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Modular.R
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Modular.R
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##Jeremy Koelmel [email protected]
##Michael Kummer [email protected]
##Paul Stelben [email protected]
##Nick Kroeger [email protected]
##David Schiessel [email protected]
rm(list = ls()) #remove all R objects from memory, this program can be memory intensive as it is dealing with huge datasets
closeAllConnections()
#### Mandatory Parameter to Change ####
#If you want to manually input your variables...
# 1. Set ManuallyInputVariables <- TRUE (all caps)
# 2. Assign variables under the next if statement, "if (ManuallyInputVariables==TRUE)"
FLOW <- TRUE
csvInput <- TRUE
ManuallyInputVariables <- FALSE
RT_flagging <- TRUE #JPK: for PFAS analysis
ParallelComputing <- TRUE
Lipid <- FALSE
TWeen_pos <- FALSE #PJS: for PolyMatch
FilterAbovePrecursor <- 1 #how far from the precursor should fragment masses be kept (e.g. if precursor is 700, should 702 be considered?)
TargetEIC_Only <- TRUE
alignMS2_ppm <- FALSE #selection accuracy will be divided by 4, eg. 1 Da Window = +/- 0.25 difference, if FALSE, Otherwise the mass accuracy in ppm will be used to align MSMS scans to the feature table
EICcpp <- FALSE #Whether to use the C++ version of the EIC / MS1 generation code, seems to give the same results, faster but less well tested
IMfirst<-FALSE #For running Modular after Running FluoroMatch IM using PNNL processed DIA to DDA workflow or similar
#### END Mandatory Parameter to Change ####
#### END "Read Me" Section ####
#Force the directory for packages to be the one that is distributed not the local R directory
if(length(.libPaths())>1){
R_DistrDir<-.libPaths()[2]
.libPaths(R_DistrDir)
}
#Or just force .libPaths()
# .libPaths("C:/NEW_SOFTWARE/2023_24_UPDATES_FM_LM/FluoroMatch-4.4_GitHub/Flow/R-4.2.1/library/")
if (FLOW == TRUE) {
csvInput <- TRUE
ManuallyInputVariables <- FALSE
}
#Checks for updates, installs packagaes: "installr" "stringr" "sqldf" "gWidgets" "gWidgetstcltk" and "compiler"
# if(!require(stringr)) {
# install.packages("stringr"); require(installr)}
# library(installr)
if (!require("stringr", quietly = TRUE))install.packages("stringr", repos = "http://cran.us.r-project.org")
library("stringr")
if (EICcpp==TRUE) {
#These are distributed by us (thanks Jonathan)
#cannot installed online
if("Rcpp" %in% (.packages())){
detach("package:mzR", unload=TRUE)
detach("package:Rcpp", unload=TRUE)
}
library("EICim")
library("MS1im")
}
if (!require("BiocManager", quietly = TRUE))install.packages("BiocManager", repos = "http://cran.us.r-project.org")
if (ParallelComputing == TRUE) {
if("foreach" %in% rownames(installed.packages()) == FALSE) {install.packages("foreach", repos = "http://cran.us.r-project.org")}
library(foreach)
if("doParallel" %in% rownames(installed.packages()) == FALSE) {install.packages("doParallel", repos = "http://cran.us.r-project.org")}
library(doParallel)
if("parallelly" %in% rownames(installed.packages()) == FALSE) {install.packages("parallelly", repos = "http://cran.us.r-project.org")}
library("parallelly")
# tic("Main")
###NOTE not sure if any of this is necessary but was causing error
DC <- as.numeric(availableCores(constraints = "connections"))
print(paste0(freeConnections()," connections are free and will be used of ",availableConnections()," R hard limited connections; the following numbers of cores exist on your system: ",as.numeric(detectCores())))
if (is.na(DC)) {
DC <- makePSOCKcluster(4)
registerDoParallel(DC)
} else if (DC <= 4) {
DC <- makePSOCKcluster(DC)
registerDoParallel(DC)
} else {
DC <- makePSOCKcluster(DC-2)
registerDoParallel(DC)
}
# Force the directory for parallel computing to be the one that is distributed not the local R directory (doPar error)
invisible(clusterEvalQ(DC, .libPaths()[2]))
}
if("tictoc" %in% rownames(installed.packages()) == FALSE) {install.packages("tictoc", repos = "http://cran.us.r-project.org")}
library(tictoc)
tic.clear()
# tic("Main")
if("remotes" %in% rownames(installed.packages()) == FALSE) {install.packages("remotes", repos = "http://cran.us.r-project.org")}
library("remotes")
if("MassTools" %in% rownames(installed.packages()) == FALSE) {remotes::install_github("mjhelf/MassTools")}
library("MassTools")
#library(MetaboCoreUtils)
if("enviPat" %in% rownames(installed.packages()) == FALSE) {install.packages("enviPat", repos = "http://cran.us.r-project.org")}
library(enviPat, quietly=T)
if("RMassBank" %in% rownames(installed.packages()) == FALSE) {BiocManager::install("RMassBank")}
library("RMassBank")
if("MetaboCoreUtils" %in% rownames(installed.packages()) == FALSE) {BiocManager::install("MetaboCoreUtils")}
library("MetaboCoreUtils")
if("sqldf" %in% rownames(installed.packages()) == FALSE) {install.packages("sqldf", repos = "http://cran.us.r-project.org")}
library("sqldf")
if("Rdisop" %in% rownames(installed.packages()) == FALSE) {install.packages("Rdisop", repos = "http://cran.us.r-project.org")}
library("Rdisop")
if("RSQLite" %in% rownames(installed.packages()) == FALSE) {install.packages("RSQLite", repos = "http://cran.us.r-project.org")}
library("RSQLite")
if("gWidgets" %in% rownames(installed.packages()) == FALSE) {install.packages("gWidgets", repos = "http://cran.us.r-project.org")}
if("gWidgetstcltk" %in% rownames(installed.packages()) == FALSE) {install.packages("gWidgetstcltk", repos = "http://cran.us.r-project.org")}
if("agricolae" %in% rownames(installed.packages()) == FALSE) {install.packages("agricolae", repos = "http://cran.us.r-project.org")}
library("agricolae")
# if (FLOW == FALSE && csvInput == FALSE && ManuallyInputVariables == FALSE) {
require(gWidgets)
require(gWidgetstcltk)
options(guiToolkit="tcltk")
# }
#library(Rdisop)
#BiocManager::install("Rdisop")
library(RSQLite)
library(sqldf)
library(agricolae)
library(Rdisop)
options(warn=-1)#suppress warning on
errorBox <- function(message) {
window <- gwindow("Confirm")
group <- ggroup(container = window)
## A group for the message and buttons
inner.group <- ggroup(horizontal=FALSE, container = group)
glabel(message, container=inner.group, expand=TRUE, icon="error")
## A group to organize the buttons
button.group <- ggroup(container = inner.group)
## Push buttons to right
addSpring(button.group)
gbutton("ok", handler=function(h,...) dispose(window), container=button.group)
return()
}
if("data.table" %in% rownames(installed.packages()) == FALSE) {install.packages("data.table", repos = "http://cran.us.r-project.org")}
library(data.table)
if("SearchTrees" %in% rownames(installed.packages()) == FALSE) {install.packages("SearchTrees", repos = "http://cran.us.r-project.org")}
library(SearchTrees)
if("comprehenr" %in% rownames(installed.packages()) == FALSE) {install.packages("comprehenr", repos = "http://cran.us.r-project.org")}
library(comprehenr)
if("mzR" %in% rownames(installed.packages()) == FALSE) {
BiocManager::install("mzR")}
library(mzR)
if (ManuallyInputVariables==TRUE){
#Retention Time plus or minus
RT_Window <- .3 #window of .2 => +/- .1
#parts-per-million window for matching the m/z of fragments obtained in the library to those in experimentally obtained
ppm_Window <- 10 #window of 10 => +/- 5 ppm error
#Tolerance for mass-to-charge matching at ms1 level (Window)
PrecursorMassAccuracy<-0.01
#Plus minus range for the mass given after targeting parent ions portrayed in excalibur to match exact mass of Lipid in library
SelectionAccuracy<-1
#Threshold for determining that the average signal intensity for a given MS/MS ion should be used for confirmation
intensityCutOff<-1000
#Feature Table information
CommentColumn <- 1
MZColumn <- 2
RTColumn <- 3
RowStartForFeatureTableData <- 2 #Look at your Feature Table (.csv)...What row do you first see numbers?
#ddMS data? set this parameter. If not? Leave it.
#The minimum number of scans required for the result to be a confirmation
ScanCutOff<-1
#Have AIF data? set these parameters. If not? Leave them.
corrMin <-.6
minNumberOfAIFScans <- 5
# Input Directory for Feature Table and MS2 files ...must have forward slashes but nothing at the end of the directory
InputDirectory<-"C:/Users/Jeremy Koelmel/Downloads/LipidMatch_Flow_3.5/LipidMatch_Flow_3.5/LipidMatch_Modular/ExampleData"
# Input Directory for Libraries ...must have forward slashes but nothing at the end of the directory
InputLibrary<-"C:/Users/Jeremy Koelmel/Downloads/LipidMatch_Flow_3.5/LipidMatch_Flow_3.5/LipidMatch_Modular/LipidMatch_Libraries_Acetate"
## PFAS Specific parameters
# RT_flagging <- TRUE #JPK: for PFAS analysis
# Mass defect filtering
upper <- 0.12 #JPK: Upper limit for PFAS mass defect
lower <- -0.11 #JPK: Lower limit for PFAS mass defect
#the isotopes that you can add for MS1 isotope labeling and isotope ratio calculations, if you don't differentiate the mass number it will use all from "secondary_isotopes.csv"
ISOstring <- "13C3;15N;33S;34S;Cl3;18O;Br3;29Si;30Si"
#Check your parameters and see that they are all correct.
#Then press ctrl+shift+s to execute all code.
####################### END MANUALLY INPUTTING VARIABLES SECTION #######################
}else if(csvInput == TRUE){
####################### Get input from csv VARIABLES SECTION ###############################
#parametersDirectory
parametersDir <- "C:/SOFTWARE/FluoroMatch_5.3/Flow/LipidMatch_Distribution/"
parametersFile <- paste(parametersDir, "PARAMETERS.csv", sep="")
parametersInput_csv <- read.csv(parametersFile, sep=",", na.strings="NA", dec=".", strip.white=TRUE,header=FALSE)
parametersInput_csv <- as.matrix(parametersInput_csv)
ErrorOutput<-0
#Retention Time plus or minus
RT_Window <- as.numeric(parametersInput_csv[2,2]) #window of .2 => +/- .1
#parts-per-million window for matching the m/z of fragments obtained in the library to those in experimentally obtained
ppm_Window <- as.numeric(parametersInput_csv[4,2]) #window of 10 => +/- 5 ppm error
#Tolerance for mass-to-charge matching at ms1 level (Window)
PrecursorMassAccuracy <- as.numeric(parametersInput_csv[3,2])
#Plus minus range for the mass given after targeting parent ions portrayed in excalibur to match exact mass of Lipid in library
SelectionAccuracy <- as.numeric(parametersInput_csv[5,2])
#Threshold for determining that the average signal intensity for a given MS/MS ion should be used for confirmation
intensityCutOff <- as.numeric(parametersInput_csv[7,2])
#Feature Table information
CommentColumn <- as.numeric(parametersInput_csv[10,2])
MZColumn <- as.numeric(parametersInput_csv[11,2])
RTColumn <- as.numeric(parametersInput_csv[12,2])
RowStartForFeatureTableData <- as.numeric(parametersInput_csv[13,2]) #Look at your Feature Table (.csv)...What row do you first see numbers?
#ddMS data? set this parameter. If not? Leave it.
#The minimum number of scans required for the result to be a confirmation
ScanCutOff<-as.numeric(parametersInput_csv[6,2])
#Have AIF data? set these parameters. If not? Leave them.
corrMin <-as.numeric(parametersInput_csv[9,2])
minNumberOfAIFScans <- as.numeric(parametersInput_csv[8,2])
# Input Directory for Feature Table and MS2 files ...must have \\ (double backslash) at the end of the directory
InputDirectory<-as.character(parametersInput_csv[14,2])
InputDirectory = substring(InputDirectory,1, nchar(InputDirectory)-1)
# Input Directory for Libraries ...must have \\ (double backslash) at the end of the directory
InputLibrary<-as.character(parametersInput_csv[15,2])
InputLibrary <- substring(InputLibrary,1, nchar(InputLibrary)-1)
GroupCSVDirectory<-as.character(parametersInput_csv[20,2])
ISOstring <- as.character(parametersInput_csv[23,2])
## PFAS Specific parameters
RT_flagging <- TRUE #JPK: for PFAS analysis
# Mass defect filtering
upper <- 0.12 #JPK: Upper limit for PFAS mass defect
lower <- -0.11 #JPK: Lower limit for PFAS mass defect
}else{
####################### Pop-up boxes input VARIABLES SECTION ###############################
# check if R version is equal to, or between, version 2.0.3 and 3.3.3, otherwise present pop-up box warning
# Comment out for now
# if(!((as.numeric(paste(version$major,version$minor,sep=""))>=20.3) && (as.numeric(paste(version$major,version$minor,sep=""))<=33.3))) {
# errorBox(message=paste("ERROR: R version must be equal to, or between, 2.0.3 and 3.3.3. Please download 3.3.3. You are using version: ", paste(version$major,version$minor,sep=".")))
# stop(paste("R version must be equal to, or between, 2.0.3 and 3.3.3. Please download 3.3.3. You are using version: ", paste(version$major,version$minor,sep=".")))
# }
ISOstring <- "13C3;15N;33S;34S;Cl3;18O;Br3;29Si;30Si"
## Input Directory that holds each folder of .ms2 files & feature table (contains features, peak heights/areas, etc.) (.csv file)
InputDirectory<-tk_choose.dir(caption="Input Directory of MS2 + Feature Tables")
if(is.na(InputDirectory)){
stop()
}
foldersToRun <- list.dirs(path=InputDirectory, full.names=FALSE, recursive=FALSE)
ErrorOutput<-0
for (i in seq_len(length(foldersToRun))){
if(foldersToRun[i] == "Output"){
ErrorOutput<-1
errorBox(message=paste("ERROR: Remove your 'Output' folder\nfrom the current Input Directory:\n", InputDirectory))
stop("Warning: Remove your 'Output' folder from the current Input Directory: ", InputDirectory)
}
}
## Input Directory for Libraries
InputLibrary<-tk_choose.dir(caption="Input Directory of libraries (came with LipidMatch)")
if(is.na(InputLibrary)){
stop()
}
GroupCSVDirectory <- tk_choose.files(caption="If there are no groupings select CANCEL, Otherwise: \nInput file containing groupings \none column file with characters unique to each grouping\neach row as a seperate group",multi=FALSE)
GetInputAndErrorHandle <- function(typeOfVariable, message, title){
isValidInput <- FALSE
inputVariable <- ginput(message=message, title=title,icon="question")
while(!isValidInput){
##Retention Time plus or minus
if(inputVariable == "d" || inputVariable == "D"){
if(typeOfVariable=="RT"){ inputVariable <- 0.3
}else if(typeOfVariable=="ppm"){ inputVariable <- 10
}else if(typeOfVariable=="precMassAccuracy"){ inputVariable <- 0.01
}else if(typeOfVariable=="selectionAccuracy"){ inputVariable <- 1
}else if(typeOfVariable=="maxInt"){ inputVariable <- 1000
}else if(typeOfVariable=="scanCutOff"){ inputVariable <- 1
}else if(typeOfVariable=="FeatRT"){inputVariable<-0.1
}else if(typeOfVariable=="FeatMZ"){inputVariable<-0.006
}else if(typeOfVariable=="corrMin"){inputVariable<-0.6
}else if(typeOfVariable=="minAIFScans"){inputVariable<-5
}else if(typeOfVariable=="upper"){inputVariable<-.12
}else if(typeOfVariable=="lower"){inputVariable<-(-0.11)
}
isValidInput <- TRUE
}else if(suppressWarnings(!is.na(as.numeric(inputVariable)))){
inputVariable <- as.numeric(inputVariable)
isValidInput <- TRUE
}else{
inputVariable <- ginput(message=paste("Error! Invalid Input.\n\n",message), title=title, icon="error")
}
}
return(inputVariable)
}
RT_Window <- GetInputAndErrorHandle("RT", message="Retention Time Window\n(Window of .3 => +/- .15)\nOr type \"d\" for default: .3", title="RT_Window")
ppm_Window <- GetInputAndErrorHandle("ppm", message="Parts-per-million window for matching experimental and in-silico fragments m/z \n(Window of 10 => +/- 5)\nOr type \"d\" for default value: 10", title="ppm_Window")
PrecursorMassAccuracy <- GetInputAndErrorHandle("precMassAccuracy", message="Mass accuracy window for matching experimental and in-silico precursors m.z\nfor full scan (precursor) mass matching \n(Window of .01 Da => +/- .005 Da)\nOr type \"d\" for default: .01", title="PrecMassAccuracyWindow")
SelectionAccuracy <- GetInputAndErrorHandle("selectionAccuracy", message="MS/MS Isolation Window (Da) \n(For determining MS/MS scans for each feature)\nOr type \"d\" for default: 1", title="SelectionAccuracy")
intensityCutOff <- GetInputAndErrorHandle("maxInt", message="Threshold for determining what the minimum signal intensity cut off for a\ngiven MS/MS ion should be (used for confirmations)\nOr type \"d\" for default: 1000", title="intensityCutOff")
CommentColumn <- GetInputAndErrorHandle("CommentCol", message= "Feature Table Info: Comment Column/Row ID \n(look at feature table .csv, first column is 1)\nNote that the column should be the same in negative and positive feature tables \nNo default value.",title="CommentColumn")
MZColumn <- GetInputAndErrorHandle("MZCol", message= "Feature Table Info: Mass-to-Charge Column \n(look at feature table .csv, first column is 1)\nNote that the column should be the same in negative and positive feature tables \nNo default value.", title="MZColumn")
RTColumn <- GetInputAndErrorHandle("RTCol", message= "Feature Table Info: Retention Time Column \n(look at feature table .csv, first column is 1)\nNote that the column should be the same in negative and positive feature tables \nNo default value.", title="RTColumn")
RowStartForFeatureTableData <- GetInputAndErrorHandle("RowStart", message= "Feature Table Info: What row does your numeric data start? \n(First row of .csv starts counting at 1)\nNote that the row should be the same in negative and positive feature tables \nNo default value.", title="RowStartForFeatureTableData")
upper <- GetInputAndErrorHandle("upper", message= "what is the upper limit for mass defect filtering (scoring, wont remove)? \nOr type \"d\" for default: 0.12 \n(0.12 is the upper 95 percentile of EPA Masterlist mass defects)", title="upper")
lower <- GetInputAndErrorHandle("lower", message= "what is the lower limit for mass defect filtering (scoring, wont remove)? \nOr type \"d\" for default: -0.11 \n(-0.11 is the lowest 5 percentile of EPA Masterlist mass defects)", title="lower")
RT_flagging <- TRUE #JPK: will simpy be default, changeable in code for now
hasAIF <- FALSE
hasdd <- FALSE
foldersToRun <- list.dirs(path=InputDirectory, full.names=FALSE, recursive=FALSE)
if(length(foldersToRun)==0){
lengthFoldersToRun <- 1 #if there are no subfolders, that means you have the faeture table and ms2s in that current directory, therefore, run analysis on those files.
}else{
lengthFoldersToRun <- length(foldersToRun)#run analysis on all subfolders
}
#checks for output folder, we don't want to overwrite your files
for (i in seq_len(lengthFoldersToRun)){
numOfAIFFiles <- vector()
if(length(foldersToRun)==0){#we're in current (and only) folder that contains feature table and ms2
fpath <- InputDirectory
}else if(foldersToRun[i] == "Output"){
fpath <- InputDirectory
print(paste("Warning: Remove your 'Output' folder from the current Input Directory:", InputDirectory))
}else{
fpath <- file.path(InputDirectory, foldersToRun[i])
}
#separate ddMS and AIF
numOfddFiles <- length(list.files(path=fpath, pattern="[dD][dD][Mm][Ss]", ignore.case=FALSE))
numOfAIFFiles <- length(list.files(path=fpath, pattern="[aA][iI][fF]", ignore.case=FALSE))
if(numOfAIFFiles > 0){ #if # of AIF .ms2 or .ms1 > 0, ask for variable input
hasAIF <- TRUE
}else{
print(paste("Warning: Couldn't find .ms1 or .ms2 files. Make sure you have 'aif' in your .ms1 or .ms2 file name (for data independent analysis)."))
}
if(numOfddFiles > 0){
hasdd <- TRUE
}else{
print(paste("Warning: Couldn't find data dependent .ms2 files. Make sure you have 'dd' in your .ms2 file name (for data dependent analysis)."))
}
}
if(hasAIF){
corrMin <- GetInputAndErrorHandle("corrMin", message= "Minimum adjusted R2 value for AIF confirmation\n(Used in correlating fragment ms1 and ms2 intensities).\nOr type \"d\" for default: 0.6", title="corrMin")
minNumberOfAIFScans <- GetInputAndErrorHandle("minAIFScans", message= "Minimum number of scans for confirming fragments (AIF)\nOr type \"d\" for default: 5", title="minNumberOfAIFScans")
}
if(hasdd){#then has ddMS
ScanCutOff <- GetInputAndErrorHandle("scanCutOff", message="Minimum number of scans required for the result to be a confirmation (ddMS)\nOr type \"d\" for default: 1", title="ScanCutOff")
}
} ####################### END AUTO INPUTTING VARIABLES SECTION ####################
#Error handling for manual input section... specifically the Input Directory. Checks for Output folder, and stops.
if(ManuallyInputVariables==TRUE || csvInput == TRUE){
#checks for output folder, we don't want to overwrite your files
foldersToRun <- list.dirs(path=InputDirectory, full.names=FALSE, recursive=FALSE)
if(length(foldersToRun)==0){
lengthFoldersToRun <- 1 #if there are no subfolders, that means you have the faeture table and ms2s in that current directory, therefore, run analysis on those files.
}else{
lengthFoldersToRun <- length(foldersToRun)#run analysis on all subfolders
}
for (i in seq_len(length(foldersToRun))){
if(foldersToRun[i] == "Output"){
errorBox(message=paste("Warning: Remove your 'Output' folder\nfrom the current Input Directory:\n", InputDirectory))
stop("Warning: Remove your 'Output' folder from the current Input Directory: ", InputDirectory)
}
if(length(foldersToRun)==0){#we're in current (and only) folder that contains feature table and ms2
fpath <- InputDirectory
}else if(foldersToRun[i] == "Output"){
fpath <- InputDirectory
print(paste("Warning: Remove your 'Output' folder from the current Input Directory:", InputDirectory))
}else{
fpath <- file.path(InputDirectory, foldersToRun[i])
}
numOfAIFFiles <- length(list.files(path=fpath, pattern="[AIFaif][AIFaif][AIFaif]", ignore.case=FALSE))
if(numOfAIFFiles %% 2 == 1){ #if # of AIF .ms2 or .ms1 are odd, STOP!
stop("Warning: You should have a one-to-one relationship between AIF.ms1 and AIF.ms2 files...We found ", numOfAIFFiles," AIF .ms1 and/or .ms2 files. You should have an even number of AIF files.",sep="")
}#else, do nothing.
}
}
#### END VARIABLE DECLARATIONS SECTION ####
#### CODE - DO NOT TOUCH ####
#Library Information
# NAME AND DIRECTORY for exact mass library
ImportLibPOS<-file.path(InputLibrary, "Precursor_Library_POS.csv")
ImportLibNEG<-file.path(InputLibrary, "Precursor_Library_NEG.csv")
# if (Lipid == TRUE || FLOW == TRUE) {
# LibCriteria<- file.path(InputLibrary, "PFAS_ID_CRITERIA.csv")
# } else {
# LibCriteria<- file.path(InputLibrary, "PFAS_ID_CRITERIA.csv")
# }
##Bernard Brooks 12/15/2022 9:05 AM - Error I found##
# if (FLOW == TRUE || Lipid == TRUE) {
# LibCriteria<- file.path(InputLibrary, "LIPID_ID_CRITERIA.csv")
# } else {
LibCriteria<- file.path(InputLibrary, "PFAS_ID_CRITERIA.csv")
# }
LibColInfo<-1 #Look at your Library. What column are your IDs? (Columns to retrieve ID information and align with data)
ParentMZcol_in<-2 #Look at your Library. What column are your precursor masses in? (Columns to retrieve ID information and align with data)
ddMS2_Code<-"1"
AIF_Code<-"2"
Class_Code<-"3"
ExactMass_Code<-"4"
NoID_Code<-"5"
# PrecursorMassAccuracy<-PrecursorMassAccuracy/2
PPM_CONST <- (10^6 + ppm_Window/2) / 10^6
################### FUNCTIONS ###################
RunAIF <- function(ms1_df, ms2_df, FeatureList, LibraryLipid_self, ParentMZcol, OutputDirectory, ExtraFileNameInfo, ConfirmORcol, ConfirmANDcol, OutputInfo){
tStart<-Sys.time()
ColCorrOR <- ConfirmORcol
ColCorrAND <- ConfirmANDcol
if(!dir.exists(file.path(OutputDirectory,"Confirmed_Compounds"))){
dir.create(file.path(OutputDirectory,"Confirmed_Compounds"))
}
if(!dir.exists(file.path(OutputDirectory,"Additional_Files"))){
dir.create(file.path(OutputDirectory,"Additional_Files"))
}
LibraryLipid<-read.csv(LibraryLipid_self, sep=",", na.strings="NA", dec=".", strip.white=TRUE,header=FALSE)
libraryLipidMZColumn <- ParentMZcol
FeatureListMZColumn <- 1
## Create a Library of Lipids and fragments for those masses in Feature List
FeatureListHeader <- FeatureList[1,]
LibraryLipidHeader <- LibraryLipid[1,]
NewLibraryLipidHeader <- cbind(LibraryLipidHeader, FeatureListHeader)
sqlMerged <- sqldf(paste("select * from LibraryLipid lib inner join FeatureList iList on lib.", colnames(LibraryLipid)[libraryLipidMZColumn], "-", PrecursorMassAccuracy, "<= iList.", colnames(FeatureList)[FeatureListMZColumn], " and iList.", colnames(FeatureList)[FeatureListMZColumn], " <= lib.", colnames(LibraryLipid)[libraryLipidMZColumn], "+", PrecursorMassAccuracy, sep = ""))
# sqlMerged <- sqldf(paste("select * from LibraryLipid lib inner join FeatureList iList on iList.", colnames(FeatureList)[FeatureListMZColumn], " >= lib.", colnames(LibraryLipid)[libraryLipidMZColumn], " -.005 and iList.", colnames(FeatureList)[FeatureListMZColumn], " <= lib.", colnames(LibraryLipid)[libraryLipidMZColumn], "+ .005", sep = ""))
NewLibraryLipid <- as.matrix(sqlMerged)
# if sqlMerged is not empty the names of the columns need to be adjusted, maybe better to always adjust them
colnames(NewLibraryLipid) <- colnames(NewLibraryLipidHeader)
NewLibraryLipid <- rbind(NewLibraryLipidHeader,NewLibraryLipid)
NewLibraryLipid <- NewLibraryLipid[,-(ncol(LibraryLipid)+1)] #removes mz from Feature list
#Removes 1 rt col and creates RT_min and RT_max cols based on RT_Window
RT_Col <- as.numeric(NewLibraryLipid[2:nrow(NewLibraryLipid),ncol(NewLibraryLipid)-1])
NewLibraryLipid <- NewLibraryLipid[,-(ncol(NewLibraryLipid)-1)]
RT_Window_Vector <- rep(RT_Window/2, each=nrow(NewLibraryLipid)-1)
#Issue found 10/30 Removed Levels function
RT_min <- RT_Col - as.numeric(RT_Window_Vector)
RT_max <- RT_Col + as.numeric(RT_Window_Vector)
RT_min <- c("RT_min",RT_min)
RT_max <- c("RT_max",RT_max)
Comment <- NewLibraryLipid[,ncol(NewLibraryLipid)]
NewLibraryLipid <- cbind(NewLibraryLipid[,1:(ncol(NewLibraryLipid)-1)], RT_min, RT_max, Comment)
colnames(NewLibraryLipid) <- as.character(as.matrix(NewLibraryLipid[1,]))
NewLibraryLipid <- NewLibraryLipid[-1,]
NewLibraryLipid <- NewLibraryLipid[!is.na(NewLibraryLipid[,1]),!is.na(NewLibraryLipid[1,])]
startRTCol <- ncol(NewLibraryLipid)-2
endRTCol <- startRTCol + 1
LibParentHits_df <- NewLibraryLipid #name change
nrowLibParentHits <- nrow(LibParentHits_df)
ncolLibParentHits <- ncol(LibParentHits_df)
NoMatches_dir<-file.path(OutputDirectory,"Additional_Files", paste0(ExtraFileNameInfo,"_NoIDs.csv"))
if(nrowLibParentHits == 0){#No hits between Feature List and Library
write.table("No Matches Found between Library and Feature List", NoMatches_dir, col.names=FALSE, row.names=FALSE, quote=FALSE)
}else{#If there was at least one match between Feature List and the Library
#Output dataframes. Used to build the All Confirmed
ConfirmedFragments_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
RTMaxIntensity_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
MaxIntensity_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
NumOfScans_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
AverageMZ_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
outputHeader <- colnames(LibParentHits_df) #get header from LibParentHits_df
colnames(ConfirmedFragments_df) <- outputHeader
colnames(RTMaxIntensity_df) <- outputHeader
colnames(MaxIntensity_df) <- outputHeader
colnames(NumOfScans_df) <- outputHeader
colnames(AverageMZ_df) <- outputHeader
outputLibData <- LibParentHits_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from LibParentHits_df
ConfirmedFragments_df[,c(1,startRTCol:ncolLibParentHits)] <- outputLibData
RTMaxIntensity_df[,c(1,startRTCol:ncolLibParentHits)] <- outputLibData
MaxIntensity_df[,c(1,startRTCol:ncolLibParentHits)] <- outputLibData
NumOfScans_df[,c(1,startRTCol:ncolLibParentHits)] <- outputLibData
AverageMZ_df[,c(1,startRTCol:ncolLibParentHits)] <- outputLibData
########################################################################################
# loop through lib parent hits M+H column, see #
# 1) if the precursor(from ms2) falls within this targetAcccuracy mz window #
# 2) if so... does this ms2 RT fall within the start and end RT from LibParentHits?#
# 3) if mz frag from ms2 falls within ppm of LibParentHits #
# Then fragment is confirmed. #
# #
# ms2_df[[1, 3]][1, 1] #
# ^matrix ^[row, column] from matrix #
########################################################################################
AIFms2SubsettedRTList <- list()
for(p in seq_len(nrowLibParentHits)){
RTConditional <- as.numeric(as.character(LibParentHits_df[p,startRTCol])) <= as.numeric(ms2_df[,2]) & as.numeric(ms2_df[,2]) <= as.numeric(as.character(LibParentHits_df[p,endRTCol]))
AIFms2SubsettedRTList[[p]] <- subset(ms2_df, RTConditional)
}
AIFms1SubsettedRTList <- list()
for(p in seq_len(nrowLibParentHits)){
RTConditional <- as.numeric(as.character(LibParentHits_df[p,startRTCol])) <= as.numeric(ms1_df[,2]) & as.numeric(ms1_df[,2]) <= as.numeric(as.character(LibParentHits_df[p,endRTCol]))
AIFms1SubsettedRTList[[p]] <- subset(ms1_df, RTConditional)
}
############################################################################
#Building All_Confirmed data frame for output (ddMS and/or AIF) #
# Creates a list of dataframes for each section of the All_Confirmed.csv. #
# Why? To organize our data. #
# Each list's element corresponds to a row from LibParentHits #
# Each list's element holds a fragment found in the scans #
############################################################################
#building ALL_Confirmed for AIF
for(c in ParentMZcol:((startRTCol - ParentMZcol)+1)){ #loop all fragment columns
subsettedFragList <- list() #has nrow(LibParentHits_df) elements
for(p in 1:nrowLibParentHits){ #loop through all AIFms2SubsettedRTList elements
# if(nrow(AIFms2SubsettedRTList[[p]]) != 0){ #don't loop if the list is empty! (for loops run once when the size is 0... :(...)
subsettedFrag_df <- data.frame(scans=numeric(), rt=numeric(), frags=numeric(), intensity=numeric())
scans <- vector()
rt <- vector()
frags <- vector()
intensity <- vector()
nrow_AIFms2SubsettedRTList <- nrow(AIFms2SubsettedRTList[[p]])
for(d in seq_len(nrow_AIFms2SubsettedRTList)){ #loop through each element in the list
subsettedFrag <- vector(length=0)
#Take the theoretical LibParentHits fragment m/z and look within a ppm window through the scan's ms2 fragments
fragConditional <- as.numeric(as.character(LibParentHits_df[p, c])) - abs(as.numeric(as.character(LibParentHits_df[p, c])) - as.numeric(as.character(LibParentHits_df[p, c]))*PPM_CONST) <= as.numeric(AIFms2SubsettedRTList[[p]][,3][[d]][,1]) & as.numeric(AIFms2SubsettedRTList[[p]][,3][[d]][,1]) <= as.numeric(as.character(LibParentHits_df[p, c])) + abs(as.numeric(as.character(LibParentHits_df[p, c])) - as.numeric(as.character(LibParentHits_df[p, c]))*PPM_CONST)
subsettedFrag <- subset(AIFms2SubsettedRTList[[p]][,3][[d]], fragConditional)
nrow_subsettedFrag <- nrow(subsettedFrag)
if(nrow_subsettedFrag>1){#more than one matching frag
for(s in 1:nrow_subsettedFrag){
scans <- append(scans, as.numeric(AIFms2SubsettedRTList[[p]][d,1])) #gets scan number
rt <- append(rt, as.numeric(AIFms2SubsettedRTList[[p]][d,2])) #get rt
}
}else if(nrow_subsettedFrag==1){#only 1 mataching frag
scans <- append(scans, as.numeric(AIFms2SubsettedRTList[[p]][d,1])) #gets scan number
rt <- append(rt, as.numeric(AIFms2SubsettedRTList[[p]][d,2])) #get rt
}#else, length is 0. no fragments found. do nothing.
frags <- append(frags, as.numeric(subsettedFrag[,1])) #gets fragment from ms2
intensity <- append(intensity, as.numeric(subsettedFrag[,2])) #gets intensity associated with that fragment
}
subsettedFrag_df <- data.frame(scans, rt, frags, intensity)
subsettedFragList[[p]] <- subsettedFrag_df
#Stores information into respective dataframes for output as All_Confirmed
if(nrow(subsettedFragList[[p]])>0){#loop over list elements that have values
#Max Intensity
maxIntensity <- max(subsettedFragList[[p]][,4])
MaxIntensity_df[p, c] <- maxIntensity
#Number of Scans
numScans <- nrow(subsettedFragList[[p]])
NumOfScans_df[p, c] <- numScans
#Confirming fragments
if(sum(subsettedFragList[[p]][,4] > intensityCutOff) > 0 & (numScans >= minNumberOfAIFScans)){ #at least one fragment's intensity is above user inputed threshold and number of scans is greater than or equal to user inputed, ScansCutOff
ConfirmedFragments_df[p, c] <- 1 #1 for yes, 0 for no. (ConfirmedFragments_df was initialized with all 0s)
}
#RT at max intensity
maxIntIndex <- which.max(subsettedFragList[[p]][,4])
RTMaxInt <- subsettedFragList[[p]][maxIntIndex,2]
RTMaxIntensity_df[p, c] <- RTMaxInt
#Average m/z for confirmed fragment
avgMZ <- mean(subsettedFragList[[p]][,3])
AverageMZ_df[p, c] <- avgMZ
}
}
}
#Creates df for output "AllConfirmed"
AllConfirmed_df <- data.frame(matrix("", ncol = ncolLibParentHits*5, nrow = nrowLibParentHits))
blankColumn <- rep("", nrowLibParentHits)
AllConfirmed_df <- cbind(blankColumn, ConfirmedFragments_df, blankColumn, RTMaxIntensity_df, blankColumn, NumOfScans_df, blankColumn, MaxIntensity_df, blankColumn, AverageMZ_df, blankColumn, LibParentHits_df)
colnames(AllConfirmed_df)[1] <- paste("Confirmed fragments if intensity is above", intensityCutOff,"and number of scans is greater than or equal to", minNumberOfAIFScans)
colnames(AllConfirmed_df)[ncolLibParentHits+2] <- "RT at Max Intensity"
colnames(AllConfirmed_df)[ncolLibParentHits*2+3] <- "Number of Scans"
colnames(AllConfirmed_df)[ncolLibParentHits*3+4] <- "Max Intensity"
colnames(AllConfirmed_df)[ncolLibParentHits*4+5] <- "Average m/z"
colnames(AllConfirmed_df)[ncolLibParentHits*5+6] <- "Theoretical, Library Parent Hits"
#DON'T WRITE THIS OUT. WE want to write out the final confirmed with Adj R2 and Slope
# ConfirmedAll_dir<-paste(OutputDirectory,"Additional_Files//", ExtraFileNameInfo,"_All_confirmed.csv",sep="")
# write.table(AllConfirmed_df, ConfirmedAll_dir, sep=",", col.names=TRUE, row.names=FALSE, quote=TRUE, na="NA")
## Code to reduce the dataset to lipids containing necessary fragments
## Start with both true, if no inputs then all rows are retained
AllConfirmed_matrix<-as.matrix(AllConfirmed_df)
OR_BinTruth<-1
AND_BinTruth<-1
## For any given confirmed fragment/precursor
## add an extra row to avoid error in an apply function encase of a one row matrix
DataCombined<-rbind(AllConfirmed_matrix,((1:ncol(AllConfirmed_matrix))*0))
if ((length(ConfirmORcol)>0)&&(is.matrix(DataCombined[1:nrow(DataCombined),ConfirmORcol+1]))) {
## Sum all the elements from the binary confirmation table, if any fragment is 1 (above threshold & minimum # of scans) the element is TRUE
OR_BinTruth<-as.numeric(c((apply(apply(DataCombined[1:nrow(DataCombined),ConfirmORcol+1],2,as.numeric),1,sum)>0)))
}
if ((length(ConfirmANDcol)>0)&&(is.matrix(DataCombined[1:nrow(DataCombined),ConfirmANDcol+1]))) {
## Sum all the elements from the binary confirmation table, if ALL fragments are 1 (above threshold & minimum # of scans) the element is TRUE
AND_BinTruth<-as.numeric(c((apply(apply(DataCombined[1:nrow(DataCombined),ConfirmANDcol+1],2,as.numeric),1,sum)>(length(ConfirmANDcol)-1))))
}
if ((length(ConfirmANDcol)>0)&&(is.vector(DataCombined[1:nrow(DataCombined),ConfirmANDcol+1]))) {
AND_BinTruth<-as.numeric(c(DataCombined[1:nrow(DataCombined),ConfirmANDcol+1]))
}
## Reduce the data set to those values which are TRUE for both the OR and AND fragments calculated in the two IF statements above
DataCombinedConfirmed<-DataCombined[(OR_BinTruth+AND_BinTruth)>1,,drop=FALSE]
NoMatches_dir<-file.path(OutputDirectory,"Additional_Files", paste0(ExtraFileNameInfo,"_NoIDs.csv"))
if(nrow(DataCombinedConfirmed)==0){
write.table("No Confirmed Compounds Found",NoMatches_dir, col.names=FALSE, row.names=FALSE, quote=FALSE)
}else{
# ConfirmedReduced_dir <- paste(OutputDirectory,"Confirmed_Lipids//",ExtraFileNameInfo,"_reduced_confirmed.csv",sep="")
# write.table(DataCombinedConfirmed, ConfirmedReduced_dir, sep=",",col.names=TRUE, row.names=FALSE, quote=TRUE, na="NA")
rowsToKeep <- as.numeric(row.names(DataCombinedConfirmed))-1 #used to reduce AIFms1SubsettedList and AIFms2SubsettedList
AIFms1SubsettedRTList <- AIFms1SubsettedRTList[rowsToKeep]
AIFms2SubsettedRTList <- AIFms2SubsettedRTList[rowsToKeep]
DataCombinedConfirmed_df <- as.data.frame(DataCombinedConfirmed)#reduced confirmed df
nrowReduced <- nrow(DataCombinedConfirmed_df)
ncolReduced <- ncol(DataCombinedConfirmed_df)
#gets the original theoretical library that was reduced
LibInfoFromDataComb_df<- DataCombinedConfirmed_df[,(ncolReduced-ncolLibParentHits+1):ncolReduced]
colsToCorr <- c(ColCorrAND, ColCorrOR)
numColsForAIF <- 4 + length(colsToCorr) #4 because ID, RTmin, RTmax, Comment
nameOfMiddleColsForAIF <- vector()
for(c in colsToCorr){#get "precursor vs fragment" header for output
nameOfMiddleColsForAIF <- append(nameOfMiddleColsForAIF, paste(colnames(LibParentHits_df[ParentMZcol]), " vs ", colnames(LibParentHits_df[c]), sep=""))
}
AIFHeader <- c(colnames(LibParentHits_df[1]), nameOfMiddleColsForAIF, colnames(LibParentHits_df[startRTCol:ncolLibParentHits])) #get header from LibParentHits_df
AdjR2_df <- data.frame(matrix(0, ncol = numColsForAIF, nrow = nrowReduced))
Slope_df <- data.frame(matrix(0, ncol = numColsForAIF, nrow = nrowReduced))
colnames(AdjR2_df) <- AIFHeader
colnames(Slope_df) <- AIFHeader
outputLibData <- LibInfoFromDataComb_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from DataCombinedConfirmed_df
AdjR2_df[,c(1,(numColsForAIF-2):numColsForAIF)] <- outputLibData
Slope_df[,c(1,(numColsForAIF-2):numColsForAIF)] <- outputLibData
#correlate precursor from ms1 against predefined columns to correlate against from ms2
for(c in colsToCorr){ #loop fragment columns to correlate against ms1
for(p in seq_len(nrowReduced)){#loop all rows of reduced confirmed data frame
fragCorr_df<-NULL
ms1ScanNum <- vector()
ms2ScanNum <- vector()
ms1Intensity <- vector()
ms2Intensity <- vector()
length_AIFms1SubsettedRTList <- length(AIFms1SubsettedRTList[[p]][,1])
for(d in seq_len(length_AIFms1SubsettedRTList)){#store ms1 scan number and PRECURSOR intensity
subsettedFrag <- vector(length=0)
#Take the theoretical LibParentHits fragment m/z and look within a ppm window through the scan's ms2 fragments
AIFms1fragConditional <- (as.numeric(as.character(LibInfoFromDataComb_df[p, ParentMZcol])) - abs(as.numeric(as.character(LibInfoFromDataComb_df[p, ParentMZcol])) - as.numeric(as.character(LibInfoFromDataComb_df[p, ParentMZcol]))*PPM_CONST) <= as.numeric(AIFms1SubsettedRTList[[p]][,3][[d]][,1])) & (as.numeric(AIFms1SubsettedRTList[[p]][,3][[d]][,1]) <= as.numeric(as.character(LibInfoFromDataComb_df[p, ParentMZcol])) + abs(as.numeric(as.character(LibInfoFromDataComb_df[p, ParentMZcol])) - as.numeric(as.character(LibInfoFromDataComb_df[p, ParentMZcol]))*PPM_CONST))
subsettedFrag <- subset(AIFms1SubsettedRTList[[p]][,3][[d]], AIFms1fragConditional)
#error handling if you find 2 fragments masses within a ppm window. Then what?
if(nrow(subsettedFrag)>1){
LibMass<-as.numeric(as.character(LibInfoFromDataComb_df[p, c]))
fragMass<-as.numeric(subsettedFrag[,1])
closestMass<-which(abs(LibMass-fragMass)==min(abs(LibMass-fragMass)))
subsettedFrag<-subsettedFrag[closestMass,]
subsettedFrag<-matrix(subsettedFrag,1,2)
if(length(closestMass)>1){#edge case: if the two fragment masses are equidistant from the lib mass... then avg their intensities
#average the intensities (and masses, but I don't use the mass later, so it doesn't matter)
subsettedFrag<-matrix(c(mean(as.numeric(subsettedFrag[,1])),mean(as.numeric(subsettedFrag[,2]))), 1, 2)
}
}
# print("------------MS1-----------------")
# print(paste("d: ", d, "subsettedFrag:", subsettedFrag))
if(nrow(subsettedFrag)==0){
ms1Intensity <- append(ms1Intensity, 0)
}else{# if there's 1 fragment
ms1Intensity <- append(ms1Intensity, subsettedFrag[,2])
}
ms1ScanNum <- append(ms1ScanNum, as.numeric(AIFms1SubsettedRTList[[p]][d,1])) #gets scan number
}
length_AIFms2SubsettedRTList <- length(AIFms2SubsettedRTList[[p]][,1])
for(d in seq_len(length_AIFms2SubsettedRTList)){#store ms2 intensity
subsettedFrag <- vector(length=0)
#Take the theoretical LibParentHits fragment m/z and look within a ppm window through the scan's ms2 fragments
AIFms2fragConditional <- as.numeric(as.character(LibInfoFromDataComb_df[p, c])) - abs(as.numeric(as.character(LibInfoFromDataComb_df[p, c])) - as.numeric(as.character(LibInfoFromDataComb_df[p, c]))*PPM_CONST) <= as.numeric(AIFms2SubsettedRTList[[p]][,3][[d]][,1]) & as.numeric(AIFms2SubsettedRTList[[p]][,3][[d]][,1]) <= as.numeric(as.character(LibInfoFromDataComb_df[p, c])) + abs(as.numeric(as.character(LibInfoFromDataComb_df[p, c])) - as.numeric(as.character(LibInfoFromDataComb_df[p, c]))*PPM_CONST)
subsettedFrag <- subset(AIFms2SubsettedRTList[[p]][,3][[d]], AIFms2fragConditional)
if(nrow(subsettedFrag)>1){
LibMass<-as.numeric(as.character(LibInfoFromDataComb_df[p, c]))
fragMass<-as.numeric(subsettedFrag[,1])
closestMass<-which(abs(LibMass-fragMass)==min(abs(LibMass-fragMass)))
subsettedFrag<-subsettedFrag[closestMass,]
subsettedFrag<-matrix(subsettedFrag,1,2)
if(length(closestMass)>1){#edge case: if the two fragment masses are equidistant from the lib mass... then avg their intensities
#average the intensities (and masses, but I don't use the mass later, so it doesn't matter)
subsettedFrag<-matrix(c(mean(as.numeric(subsettedFrag[,1])),mean(as.numeric(subsettedFrag[,2]))), 1, 2)
}
}
# print("------------MS2-----------------")
# print(paste("d: ", d, "subsettedFrag:", subsettedFrag))
if(nrow(subsettedFrag)==0){
ms2Intensity <- append(ms2Intensity, 0)
}else{
ms2Intensity <- append(ms2Intensity, subsettedFrag[,2])
}
ms2ScanNum <- append(ms2ScanNum, as.numeric(AIFms2SubsettedRTList[[p]][d,1])) #gets scan number
}
ms1ScanNum<-as.numeric(ms1ScanNum)
ms2ScanNum<-as.numeric(ms2ScanNum)
ms1Intensity <- as.numeric(ms1Intensity)
ms2Intensity <- as.numeric(ms2Intensity)
ms1IntensityAveraged<-vector()
ms1ScanNumAveraged <- vector()
#average each ms1's intensity that are next to eachother so we can correlate it against ms2
length_ms1Intensity <- length(ms1Intensity)
for(a in seq_len(length_ms1Intensity-1)){#-1 so I don't go out of bounds
temp <- ms1Intensity[a+1]#next intensity
averaged <- mean(c(ms1Intensity[a], temp))
ms1IntensityAveraged <- append(ms1IntensityAveraged, averaged)
}
length_ms1ScanNum <- length(ms1ScanNum)
for(b in seq_len(length_ms1ScanNum-1)){#-1 so I don't go out of bounds
temp <- ms1ScanNum[b+1]#next intensity
averaged <- mean(c(ms1ScanNum[b], temp))
ms1ScanNumAveraged <- append(ms1ScanNumAveraged, averaged)
}
intensityCol1 <- ms1IntensityAveraged[ms1ScanNumAveraged %in% ms2ScanNum]
intensityCol2 <- ms2Intensity[ms2ScanNum %in% ms1ScanNumAveraged]
#test for mininum number of couples (found fragments in both ms1 and ms2)
intensitiesMultiplied <- intensityCol1*intensityCol2
numOfCouples <- length(subset(intensitiesMultiplied, intensitiesMultiplied != 0))
columnToFill <- match(c, colsToCorr) + 1 # +1 because the ID column in AdjR2_df is column 1.
#are there min number of user specified couples (Definition: Couples (noun) := fragment found in both ms1 and ms2 at the same scan time)
if(numOfCouples < minNumberOfAIFScans){#bad, you get no adj R2
# AdjR2_df[p,columnToFill] <- paste("Not enough ms1 averaged scans & ms2 scans for a linear model. We found: ", numOfCouples, " ms1 and ms2 scan couples. You specified: ", minNumberOfAIFScans, " number of coupled scans.",sep="")
# Slope_df[p,columnToFill] <- paste("Not enough ms1 averaged scans & ms2 scans for a linear model. We found: ", numOfCouples, " ms1 and ms2 scan couples. You specified: ", minNumberOfAIFScans, " number of coupled scans.",sep="")
AdjR2_df[p,columnToFill] <- NA
Slope_df[p,columnToFill] <- NA
}else{
fragCorr_df <- data.frame(ms1ScanNumAveraged, intensityCol1, intensityCol2)
intensityFit<-lm(fragCorr_df[,3]~fragCorr_df[,2])
intensitySummary <- summary(intensityFit)
intensityAdjR2 <- intensitySummary$adj.r.squared
intensitySlope <- intensityFit$coefficients[2]
# if(intensityAdjR2 > corrMin){
AdjR2_df[p,columnToFill] <- round(intensityAdjR2, 3)
Slope_df[p,columnToFill] <- round(intensitySlope, 5)
# }else{
# AdjR2_df[p,columnToFill] <- NA
# Slope_df[p,columnToFill] <- NA
# }
# print(fragCorr_df)
##We don't use this. But I'll keep it here. intensityCorr <- cor(fragCorr_df[,2],fragCorr_df[,3],use="complete.obs")##
}
}#end row loop
}#end column loop
# write the table out.
#remove theoretical library from end of DataCombinedConfirmed_df
DataCombinedConfirmed_df <- DataCombinedConfirmed_df[,-((ncolReduced-ncolLibParentHits):ncolReduced)] #CAREFUL NICK! THIS MESSES WITH THE HEADER!...but I kinda like the .1 .2 .3. .4
blankColumn <- rep("", nrowReduced)
DataCombinedConfirmed_df <- cbind(DataCombinedConfirmed_df, blankColumn, AdjR2_df, blankColumn, Slope_df, blankColumn, LibInfoFromDataComb_df)
colnames(DataCombinedConfirmed_df)[ncolLibParentHits*5+6] <- paste("Adjusted R2, Precursor Intensity vs Fragment Intensity (AIF). AdjR2 > ", corrMin, sep="")
colnames(DataCombinedConfirmed_df)[ncolLibParentHits*5+6 + numColsForAIF + 1] <- "Slope, Precursor Intensity vs Fragment Intensity (AIF)"
colnames(DataCombinedConfirmed_df)[ncolLibParentHits*5+6 + numColsForAIF*2 + 2] <- "Theoretical, Library Parent Hits"
ConfirmedReduced_dir <- file.path(OutputDirectory,"Additional_Files", paste0(ExtraFileNameInfo,"_AdjR2Info.csv"))
write.table(DataCombinedConfirmed_df, ConfirmedReduced_dir, sep=",",col.names=TRUE, row.names=FALSE, quote=TRUE, na="NA")
#Reduce matches based on ConfirmAndCol
if(length(ConfirmANDcol) > 0){
columnToFill<-vector()
# AdjR2_df[] <- lapply(AdjR2_df, as.numeric)
for(i in ConfirmANDcol){
columnToFill <- append(columnToFill, match(i, ConfirmANDcol) + 1)
}
#get R2 rows with text in them, and remove them.
if(!is.numeric(AdjR2_df[,columnToFill])){#if the adjR2 column is already numeric, (don't change to as.numeric...this gives an error)
AdjR2_df[,columnToFill] <- lapply(AdjR2_df[,columnToFill], as.numeric)
}
if(!is.numeric(Slope_df[,columnToFill])){#if the adjR2 column is already numeric, (don't change to as.numeric...this gives an error)
Slope_df[,columnToFill] <- lapply(Slope_df[,columnToFill], as.numeric)
}
#We want AdjR2 to be > corrMin and slopes to be positive!
#set non positive slopes to NA
Slope_df[,columnToFill] <- replace(Slope_df[,columnToFill],Slope_df[,columnToFill]<=0,NA)
#remove negative slopes
AdjR2_df <- AdjR2_df[apply(cbind(AdjR2_df[,columnToFill],Slope_df[,columnToFill]), 1, function(x) all(!is.na(x))),]
AdjR2_df <- AdjR2_df[apply(AdjR2_df[,columnToFill, drop=F], 1, function(x) all(x > corrMin)),]
rowsToKeep <- as.numeric(row.names(AdjR2_df)) #used to reduce final output table based on the reduced AdjR2_df rows from above ConfirmAND and ConfirmOR columns
Slope_df <- Slope_df[rowsToKeep,]
# AdjR2_df <- AdjR2_df[apply(AdjR2_df[columnToFill], 1, function(x) all(x > corrMin & !is.na(x))),]
# AdjR2_df <- AdjR2_df[apply(AdjR2_df[c(2,3)], 1, function(x) all((x > corrMin) & is.numeric(x))),]
}
#difference here is in the apply function.... "all" vs "any"
if(length(ConfirmORcol) > 0){
columnToFill<-vector()
# AdjR2_df[] <- lapply(AdjR2_df, as.numeric)
length_ConfirmANDcol <- length(ConfirmANDcol)
for(i in ConfirmORcol){
columnToFill <- append(columnToFill, match(i, ConfirmORcol) + 1 + length_ConfirmANDcol)
}
if(nrow(AdjR2_df[,columnToFill]) !=0){#The AND could've removed all rows, so now we have nothing to work with.
if(!is.numeric(AdjR2_df[,columnToFill])){#if the adjR2 column is already numeric, (don't change to as.numeric...this gives an error)
AdjR2_df[,columnToFill] <- lapply(AdjR2_df[,columnToFill], as.numeric)
}
if(!is.numeric(Slope_df[,columnToFill])){#if the adjR2 column is already numeric, (don't change to as.numeric...this gives an error)
Slope_df[,columnToFill] <- lapply(Slope_df[,columnToFill], as.numeric)
}
#We want AdjR2 to be > corrMin and slopes to be positive!
#set non positive slopes to NA
Slope_df[,columnToFill] <- replace(Slope_df[,columnToFill],Slope_df[,columnToFill]<=0,NA)
#remove negative slopes
AdjR2_df <- AdjR2_df[apply(cbind(AdjR2_df[,columnToFill],Slope_df[,columnToFill]), 1, function(x) all(!is.na(x))),]
AdjR2_df <- AdjR2_df[apply(AdjR2_df[,columnToFill, drop=F], 1, function(x) any(x > corrMin)),]
}
}
rowsToKeep <- as.numeric(row.names(AdjR2_df)) #used to reduce final output table based on the reduced AdjR2_df rows from above ConfirmAND and ConfirmOR columns
DataCombinedConfirmed_df <- DataCombinedConfirmed_df[rowsToKeep,]
ConfirmedReduced_dir <- file.path(OutputDirectory,"Confirmed_Compounds", paste0(ExtraFileNameInfo,"_IDed.csv"))
if(nrow(DataCombinedConfirmed_df)>0){#don't output if there's nothing in the reduced file
write.table(DataCombinedConfirmed_df, ConfirmedReduced_dir, sep=",",col.names=TRUE, row.names=FALSE, quote=TRUE, na="NA")
}
}#end else (if there are matches after reduction step)
tEnd<-Sys.time()
# Capture parameters used for this run and save them
RunInfo<-matrix("",24,2)
RunInfo[,1]<-c("Parameters Used","","Run Time","Computation Started:","Computation Ended:","Elapsed Time (largest time unit)","","Files","MS2 file name and Directory:","MS1 file name and Directory","Feature Table name and directory:","Library name and directory:","","MS/MS confirmation criteria","Library columns for confirmation (AND)","Library columns for confirmation (OR)","m/z ppm window:","RT window (min):","minimum scans:","minimum intensity:", "","AIF confirmation criteria","Minimum adjusted R2 value","Minimum scans:")
RunInfo[,2]<-c("Values","","",as.character(tStart),as.character(tEnd),tEnd-tStart,"","",OutputInfo[1],OutputInfo[4],OutputInfo[3],LibraryLipid_self,"","",paste(ConfirmANDcol,collapse=","),paste(ConfirmORcol,collapse=","),ppm_Window,RT_Window,minNumberOfAIFScans,intensityCutOff,"","",corrMin,minNumberOfAIFScans)
Info_dir<-file.path(OutputDirectory,"Additional_Files", paste0(ExtraFileNameInfo,"_Parameters.csv"))
write.table(RunInfo, Info_dir, sep=",",col.names=FALSE, row.names=FALSE, quote=TRUE, na="NA")
}
}#end RunAIF
# ms2_df <- MS2_df_list[[c]]
# FeatureList <- FeatureList_in
# LibraryLipid_self <- file.path(InputLibrary, LibraryCriteria[i,1])
# ParentMZcol <- ParentMZcol_in
# OutputDirectory <- OutputDirectoryddMSNeg_in
# ExtraFileNameInfo <- paste(ExtraSample,"_",gsub('.{4}$', '', LibraryCriteria[i,1]), sep="")
# ms2FileName <- ddMS2NEG_in[c]
# NegPos <- "Neg"
#Function for MS/MS ID
RunTargeted <- function(ms2_df, FeatureList, LibraryLipid_self, ParentMZcol, OutputDirectory, ExtraFileNameInfo, ConfirmORcol, ConfirmANDcol, OutputInfo, ms2FileName, NegPos){
# message("RunTar")
if(!dir.exists(file.path(OutputDirectory,"Confirmed_Compounds"))){
dir.create(file.path(OutputDirectory,"Confirmed_Compounds"))
}
if(!dir.exists(file.path(OutputDirectory,"Additional_Files"))){
dir.create(file.path(OutputDirectory,"Additional_Files"))
}
#store starting time
tStart<-Sys.time()
LibraryLipid<-read.csv(LibraryLipid_self, sep=",", na.strings="NA", dec=".", strip.white=TRUE,header=FALSE)
libraryLipidMZColumn <- ParentMZcol
FeatureListMZColumn <- 1
## Create a Library of Lipids and fragments for those masses in Feature List
FeatureListHeader <- FeatureList[1,]
LibraryLipidHeader <- LibraryLipid[1,]
NewLibraryLipidHeader <- cbind(LibraryLipidHeader, FeatureListHeader)
sqlMerged <- sqldf(paste("select * from LibraryLipid lib inner join FeatureList iList on lib.", colnames(LibraryLipid)[libraryLipidMZColumn], "-", PrecursorMassAccuracy, "<= iList.", colnames(FeatureList)[FeatureListMZColumn], " and iList.", colnames(FeatureList)[FeatureListMZColumn], " <= lib.", colnames(LibraryLipid)[libraryLipidMZColumn], "+", PrecursorMassAccuracy, sep = ""))
# sqlMerged <- sqldf(paste("select * from LibraryLipid lib inner join FeatureList iList on iList.", colnames(FeatureList)[FeatureListMZColumn], " >= lib.", colnames(LibraryLipid)[libraryLipidMZColumn], " -.005 and iList.", colnames(FeatureList)[FeatureListMZColumn], " <= lib.", colnames(LibraryLipid)[libraryLipidMZColumn], "+ .005", sep = ""))
NewLibraryLipid <- as.matrix(sqlMerged)
# if sqlMerged is not empty the names of the columns need to be adjusted, maybe better to always adjust them
colnames(NewLibraryLipid) <- colnames(NewLibraryLipidHeader)
NewLibraryLipid <- rbind(NewLibraryLipidHeader,NewLibraryLipid)
NewLibraryLipid <- NewLibraryLipid[,-(ncol(LibraryLipid)+1)] #removes mz from Feature list
#Removes 1 rt col and creates RT_min and RT_max cols based on RT_Window
RT_Col <- as.numeric(NewLibraryLipid[2:nrow(NewLibraryLipid),ncol(NewLibraryLipid)-1])
NewLibraryLipid <- NewLibraryLipid[,-(ncol(NewLibraryLipid)-1)]
RT_Window_Vector <- rep(RT_Window/2, each=nrow(NewLibraryLipid)-1)
#Issue found 10/30 Removed Levels function
RT_min <- RT_Col - as.numeric(RT_Window_Vector)
RT_max <- RT_Col + as.numeric(RT_Window_Vector)
RT_min <- c("RT_min",RT_min)
RT_max <- c("RT_max",RT_max)
Comment <- NewLibraryLipid[,ncol(NewLibraryLipid)]
NewLibraryLipid <- cbind(NewLibraryLipid[,1:(ncol(NewLibraryLipid)-1)], RT_min, RT_max, Comment)
colnames(NewLibraryLipid) <- as.character(as.matrix(NewLibraryLipid[1,]))
NewLibraryLipid <- NewLibraryLipid[-1,]
NewLibraryLipid <- NewLibraryLipid[!is.na(NewLibraryLipid[,1]),!is.na(NewLibraryLipid[1,])]
startRTCol <- ncol(NewLibraryLipid)-2
endRTCol <- startRTCol + 1
LibParentHits_df <- NewLibraryLipid #name change
nrowLibParentHits <- nrow(LibParentHits_df)
ncolLibParentHits <- ncol(LibParentHits_df)
NoMatches_dir<-file.path(OutputDirectory,"Additional_Files", paste0(ExtraFileNameInfo,"_NoIDs.csv"))
if(nrowLibParentHits == 0){#No hits between Feature List and Library
write.table("No Matches Found between Library and Feature List", NoMatches_dir, col.names=FALSE, row.names=FALSE, quote=FALSE)
}else{#If there was at least one match between Feature List and the Library
#Output dataframes. Used to build the All Confirmed
ConfirmedFragments_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
RTMaxIntensity_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
MaxIntensity_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
NumOfScans_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
AverageMZ_df <- data.frame(matrix(0, ncol = ncolLibParentHits, nrow = nrowLibParentHits))
colnames(ConfirmedFragments_df) <- colnames(LibParentHits_df) #get header from LibParentHits_df
colnames(RTMaxIntensity_df) <- colnames(LibParentHits_df) #get header from LibParentHits_df
colnames(MaxIntensity_df) <- colnames(LibParentHits_df) #get header from LibParentHits_df
colnames(NumOfScans_df) <- colnames(LibParentHits_df) #get header from LibParentHits_df
colnames(AverageMZ_df) <- colnames(LibParentHits_df) #get header from LibParentHits_df
ConfirmedFragments_df[,c(1,startRTCol:ncolLibParentHits)] <- LibParentHits_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from LibParentHits_df
RTMaxIntensity_df[,c(1,startRTCol:ncolLibParentHits)] <- LibParentHits_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from LibParentHits_df
MaxIntensity_df[,c(1,startRTCol:ncolLibParentHits)] <- LibParentHits_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from LibParentHits_df
NumOfScans_df[,c(1,startRTCol:ncolLibParentHits)] <- LibParentHits_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from LibParentHits_df
AverageMZ_df[,c(1,startRTCol:ncolLibParentHits)] <- LibParentHits_df[,c(1,startRTCol:ncolLibParentHits)] #gets data from LibParentHits_df
########################################################################################
# loop through lib parent hits M+H column, see #
# 1) if the precursor(from ms2) falls within this targetAcccuracy mz window #
# 2) if so... does this ms2 RT fall within the start and end RT from LibParentHits?#
# 3) if mz frag from ms2 falls within ppm of LibParentHits #
# Then fragment is confirmed. #
# #
# ms2_df[[1, 3]][1, 1] #
# ^matrix ^[row, column] from matrix #