$Base.Peak[h] <- maxabundance
if (GGReport=="Yes (Slower analysis speed)"){
Graphic.df<-rbind.fill(Graphic.df,data.frame(Metabolite.Names=paste(metabolite$Name," (m/z:",R.Ion,")",sep="") ,Retention.Time=(abundance$rt)/60,
Intensity=abundance$intensity ,Sample.Names=name.file ))
}
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
}
for (r in 1:nrow(replicates)) {
rep.row <- replicates[r, ]
rep.row <- rep.row[!duplicated(rep.row)]
rep.name <- rep(conditions[r], length(rep.row))
if (r == 1) {
rep.name.final <- c(rep.name)
rep.name.final <- c(rep("Replicates",4), rep.name.final)
}
else {
rep.name.final <- c(rep.name.final, rep.name)
}
}
final.df$Name<-as.character(final.df$Name)
final.df<-cbind(CAS=ion.lib$CAS, Num.ID=ion.lib$Number.of.ID, Ref.Ion=ion.lib$ref_ion, Ret.Time=ion.lib$RT.median, final.df)
final.df$CAS<-as.character(final.df$CAS)
final.df <- final.df[order(final.df$Ret.Time, decreasing = F),]
final.df <- rbind(rep.name.final, final.df)
row.names(final.df) <- 1:nrow(final.df)
}
if (save == TRUE) {
sheet <- output
store <- paste(main.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
if (GGReport=="Yes (Slower analysis speed)"){
write.csv(Graphic.df,file="GC-Peak Diagnosis Report.csv")
cat(paste("Data process is completed and called",sheet, sep=" "))
}
}
return(final.df)
}
######################################### IonExtractor_Window #########################################
IonExtractor_Window <- function(){
require(RColorBrewer)
require(flux)
IonExtractor<- function(folder = tk_choose.dir(caption = "Select working directory"),
mz = 106,
RT.Time1 = 10.5,
RT.Time2 = 11,
Ion.bin = 0.5,
heightMAX= 50000,
Metabolite.Names = "D4-Methanol",
save = TRUE,
peak="Peak Height"
){
require(xcms)
old.wd <- getwd()
main.folder <- folder
folderRE <<- folder
setwd(main.folder)
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre <- c(files[isDir == TRUE])
conditions <- tk_select.list(conditions_pre, multiple = FALSE,
title = paste("Folder conatins all cdf files"))
condtions1<<-conditions
final.df<-NULL
plot_1<-NULL
rm(final.df)
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
colors<-rainbow(length(filenames))
# dev.new.OS()
for (q in 1:length(filenames)) {
raw_data <- xcmsRaw(filename = paste(conditions, filenames[q], sep = "\\"))
name.file <- filenames[q]
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
IonExtract <-getEIC(raw_data,mzrange=cbind(mz-Ion.bin,mz+Ion.bin),rtrange=cbind(RT.Time1*60,RT.Time2*60))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
ifelse(peak=="Peak Height", Peakvalue <- max(abundance$intensity) ,Peakvalue<-auc(abundance$rt, abundance$intensity*10))
if (!exists("final.df")){
final.df <- data.frame(t(c(name.file,Peakvalue)))
} else {
final.df <- rbind(final.df, data.frame(t(c(name.file,Peakvalue))))
}
print(paste("File", q, name.file,"done!"))
## Plotting
Intensity=abundance$intensity
Retention.Time<-abundance$rt/60
#CombinRT <- as.data.frame(cbind(Retention.Time,Intensity))
if(q==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= colors[q],bty="n", type="l",main=paste(Metabolite.Names," (",mz," m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""),
xlab="Retention Time (min)",ylab="Intensity", ylim=c(0,heightMAX))
## test new scrip
#plot_1 <- ggplot(CombinRT , aes(x=Retention.Time, y=Intensity)) + geom_line(col= colors[q]) + labs(title=paste(Metabolite.Names," (",mz," m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""), x = "Retention Time (min)", y ="Intensity")
}else{
lines(Intensity~Retention.Time,col= colors[q])
#plot_1 <- plot_1 + geom_line( aes(x=Retention.Time, y=Intensity*q), col=colors[q])
#print(plot_1)
}
}
final.df2<-NULL
final.df2<- data.frame(t(final.df[2]))
names(final.df2) <- t(final.df[1])
row.names(final.df2) <- Metabolite.Names
if (save == TRUE) {
ifelse(peak=="Peak Height",ValueType<-"PeakHeight",ValueType<-"PeakArea")
store <- paste(main.folder, "\\", Metabolite.Names,"(",mz,")RT",RT.Time1,"-",RT.Time2,"_",ValueType,".csv", sep = "")
write.csv(final.df2, file = store, row.names = TRUE)
print("Complete!")
print(paste("The file ",Metabolite.Names, ".csv", " was saved in the folder ", folder, sep=""))
} else {
print("No file was saved because the argument save was set as FALSE")
}
return(final.df2)
}
ReIonExtractor<- function( folder = tk_choose.dir(caption = "Select working directory"),
mz = 106,
RT.Time1 = 10.5,
RT.Time2 = 10.7,
Ion.bin = 0.5,
heightMAX= 50000,
Metabolite.Names = "D4-alanine",
conditions=1,
peak="Peak Height",
save = TRUE){
require(xcms)
old.wd <- getwd()
main.folder <- folder
setwd(main.folder)
if(conditions==1){
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre <- c(files[isDir == TRUE])
conditions <- tk_select.list(conditions_pre, multiple = FALSE, title = paste("Folder conatins all cdf files"))
}
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
final.df<-NULL
rm(final.df)
colors<-rainbow(length(filenames))
for (q in 1:length(filenames)) {
raw_data <- xcmsRaw(filename = paste(conditions, filenames[q], sep = "\\"))
name.file <- filenames[q]
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
IonExtract <-getEIC(raw_data,mzrange=cbind(mz-Ion.bin,mz+Ion.bin),rtrange=cbind(RT.Time1*60,RT.Time2*60))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
ifelse(peak=="Peak Height", Peakvalue <- max(abundance$intensity) ,Peakvalue<-auc(abundance$rt, abundance$intensity*10))
if (!exists("final.df")){
final.df <- data.frame(t(c(name.file, Peakvalue)))
} else {
final.df <- rbind(final.df, data.frame(t(c(name.file, Peakvalue))))
}
print(paste("File",q, name.file,"done!"))
## Plotting
Intensity=abundance$intensity
Retention.Time<-abundance$rt/60
if(q==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= colors[q],bty="n", type="l",main=paste(Metabolite.Names," (",mz," m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""),
xlab="Retention Time (min)",ylab="Intensity", ylim=c(0,heightMAX))
}else{
lines(Intensity~Retention.Time,col= colors[q])
}
}
final.df2<-NULL
final.df2<- data.frame(t(final.df[2]))
names(final.df2) <- t(final.df[1])
row.names(final.df2) <- Metabolite.Names
if (save == TRUE) {
ifelse(peak=="Peak Height",ValueType<-"PeakHeight",ValueType<-"PeakArea")
store <- paste(main.folder, "\\", Metabolite.Names,"(",mz,")RT",RT.Time1,"-",RT.Time2,"_",ValueType,".csv", sep = "")
write.csv(final.df2, file = store, row.names = TRUE)
print("Complete!")
print(paste("The file ",Metabolite.Names, ".csv", " was saved in the folder ", folder, sep=""))
} else {
print("No file was saved because the argument save was set as FALSE")
}
return(final.df2)
}
### interphase
require(tcltk)
tclRequire("BWidget")
kk <- tktoplevel()
frame_All <- tkframe(kk)
tkwm.title(kk,"GC-MS IonExtractor")
xvar <- tclVar("106")
yvar <- tclVar("10.5")
zvar <- tclVar("10.9")
wvar <- tclVar("D4-alanine")
hvar <- tclVar("300000")
Peakvar <- tclVar("Peak Height")
x.entry <- tkentry(background="white",kk, textvariable=xvar, width=17)
y.entry <- tkentry(background="white",kk, textvariable=yvar, width=17)
z.entry <- tkentry(background="white",kk, textvariable=zvar, width=17)
w.entry <- tkentry(background="white",kk, textvariable=wvar, width=17)
h.entry <- tkentry(background="white",kk, textvariable=hvar, width=17)
## choose between peakheight or area
Peak <- c("Peak Height","Peak Area")
comboBox <- tkwidget(kk,"ComboBox",editable=FALSE,values=Peak,textvariable=tclVar("Peak Height"),width=15)
####################
Re_analysis <-function () {
w <- as.character(tclvalue(wvar))
x <- as.numeric(tclvalue(xvar))
y <- as.numeric(tclvalue(yvar))
z <- as.numeric(tclvalue(zvar))
h <- as.numeric(tclvalue(hvar))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
if (!exists("folderRE")){c(folder<- tk_choose.dir(caption = "Select working directory"),conditions<-1)
}else{c(folder<-folderRE, conditions <- condtions1)}
ReIonExtractor(Metabolite.Names=w,mz=x,RT.Time1=y,RT.Time2=z, heightMAX=h, folder = folder, conditions =conditions ,peak= Peakvar)
}
re_analysis.but <- tkbutton(kk, text="Re-analysis", command=Re_analysis)
reset <- function() {
tclvalue(xvar)<-""
tclvalue(yvar)<-""
tclvalue(zvar)<-""
tclvalue(wvar)<-""
tclvalue(hvar)<-"300000"
}
reset.but <- tkbutton(kk, text="Reset", command=reset)
submit <- function() {
w <- as.character(tclvalue(wvar))
x <- as.numeric(tclvalue(xvar))
y <- as.numeric(tclvalue(yvar))
z <- as.numeric(tclvalue(zvar))
h <- as.numeric(tclvalue(hvar))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
IonExtractor(Metabolite.Names=w,mz=x,RT.Time1=y,RT.Time2=z, heightMAX=h ,peak= Peakvar)
}
submit.but <- tkbutton(kk, text="Submit", command=submit)
quit.but <- tkbutton(kk, text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
# outline
frameL<- tkframe(frame_All,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameL,text="Metabolite Name"), w.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Reference Ion"), x.entry, tklabel(frameL, text="m/z"), pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Initial Retention Time"), y.entry, tklabel(frameL, text="minutes"), pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="End Retention Time"), z.entry, tklabel(frameL, text="minutes"), pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Min Intensitiy for y-axis"), h.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameL,text="Integration Types"),comboBox, sticky="w",pady= 10, padx= 10)
frameButton<- tkframe(frame_All)
tkgrid(tklabel(frameButton,text=""), submit.but, re_analysis.but, reset.but, quit.but, pady= 10, padx= 10)
## load images
image.path_GC_IonExtractor<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCMS_IonExtractor_logo.gif", sep=""))
GC_IonExtractor_logo <-tcl("image", "create", "photo", "IonExtract_GC.image", file=image.path_GC_IonExtractor)
# Interphase
tkgrid(tklabel(frame_All,image=GC_IonExtractor_logo))
tkgrid(tklabel(frame_All,text="ENTER PARAMETERS"),columnspan=4, pady = 10, sticky="w")
tkgrid(frame_All)
tkgrid(frameL,pady= 0, padx= 10)
tkgrid(frameButton)
}
######################################### LcOrbitalTRap #########################################
LcOrbitalTRap <- function(){
# Load all require library
require(multtest)
require(xcms)
require(faahKO)
require(CAMERA)
require(flux)
require(tcltk)
if (!require(mzmatch.R)){
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS=T)
devtools::install_github("andzajan/mzmatch.R",upgrade ="never",quiet = T)
require(mzmatch.R)
}
mzmatch.init(version.1=FALSE)
#require(RColorBrewer)
################# Setup working directory ######################
WorkingDir <-function(){
Main.Folder <- choose.dir(caption = "Select working directory")
all.Main.Folder<<-Main.Folder
setwd(all.Main.Folder)
}
################# RUN LC Orbital Trap peak area integration ####
Run.LCMS.Analysis<-function(ppm_b=2.5, peakwidth_b=c(5,20), AllScangRange=TRUE, RtRangeM=c(0,13), SignalNoiseR_b=50, Prefilter_b=c(3,5000), mzwid_b=0.015, bw_b=2, modeP="positive"){
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
cdfpath<-all.Main.Folder
## Load all raw files
print("STEP 1a: Upload all LC-orbitral raw data in .mzXML formate")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
cdffiles <- cdffiles[grep(c(".cdf$|.mzXML$"),cdffiles, ignore.case=TRUE)]
print(cdffiles)
## Print parameters
print("-----XCMS Parameter Settings----")
print(paste("Mass Resolution:", ppm_b, "ppm"))
print(paste("Peak width:", toString(peakwidth_b), "s"))
print(paste("RT bin (bw):", bw_b, "s"))
print(paste("mz varation (mzwid):", mzwid_b))
print(paste("Signal-to-noise ratio:", SignalNoiseR_b))
print(paste("Pre-filter:", toString(Prefilter_b)))
print(paste("Polarity:", modeP))
## Peak identitication
print("STEP 1b: Peak Identications (long waiting time: ~2 min per sample)")
RtRangeS<-round(RtRangeM*60,0) # convert minute to second and no decemal place
if(AllScangRange==TRUE) {xset <- xcmsSet(cdffiles, ppm = ppm_b, peakwidth = peakwidth_b, method = "centWave", nSlaves=4, snthresh=SignalNoiseR_b, prefilter= Prefilter_b, integrate = 1, fitgauss= TRUE, verbose.columns=TRUE) } else {
xset <-xcmsSet(cdffiles, ppm = ppm_b, peakwidth = peakwidth_b, method = "centWave" , scanrange=RtRangeS, nSlaves=4, snthresh=SignalNoiseR_b, prefilter= Prefilter_b, integrate = 1, fitgauss= TRUE, verbose.columns=TRUE) }
## save the file
# xset_RT<-xset
#save(xset_RT,file="BackUp_ForRtFail.rda")
## Peak Match 1
# print("STEP 3: Matching Peaks Across Samples")
# xset <- group(xset, bw = 2, mzwid = 0.015)
## RT correction
print("STEP 1c: Retention Time Correction")
xset2 <- retcor(xset, method ="obiwarp", plottype = c("deviation"), profStep=0.5, center=3)
## Peak Match 2
print("STEP 1d: Matching Peaks Across Samples")
xset2 <- group(xset2, bw = bw_b , mzwid = mzwid_b)
print(xset2)
## Fill up the missing values
print("STEP 1e: Filling in Missing Peak Data")
xset3 <- fillPeaks(xset2)
## Save the file in the working folder
# peakTable(xset3, filebase="Final LC-MS result")
# print("Data process is completed and save Final LC-MS result.tsv")
## save the process LC-MS data in the working folder and can be load for futher downstream analysis
if(AllScangRange==TRUE) {RtRangFinal=" AllScan"} else { RtRangFinal=toString(RtRangeM) }
LongFileName <-paste("Processed raw data(ppm_",ppm_b,")(peakwidth_",toString(peakwidth_b),")(RtRange_",RtRangFinal,")(bw_",bw_b,")(mzwid_",
mzwid_b,")(SignalNoise_",SignalNoiseR_b,")(Pre-filter_",toString(Prefilter_b),")(Polarity_",modeP,").rda",sep="")
#rawdata_FileName <- tclvalue(tkgetSaveFile(initialfile= LongFileName))
save(xset3,file= LongFileName)
## Annotation by CAMERA packages
xset3_annotation <- xset3
print("STEP 1f: Annotation of adduct fragments and isotopes in progress....")
if(length(levels(xset3_annotation@phenoData$class))==2){
peaklist<- annotateDiffreport(xset3_annotation,polarity=modeP)
write.csv(peaklist, file=paste("Final LC-MS result(XCMS)_TTtest","_",modeP,".csv",sep=""))
} else {
an <- xsAnnotate(xset3_annotation)
an <- groupFWHM(an)
an <- findIsotopes(an) # optional but recommended.
an <- groupCorr(an) # optional but very recommended step
an <- findAdducts(an,polarity=modeP)
peaklist <- getPeaklist(an)
write.csv(peaklist, file=paste("LC-MS result(XCMS)","_",modeP,".csv",sep=""))
}
print("LC-MS data extraction is completed and saved as LC-MS result(XCMS).csv")
tkmessageBox(title = "MassOmics-LCMS processing", message = paste("LC-MS data extraction is completed and saved as LC-MS result(XCMS).csv"))
}
########################## If the RT doesn't work
# RT_correct_error <- function(){
# Save_xsetRT<-choose.files(caption = "Select a processed raw data (e.g.BackUp_ForRtFail.rda")
# load(Save_xsetRT)
# print("STEP 3: Matching Peaks Across Samples")
# xset_RT <- group(xset_RT)
# xset_RT <- fillPeaks(xset_RT)
# xset3 <- xset_RT
# peakTable(xset_RT, filebase="Final LC-MS result(No RT correction)")
## need to save the files
# rawdata_FileName <- tclvalue(tkgetSaveFile(initialfile="Processed raw data(ppm)(peakwidth)(RrRange)_ForRTFail.rda"))
# save(xset3,file=rawdata_FileName)
# }
########################## mzMatch ####################################
mzMatch<-function(mode_b = "positive", ppm_b = 5, rtwindow_b = 20, BlankName= "Blank", BlankFilter= TRUE, javaMemorySizeG=4){
print("-----mzMatch Parameter Settings----")
print(paste("Mass Resolution:", ppm_b, "ppm"))
print(paste("Retention time window:", rtwindow_b, "s"))
print(paste("Blank filter:", BlankFilter))
print(paste("Name for blank class:", BlankName))
print(paste("Polarity:", mode_b))
print(paste("Memory usuage:", javaMemorySizeG, "GB"))
javaMemorySize<-javaMemorySizeG*1000
mzmatch.init(memorysize= javaMemorySize, version.1=FALSE)
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
Save_xset3<-choose.files(caption = "Select a XCMS processed raw data (e.g.Processed raw data(ppm)(peakwidth)....rda)")
load(Save_xset3)
## Convert xcms process file to .PeakML
print("STEP 2a: Convert xcms process data to .peakML")
PeakML.xcms.write(xset3, filename= "1_LCMS.peakML", ionisation= mode_b)
## Remove noise using blank samples
print("STEP 2b:Remove noise using blank")
if(BlankFilter==TRUE){
PeakML.BlankFilter(filename="1_LCMS.peakML",
ionisation= mode_b,
outputfile="1b_LCMS_BlankFilter.peakml",
BlankSample=BlankName)
}
## fill the gap
print("STEP 2c: Gap Filling the missing values")
if(BlankFilter==TRUE){
PeakML.GapFiller(filename = "1b_LCMS_BlankFilter.peakml", ionisation = mode_b, outputfile = "2_LCMS_GapFilling.peakml", ppm = 0, rtwin = 0, fillAll=FALSE, Rawpath=NULL, nSlaves = 4)
} else {
PeakML.GapFiller(filename = "1_LCMS.peakML", ionisation = mode_b, outputfile = "2_LCMS_GapFilling.peakml", ppm = 0, rtwin = 0, fillAll=FALSE, Rawpath=NULL, nSlaves = 4)
}
## Group peaks
print('STEP 3c: Group realtive peak together')
mzmatch.ipeak.sort.RelatedPeaks(i="2_LCMS_GapFilling.peakml", v=T,
o="3_LCMS_GapFilling_GroupPeak.peakml", ppm=ppm_b, rtwindow=rtwindow_b, JHeapSize= javaMemorySize)
## Identify peaks from databases
print("STEP 4c: Peak annoatation by databases")
DBS <- dir(paste(find.package("mzmatch.R"), "/dbs", sep=""), full.names=TRUE)
DBS # print a list of library
DBS <- paste(DBS[c(1:15)],collapse=",")
if(mode_b=="positive"){
mzmatch.ipeak.util.Identify(i="3_LCMS_GapFilling_GroupPeak.peakml", v=T,
o="4_LCMS_GapFilling_GroupPeak_Annotation(Positive).peakml", ppm=ppm_b, databases=DBS, polarity= mode_b,
adducts="M+H,M+ACN+Na,M+Na,M+K,M+ACN+H", JHeapSize= javaMemorySize) ## need to fix
mzmatch.ipeak.convert.ConvertToText (
i="4_LCMS_GapFilling_GroupPeak_Annotation(Positive).peakml",
o="Final_annoated_LCMS_data(positive).txt", databases=DBS,
annotations="identification,moleculeName,npeaks,ppm,adduct,relation.ship,charge", JHeapSize= javaMemorySize)
} else {
mzmatch.ipeak.util.Identify(i="3_LCMS_GapFilling_GroupPeak.peakml", v=T,
o="4_LCMS_GapFilling_GroupPeak_Annotation(Negative).peakml", ppm=ppm_b, databases=DBS, polarity= mode_b,
adducts="M-H", JHeapSize= javaMemorySize) ## need to fix
mzmatch.ipeak.convert.ConvertToText (
i="4_LCMS_GapFilling_GroupPeak_Annotation(Negative).peakml",
o= "Final_annoated_LCMS_data(Negative).txt", databases=DBS,
annotations="identification,moleculeName,npeaks,ppm,adduct,relation.ship,charge", JHeapSize= javaMemorySize)
}
tkmessageBox(title = "MassOmics-LCMS processing", message = paste("LC-MS data filter and annotation is completed and saved as Final_annoated_LCMS_data.txt"))
print("Analysis is completed and saved as - Final_annoated_LCMS_data.txt")
## end if mzmatch
}
## manually check data
ManualCheck <-function(){
require (tcltk)
require (mzmatch.R)
mzmatch.init(version.1=FALSE)
PeakML.Viewer (JHeapSize=5000)
tkmessageBox(title = "PeakML.Viewer", message = paste("May take long time to open PeakML.Viewer and open a file in .peakml format (e.g.4_LCMS_GapFilling_GroupPeak_Annotation.peakml"))
}
ExportToTXT <- function(javaMemorySizeG=4){
require (mzmatch.R)
require (tcltk)
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
javaMemorySize<-javaMemorySizeG*1000
mzmatch.init(memorysize= javaMemorySize, version.1=FALSE)
DBS <- dir(paste(find.package("mzmatch.R"), "/dbs", sep=""), full.names=TRUE)
DBS <- paste(DBS[c(1:15)],collapse=",")
InputName <- basename(choose.files(caption = "Select a .peakmL file (e.g.manual check.peakml)"))
OutputName <- basename(tclvalue(tkgetSaveFile(initialfile= "LC-MS_manual_check.txt")))
mzmatch.ipeak.convert.ConvertToText (
i=InputName,
o=OutputName, databases=DBS,
annotations="identification,moleculeName,npeaks,ppm,adduct,relation.ship,charge",
JHeapSize= javaMemorySize
)
print ("Done!")
}
################# stataiscally analysis for 2 conditions ####
LCMS_TTest<-function(nplot=100){
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
#Auotmatically load the proces data if xset3 not present
#if(!exists("xset3")){
# Save_xset3<-choose.files(caption = "Select Processed raw data")
# load(Save_xset3)
#}
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
condition_1<- as.character(unique(xset3@ phenoData $class)[[1]])
condition_2<- as.character(unique(xset3@ phenoData $class)[[2]])
print("Statistical analysis in progress....")
reporttab <- diffreport(xset3, condition_1, condition_2, "Final LC-MS result(T-Test)", nplot, metlin = 0.15, h=480, w=640)
print("The TTest report is completed and save as Final LC-MS result(T-Test.tsv")
}
################# Plot ion extracted chromatogram base on ID
choose.xset3<-function(){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_ID<<-xset3
}
plot.ID<-function(ID.NO=712){
if(!exists("xset3_ID")){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_ID<<-xset3
}
eiccor <- getEIC(xset3_ID, groupidx = ID.NO)
# Mass range
mzmin<-round(as.numeric(eiccor@ mzrange[1,1]),5)
mzmax<-round(as.numeric(eiccor@ mzrange[1,2]),5)
# RT range
RTmin<-round(as.numeric(eiccor@ rtrange[1,1]),1)
RTmax<-round(as.numeric(eiccor@ rtrange[1,2]),1)
# plot and save image
fig.names<-paste("ID(",ID.NO,")_Ion(",mzmin,"-",mzmax, "mz)_RT(", RTmin,"-",RTmax,"s).png", sep="")
png(fig.names)
plot(eiccor, xset3_ID)
dev.off() ## Need to make it plot the figure.....>_<
}
################# LC-MS Single ion extraction ##############
RUN.LCMS.Ion.Extractor<-function(){
require(xcms)
require(flux)
# Setup working dirctory
WorkingDir <-function(){
Main.Folder <- choose.dir(caption = "Select working directory")
all.Main.Folder<<-Main.Folder
setwd(all.Main.Folder)
}
# Located the process file
choose.xset3_Ion<-function(){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_Ion<<-xset3
}
# LC-MS IonExtractor
LCMS.Ion.Extractor<-function(mz.1 = 210.1281,
mz.2 = 210.1289,
RT.Time1 = 190,
RT.Time2 = 210,
heightMAX= 1500000000,
Metabolite.Names = "D5-tryptophan",
peak="Peak Area"){
if(exists("all.Main.Folder")){
setwd(all.Main.Folder)
} else {
all.Main.Folder<<-choose.dir(caption = "Select working directory")
setwd(all.Main.Folder)
}
if(!exists("xset3_Ion")){
Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
load(Save_xset3)
xset3_Ion<<-xset3
}
# Extract specific ion and rentension time
IonExtract <-getEIC(xset3_Ion,mzrange=cbind(mz.1,mz.2),rtrange=cbind(RT.Time1,RT.Time2))
# Determine colour of figure
#mycolors<-rainbow(length(t(unique(xset3@ phenoData))))
#mycolors<-brewer.pal(length(t(unique(xset3@ phenoData))),"Set1")
#names(mycolors) <- levels(xset3@ phenoData$class)
#list.df<-t(xset3@ phenoData)
#list.color<-mycolors[match(list.df,names(mycolors))]
list.color<-rainbow(length(IonExtract@eic))
IonExctor.df<-NULL
for (i in 1:length(IonExtract@eic)){
#Extraction
Extra.Data<-data.frame(IonExtract@eic[i])
ifelse(peak=="Peak Height", Peakvalue <- max(Extra.Data[,2]) ,Peakvalue<-auc(Extra.Data[,1], Extra.Data[,2]))
name.file<-names(IonExtract@eic)[i]
if (!exists("IonExctor.df")){
IonExctor.df <- data.frame(t(c(name.file,Peakvalue)))
} else {
IonExctor.df <- rbind(IonExctor.df, data.frame(t(c(name.file,Peakvalue))))
}
print(paste("File", i, name.file,"done!"))
# Plot
Intensity<-Extra.Data[,2]
Retention.Time<-Extra.Data[,1]
if(i==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= list.color[i],bty="n", type="l",main=paste(Metabolite.Names," (",mz.1,"-",mz.2, " m/z",") ","RT:",RT.Time1,"-",RT.Time2," min", sep=""),
xlab="Retention Time (Sec)",ylab="Intensity", ylim=c(0,heightMAX))
}else{
lines(Intensity~Retention.Time,col= list.color[i])
}
}
final.df2<- data.frame(t(IonExctor.df[2]))
names(final.df2) <- t(IonExctor.df[1])
row.names(final.df2) <- Metabolite.Names
# Save files
ifelse(peak=="Peak Height",ValueType<-"PeakHeight",ValueType<-"PeakArea")
store <- paste(all.Main.Folder, "\\", Metabolite.Names,"(",mz.1,"-",mz.2," m.z",")RT",RT.Time1,"-",RT.Time2,"_",ValueType,".csv", sep = "")
write.csv(final.df2, file =store, row.names = TRUE)
print("Complete!")
return(final.df2)
print(paste("The file ",Metabolite.Names, ".csv", " was saved in ", all.Main.Folder, sep=""))
}
## User interphse specific for LC-MS IonExtractor
require(tcltk)
tclRequire("BWidget")
# Setup frame
kk <- tktoplevel()
frameAll <- tkframe(kk)
tkwm.title(kk,"LCMS-IonExtractor")
# Setup parameters
xvar <- tclVar("210.1281")
vvar <- tclVar("210.1289")
yvar <- tclVar("190")
zvar <- tclVar("210")
wvar <- tclVar("D5-tryptophan")
hvar <- tclVar("5000000")
Peakvar <- tclVar("Peak Area")
x.entry <- tkentry(background="white",kk, textvariable=xvar, width=17)
v.entry <- tkentry(background="white",kk, textvariable=vvar, width=17)
y.entry <- tkentry(background="white",kk, textvariable=yvar, width=17)
z.entry <- tkentry(background="white",kk, textvariable=zvar, width=17)
w.entry <- tkentry(background="white",kk, textvariable=wvar, width=17)
h.entry <- tkentry(background="white",kk, textvariable=hvar, width=17)
# Choose between peakheight or area
Peak <- c("Peak Area","Peak Height")
comboBox <- tkwidget(kk,"ComboBox",editable=FALSE,values=Peak,textvariable=tclVar("Peak Area"),width=15)
# Reset function
reset <- function() {
tclvalue(xvar)<-""
tclvalue(vvar)<-""
tclvalue(yvar)<-""
tclvalue(zvar)<-""
tclvalue(wvar)<-""
tclvalue(hvar)<-"5000000"
Peakvar <- tclVar("Peak Area")
}
reset.but <- tkbutton(kk, text="Reset", command=reset, width=9)
# Run function
submit <- function() {
w <- as.character(tclvalue(wvar))
x <- as.numeric(tclvalue(xvar))
v <- as.numeric(tclvalue(vvar))
y <- as.numeric(tclvalue(yvar))
z <- as.numeric(tclvalue(zvar))
h <- as.numeric(tclvalue(hvar))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
LCMS.Ion.Extractor(Metabolite.Names=w, mz.1=x, mz.2=v, RT.Time1=y, RT.Time2=z, heightMAX=h ,peak= Peakvar)
}
submit.but <- tkbutton(kk, text="Run", command=submit, width= 9)
# Quit function
quit.but <- tkbutton(kk, text = "Close Session",
command = function() {
tkdestroy(kk)
}
)
Wdir.but <- tkbutton(kk, text="Choose", command= WorkingDir ,width=9)
Process.but <- tkbutton(kk, text="Choose", command= choose.xset3_Ion ,width=9)
open.but <- tkbutton(kk,text="Open folder",width= 9,
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path2<-c(paste(file.path(path.package(package="MassOmics")),"/R/LCMS_IonExtractor.gif", sep=""))
logo_1<-tcl("image", "create", "photo", "MaxC4.image", file=image.path2)
# Upper frame
frameUpper<- tkframe(frameAll,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Setup Working Directory "), Wdir.but, pady = 2, padx= 10, sticky="w") #columnspan=4
tkgrid(tklabel(frameUpper,text="Select processed LC-MS file "), Process.but, pady = 2, padx= 10, sticky="w")
#middle frame
frameMiddle<- tkframe(frameAll,borderwidth=2, relief="groove",padx=5,pady=5)
tkgrid(tklabel(frameMiddle,text="Metabolite Name"), w.entry, pady= 10, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Reference Ion (min,max)"), x.entry, v.entry, tklabel(frameMiddle,text="m/z"), pady= 8, 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")
tkgrid(tklabel(frameMiddle,text="Min Intensitiy for y-axis"), h.entry, pady= 8, padx= 10, sticky="w")
tkgrid(tklabel(frameMiddle,text="Integration type"),comboBox, pady= 8, padx= 10, sticky="w")
# Button
frameButton<- tkframe(frameAll)
tkgrid(tklabel(frameButton,text=""), reset.but, submit.but, open.but, quit.but, pady= 10, padx= 10, sticky="w")
# Interphase structure
tkgrid(tklabel(kk,image=logo_1))
tkgrid(tklabel(frameAll,text="1. SETUP"), pady = 10, padx= 20, sticky="w")
tkgrid(frameAll)
tkgrid(frameUpper, pady= 0, padx= 30, sticky="w")
tkgrid(tklabel(frameAll,text="2. ENTER PARAMETERS"), pady = 10, padx= 20, sticky="w") #columnspan=4
tkgrid(frameMiddle, pady= 0, padx= 30)
tkgrid(frameButton, pady= 10, padx= 30)
}
## Annotation of Adduct and isotopes
# LCMS_Adduct_isotpes<-function(mode="positive"){
# if(exists("all.Main.Folder")){
# setwd(all.Main.Folder)
# } else {
# all.Main.Folder<<-choose.dir(caption = "Select working directory")
# setwd(all.Main.Folder)
# }
#
# Save_xset3<-choose.files(caption = "Select a processed raw data (e.g.Processed raw data(ppm)(peakwidth).rda)")
# load(Save_xset3)
# xset3_annotation <- xset3
#
# print("Annotation of adduct fragments and isotopes in progress....")
#
# if(length(levels(xset3_annotation@phenoData$class))==2){
# peaklist<- annotateDiffreport(xset3_annotation,polarity=mode)
# write.csv(peaklist, file=paste("Final LC-MS result(Adduct_Isotope)_TTtest","_",mode,".csv",sep=""))
# } else {
#
# an <- xsAnnotate(xset3_annotation)
# an <- groupFWHM(an)
# an <- findIsotopes(an) # optional but recommended.
# an <- groupCorr(an) # optional but very recommended step
# an <- findAdducts(an,polarity=mode)
# peaklist <- getPeaklist(an)
# write.csv(peaklist, file=paste("Final LC-MS result(Adduct_Isotope)","_",mode,".csv",sep=""))
# }
### Visilization ##
#plotEICs(an,1)
#plotPsSpectrum(an,1,maxlabel=10)
## find nature lost
#xs.pseudo <- findNeutralLoss(an,mzdiff=18.01,mzabs=0.01)
#xs.pseudo@peaks #WATER LOST
## Calcualte moleuclar composition
#library(Rdisop)
#isolist <- getIsotopeCluster(an)
# test<-isolist[5][[1]]$peaks
# molecules<- decomposeMass(488.158)
# molecules<- decomposeIsotopes(test[,1],test[,2],ppm=10)
# errors = MFerrors(molecules,test[,1],test[,2])
# cbind(getFormula(molecules), getScore(molecules), getValid(molecules))
# print("Annotation of adduct fragments and isotopes are completed and save as Final LC-MS result(Adduct_Isotope).csv")
## save the process LC-MS data in the working folder and can be load for futher downstream analysis
# rawdata_FileName <- tclvalue(tkgetSaveFile(initialfile=paste("Annotation raw data","(",mode, ").rda",sep="")))
# save(an,file=rawdata_FileName)
# }
################# User interphase ####################
## Load library
require(tcltk)
require(rgl)
tclRequire("BWidget")
## Setup tk enviroment ##
LC_Orbital <- tktoplevel()
frameOverall0 <- tkframe(LC_Orbital)
frameOverall <- tkframe(LC_Orbital)
frameOverall2 <- tkframe(LC_Orbital)
frameOverall3 <- tkframe(LC_Orbital)
tkwm.title(LC_Orbital ,"LC-MS data analysis")
## Functions in the buttons
workingdirctory<-function(){WorkingDir()
}
LC_MS_analysis<-function(){
ppm_a <- as.numeric(tclvalue(ppm.var))
peakwidth_a <- as.numeric(strsplit(tclvalue(peakwidth.var), ",",)[[1]])
RtRangeMa <- as.numeric(strsplit(tclvalue(RtRangeM.var), ",",)[[1]])
SignalNoiseR_a <-as.numeric(tclvalue(SignalNoiseR.var))
mzwid_a <- as.numeric(tclvalue(mzwid.var))
bw_a <- as.numeric(tclvalue(bw.var))
Prefilter_a <- as.numeric(strsplit(tclvalue(Prefilter.var), ",",)[[1]])
modeB <- modeA[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
rtVal <- as.character(tclvalue(rtValue))
if (rtVal=="1")
AllScangRangeA<-TRUE
if (rtVal=="0")
AllScangRangeA<-FALSE
Run.LCMS.Analysis(ppm_b=ppm_a,peakwidth_b=peakwidth_a, AllScangRange=AllScangRangeA, RtRangeM = RtRangeMa, SignalNoiseR_b=SignalNoiseR_a, mzwid_b=mzwid_a, Prefilter_b= Prefilter_a, bw_b= bw_a, modeP=modeB)
}
LCMS.hlep.page<-function(){
TMS.help <- tktoplevel()
tkwm.title(TMS.help,"Help")
scr <- tkscrollbar(TMS.help, repeatinterval=5,
command=function(...)tkyview(txt,...))
txt <- tktext(TMS.help,bg="white",font="courier",yscrollcommand=function(...)tkset(scr,...))
tkgrid(txt,scr)
tkgrid.configure(scr,sticky="ns")
## Insert text here
tkinsert(txt,"end","Under construction\n")
tkconfigure(txt, state="disabled")
tkfocus(txt)
}
TT.Test<-function(){
nplot_a <- as.numeric(tclvalue(nPlot.var))
LCMS_TTest(nplot=nplot_a)
}
workingdirctoryID<-function(){choose.xset3()
}
ID.Ion.Extraction<-function(){
ID.NO_a <- as.numeric(tclvalue(ID.NO.var))
plot.ID(ID.NO=ID.NO_a)
}
# adduct_isotope<-function(){
# modeB <- modeA[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
# LCMS_Adduct_isotpes(mode=modeB)
# }
# RT_Error<-function(){
# RT_correct_error()
# }
xcms.Tips<-function () {
require(tcltk)
kk <- tktoplevel()
tktitle(kk) <- "Suggested XCMS parameter settings"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/XCMS_parameter_tips.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "setting.image", file=image.path)
tkgrid(tklabel(kk,image=FrontImage))
}
MzMatch.ini <- function() {
ppm_a <- as.numeric(tclvalue(ppm_mz.var))
memory_a <-as.numeric(tclvalue( memory.var))
rtwindow_a <- as.numeric(tclvalue(rtwindow_mz.var))
BlankName_a <- as.character(tclvalue(BlankName_mz))
BnaVal <- as.character(tclvalue(BlankName_apply_Value))
if (BnaVal=="1")
BlankFilter_a<-TRUE
if (BnaVal=="0")
BlankFilter_a<-FALSE
mode_a <- mode_mz.var[[as.numeric(tclvalue(tcl(comboBox_MzMatch,"getvalue")))+1]]
mzMatch(mode_b = mode_a, ppm_b = ppm_a, rtwindow_b = rtwindow_a, BlankName= BlankName_a, BlankFilter= BlankFilter_a, javaMemorySizeG= memory_a)
}
manualCheck.ini <- function() {
ManualCheck()
}
exportText.ini <- function() {
memory_a <-as.numeric(tclvalue(memory_b.var))
ExportToTXT(javaMemorySizeG= memory_a)
}
## Creat buttons
Wdir.but <- tkbutton(LC_Orbital, text="Choose", command=workingdirctory,width=6)
integrate.but <- tkbutton(LC_Orbital, text="Run", command=LC_MS_analysis,width=6)
help.but <-tkbutton(LC_Orbital, text="Help", command= LCMS.hlep.page,width=6)
TT.Test.but <- tkbutton(LC_Orbital, text="Run", command=TT.Test, width=6)
WdirID.but <- tkbutton(LC_Orbital, text="Choose", command=workingdirctoryID,width=6)
ID.Ion.Extraction.but <- tkbutton(LC_Orbital, text="Run", command=ID.Ion.Extraction, width=6)
Ion.Extraction.but <- tkbutton(LC_Orbital, text="Run", command=RUN.LCMS.Ion.Extractor, width=6)
# adduct_isotope.but <- tkbutton(LC_Orbital, text="Run", command=adduct_isotope, width=6)
# RT.Error.but <- tkbutton(LC_Orbital, text="Run", command=RT_Error, width=6)
xcms.setting.but <- tkbutton(LC_Orbital, text="Tip", command= xcms.Tips, width=6)
MzMatch.but <- tkbutton(LC_Orbital, text="Run", command= MzMatch.ini, width=6)
ManualCheck.but <- tkbutton(LC_Orbital, text="Run", command= manualCheck.ini, width=6)
exportText.but <- tkbutton(LC_Orbital, text="Run", command= exportText.ini, width=6)
## create entry box and define their default values
# PPM
ppm.var <- tclVar("2.5")
ppm.entry <- tkentry(background="white",LC_Orbital, textvariable=ppm.var, width=8)
# +6Width
peakwidth.var <- tclVar("5,20")
peakwidth.entry <- tkentry(background="white",LC_Orbital, textvariable=peakwidth.var, width=8)
# ScangeRange
RtRangeM.var <- tclVar("0,13")
RtRangeM.entry <- tkentry(background="white",LC_Orbital, textvariable=RtRangeM.var, width=8)
rtx <- tkcheckbutton(LC_Orbital)
rtValue <- tclVar("1")
tkconfigure(rtx,variable=rtValue)
# Signal noise ratio
SignalNoiseR.var <- tclVar("50")
SignalNoiseR.entry <- tkentry(background="white",LC_Orbital, textvariable=SignalNoiseR.var, width=8)
# Prefilter
Prefilter.var <- tclVar("3,5000")
Prefilter.entry <- tkentry(background="white",LC_Orbital, textvariable= Prefilter.var, width=8)
# mzwid
mzwid.var <- tclVar("0.015")
mzwid.entry <- tkentry(background="white",LC_Orbital, textvariable= mzwid.var, width=8)
#bw
bw.var <- tclVar("2")
bw.entry <- tkentry(background="white",LC_Orbital, textvariable= bw.var, width=8)
# Number of plot
nPlot.var<- tclVar("10")
nPlot.entry <- tkentry(background="white",LC_Orbital, textvariable=nPlot.var, width=8)
# Chramtogram ID
ID.NO.var<- tclVar("1")
ID.NO.entry <- tkentry(background="white",LC_Orbital, textvariable=ID.NO.var, width=8)
# Positive or negative for addcut determination
modeA <- c("positive","negative")
comboBox <- tkwidget(LC_Orbital,"ComboBox",editable=FALSE,values=modeA,textvariable=tclVar("positive"),width=7)
## variable for mzMatch
ppm_mz.var <- tclVar("5")
ppm_mz.entry <- tkentry(background="white",LC_Orbital, textvariable= ppm_mz.var, width=8)
rtwindow_mz.var <- tclVar("10")
rtwindow_mz.entry <- tkentry(background="white",LC_Orbital, textvariable= rtwindow_mz.var, width=8)
BlankName_mz <- tclVar("Blank")
BlankName_mz.entry <- tkentry(background="white",LC_Orbital, textvariable= BlankName_mz, width=8)
BlankName_apply <- tkcheckbutton(LC_Orbital)
BlankName_apply_Value <- tclVar("1")
tkconfigure(BlankName_apply,variable=BlankName_apply_Value)
mode_mz.var <- c("positive","negative")
comboBox_MzMatch <- tkwidget(LC_Orbital,"ComboBox", editable=FALSE, values= mode_mz.var, textvariable=tclVar("positive"), width=7)
## memory
memory.var <-tclVar("4")
memory.entry <- tkentry(background="white",LC_Orbital, textvariable= memory.var, width=8)
memory_b.var <-tclVar("4")
memory_b.entry <- tkentry(background="white",LC_Orbital, textvariable= memory_b.var, width=8)
### Frame structure of user interphase
## UpperFrame
frameUpper <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Setup Working Directory "),Wdir.but, tklabel(frameUpper,text=" "), sticky="w")
## MiddleFrame
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameMid,text="Mass Resolution"), ppm.entry, tklabel(frameMid,text="ppm"),sticky="w")
tkgrid(tklabel(frameMid,text="Peak width (min,max)"), peakwidth.entry,tklabel(frameMid,text="second"),sticky="w")
tkgrid(tklabel(frameMid,text="RT bin (bw)"), bw.entry, tklabel(frameMid,text="second"), sticky="w")
tkgrid(tklabel(frameMid,text="mz varation (mzwid)"), mzwid.entry, sticky="w")
tkgrid(tklabel(frameMid,text="Signal-to-noise ratio"), SignalNoiseR.entry, sticky="w")
tkgrid(tklabel(frameMid,text="Pre-filter "), Prefilter.entry, sticky="w")
tkgrid(tklabel(frameMid,text="All RT range (Ignore below)"), rtx, sticky="w")
tkgrid(tklabel(frameMid,text="RT range (min,max)"), RtRangeM.entry, tklabel(frameMid,text="minute"),sticky="w")
tkgrid(tklabel(frameMid,text="MS modes"), comboBox, tklabel(frameMid,text=" "),sticky="w")
tkgrid(tklabel(frameMid,text="Suggested XCMS settings"),xcms.setting.but, sticky="w")
tkgrid(tklabel(frameMid,text="Automatic LC-peak integration"),integrate.but,sticky="w")
## MiddleFrame_b
# frameRtError <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameRtError,text="No retention correction "),RT.Error.but, tklabel(frameRtError,text=" "),sticky="w")
## Lower Frame_adduct&Isotope
# frameLowerAdductIsotope <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameLowerAdductIsotope,text="MS modes"), comboBox, tklabel(frameLowerAdductIsotope,text=" "),sticky="w")
# tkgrid(tklabel(frameLowerAdductIsotope,text="Annotate Adducts and Isotopes"), adduct_isotope.but ,sticky="w")
# Mzmatch
framLowerMzMatch <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
#tkgrid(tklabel(framLowerMzMatch,text="Select processed XCMS raw data "), MzMatch.but, tklabel(framLowerMzMatch,text=" "), sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Mass Resolution"), ppm_mz.entry, tklabel(framLowerMzMatch,text="ppm"),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="RT bin to group related peak "), rtwindow_mz.entry, tklabel(framLowerMzMatch,text="second"),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="RAM memory reserve for java"), memory.entry, tklabel(framLowerMzMatch,text="GB"),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Use blank filter"), BlankName_apply, sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Name of blank folder"), BlankName_mz.entry, sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="MS modes"), comboBox_MzMatch, tklabel(framLowerMzMatch,text=" "),sticky="w")
tkgrid(tklabel(framLowerMzMatch,text="Peak annotations"), MzMatch.but,sticky="w")
## Manually check data
framLowerCheck <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(framLowerCheck, text="Manually check peaks "), ManualCheck.but, tklabel(framLowerCheck, text=" "), sticky="w")
tkgrid(tklabel(framLowerCheck,text="RAM memory reserve for java"), memory_b.entry, tklabel(framLowerCheck,text="GB"),sticky="w")
tkgrid(tklabel(framLowerCheck, text="Export .peakml to .txt "), exportText.but, tklabel(framLowerCheck, text=" "), sticky="w")
## lowerFrame_ID
# frameLowerID <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameLowerID,text="Select processed raw data"),WdirID.but, tklabel(frameLowerID,text=" "), sticky="w")
# tkgrid(tklabel(frameLowerID ,text="Chromatrogram ID #"), ID.NO.entry, tklabel(frameLowerID ,text=" "),sticky="w")
# tkgrid(tklabel(frameLowerID ,text="Ion Extraction based on ID "), ID.Ion.Extraction.but ,sticky="w")
## lowerFrame_ionExtractor
frameLowerIon <- tkframe(frameOverall2,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameLowerIon, text="Re-integrate a single peak "), Ion.Extraction.but, tklabel(frameLowerIon, text=" "),sticky="w")
## lowerFrame_1
frameLower1 <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameLower1,text="Dispaly most significant ion "), nPlot.entry, tklabel(frameLower1,text=" plot "),sticky="w")
tkgrid(tklabel(frameLower1,text="TT-Test"), TT.Test.but ,sticky="w")
# Quit button
quit.but <- tkbutton(LC_Orbital, text = "Close Session",
command = function() {
tkdestroy(LC_Orbital)
rgl.close()
}
)
# Open working directory
open.but <- tkbutton(LC_Orbital,text="Open folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path1<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_SIlas_Gravida_logo.gif", sep=""))
logo<-tcl("image", "create", "photo", "LCMS.image", file=image.path1)
## 3 bottom buttons
#button <- tkframe(frameOverall3,relief="groove", borderwidth=0,padx=5,pady=5)
#tkgrid(tklabel(button ,text=" "),quit.but,tklabel(button ,text=" "),open.but, tklabel(button ,text=" "),help.but)
## change to right
button <- tkframe(frameOverall2,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=""),quit.but,tklabel(button ,text=""),open.but, tklabel(button ,text=""),help.but)
## Add window scroll
## Userinterphase
tkgrid(frameOverall0,columnspan=2)
tkgrid(frameOverall,frameOverall2)
# tkgrid(frameOverall3,columnspan=2)
tkgrid(tklabel(frameOverall0,image=logo),pady= 10, padx= 10)
# tkgrid(tklabel(frameOverall,text=""),sticky="w")
tkgrid(tklabel(frameOverall,text=" STEP 1. Setup"),sticky="w")
tkgrid(frameUpper,pady= 10, padx= 10)
#tkgrid(tklabel(frameOverall,text=""),sticky="w")
tkgrid(tklabel(frameOverall,text=" STEP 2. Peak extraction & RT correction (XCMS)"),sticky="w")
tkgrid(frameMid,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall ,text=" STEP 2b. Statsitcal anaylsis for 2 conditions (OPTION for XCMS) "),sticky="w")
tkgrid(frameLower1 ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall,text=""),sticky="w")
#tkgrid(tklabel(frameOverall,text=" STEP 2b. Use When RT Correction Fail"),sticky="w")
#tkgrid(tklabel(frameOverall,text=""),sticky="w")
#tkgrid(frameRtError,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall,text=" STEP 3. Annotation of Adducts and Isotopes"),sticky="w")
#tkgrid(frameLowerAdductIsotope ,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall2,text=""),sticky="w")
#tkgrid(tklabel(frameOverall2,text=" STEP 4. Singel ion chromatogram view based on ID"),sticky="w")
#tkgrid(frameLowerID ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=" STEP 3. Peak filter, filling, annotation, and identifications"),sticky="w")
tkgrid(framLowerMzMatch ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=" STEP 4. Manully check peaks using PeakMLViewer"),sticky="w")
tkgrid(framLowerCheck ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=" STEP #. IonExtractor For LC-MS (OPTION)"),sticky="w")
tkgrid(frameLowerIon ,pady= 10, padx= 30)
tkgrid(tklabel(frameOverall2,text=""),sticky="w")
#tkgrid(tklabel(frameOverall,text=""),sticky="w")
#tkgrid(tklabel(frameOverall,text=" STEP #. Check LC-MS results"),sticky="w")
#tkgrid(frameLower,pady= 10, padx= 10)
tkgrid(button, pady= 10, padx= 10)
}
######################################### GCMS_analysis #########################################
GCMS_analysis <- function(workdir=tk_choose.dir(caption = "Select working directory")){
require(xcms)
require(lattice)
require(plyr)
require(flux)
library(tcltk)
library(stringr)
WorkingDir <- function(workdir = tk_choose.dir(caption = "Select working directory")){
workdir <<- workdir
setwd(workdir)
}
# Peak integration
## Help page
TMS.hlep.page<-function(){
TMS.help <- tktoplevel()
tkwm.title(TMS.help,"Help")
scr <- tkscrollbar(TMS.help, repeatinterval=5,
command=function(...)tkyview(txt,...))
txt <- tktext(TMS.help,bg="white",font="courier",yscrollcommand=function(...)tkset(scr,...))
tkgrid(txt,scr)
tkgrid.configure(scr,sticky="ns")
tkinsert(txt,"end","Step 1\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Prepare a working folder containing:\n")
tkinsert(txt,"end"," AMDIS Batch report in .txt formate\n")
tkinsert(txt,"end"," A folder contains all GC-MS raw data in .cdf formate\n")
tkinsert(txt,"end"," A MS library in .msl formate\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Suggested AMIDS setting\n")
tkinsert(txt,"end"," Minimum match factor : 80\n")
tkinsert(txt,"end"," Deconv. : one, High, Very High, Low\n")
tkinsert(txt,"end"," Batch Job : include only first 1 hit\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 2\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 3\n")
tkconfigure(txt, state="disabled")
tkfocus(txt)
}
## Building an interfance for lIZ mcf anlaysis
library(tcltk)
require(rgl)
tclRequire("BWidget")
#Setup
TMS <- tktoplevel()
frameOverall <- tkframe(TMS)
tkwm.title(TMS,"GC-MS data processing")
rb3 <- tkradiobutton(TMS,text=" AMDIS ",indicatoron=F)
rb4 <- tkradiobutton(TMS,text="MassHunter",indicatoron=F)
lib_search_mode <- tclVar("AMDIS")
tkconfigure(rb3,variable=lib_search_mode,value="AMDIS")
tkconfigure(rb4,variable=lib_search_mode,value="MassHunter")
#All the functions
workingdirctory<-function(){WorkingDir()}
Id_report1<-function(){
Ret.Time.Filter_s <- as.numeric(tclvalue(RT.filter.var))
NbVal <- as.character(tclvalue(NbValue))
generate_graph<-F
generate_rt_shift_graph_s=as.numeric(tclvalue(generate_rt_shift_graph))
rtchecks=tclvalue(RTcorrection)
if (rtchecks=="0"){rtchecks<-FALSE}
if (rtchecks=="1"){rtchecks<-TRUE}
if (NbVal=="0"){MsLibrary_s<-"InHouse"}
if (NbVal=="1"){MsLibrary_s<-"NIST"}
if (generate_rt_shift_graph_s=="0"){generate_graph<-F}
if (generate_rt_shift_graph_s=="1"){generate_graph<-T}
RT.shift.limt_s <- as.numeric(tclvalue(multimode.var))
mz_L_s=as.numeric(tclGetValue("mz_L"))
mz_U_s=as.numeric(tclGetValue("mz_U"))
if (as.character(tclvalue(lib_search_mode))=="AMDIS"){
amdis_id_Summary(workdir=workdir,
MsLibrary=MsLibrary_s,
Ret.Time.Filter=Ret.Time.Filter_s,
RT.shift.limt=RT.shift.limt_s,
mz_L=mz_L_s,
mz_U=mz_U_s,
generate_rt_shift_graph=generate_graph,
RTcorrection=rtchecks
)
} else if (as.character(tclvalue(lib_search_mode))=="MassHunter"){
MassHunter_id_Summary(workdir=workdir,
MsLibrary=MsLibrary_s,
Ret.Time.Filter=Ret.Time.Filter_s,
RT.shift.limt=RT.shift.limt_s,
mz_L=mz_L_s,
mz_U=mz_U_s,
generate_rt_shift_graph=generate_graph,
RTcorrection=rtchecks
)
}
message(paste("Summary report generated:",as.character(tclvalue(lib_search_mode)), MsLibrary_s,"library","\nRetention time:",Ret.Time.Filter_s,"Retention time shift:",RT.shift.limt_s,"\nmz range:",mz_L_s,"-",mz_U_s))
}
Tips1<-function(){Tips()}
GCMS_integration1<-function(){
# RT.bin_s <-as.numeric(tclvalue(SliderValue))
Peakvar <- Peak[[as.numeric(tclvalue(tcl(comboBox,"getvalue")))+1]]
cbVal <- as.character(tclvalue(cbValue))
if (cbVal=="0"){GGReport_s<-"Slow"}
if (cbVal=="1"){GGReport_s<-"Fast"}
Mzbin<-as.numeric(tclvalue(MzBinValue))
GCMS_integration(workdir=workdir, intensity_type=Peakvar,GGReport=GGReport_s,Ion.bin=Mzbin)
}
merage_RT_standard1<-function(){merage_RT_standard(workdir=workdir)}
TMS.hlep.page1<-function(){TMS.hlep.page()}
MZrange_autodetect1<-function(){
mzrange=MZrange_autodetect()
try(tclSetValue("mz_L", mzrange[1]))
try(tclSetValue("mz_U", mzrange[2]))
}
### all the button function
Wdir.but <- tkbutton(TMS, text="Choose", command=workingdirctory,width=6)
ID_report.but <- tkbutton(TMS, text="Run", command=Id_report1,width=6)
#Tips.but<-tkbutton(TMS, text="Tips", command=Tips1,width=6)
integrate.but<-tkbutton(TMS, text="Run", command= GCMS_integration1,width=6)
#mergeSM.but<-tkbutton(TMS, text="Run", command= merage_RT_standard1,width=6)
help.but<-tkbutton(TMS, text="Help", command= TMS.hlep.page1,width=6)
## fast or slow mode
cb <- tkcheckbutton(TMS)
cbValue <- tclVar("1")
tkconfigure(cb,variable=cbValue)
## NIST library mode
Nb <- tkcheckbutton(TMS)
NbValue <- tclVar("1")
tkconfigure(Nb,variable=NbValue)
## generate_rt_shift_graph_check
generate_rt_shift_graph_check <- tkcheckbutton(TMS)
generate_rt_shift_graph<- tclVar("0")
tkconfigure(generate_rt_shift_graph_check,variable=generate_rt_shift_graph)
## RTcorrection
RTcorrection_check <- tkcheckbutton(TMS)
RTcorrection<- tclVar("0")
tkconfigure(RTcorrection_check,variable=RTcorrection)
## NIST library mode
#MZrange_L <- tclVar(TMS)
# MZrange_U <- tclVar(TMS)
mz_L <- 0
mz_U <- 1000
#tkconfigure(MZrange_L,variable=mz_L)
#tkconfigure(MZrange_U,variable=mz_U)
#MZrange_L <- tkentry(background="white",TMS, textvariable=mz_L ,width=6)
#MZrange_L <- tkentry(background="white",TMS,width="6",textvariable=mz_L,validate="key",validatecommand="string is double %P")
tclSetValue("mz_L",mz_L)
tclSetValue("mz_U",mz_U)
MZrange_L <- tkentry(background="white",TMS,width="6",textvariable="mz_L",validate="key",validatecommand="string is double %P")
MZrange_U <- tkentry(background="white",TMS,width="6",textvariable="mz_U",validate="key",validatecommand="string is double %P")
MZrange_autodetect.but <- tkbutton(TMS, text="Detect", command=MZrange_autodetect1,width=6)
## RT filter
RT.filter.var <- tclVar("2.5")
RT.entry <- tkentry(background="white",TMS, textvariable=RT.filter.var,width=8)
## MZ bin
MzBinValue <- tclVar("0.5")
MzBin.entry <- tkentry(background="white",TMS, textvariable=MzBinValue ,width=8)
## find mutiple peak
multimode.var <- tclVar("60")
multimode.entry <- tkentry(background="white",TMS, textvariable=multimode.var ,width=8)
## Upper
frameUpper <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Select library search mode"),rb3,rb4, sticky="w")
tkgrid(tklabel(frameUpper,text="Setup Working Directory"),Wdir.but, sticky="w")
tkgrid(tklabel(frameUpper,text="AutoSelect Reference Ions (NIST)"),Nb,sticky="w")
tkgrid(tklabel(frameUpper,text="Specify the MZ range"),MZrange_L,MZrange_U,sticky="w")
tkgrid(tklabel(frameUpper,text="Detect the MZ range"),MZrange_autodetect.but,sticky="w")
#tkgrid(tklabel(frameUpper,text="Add StandardMix RT (option)"),mergeSM.but,sticky="w ")
tkgrid(tklabel(frameUpper,text="Perform RT correction"), RTcorrection_check, sticky="w")
tkgrid(tklabel(frameUpper,text="Detect Multiple Peaks - RT Range above"), multimode.entry,tklabel(frameUpper,text="s"),sticky="w")
tkgrid(tklabel(frameUpper,text="Retention Time filter by library"), RT.entry,tklabel(frameUpper,text="min "),sticky="w")
tkgrid(tklabel(frameUpper,text="Generate plots for RT shift"),generate_rt_shift_graph_check,sticky="w")
tkgrid(tklabel(frameUpper,text="Create a Summary Report"),ID_report.but,sticky="w")
## Middle
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
## Slider
# SliderValue <- tclVar("2")
# SliderValueLabel <- tklabel(TMS,text=as.character(tclvalue(SliderValue)),widt=2)
# tkconfigure(SliderValueLabel,textvariable=SliderValue)
# slider <- tkscale(frameMid, from=1, to=5,
# showvalue=F, variable=SliderValue,
# resolution=0.5, orient="horizontal")
## choose between peakheight or area
Peak <- c("Peak Area","Peak Height")
comboBox <- tkwidget(TMS,"ComboBox",editable=FALSE,values=Peak,textvariable=tclVar("Peak Area"),width=12)
# tkgrid(tklabel(frameMid,text="Manually correct the Identification Report"),Tips.but,sticky="w")
tkgrid(tklabel(frameMid,text="Fast Mode (No Chromatograms)"),cb,sticky="w")
tkgrid(tklabel(frameMid,text="Peak Height or Peak Area "),comboBox, tklabel(frameMid,text=" "), sticky="w")
# tkgrid(tklabel(frameMid,text="Minimum RT Bin (Default = 2 s) "),slider,SliderValueLabel,tklabel(frameMid,text="s"),sticky="w")
tkgrid(tklabel(frameMid,text="Ion Mass Bin (m/z)"), MzBin.entry,sticky="w")
tkgrid(tklabel(frameMid,text="Automatic GC-Peak Integration "),integrate.but,sticky="w")
## lower
font.size.var <- tclVar("0.5")
font.size.entry <- tkentry(background="white",TMS, textvariable=font.size.var,width=4)
rb1 <- tkradiobutton(TMS)
rb2 <- tkradiobutton(TMS)
rbValue <- tclVar("PeakDiagnosis")
tkconfigure(rb1,variable=rbValue,value="PeakDiagnosis")
tkconfigure(rb2,variable=rbValue,value="IonExtractor_Window")
OnOK <- function(){
rbVal <- as.character(tclvalue(rbValue))
if (rbVal=="PeakDiagnosis"){
font.size<- as.numeric(tclvalue(font.size.var))
ifelse(sum(dir()=="GC-Peak Diagnosis Report.csv")==1,PeakDiagnosis(font.size=font.size),stop("Please select correct working directory"))
}
if (rbVal=="IonExtractor_Window"){
IonExtractor_Window()}
}
OK.but <- tkbutton(TMS, text="Run", command=OnOK, width=6)
frameLower <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
#tkgrid(tklabel(frameLower,text="Check TMS results"))
tkgrid(tklabel(frameLower,text="Overlay All Chromatograms "),rb1,tklabel(frameLower,text="Font Size "),font.size.entry, sticky="w")
tkgrid(tklabel(frameLower,text="IonExtractor "), rb2,OK.but,sticky="w")
quit.but <- tkbutton(TMS, text = "Close Session",
command = function() {
tkdestroy(TMS)
rgl.close()
}
)
## Open working directory
open.but <- tkbutton(TMS,text="Open Folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path1a<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_SIlas_logo.gif", sep=""))
logo_2<-tcl("image", "create", "photo", "MaxC3.image", file=image.path1a)
button <- tkframe(frameOverall,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=" "),quit.but,open.but, tklabel(button ,text=" "),help.but)
## Userinterphase
tkgrid(tklabel(frameOverall,image=logo_2))
tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" STEP 1. Setup"),sticky="w")
tkgrid(frameUpper,pady= 10, padx= 10)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" STEP 2. Peak Integration"),sticky="w")
tkgrid(frameMid,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" STEP 3. Check GC-MS results"),sticky="w")
tkgrid(frameLower,pady= 10, padx= 10)
tkgrid(frameOverall)
#tkgrid(quit.but,help.but, pady= 10, padx= 10)
tkgrid(button,pady= 10, padx= 10)
tkgrid(tklabel(frameOverall,text=" "))
}
######################################### raw.DIC #########################################
raw.DIC <- function (main.folder = choose.dir(caption = "Select Working Directory"),
save = TRUE, output = "DI findal data",ppm = 3)
{
require(xcms)
old.wd <- getwd()
main.folder <- main.folder
setwd(main.folder)
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
top<-100
final.df<-NULL
remove(final.df)
if (length(conditions)==1){
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
file.name <- paste(conditions,name.file, sep = "\\")
spectrum<-xcmsRaw(file.name,includeMSn=F)
scans<-lapply(1:length(spectrum@scantime), function(i) getScan(spectrum,i))
lens<-sapply(scans, nrow)
#scans<-scans[lens < median(lens)*1.2 ]
tops<-lapply(scans, function(x) {
tmp=x[order(x[,2],decreasing=T)[1:top],]
tmp[order(tmp[,1]),]
})
topss<-matrix(unlist(tops), nrow=top)
all.reads<-data.frame(
mz=as.vector(topss[,ii<-seq(1,ncol(topss),by=2)]),
I=as.vector(topss[,ii+1]),
scN=rep(1:(ncol(topss)/2), each=top)
)
all.reads<-all.reads[order(all.reads$mz),]
clusters<-vector()
clusters<-unlist(c((all.reads[1,]), clust=1))
curClust<-1
for (i in 2:nrow(all.reads)){
# i<-2
# print(i)
temp<-all.reads[i,]
if((temp$mz - all.reads[i-1, "mz"]) >=0.07){
curClust<-curClust+1
print(i)
}
clusters<-rbind(clusters, data.frame((temp), clust=curClust))
}
#######show cluster number "k". Show all.reads
############
all.reads[120:150,]
k<-5 ################
clusters[clusters$clus==k,]
#plot(clusters[clusters$clus==k,][,c(1,3)], pch="o", cex=1)
############################################################
singleClust<-as.numeric(names(table(clusters$clust)[table(clusters$clust) < round(0.1*length(spectrum@scanindex),0)]))# removes clusters which have less than 10 per cent of elements
clusters.clean<-clusters[!clusters$clust %in% singleClust,]
length(unique(clusters$clust))
length(unique(clusters.clean$clust))
means<-vector()
for(i in levels(factor(as.character((clusters.clean$clust))))){
print(i)
means<-rbind(means, c(i, colMeans(clusters.clean[clusters.clean$clust == i, c("mz", "I")])))
}
rez<-data.frame(means)[,-1]
names(rez)[2] <- name.file
rez[1]<-round(as.numeric(as.character(rez$mz)),ppm)
if (!exists("final.df")){
final.df <- rez
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- merge(final.df, rez, by.x = "mz",
by.y = "mz", all = TRUE)
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
}else {
stop("place all the cdf files in a folder")
}
final.df <- final.df[order(final.df$mz, decreasing = F),]
if (save == TRUE) {
sheet <- output
store <- paste(main.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
}
return(final.df)
}
######################################### raw.peaks #########################################
raw.peaks <- function (main.folder = choose.dir(caption = "Select Working Directory"),
correct.RT = TRUE, method = "loess", save = TRUE, output = "Total MassFragments Result")
{
require(xcms)
old.wd <- getwd()
main.folder <- main.folder
setwd(main.folder)
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions <- c(files[isDir == TRUE])
if (length(conditions)==1){
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions, full.names = TRUE)),
ignore.case = TRUE, value = TRUE)
replicates <- (c(1:length(samples)))
xset <- xcmsSet(samples)
xset <- group(xset)
if (correct.RT == TRUE) {
xset <- retcor(xset, method = method)
xset <- group(xset)
}
mat.raw <- groupval(xset, "medret", "into")
mat.raw <- mat.raw[!duplicated(row.names(mat.raw)), ]
Names <- as.character(row.names(mat.raw))
mat.raw <- cbind(Names, mat.raw)
}else {
num.rep <- length(grep(".CDF", c(list.files(conditions[1],
full.names = TRUE)), ignore.case = TRUE))
samples <- grep(".CDF", c(list.files(conditions[1], full.names = TRUE)),
ignore.case = TRUE, value = TRUE)
replicates <- (c(1:length(samples)))
for (i in 2:length(conditions)) {
num.rep <- c(num.rep, length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE)))
samples <- c(samples, grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
up.num <- length(grep(".CDF", c(list.files(conditions[i],
full.names = TRUE)), ignore.case = TRUE, value = TRUE))
if (i < 3) {
low.num <- (replicates[length(replicates)] + 1)
}
else {
low.num <- max(replicates[i - 1, !duplicated(replicates[i -
1, ])]) + 1
}
replicates <- rbind(replicates, c((low.num):(low.num +
(up.num - 1))))
}
xset <- xcmsSet(samples)
xset <- group(xset)
if (correct.RT == TRUE) {
xset <- retcor(xset, method = method)
xset <- group(xset)
}
mat.raw <- groupval(xset, "medret", "into")
mat.raw <- mat.raw[!duplicated(row.names(mat.raw)), ]
Names <- as.character(row.names(mat.raw))
mat.raw <- cbind(Names, mat.raw)
for (r in 1:nrow(replicates)) {
rep.row <- replicates[r, ]
rep.row <- rep.row[!duplicated(rep.row)]
rep.name <- rep(conditions[r], length(rep.row))
if (r == 1) {
rep.name.final <- c(rep.name)
rep.name.final <- c("Replicates", rep.name.final)
}
else {
rep.name.final <- c(rep.name.final, rep.name)
}
}
mat.raw <- rbind(rep.name.final, mat.raw)
}
row.names(mat.raw) <- 1:nrow(mat.raw)
mat.raw <- data.frame(mat.raw)
if (save == TRUE) {
store <- paste(main.folder, "\\", output,".csv", sep = "")
write.csv(mat.raw, file = store, row.names = FALSE)
cat(paste("The file", output, "was saved in the folder",
main.folder, "\n"))
}
setwd(old.wd)
return(mat.raw)
print("Data processing is completed and saved as Meta Result!")
}
######################################### run #########################################
run <- function(){
library("pacman")
p_load(tcltk2)
tt <- tktoplevel(width=600, height=200)
tktitle(tt) <- "MassOmics"
topMenu <- tk2menu(tt)
tkconfigure(tt, menu = topMenu)
fileMenu <- tk2menu(topMenu, tearoff = FALSE)
software <- tk2menu(topMenu, tearoff = FALSE) # Our cascaded menu
installation <- tk2menu(topMenu, tearoff = FALSE)
Update<-tkmenu(topMenu, tearoff = FALSE)
Statistics <- tk2menu(topMenu, tearoff = FALSE)
PAPi<- tkmenu(topMenu, tearoff = FALSE)
Graph<-tkmenu(topMenu, tearoff = FALSE)
tkadd(fileMenu, "cascade", label = "Installation", menu = installation)
tkadd(fileMenu, "command", label = "Quit", command = function() tkdestroy(tt))
tkadd(topMenu, "cascade", label = "File", menu = fileMenu)
tkadd(topMenu, "cascade", label = "Softwares", menu = software)
tkadd(topMenu, "cascade", label = "Statistics",menu=Statistics)
tkadd(topMenu, "cascade", label = "Graph",menu=Graph)
tkadd(topMenu, "cascade", label = "Update", menu=Update)
#tkadd(software, "command", label = "Run IonExtractor (Batch)",command = function() IonE())
#tkadd(software, "command", label = "Run Metab",command = function() clean.fix())
# tkadd(software, "command", label = "Run Metab 3",command = function() clean.fix3())
# tkadd(software, "command", label = "Run GC-MS data anlaysis (Liz version)",command = function() Liz_analysis())
tkadd(software, "command", label = "GC-MS data processing",command = function() GCMS_analysis())
# tkadd(software, "command", label = "Run TMS data anlaysis",command = function() TMS_analysis())
#tkadd(software, "command", label = "IonExtractor Single Mode",command = function() IonExtractor_Window())
# tkadd(software, "command", label = "IonExtractor Single Mode_5ppm",command = function() IonExtractor_Window_5ppm())
# tkadd(software, "command", label = "Run Total MassFragment Analysis",command = function() raw.peaks())
# tkadd(software, "command", label = "Run DI-TOF data analysis",command = function() raw.DIC())
# tkadd(software, "command", label = "Remove false positive results",command = function() runFalsePostive())
# tkadd(software, "command", label = "ChemStation data analysis I (OLD)",command = function() Chemstation_DataMining())
# tkadd(software, "command", label = "ChemStation data analysis II (Advance)",command = function() Liz_analysis_chemstation())
tkadd(software, "command", label = "LC-MS data processing",command = function() LcOrbitalTRap())
tkadd(software, "command", label = "Create a sub-NIST library",command = function() create_sub_library())
tkadd(software, "command", label = "Data clean-up and normalization",command = function() DataCorrection())
tkadd(software, "command", label = "Accurate mass extraction",command = function() RunGangliosides())
tkadd(installation, "command", label = "Required R packages",command = function() install.MassOmics_dependency())
tkadd(installation, "command", label = "Required R packages for MzMatch",command = function() install.mzmatch())
tkadd(Update, "cascade", label = "Version 2.5 (11June2017)")
## Statistics
tkadd(Statistics, "command", label = "Run ANOVA & T-test",command = function() Omics_htest())
tkadd(Statistics, "command", label = "Run PCA analysis",command = function() Omics_PCA())
#tkadd(Statistics, "command", label = "Run sparse PCA analysis",command = function() Omics_sPCA())
tkadd(Statistics, "command", label = "Run PLSDA analysis",command = function() Omics_PLSDA())
# Graph
tkadd(Graph, "command", label = "Diagnose GC-Peak Auto Integration",command = function() PeakDiagnosis())
##PAPi
tkadd(software, "cascade", label = "Pathway analysis", menu = PAPi)
tkadd(PAPi ,"command", label = "Format massomics data for pathway analysis", command = function() PAPi_formatting())
tkadd(PAPi ,"command", label = "Building KEGG database in your PC", command = function() PAPi::buildDatabase())
tkadd(PAPi ,"command", label = "Replace name by KeggCodes", command = function() convert_PAPi())
tkadd(PAPi ,"command", label = "Run pathway analysis", command = function() run_PAPi())
#image
status_text<-"Maintainer: George GUO (George.GUO@auckland.ac.nz)"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/LabLogo.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "MaxC1.image", file=image.path)
tkgrid(tklabel(tt,image=FrontImage))
tkgrid(tklabel(tt,text=status_text))
tkfocus(tt)
##Update 29April2016
}
######################################### runFalsePostive #########################################
runFalsePostive <- function(){
FalsePostive<-function (true = 0.2, conditions=1:3, save = TRUE,
output = "Remove false positive results")
{
report_folder <- choose.dir(caption = "Select the Working directory")
setwd(report_folder)
final.df<- read.csv(choose.files(default = "Select the CSV file containing the input data",
multi = FALSE, caption = "Select the CSV file containing the input data"))
final.stat <- final.df
abc <- 1:100
selected.samples <- numeric()
for (q in 1:length(conditions)) {
cat(paste("Select samples from condition:", q, "\n"))
samples <- tk_select.list(names(final.stat), multiple = TRUE,
title = paste("Samples in condition", q))
columns <- names(final.stat)
equal <- 0
equal_list <- 0
for (t in 1:length(samples)) {
equal <- which(columns == samples[t])
if (length(equal) != 0) {
equal_list <- rbind(equal_list, equal)
}
}
equal_list <- (equal_list[-1])
names(final.stat)[equal_list] <- abc[q]
if (length(selected.samples) == 0) {
selected.samples <- equal_list
}
else {
selected.samples <- c(selected.samples, equal_list)
}
}
group <- names(final.stat)[selected.samples]
data.df<- final.stat[,selected.samples]
names(data.df)<-names(final.df[,selected.samples])
data.df1<-rbind(group, data.df)
group<-as.numeric(group)
reps <- factor(group)
cat(paste("Data clean up is in progress..", "\n"))
for (z in 1:length(levels(reps))) {
column <- which(as.character(levels(reps)[z]) ==
as.character( data.df1[1, ]))
yMat <- data.df1[column]
pb <- txtProgressBar(min = 0, max = nrow(yMat), style = 3, width = 50)
for (i in 1:nrow(yMat)) {
setTxtProgressBar(pb, i)
if (sum(!is.na(yMat[i, ]))/ncol(yMat) < true) {
yMat[i, ] <- NA
}
}
cat(paste(" Finish Condition",z, "\n"))
data.df1[column] <- yMat
}
yMat <- data.df1[-1,]
missing <- t(apply(yMat, 1, function(y) tapply(as.numeric(y),
reps, function(x) sum(!is.na(x)))))
missing2 <- apply(t(apply(missing, 1, function(x) x == 0)),
1, sum)
missing2 <- as.data.frame(missing2)
data.df2<-cbind(final.df[,-selected.samples],yMat)
data.df2 <- merge( data.df2, missing2, by = 0)
data.df2 <- subset( data.df2, data.df2$missing2 < ((length(levels(reps)))-(length(levels(reps)))*true)) ## Change the
data.df2$missing2 <- NULL
data.df2$Row.names <- NULL
if (save == TRUE) {
sheet <- output
store <- paste(report_folder, "\\", sheet,"(",true*100,"%",")", ".csv", sep = "")
write.csv( data.df2, file = store, row.names = FALSE)
cat(paste("Process Complete!",sheet ,"was saved in the folder",
report_folder, "\n"))
}
return( data.df2)
}
####### user interphase
require(tcltk)
ConditionN <- tclVar("")
true <- tclVar("50")
gg <- tktoplevel()
tkwm.title(gg,"Remove false postive data")
c.entry <- tkentry(background="white",gg, textvariable=ConditionN)
p.entry <- tkentry(background="white",gg, textvariable=true)
reset <- function() {
tclvalue(ConditionN)<-""
tclvalue(true)<-""
}
reset.but <- tkbutton(gg, text="Reset", command=reset)
submit <- function() {
c <- as.numeric(tclvalue(ConditionN))
conditions<-1:c
True<-as.numeric(tclvalue(true))
FalsePostive(conditions=conditions,true=True/100)
}
submit.but <- tkbutton(gg, text="submit", command=submit)
quit.but <- tkbutton(gg, text = "Close Session",
command = function() {
tkdestroy(gg)
}
)
tkgrid(tklabel(gg,text="Enter Parameters"),columnspan=3, pady = 10)
tkgrid(tklabel(gg,text="Number of conditions"), c.entry, pady= 10, padx= 10)
tkgrid(tklabel(gg,text="Exclude compounds less than"), p.entry, tklabel(gg,text="%"), pady= 10, padx= 10)
tkgrid(submit.but, reset.but, quit.but, pady= 10, padx= 10)
}
######################################### RunGangliosides #########################################
RunGangliosides <- function (){
runG <-function( output = "Peak area for gangliosides",
Ion.bin= 0.01,
WorkingFolder= tk_choose.dir(caption = "Select working directory"),
heightMAX= 300000,
info = read.csv(tk_choose.files(caption = "Accurate mass list for gangliosides.csv", multi = FALSE), stringsAsFactors=FALSE),
RTrange=c(8,14)
) {
require(xcms)
require(lattice)
require(plyr)
require(flux)
library(tcltk)
print(paste("Mass Resolution:", Ion.bin))
print(paste("heightMAX:", heightMAX))
print(paste("RT range:", RTrange))
setwd(WorkingFolder)
final.check.data <- info
library_file <-final.check.data
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre1 <- c(files[isDir == TRUE])
conditions_pre<-c(conditions_pre1 ,"Manually choose folder")
conditions <- tk_select.list(conditions_pre, multiple = FALSE,
title = paste("Folder contains all cdf files"))
if (conditions=="Manually choose folder"){ conditions<-tk_choose.dir(default = "", caption = "A folder contains all cdf files")}
###
final.df<-NULL
remove(final.df)
Graphic.df<-data.frame()
filenames <- dir(conditions)
filenames <- filenames[grep(c(".cdf$|.mzXML$"),filenames, ignore.case=TRUE)]
colors<-rainbow(length(library_file[,1]))
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=paste(library_file[,"Gangliosides"],"_", library_file[,"Ion"],sep=""),Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions,
filenames[q], sep = "\\"))
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$Ion
R.Time<-metabolite$RT.min.*60
RT.lowerset<- R.Time-metabolite$bin.min.*60
RT.upperset<- R.Time+metabolite$bin.min.*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(RT.lowerset, RT.upperset))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
Peakvalue<-auc(abundance$rt, abundance$intensity*10)
surefinal$Base.Peak[h] <- Peakvalue
## Plotting
Intensity <- abundance$intensity
Retention.Time<-abundance$rt/60
if(h==1){
dev.new.OS()
ifelse(heightMAX<max(Intensity),heightMAX<-max(Intensity),heightMAX)
plot(Intensity~Retention.Time,col= colors[h],bty="n", type="l",main=filenames[q],
xlab="Retention Time (min)",ylab="Intensity", ylim=c(0,heightMAX),xlim=RTrange)
}else{
lines(Intensity~Retention.Time,col= colors[h])
}
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
finalF.df <- cbind( Ref.Ion=library_file$Ion, Ret.Time=library_file$RT.min., RT.bin=library_file$bin.min., final.df)
finalF.df <- finalF.df[order(finalF.df$Ret.Time , decreasing = FALSE),]
## Save Files
print("Save file")
store <- paste(WorkingFolder, "\\", output, ".csv", sep = "")
FileName <- tclvalue(tkgetSaveFile(initialfile=store))
write.csv(finalF.df, file = FileName, row.names = FALSE)
print(paste("Ganglioside data extraction is completed and saved as: ",output,".csv", sep=""))
}
################################################################################################################
## Building an interfance
library(tcltk)
require(rgl)
tclRequire("BWidget")
#Setup
TMS <- tktoplevel()
frameOverall <- tkframe(TMS)
tkwm.title(TMS,"Accurate mass extraction for gangliosides")
# Parameters
## MZ bin
MzBinValue <- tclVar("0.01")
MzBin.entry <- tkentry(background="white",TMS, textvariable=MzBinValue ,width=8)
heightValue <- tclVar("300000")
height.entry <- tkentry(background="white",TMS, textvariable=heightValue ,width=8)
RangeM.var <- tclVar("8,14")
RangeM.entry <- tkentry(background="white",TMS, textvariable=RangeM.var, width=8)
# All the functions
GCMS_integration1<-function(){
Mzbin<-as.numeric(tclvalue(MzBinValue))
Range <- as.numeric(strsplit(tclvalue(RangeM.var),",")[[1]])
height <- as.numeric(tclvalue(heightValue))
runG(RTrange=Range, heightMAX=height ,Ion.bin=Mzbin)
}
## the button function
integrate.but<-tkbutton(TMS, text="Run", command= GCMS_integration1,width=6)
quit.but <- tkbutton(TMS, text = "Close Session",
command = function() {
tkdestroy(TMS)
rgl.close()
}
)
## Interphase
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
# tkgrid(tklabel(frameMid,text="Minimum RT Bin (Default = 2 s) "),slider,SliderValueLabel,tklabel(frameMid,text="s"),sticky="w")
tkgrid(tklabel(frameMid,text="Mass resolution"), MzBin.entry,tklabel(frameMid,text="(m/z)"), sticky="w")
tkgrid(tklabel(frameMid,text="RT range (min,max)"), RangeM.entry, tklabel(frameMid,text="min"),sticky="w")
tkgrid(tklabel(frameMid,text="Min Intensitiy for y-axis"), height.entry, sticky="w")
tkgrid(tklabel(frameMid,text="Automatic accurate mass extraction "),integrate.but,sticky="w")
## Open working directory
open.but <- tkbutton(TMS,text="Open Folder",
command= function(){
dir = getwd()
shell.exec(dir)
}
)
## load images
image.path1b<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_ganglioside_logogif.gif", sep=""))
logo_3<-tcl("image", "create", "photo", "MaxC3X.image", file=image.path1b)
button <- tkframe(frameOverall,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=" "),quit.but,open.but)
## Userinterphase
tkgrid(tklabel(frameOverall,image=logo_3))
tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text=" Accurate mass extraction"),sticky="w")
tkgrid(frameMid,pady= 10, padx= 30)
tkgrid(frameOverall)
#tkgrid(quit.but,help.but, pady= 10, padx= 10)
tkgrid(button,pady= 10, padx= 10)
tkgrid(tklabel(frameOverall,text=" "))
}
######################################### TMS_analysis #########################################
TMS_analysis <- function(main.TMS.folder=tk_choose.dir(caption = "Select working directory")){
require(xcms)
require(lattice)
require(plyr)
require(MassOmics)
WorkingDir <-function(main.TMS.folder = tk_choose.dir(caption = "Select working directory")){
main.TMS.folder <<- main.TMS.folder
setwd(main.TMS.folder)
}
Id_report<-function( main.TMS.folder=main.TMS.folder,
MS.L= tk_choose.files(caption="Select MS library (e.g.AgilentGCMS_TMS_Library) in .msl"),
amdis.report = read.csv(tk_choose.files(default = "Select the AMDIS report in .TXT",
multi = FALSE, caption = "Select the AMDIS report in .TXT"),
sep = "\t", stringsAsFactors=FALSE),
File.name = "Identification Report (TMS).csv"){
main.TMS.folder <- main.TMS.folder
setwd(main.TMS.folder)
getIonLib<-function(lib.fn = MS.L){
lib.txt<-readLines(lib.fn)
lib.txt<-lib.txt[-grep("^[ ]*$",lib.txt)]
att.nms<-unique(sapply(strsplit(lib.txt[grep(":",lib.txt)],":"),function(x) x[1]))
entry.nms<-sapply(strsplit(lib.txt[grep("NAME:",lib.txt)],":"), function(x) x[2])
rez<-matrix(NA, nrow=length(entry.nms), ncol=length(att.nms), dimnames=list(NULL,att.nms ))
starts=grep("NAME:", lib.txt)
stops=c(starts[1:(length(starts)-1)]+diff(starts)-1,length(lib.txt))
for (i in 1:length(starts)){
#i=1
tmp<-lib.txt[starts[i]:stops[i]]
sapply(strsplit(tmp[grep(":",tmp)],":"), function(x) rez[i,x[1]]<<-x[2])
}
rez<-data.frame(rez,stringsAsFactors=FALSE)
rez=within(rez, {
RT=as.numeric(RT)
RSN=as.numeric(RSN)
NUM.PEAKS=as.numeric(NUM.PEAKS)
})
return(rez)
}
libr<-getIonLib()## returns list of peaks
############ Step 2. Generate the list of metabolites and clean up
AmRep<-amdis.report
AmRep<-AmRep[!AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)+2 & AmRep$RT.RT.lib.>median(AmRep$RT.RT.lib.)-2,]## check this one ##
AmRep$Name <- gsub("?", "", AmRep$Name, fixed = TRUE)
AmRep$Name <- gsub("^ ", "", AmRep$Name, perl=T)
## RT statistics from the AReport
RT.stats<-t(sapply(split(AmRep$RT, AmRep$Name),function(x) c(RT.median=round(median(x,na.rm=T),3),
RT.mean=round(mean(x,na.rm=TRUE),3),
RT.sd=sd(x,na.rm=T),
Number.of.ID=length(x[!is.na(x)]))))
AmRep.RT.stats <- as.data.frame(cbind(Name=0,RT.stats,Quality=0, Exp.RT=0,Diff.RT=0,Diff.RT.by.Ribitol=0,ref_ion=0))##data frame for list of metabolites
AmRep.RT.stats$ref_ion <- libr$RSN[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Exp.RT <- libr$RT[match(rownames(RT.stats),libr$NAME)]
AmRep.RT.stats$Diff.RT <- AmRep.RT.stats$Exp.RT- AmRep.RT.stats$RT.median
AmRep.RT.stats$Diff.RT.by.Ribitol <-AmRep.RT.stats$Diff.RT- AmRep.RT.stats["16.1473 min RIBITOL","Diff.RT"]
AmRep.RT.stats$Quality <- "Good/Bad"
AmRep.RT.stats$Name <- rownames(RT.stats)
AmRep.RT.stats <- AmRep.RT.stats[order(AmRep.RT.stats$RT.median, decreasing = F),]
write.csv(AmRep.RT.stats, file = File.name,row.names = FALSE)
print(paste("Identificaiton report was created and save in",main.TMS.folder, sep=" "))
}
### MERAGE TWO FILE TOGETHER
merage_RT_standard<-function(main.TMS.folder=main.TMS.folder,
File.name = "Identification Report (TMS) with StandardMix_RT.csv"
){
main.TMS.folder <- main.TMS.folder
setwd(main.TMS.folder)
data.df=read.csv(tk_choose.files(caption = "Load Samples Identification Report.csv", multi = FALSE),stringsAsFactors=FALSE)
standard.df=read.csv(tk_choose.files(caption = "Load Standard Mixture Identification Report.csv", multi = FALSE),stringsAsFactors=FALSE)
RT_standard_names <- tk_select.list(names(standard.df), multiple = FALSE, title = paste("Select Rentention time"))
SelectStandard<-standard.df[,RT_standard_names]
standard.df.final<-data.frame(cbind(Name=standard.df$Name,StandardMixture_RT=SelectStandard))
Merge.data <- merge(data.df, standard.df.final, by.x = "Name",
by.y = "Name", all = TRUE)
Merge.data <- Merge.data [order(Merge.data $RT.median, decreasing = F),]
write.csv(Merge.data, file = File.name,row.names = FALSE)
print(paste("Identificaiton report with StandardMix RT was created and save in",main.TMS.folder))
}
Tips<-function () {
require(tcltk)
kk <- tktoplevel()
tktitle(kk) <- "GC-Chromatogram of TMS standard mixtures"
image.path<-c(paste(file.path(path.package(package="MassOmics")),"/R/TMSstandardMix.gif", sep=""))
FrontImage<-tcl("image", "create", "photo", "MaxC2.image", file=image.path)
tkgrid(tklabel(kk,image=FrontImage))
}
RunTMS<-function(save = TRUE,
output = "Final TMS Results",
Ion.bin= 0.5,
main.TMS.folder=main.TMS.folder,
RT.bin = 5) {
require(xcms)
require(lattice)
require(plyr)
setwd(main.TMS.folder)
final.check.data <- read.csv(tk_choose.files(caption = "Load a corrected Identification Report (TMS).csv", multi = FALSE),stringsAsFactors=FALSE)
ion.lib<-final.check.data
files <- c(dir())
info <- file.info(files)
isDir <- info$isdir
conditions_pre <- c(files[isDir == TRUE])
conditions <- tk_select.list(conditions_pre, multiple = FALSE,
title = paste("Folder contains all cdf files"))
###
library_file <- ion.lib[,c("Name","ref_ion","RT.median")]
final.df<-NULL
remove(final.df)
Graphic.df<-data.frame()
filenames <- dir(conditions)
filenames <- filenames[grep(".cdf$",filenames, ignore.case=TRUE)]
for (q in 1:length(filenames)) {
file1 <- filenames[q]
name.file <- file1
name.file <- gsub(".CDF", "", name.file, ignore.case=TRUE)
surefinal <- data.frame(Name=ion.lib[,"Name"],Base.Peak=0)
raw_data <- xcmsRaw(filename = paste(conditions,
filenames[q], sep = "\\"))
dev.new.OS()
plotChrom(raw_data) # plot TIC
for (h in 1:nrow(library_file)) {
metabolite <- library_file[h, ]
R.Ion<-metabolite$ref_ion
R.Time<-metabolite$RT.median*60
IonExtract <-getEIC(raw_data,mzrange=cbind(R.Ion-Ion.bin,R.Ion+Ion.bin),rtrange=cbind(R.Time-RT.bin,R.Time+RT.bin))
abundance<-data.frame(IonExtract@eic$xcmsRaw[1])
maxabundance <- max(abundance$intensity)
surefinal$Base.Peak[h] <- maxabundance
Graphic.df<-rbind.fill(Graphic.df,data.frame(Metabolite.Names=paste(metabolite$Name," (m/z:",R.Ion,")",sep="") ,Retention.Time=(abundance$rt)/60,
Intensity=abundance$intensity ,Sample.Names=name.file ))
}
surefinal <- surefinal[!duplicated(surefinal$Name),]
names(surefinal)[2] <- name.file
if (!exists("final.df")){
final.df <- surefinal
confirmation <- paste("File", q, "(", name.file,")", "done!", sep = " ")
print(confirmation)
} else {
final.df <- cbind(final.df,surefinal[-1])
confirmation <- paste("File", q, "(", name.file,
")", "done!", sep = " ")
print(confirmation)
}
}
final.df<-cbind( Num.ID=ion.lib$Number.of.ID, Ref.Ion=ion.lib$ref_ion, Ret.Time=ion.lib$RT.median, Quality =ion.lib$Quality ,final.df)
final.df <- final.df[order(final.df$Ret.Time , decreasing = F),]
row.names(final.df) <- 1:nrow(final.df)
if (save == TRUE) {
sheet <- paste(output," (RT bin=", RT.bin,"s)", sep="")
store <- paste(main.TMS.folder, "\\", sheet, ".csv", sep = "")
write.csv(final.df, file = store, row.names = FALSE)
write.csv(Graphic.df,file="GC-Peak Diagnosis Report.csv")
print(paste("Data process is completed and saved as: ",sheet,".csv", sep=""))
}
}
## Help page
TMS.hlep.page<-function(){
TMS.help <- tktoplevel()
tkwm.title(TMS.help,"Help")
scr <- tkscrollbar(TMS.help, repeatinterval=5,
command=function(...)tkyview(txt,...))
txt <- tktext(TMS.help,bg="white",font="courier",yscrollcommand=function(...)tkset(scr,...))
tkgrid(txt,scr)
tkgrid.configure(scr,sticky="ns")
tkinsert(txt,"end","Step 1\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Prepare a working folder containing:\n")
tkinsert(txt,"end"," AMDIS Batch report in .txt formate\n")
tkinsert(txt,"end"," A folder contains all GC-MS raw data in .cdf formate\n")
tkinsert(txt,"end"," A TMS MS library in .msl formate\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," -Suggested AMIDS setting\n")
tkinsert(txt,"end"," Minimum match factor : 80\n")
tkinsert(txt,"end"," Deconv. : one, High, Very High, Low\n")
tkinsert(txt,"end"," Batch Job : include only first 1 hit\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 2\n")
tkinsert(txt,"end"," \n")
tkinsert(txt,"end","Step 3\n")
tkconfigure(txt, state="disabled")
tkfocus(txt)
}
## Building an interfance for TMS anlaysis
library(tcltk)
require(rgl)
TMS <- tktoplevel()
frameOverall <- tkframe(TMS)
tkwm.title(TMS,"TMS analysis")
workingdirctory<-function(){WorkingDir()}
Id_report1<-function(){Id_report(main.TMS.folder=main.TMS.folder)}
Tips1<-function(){Tips()}
RunTMS1<-function(){
RT.bin_s <-as.numeric(tclvalue(SliderValue))
RunTMS(RT.bin = RT.bin_s,main.TMS.folder=main.TMS.folder)
}
merage_RT_standard1<-function(){merage_RT_standard(main.TMS.folder=main.TMS.folder)}
TMS.hlep.page1<-function(){TMS.hlep.page()}
### all the button function
Wdir.but <- tkbutton(TMS, text="Choose", command=workingdirctory,width=6)
ID_report.but <- tkbutton(TMS, text="Run", command=Id_report1,width=6)
Tips.but<-tkbutton(TMS, text="Tips", command=Tips1,width=6)
integrate.but<-tkbutton(TMS, text="Run", command= RunTMS1,width=6)
mergeSM.but<-tkbutton(TMS, text="Run", command= merage_RT_standard1,width=6)
help.but<-tkbutton(TMS, text="Help", command= TMS.hlep.page1,width=6)
## Upper
frameUpper <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
tkgrid(tklabel(frameUpper,text="Setup Working Directory"),Wdir.but, sticky="w")
tkgrid(tklabel(frameUpper,text="Create an Identification Report "),ID_report.but,sticky="w")
tkgrid(tklabel(frameUpper,text="Add StandardMix RT (option)"),mergeSM.but,sticky="w")
## Middle
frameMid <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
## Slider
SliderValue <- tclVar("5")
SliderValueLabel <- tklabel(TMS,text=as.character(tclvalue(SliderValue)),width=2)
tkconfigure(SliderValueLabel,textvariable=SliderValue)
slider <- tkscale(frameMid, from=1, to=10,
showvalue=F, variable=SliderValue,
resolution=1, orient="horizontal")
tkgrid(tklabel(frameMid,text="Manually correct the Identification Report"),Tips.but,sticky="w")
tkgrid(tklabel(frameMid,text="Automatic GC-peak integration"),integrate.but,sticky="w")
tkgrid(tklabel(frameMid,text="Rentention time bin (Default = 5 s) "),slider,SliderValueLabel,tklabel(frameMid,text="s"),sticky="w")
## lower
rb1 <- tkradiobutton(TMS)
rb2 <- tkradiobutton(TMS)
rbValue <- tclVar("PeakDiagnosis")
tkconfigure(rb1,variable=rbValue,value="PeakDiagnosis")
tkconfigure(rb2,variable=rbValue,value="IonExtractor_Window")
OnOK <- function(){
rbVal <- as.character(tclvalue(rbValue))
if (rbVal=="PeakDiagnosis")
ifelse(sum(dir()=="GC-Peak Diagnosis Report.csv")==1,PeakDiagnosis(),stop("Please select correct working directory"))
if (rbVal=="IonExtractor_Window")
IonExtractor_Window()
}
OK.but <- tkbutton(TMS,text="RUN",command=OnOK,width=6)
frameLower <- tkframe(frameOverall,relief="groove",borderwidth=2,padx=5,pady=5)
#tkgrid(tklabel(frameLower,text="Check TMS results"))
tkgrid(tklabel(frameLower,text="Diagnose GC-Peak Auto Integration "),rb1, OK.but,sticky="w")
tkgrid(tklabel(frameLower,text="IonExtractor Single Mode"),rb2,sticky="w")
quit.but <- tkbutton(TMS, text = "Close Session",
command = function() {
tkdestroy(TMS)
rgl.close()
}
)
image.path1<-c(paste(file.path(path.package(package="MassOmics")),"/R/GCPM_SIlas_logo.gif", sep=""))
logo<-tcl("image", "create", "photo", "MaxC3.image", file=image.path1)
button <- tkframe(frameOverall,relief="groove", borderwidth=0,padx=5,pady=5)
tkgrid(tklabel(button ,text=" "),quit.but,tklabel(button ,text=" "),help.but)
## Userinterphase
tkgrid(tklabel(frameOverall,image=logo))
tkgrid(tklabel(frameOverall,text="Step 1: Setup"))
tkgrid(frameUpper,pady= 10, padx= 10)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text="Step 2: Integration"))
tkgrid(frameMid,pady= 10, padx= 30)
#tkgrid(tklabel(frameOverall,text=" "))
tkgrid(tklabel(frameOverall,text="Step 3: Check TMS results"))
tkgrid(frameLower,pady= 10, padx= 10)
tkgrid(frameOverall)
#tkgrid(quit.but,help.but, pady= 10, padx= 10)
tkgrid(button,pady= 10, padx= 10)
}
###############################MassOmics.GUI##################
#' MassOmics.GUI
#'
#' This function initiate a graphical user interface for MassOmics. To initiate the GG-MS data processing software, open the R console and type the codes in the example to initate the user interface.
#' @return None
#'
#' @examples
#' MassOmics.GUI()
#'
#' @export
MassOmics.GUI<-function(){
library(tcltk2)
run()
}
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