R/batchMS2Score.optimized.R
In zhengfj1994/MetEx: Targeted extraction from untargeted metabolomics dataset by using metabolomics database
Defines functions
batchMS2Score.optimized
Documented in
batchMS2Score.optimized
#' Title batchMS2Score.optimized
#'
#' @param ms1Info the result of extracResFliter
#' @param MS1MS2DeltaMZ the difference of m/z between ms1Info and mgf files
#' @param MS1MS2DeltaTR the difference of m/z between mgf and database of MS2
#' @param mgfFile mgf file path
#' @param MS2DeltaMZ
#' @param NeedCleanSpectra
#' @param MS2NoiseRemoval
#' @param onlyKeepMax
#' @param minScore
#' @param KeepNotMatched
#' @param cores The number of CPU cores when computing.
#'
#' @return resultList
#' @export batchMS2Score.optimized
#' @importFrom snow makeSOCKcluster stopCluster
#' @importFrom progress progress_bar
#' @importFrom doSNOW registerDoSNOW
#' @importFrom utils read.table
#' @import foreach
#'
#'
batchMS2Score.optimized <- function(ms1Info,
MS1MS2DeltaMZ,
MS1MS2DeltaTR,
mgfFilePath,
MS2DeltaMZ,
NeedCleanSpectra = T,
MS2NoiseRemoval = 0.01,
onlyKeepMax = T,
minScore = 0.5,
KeepNotMatched = T,
cores = 4) {
require(openxlsx)
require(dplyr)
require(tcltk)
require(doSNOW)
require(progress)
require(stringr)
## Define mgfReader function for reading mgf files
mgfReader <- function(mgfFile){
mgfFileContent <- read.csv2(file = mgfFile, header = F)
beginIndex <- which(mgfFileContent == "BEGIN IONS")
endIndex <- which(mgfFileContent == "END IONS")
RTIndex <- which(grepl("RTINSECONDS=",mgfFileContent$V1))
pepmassIndex <- which(grepl("PEPMASS=",mgfFileContent$V1))
mgfMatrix <- as.data.frame(matrix(data = NA, nrow = length(beginIndex), ncol = 4))
colnames(mgfMatrix) <- c("FileName", "RT", "PEPMASS", "MSMS")
mgfMatrix$FileName <- unlist(strsplit(rev(unlist(strsplit(mgfFile, split = "/")))[1], split = ".mgf"))[1]
for (i in c(1:length(beginIndex))){
mgfMatrix$RT[i] <- strsplit(mgfFileContent$V1[RTIndex[i]], split = "=")[[1]][2]
mgfMatrix$PEPMASS[i] <- strsplit(strsplit(mgfFileContent$V1[pepmassIndex[i]], split = "=")[[1]][2], split = " ")[[1]][1]
ithMSMS <- mgfFileContent$V1[(beginIndex[i]):(endIndex[i])]
ithMSMS <- ithMSMS[!grepl("[a-zA-Z]", ithMSMS)]
if (length(ithMSMS) == 0){
mgfMatrix$MSMS[i] <- NA
} else {
if (length(unlist(strsplit(ithMSMS[1], split = " "))) == 2){
ithMSMS <- strsplit(ithMSMS, split = " ")
ithMSMS <- data.frame(matrix(unlist(ithMSMS), byrow = T, ncol = 2), stringsAsFactors = F)
colnames(ithMSMS) <- c("mz", "intensity")
ithMSMS$mz <- as.numeric(ithMSMS$mz)
ithMSMS$intensity <- as.numeric(ithMSMS$intensity)
ithMSMS <- ithMSMS[which(ithMSMS$intensity/max(ithMSMS$intensity) > 0.005),]
mgfMatrix$MSMS[i] <- paste(paste(ithMSMS$mz,ithMSMS$intensity, sep = " "), collapse = ";")
} else if (length(unlist(strsplit(ithMSMS[1], split = " "))) == 3){
ithMSMS <- strsplit(ithMSMS, split = " ")
ithMSMS <- data.frame(matrix(unlist(ithMSMS), byrow = T, ncol = 3), stringsAsFactors = F)
colnames(ithMSMS) <- c("mz", "intensity", "charge")
ithMSMS$mz <- as.numeric(ithMSMS$mz)
ithMSMS$intensity <- as.numeric(ithMSMS$intensity)
ithMSMS <- ithMSMS[which(ithMSMS$intensity/max(ithMSMS$intensity) > 0.005),]
mgfMatrix$MSMS[i] <- paste(paste(ithMSMS$mz,ithMSMS$intensity, sep = " "), collapse = ";")
} else {
packageStartupMessage("The format of mgf is wrong!")
}
}
# if (beginIndex[i] + 4 == endIndex[i]){
# mgfMatrix$MSMS[i] <- NA
# }
# else {
# ithMSMS <- mgfFileContent$V1[(beginIndex[i] + 4):(endIndex[i] - 1)]
# ithMSMS <- strsplit(ithMSMS, split = " ")
# ithMSMS <- data.frame(matrix(unlist(ithMSMS), byrow = T, ncol = 2), stringsAsFactors = F)
# colnames(ithMSMS) <- c("mz", "intensity")
# ithMSMS$intensity <- as.numeric(ithMSMS$intensity)
# ithMSMS <- ithMSMS[which(ithMSMS$intensity/max(ithMSMS$intensity) > 0.005),]
# mgfMatrix$MSMS[i] <- paste(paste(ithMSMS$mz,ithMSMS$intensity, sep = " "), collapse = ";")
# }
}
mgfMatrix <- mgfMatrix[which(!is.na(mgfMatrix$MSMS) & mgfMatrix$MSMS != ""),]
return(mgfMatrix)
}
## Define MS1MS2Match function
MS1MS2Match <- function(ithRow){
mz <- as.numeric(MS1RawData$mz[ithRow])
rt <- as.numeric(MS1RawData$trOfPeak[ithRow])
matchedMgfRowID <- which(abs(as.numeric(mgfMatrix$PEPMASS) - mz) < MS1MS2DeltaMZ &
abs(as.numeric(mgfMatrix$RT) - rt) < MS1MS2DeltaTR)
return(paste(matchedMgfRowID, collapse = " "))
}
## Define MS2ScoreFunction
MS2ScoreFunction <- function(ithRow, onlyKeepMax = T,
minScore = 0.5, KeepNotMatched = T){
## Get the matched MS2 ID and matched database ID
MatchedMS2ID <- MS1RawData$MatchedMS2ID[ithRow]
MatchedMS2ID <- as.numeric(unlist(strsplit(MatchedMS2ID, split = " ")))
MS2inDB <- MS1RawData$MSMS[ithRow]
## If there are no matched MS2 or matched database, output none
if (length(MatchedMS2ID) == 0 | is.na(MS2inDB)){
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = 1, ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
## Create the final result
MS2ScoreFunctionResult <- cbind(MS1RawData[0,],MS2ScoreResult[0,])
## If you want to keep the result which have no matched result
if (KeepNotMatched){
MS2ScoreFunctionResult[1,c(1:ncol(MS1RawData))] <- MS1RawData[ithRow,]
}
} else{ ## If there are matched MS2 and matched database
## Get the MS2 in mgf and MS2 in database
MS2inMgf <- mgfMatrix$MSMS[MatchedMS2ID]
## Create a matrix to save the MS2 score result
MS2ScoreMatrix <- matrix(data = NA, nrow = length(MS2inMgf), ncol = 1)
## MS2 score
for (ithMS2inMgf in c(1:length(MS2inMgf))){
spec_query <- as.data.frame(do.call("rbind", strsplit(unlist(strsplit(MS2inMgf[ithMS2inMgf], split = ";")), split = " ")))
colnames(spec_query) <- c("mz", "intensity")
spec_query <- as.data.frame(lapply(spec_query,as.numeric))
spec_reference <- as.data.frame(do.call("rbind", strsplit(unlist(strsplit(MS2inDB, split = ";")), split = " ")))
colnames(spec_reference) <- c("mz", "intensity")
spec_reference <- as.data.frame(lapply(spec_reference,as.numeric))
spec_reference <- na.omit(spec_reference)
# spec_matched = tools.match_peaks_in_spectra(spec_a=spec_query,
# spec_b=spec_reference,
# ms2_ppm=NA, ms2_da=0.01)
# print(spec_matched)
MS2score <- ms2ScoreSpectralEntropy(spec_query = spec_query,
spec_reference = spec_reference,
ms2_da = MS2DeltaMZ,
need_clean_spectra = NeedCleanSpectra,
noise_removal = MS2NoiseRemoval)
MS2ScoreMatrix[ithMS2inMgf,1] <- MS2score
}
if (onlyKeepMax){ ## If you want to only keep the result with the max score
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = 1, ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
## Find the row and column ID of the max score
MaxRowAndColID <- which(MS2ScoreMatrix == max(MS2ScoreMatrix),arr.ind=TRUE)
MaxRowID <- MaxRowAndColID[1]
MaxColID <- MaxRowAndColID[2]
## Write the result to the MS2 score result
MS2ScoreResult$MS2_file_name[1] <- mgfMatrix$FileName[MatchedMS2ID[MaxRowID]]
MS2ScoreResult$MSMS_in_file[1] <- mgfMatrix$MSMS[MatchedMS2ID[MaxRowID]]
MS2ScoreResult$MS2_similarity[1] <- MS2ScoreMatrix[MaxRowAndColID]
## Create the final result
MS2ScoreFunctionResult <- cbind(MS1RawData[ithRow,],MS2ScoreResult)
} else { ## If you want to keep the result bigger than a min score
## Reshape the MS2 score matrix
reshapedMS2ScoreMatrix <- data.frame(idrow = rep(c(1:nrow(MS2ScoreMatrix)),
ncol(MS2ScoreMatrix)),
idcol = rep(c(1:ncol(MS2ScoreMatrix)),
each = nrow(MS2ScoreMatrix)),
value = as.vector(as.matrix(MS2ScoreMatrix)))
## Order the reshaped MS2 score matrix by score
reshapedMS2ScoreMatrix <- reshapedMS2ScoreMatrix[order(reshapedMS2ScoreMatrix$value, decreasing = T),]
## Only keep the result bigger than min score
reshapedMS2ScoreMatrix <- reshapedMS2ScoreMatrix[which(reshapedMS2ScoreMatrix$value >= minScore),]
if (nrow(reshapedMS2ScoreMatrix) == 0){ ## If there are no result bigger than min score
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = 1, ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
MS2ScoreFunctionResult <- cbind(MS1RawData[0,],MS2ScoreResult[0,])
## If you want to keep the result which have no matched result
if (KeepNotMatched){
MS2ScoreFunctionResult[1,c(1:ncol(MS1RawData))] <- MS1RawData[ithRow,]
}
} else {
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = nrow(reshapedMS2ScoreMatrix), ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
MatchedRowID <- reshapedMS2ScoreMatrix[,1]
MatchedColID <- reshapedMS2ScoreMatrix[,2]
## Write the result to the data.frame
MS2ScoreResult$MS2_file_name <- mgfMatrix$FileName[MatchedMS2ID[MatchedRowID]]
MS2ScoreResult$MSMS_in_file <- mgfMatrix$MSMS[MatchedMS2ID[MatchedRowID]]
MS2ScoreResult$MS2_similarity <- reshapedMS2ScoreMatrix$value
## Create the final result
MS2ScoreFunctionResult <- cbind(MS1RawData[rep(ithRow,nrow(reshapedMS2ScoreMatrix)),],MS2ScoreResult)
}
}
}
return(MS2ScoreFunctionResult)
}
## Read the peak table and check the row names.
MS1RawData <- ms1Info
if (length(which(colnames(MS1RawData)=='tr')) >= 1){
message('Row names of MS1 file is Available.')
} else if (length(which(colnames(MS1RawData)=='tr')) == 0 & length(which(colnames(MS1RawData)=='rt')) >= 1){
colnames(MS1RawData)[which(colnames(MS1RawData)=='rt')] = 'tr'
message('Change rt to tr in row names of MS1.')
} else {
message('Row names of MS1 file is wrong!')
}
if (length(which(colnames(MS1RawData)=='mz')) >= 1){
message('Row names of MS1 file is Available.')
} else if (length(which(colnames(MS1RawData)=='mz')) == 0 & length(which(colnames(MS1RawData)=='m.z')) >= 1){
colnames(MS1RawData)[which(colnames(MS1RawData)=='m.z')] = 'mz'
message('Change m.z to mz in row names of MS1.')
} else {
message('Row names of MS1 file is wrong!')
}
## Get the file path of all mgf files
if (stringr::str_sub(mgfFilePath, start= -4) == ".mgf"){
mgfFiles <- mgfFilePath
} else {
mgfFiles <- list.files(mgfFilePath, pattern = "mgf", recursive = T, full.names = T)
}
## Read mgf files and save them in mgfMatrix
cl <- makeSOCKcluster(cores)
registerDoSNOW(cl)
# progress bar ------------------------------------------------------------
iterations <- length(mgfFiles)
pb <- progress_bar$new(
format = ":letter [:bar] :elapsed | Remaining time: :eta <br>",
total = iterations,
width = 120)
# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
pb$tick(tokens = list(letter = "Progress of reading mgf files."))
}
opts <- list(progress = progress)
## Read mgf files
mgfMatrix <- foreach(i=mgfFiles, .options.snow=opts, .combine='rbind') %dopar% mgfReader(i)
# progress bar ------------------------------------------------------------
iterations <- nrow(MS1RawData)
pb <- progress_bar$new(
format = ":letter [:bar] :elapsed | Remaining time: :eta <br>",
total = iterations,
width = 120)
# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
pb$tick(tokens = list(letter = "Progress of matching MS2 to MS1 peak table."))
}
opts <- list(progress = progress)
## Match MS1 and MS2
MS1MS2MatchResult <- foreach(i=1:nrow(MS1RawData), .options.snow=opts, .combine='rbind') %dopar% MS1MS2Match(i)
MS1MS2MatchResult <- as.data.frame(MS1MS2MatchResult)
colnames(MS1MS2MatchResult) <- "MatchedMS2ID"
MS1RawData <- cbind(MS1RawData, MS1MS2MatchResult)
# progress bar ------------------------------------------------------------
iterations <- nrow(MS1RawData)
pb <- progress_bar$new(
format = ":letter [:bar] :elapsed | Remaining time: :eta <br>",
total = iterations,
width = 120)
# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
pb$tick(tokens = list(letter = "Progress of ms2 similarity calculation."))
}
opts <- list(progress = progress)
## MS2 score
batchMS2ScoreResult <- foreach(i=1:nrow(MS1RawData), .options.snow=opts, .combine='rbind') %dopar% MS2ScoreFunction(i, onlyKeepMax = onlyKeepMax, minScore = minScore, KeepNotMatched = KeepNotMatched)
stopCluster(cl)
resultList <- list(mgfMatrix = mgfMatrix,
batchMS2ScoreResult = batchMS2ScoreResult)
return(resultList)
}
zhengfj1994/MetEx documentation built on Nov. 19, 2023, 1:34 p.m.
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Defines functions batchMS2Score.optimized
Documented in batchMS2Score.optimized
#' Title batchMS2Score.optimized
#'
#' @param ms1Info the result of extracResFliter
#' @param MS1MS2DeltaMZ the difference of m/z between ms1Info and mgf files
#' @param MS1MS2DeltaTR the difference of m/z between mgf and database of MS2
#' @param mgfFile mgf file path
#' @param MS2DeltaMZ
#' @param NeedCleanSpectra
#' @param MS2NoiseRemoval
#' @param onlyKeepMax
#' @param minScore
#' @param KeepNotMatched
#' @param cores The number of CPU cores when computing.
#'
#' @return resultList
#' @export batchMS2Score.optimized
#' @importFrom snow makeSOCKcluster stopCluster
#' @importFrom progress progress_bar
#' @importFrom doSNOW registerDoSNOW
#' @importFrom utils read.table
#' @import foreach
#'
#'
batchMS2Score.optimized <- function(ms1Info,
MS1MS2DeltaMZ,
MS1MS2DeltaTR,
mgfFilePath,
MS2DeltaMZ,
NeedCleanSpectra = T,
MS2NoiseRemoval = 0.01,
onlyKeepMax = T,
minScore = 0.5,
KeepNotMatched = T,
cores = 4) {
require(openxlsx)
require(dplyr)
require(tcltk)
require(doSNOW)
require(progress)
require(stringr)
## Define mgfReader function for reading mgf files
mgfReader <- function(mgfFile){
mgfFileContent <- read.csv2(file = mgfFile, header = F)
beginIndex <- which(mgfFileContent == "BEGIN IONS")
endIndex <- which(mgfFileContent == "END IONS")
RTIndex <- which(grepl("RTINSECONDS=",mgfFileContent$V1))
pepmassIndex <- which(grepl("PEPMASS=",mgfFileContent$V1))
mgfMatrix <- as.data.frame(matrix(data = NA, nrow = length(beginIndex), ncol = 4))
colnames(mgfMatrix) <- c("FileName", "RT", "PEPMASS", "MSMS")
mgfMatrix$FileName <- unlist(strsplit(rev(unlist(strsplit(mgfFile, split = "/")))[1], split = ".mgf"))[1]
for (i in c(1:length(beginIndex))){
mgfMatrix$RT[i] <- strsplit(mgfFileContent$V1[RTIndex[i]], split = "=")[[1]][2]
mgfMatrix$PEPMASS[i] <- strsplit(strsplit(mgfFileContent$V1[pepmassIndex[i]], split = "=")[[1]][2], split = " ")[[1]][1]
ithMSMS <- mgfFileContent$V1[(beginIndex[i]):(endIndex[i])]
ithMSMS <- ithMSMS[!grepl("[a-zA-Z]", ithMSMS)]
if (length(ithMSMS) == 0){
mgfMatrix$MSMS[i] <- NA
} else {
if (length(unlist(strsplit(ithMSMS[1], split = " "))) == 2){
ithMSMS <- strsplit(ithMSMS, split = " ")
ithMSMS <- data.frame(matrix(unlist(ithMSMS), byrow = T, ncol = 2), stringsAsFactors = F)
colnames(ithMSMS) <- c("mz", "intensity")
ithMSMS$mz <- as.numeric(ithMSMS$mz)
ithMSMS$intensity <- as.numeric(ithMSMS$intensity)
ithMSMS <- ithMSMS[which(ithMSMS$intensity/max(ithMSMS$intensity) > 0.005),]
mgfMatrix$MSMS[i] <- paste(paste(ithMSMS$mz,ithMSMS$intensity, sep = " "), collapse = ";")
} else if (length(unlist(strsplit(ithMSMS[1], split = " "))) == 3){
ithMSMS <- strsplit(ithMSMS, split = " ")
ithMSMS <- data.frame(matrix(unlist(ithMSMS), byrow = T, ncol = 3), stringsAsFactors = F)
colnames(ithMSMS) <- c("mz", "intensity", "charge")
ithMSMS$mz <- as.numeric(ithMSMS$mz)
ithMSMS$intensity <- as.numeric(ithMSMS$intensity)
ithMSMS <- ithMSMS[which(ithMSMS$intensity/max(ithMSMS$intensity) > 0.005),]
mgfMatrix$MSMS[i] <- paste(paste(ithMSMS$mz,ithMSMS$intensity, sep = " "), collapse = ";")
} else {
packageStartupMessage("The format of mgf is wrong!")
}
}
# if (beginIndex[i] + 4 == endIndex[i]){
# mgfMatrix$MSMS[i] <- NA
# }
# else {
# ithMSMS <- mgfFileContent$V1[(beginIndex[i] + 4):(endIndex[i] - 1)]
# ithMSMS <- strsplit(ithMSMS, split = " ")
# ithMSMS <- data.frame(matrix(unlist(ithMSMS), byrow = T, ncol = 2), stringsAsFactors = F)
# colnames(ithMSMS) <- c("mz", "intensity")
# ithMSMS$intensity <- as.numeric(ithMSMS$intensity)
# ithMSMS <- ithMSMS[which(ithMSMS$intensity/max(ithMSMS$intensity) > 0.005),]
# mgfMatrix$MSMS[i] <- paste(paste(ithMSMS$mz,ithMSMS$intensity, sep = " "), collapse = ";")
# }
}
mgfMatrix <- mgfMatrix[which(!is.na(mgfMatrix$MSMS) & mgfMatrix$MSMS != ""),]
return(mgfMatrix)
}
## Define MS1MS2Match function
MS1MS2Match <- function(ithRow){
mz <- as.numeric(MS1RawData$mz[ithRow])
rt <- as.numeric(MS1RawData$trOfPeak[ithRow])
matchedMgfRowID <- which(abs(as.numeric(mgfMatrix$PEPMASS) - mz) < MS1MS2DeltaMZ &
abs(as.numeric(mgfMatrix$RT) - rt) < MS1MS2DeltaTR)
return(paste(matchedMgfRowID, collapse = " "))
}
## Define MS2ScoreFunction
MS2ScoreFunction <- function(ithRow, onlyKeepMax = T,
minScore = 0.5, KeepNotMatched = T){
## Get the matched MS2 ID and matched database ID
MatchedMS2ID <- MS1RawData$MatchedMS2ID[ithRow]
MatchedMS2ID <- as.numeric(unlist(strsplit(MatchedMS2ID, split = " ")))
MS2inDB <- MS1RawData$MSMS[ithRow]
## If there are no matched MS2 or matched database, output none
if (length(MatchedMS2ID) == 0 | is.na(MS2inDB)){
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = 1, ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
## Create the final result
MS2ScoreFunctionResult <- cbind(MS1RawData[0,],MS2ScoreResult[0,])
## If you want to keep the result which have no matched result
if (KeepNotMatched){
MS2ScoreFunctionResult[1,c(1:ncol(MS1RawData))] <- MS1RawData[ithRow,]
}
} else{ ## If there are matched MS2 and matched database
## Get the MS2 in mgf and MS2 in database
MS2inMgf <- mgfMatrix$MSMS[MatchedMS2ID]
## Create a matrix to save the MS2 score result
MS2ScoreMatrix <- matrix(data = NA, nrow = length(MS2inMgf), ncol = 1)
## MS2 score
for (ithMS2inMgf in c(1:length(MS2inMgf))){
spec_query <- as.data.frame(do.call("rbind", strsplit(unlist(strsplit(MS2inMgf[ithMS2inMgf], split = ";")), split = " ")))
colnames(spec_query) <- c("mz", "intensity")
spec_query <- as.data.frame(lapply(spec_query,as.numeric))
spec_reference <- as.data.frame(do.call("rbind", strsplit(unlist(strsplit(MS2inDB, split = ";")), split = " ")))
colnames(spec_reference) <- c("mz", "intensity")
spec_reference <- as.data.frame(lapply(spec_reference,as.numeric))
spec_reference <- na.omit(spec_reference)
# spec_matched = tools.match_peaks_in_spectra(spec_a=spec_query,
# spec_b=spec_reference,
# ms2_ppm=NA, ms2_da=0.01)
# print(spec_matched)
MS2score <- ms2ScoreSpectralEntropy(spec_query = spec_query,
spec_reference = spec_reference,
ms2_da = MS2DeltaMZ,
need_clean_spectra = NeedCleanSpectra,
noise_removal = MS2NoiseRemoval)
MS2ScoreMatrix[ithMS2inMgf,1] <- MS2score
}
if (onlyKeepMax){ ## If you want to only keep the result with the max score
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = 1, ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
## Find the row and column ID of the max score
MaxRowAndColID <- which(MS2ScoreMatrix == max(MS2ScoreMatrix),arr.ind=TRUE)
MaxRowID <- MaxRowAndColID[1]
MaxColID <- MaxRowAndColID[2]
## Write the result to the MS2 score result
MS2ScoreResult$MS2_file_name[1] <- mgfMatrix$FileName[MatchedMS2ID[MaxRowID]]
MS2ScoreResult$MSMS_in_file[1] <- mgfMatrix$MSMS[MatchedMS2ID[MaxRowID]]
MS2ScoreResult$MS2_similarity[1] <- MS2ScoreMatrix[MaxRowAndColID]
## Create the final result
MS2ScoreFunctionResult <- cbind(MS1RawData[ithRow,],MS2ScoreResult)
} else { ## If you want to keep the result bigger than a min score
## Reshape the MS2 score matrix
reshapedMS2ScoreMatrix <- data.frame(idrow = rep(c(1:nrow(MS2ScoreMatrix)),
ncol(MS2ScoreMatrix)),
idcol = rep(c(1:ncol(MS2ScoreMatrix)),
each = nrow(MS2ScoreMatrix)),
value = as.vector(as.matrix(MS2ScoreMatrix)))
## Order the reshaped MS2 score matrix by score
reshapedMS2ScoreMatrix <- reshapedMS2ScoreMatrix[order(reshapedMS2ScoreMatrix$value, decreasing = T),]
## Only keep the result bigger than min score
reshapedMS2ScoreMatrix <- reshapedMS2ScoreMatrix[which(reshapedMS2ScoreMatrix$value >= minScore),]
if (nrow(reshapedMS2ScoreMatrix) == 0){ ## If there are no result bigger than min score
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = 1, ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
MS2ScoreFunctionResult <- cbind(MS1RawData[0,],MS2ScoreResult[0,])
## If you want to keep the result which have no matched result
if (KeepNotMatched){
MS2ScoreFunctionResult[1,c(1:ncol(MS1RawData))] <- MS1RawData[ithRow,]
}
} else {
## Create a data.frame to save the MS2 score result
MS2ScoreResult <- as.data.frame(matrix(data = NA, nrow = nrow(reshapedMS2ScoreMatrix), ncol = 3))
colnames(MS2ScoreResult) <- c("MS2_file_name","MSMS_in_file","MS2_similarity")
MatchedRowID <- reshapedMS2ScoreMatrix[,1]
MatchedColID <- reshapedMS2ScoreMatrix[,2]
## Write the result to the data.frame
MS2ScoreResult$MS2_file_name <- mgfMatrix$FileName[MatchedMS2ID[MatchedRowID]]
MS2ScoreResult$MSMS_in_file <- mgfMatrix$MSMS[MatchedMS2ID[MatchedRowID]]
MS2ScoreResult$MS2_similarity <- reshapedMS2ScoreMatrix$value
## Create the final result
MS2ScoreFunctionResult <- cbind(MS1RawData[rep(ithRow,nrow(reshapedMS2ScoreMatrix)),],MS2ScoreResult)
}
}
}
return(MS2ScoreFunctionResult)
}
## Read the peak table and check the row names.
MS1RawData <- ms1Info
if (length(which(colnames(MS1RawData)=='tr')) >= 1){
message('Row names of MS1 file is Available.')
} else if (length(which(colnames(MS1RawData)=='tr')) == 0 & length(which(colnames(MS1RawData)=='rt')) >= 1){
colnames(MS1RawData)[which(colnames(MS1RawData)=='rt')] = 'tr'
message('Change rt to tr in row names of MS1.')
} else {
message('Row names of MS1 file is wrong!')
}
if (length(which(colnames(MS1RawData)=='mz')) >= 1){
message('Row names of MS1 file is Available.')
} else if (length(which(colnames(MS1RawData)=='mz')) == 0 & length(which(colnames(MS1RawData)=='m.z')) >= 1){
colnames(MS1RawData)[which(colnames(MS1RawData)=='m.z')] = 'mz'
message('Change m.z to mz in row names of MS1.')
} else {
message('Row names of MS1 file is wrong!')
}
## Get the file path of all mgf files
if (stringr::str_sub(mgfFilePath, start= -4) == ".mgf"){
mgfFiles <- mgfFilePath
} else {
mgfFiles <- list.files(mgfFilePath, pattern = "mgf", recursive = T, full.names = T)
}
## Read mgf files and save them in mgfMatrix
cl <- makeSOCKcluster(cores)
registerDoSNOW(cl)
# progress bar ------------------------------------------------------------
iterations <- length(mgfFiles)
pb <- progress_bar$new(
format = ":letter [:bar] :elapsed | Remaining time: :eta <br>",
total = iterations,
width = 120)
# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
pb$tick(tokens = list(letter = "Progress of reading mgf files."))
}
opts <- list(progress = progress)
## Read mgf files
mgfMatrix <- foreach(i=mgfFiles, .options.snow=opts, .combine='rbind') %dopar% mgfReader(i)
# progress bar ------------------------------------------------------------
iterations <- nrow(MS1RawData)
pb <- progress_bar$new(
format = ":letter [:bar] :elapsed | Remaining time: :eta <br>",
total = iterations,
width = 120)
# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
pb$tick(tokens = list(letter = "Progress of matching MS2 to MS1 peak table."))
}
opts <- list(progress = progress)
## Match MS1 and MS2
MS1MS2MatchResult <- foreach(i=1:nrow(MS1RawData), .options.snow=opts, .combine='rbind') %dopar% MS1MS2Match(i)
MS1MS2MatchResult <- as.data.frame(MS1MS2MatchResult)
colnames(MS1MS2MatchResult) <- "MatchedMS2ID"
MS1RawData <- cbind(MS1RawData, MS1MS2MatchResult)
# progress bar ------------------------------------------------------------
iterations <- nrow(MS1RawData)
pb <- progress_bar$new(
format = ":letter [:bar] :elapsed | Remaining time: :eta <br>",
total = iterations,
width = 120)
# allowing progress bar to be used in foreach -----------------------------
progress <- function(n){
pb$tick(tokens = list(letter = "Progress of ms2 similarity calculation."))
}
opts <- list(progress = progress)
## MS2 score
batchMS2ScoreResult <- foreach(i=1:nrow(MS1RawData), .options.snow=opts, .combine='rbind') %dopar% MS2ScoreFunction(i, onlyKeepMax = onlyKeepMax, minScore = minScore, KeepNotMatched = KeepNotMatched)
stopCluster(cl)
resultList <- list(mgfMatrix = mgfMatrix,
batchMS2ScoreResult = batchMS2ScoreResult)
return(resultList)
}
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