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Workflow_R_Script_all_SIRIUS.r
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# ---------- Preparations ----------
# provenance library rdtLite integration
library(rdtLite)
options(prov.dir = "./prov", snapshot.size = 10000)
prov.init(prov.dir = ".")
# Load Libraries
library(Spectra)
library(MsBackendMgf)
#library(MsBackendHmdb)
library(MsCoreUtils)
library(MsBackendMsp)
library(readr)
library(dplyr)
library(rvest)
library(stringr)
library(xml2)
options(warn=-1)
library("mzR")
library(curl)
#library(CompoundDb)
download_specDB_new <- function(input_dir, db = "all"){
if (dir.exists(input_dir)){
# Track Time
start_time <- Sys.time()
# only input available as of now
databases <- 'gnps, hmdb, mbank, all'
# creat a summary file, open and store timings of download and version if possible
if (!(file.exists(paste(input_dir, "/summaryFile.txt", sep = "")))){
summaryFile <- paste(input_dir, "/summaryFile.txt", sep = "")
file.create(summaryFile, recursive = TRUE)
}
else{
summaryFile <- paste(input_dir, "/summaryFile.txt", sep = "")
}
file.conn <- file(summaryFile)
open(file.conn, open = "at")
# gnps
if (db == "all" || db =="gnps"){
# Download file
system(paste("wget -P",
input_dir,
"https://gnps-external.ucsd.edu/gnpslibrary/ALL_GNPS.msp",
sep = " "))
# load the spectra into MsBackendMgf
gnpsdb <- Spectra(paste(input_dir, "/ALL_GNPS.msp", sep = ''), source = MsBackendMsp())
save(gnpsdb, file = paste(input_dir,"/gnps.rda", sep = ""))
# delete the database in its format to free up space
system(paste("rm", (paste(input_dir, "/ALL_GNPS.mgf", sep = '')), sep = " "))
writeLines(paste("GNPS saved at", Sys.time(), sep=" "),con=file.conn)
}
#mbank
if (db == "all" || db =="mbank"){
#print("MassBank WORKS")
page <- read_html("https://github.com/MassBank/MassBank-data/releases")
page %>%
html_nodes("a") %>% # find all links
html_attr("href") %>% # get the url
str_subset("MassBank_NIST.msp") -> tmp # find those that have the name MassBank_NIST.msp
#download file
system(paste("wget ",
"https://github.com", tmp[1],
sep = ""))
mbank <- Spectra(paste(input_dir, "/MassBank_NIST.msp", sep = ''), source = MsBackendMsp())
save(mbank, file = paste(input_dir,"/mbankNIST.rda", sep = ""))
# delete the database in its format to free up space
system(paste("rm", (paste(input_dir, "/MassBank_NIST.msp", sep = '')), sep = " "))
# obtain the month and year for the database release to add to summary
res <- str_match(tmp[1], "download/\\s*(.*?)\\s*/MassBank_NIST")
writeLines(paste("MassBank saved at", Sys.time(), "with release version", res[,2], sep=" "),con=file.conn)
}
#mbank
if (db == "all" || db =="hmdb"){
# extract HMDB Current version\n",
html <- read_html("https://hmdb.ca/downloads")
strings <- html%>% html_elements("a") %>% html_text2()
ls <- unique(strings)
hmdb_curr_ver <- c()
for (i in ls){
if (grepl("Current", i)){
hmdb_curr_ver<- c(i, hmdb_curr_ver)
}
}
dbname <- "CompDb.Hsapiens.HMDB.5.0.sqlite"
db_file <- file.path(tempdir(), dbname)
curl_download(
paste0("https://github.com/jorainer/MetaboAnnotationTutorials/",
"releases/download/2021-11-02/", dbname),
destfile = db_file)
#' Load a CompDb database with compound annotation from HMDB
cdb <- CompDb(db_file)
hmdb <- Spectra(cdb)
hmdb$collisionEnergy <- as.numeric(hmdb$collisionEnergy)
hmdb <- setBackend(hmdb, backend = MsBackendDataFrame())
save(hmdb, file = paste(input_dir,"/hmdb.rda", sep = ""))
writeLines(paste("HMDB saved at", Sys.time(), "with release version", hmdb_curr_ver, sep=" "),con=file.conn)
}
#wrong input error message
else if (!grepl(db, databases, fixed = TRUE)){
stop("Wrong db input. Following inputs apply: gnps, hmdb, mbank or all")
}
close(file.conn)
end_time <- Sys.time()
print(end_time - start_time)
}
else{
stop("Your input_dir is incorrect. Please provide the directory where all your input files are stored.")
}
}
##-----------------------------------------------------------------
## filter intensity
##-----------------------------------------------------------------
#' Define a filtering function and remove peaks less than 0.05 of intensity
low_int <- function(c, ...) {
c > max(c, na.rm = TRUE) * 0.05
}
# Usage:
# filterIntensity(spectra_object, intensity = low_int)
##-----------------------------------------------------------------
## normalize intensity
##-----------------------------------------------------------------
#' Define a function to *normalize* the intensities
norm_int <- function(y, ...) {
maxint <- max(y[, "intensity"], na.rm = TRUE)
y[, "intensity"] <- 100 * y[, "intensity"] / maxint
y
}
## Specifying a function for creating result directories for each input mzml
# input for the function:
# input directory
ms2_rfilename<- function(input_dir, output_dir){
if (dir.exists(input_dir)){
#list_ms2_files <- intersect(list.files(input_dir, pattern = "_PRM_"), list.files(input_dir, pattern = ".mzML"))
list_ms2_files <- list.files(input_dir, pattern = ".mzML")
mzml_file <- paste(input_dir, "/", list_ms2_files, sep = "")
#store the result file names to return to this function as output
mzml_files <- c()
ResultFileNames <- c()
File_id <- c()
nx <- 0
# x is mzML files
for (i in 1:length(mzml_file)){
nx <- nx+1
# remove .mzML to extract just the names
mzml_filex <- str_replace(mzml_file[i], input_dir, ".")
name_mzmls <- str_remove(as.character(mzml_filex), ".mzML")
#mzml_filex2 <- str_replace(mzml_file[i], input_dir, "")
name_mzmlsd <- paste(output_dir, str_remove(name_mzmls, "."), sep = "")
#name_mzmlsd <- str_remove(mzml_file[i], ".mzML")
#name_mzml <- str_replace(name_mzmls, input_dir, "./")
#' for each file a subdirectory is created to store all results in that, add working directory
if (!file.exists(name_mzmlsd)){
dir.create(name_mzmlsd) ##create folder
}
ResultFileNames<- c(ResultFileNames, name_mzmlsd)
mzml_files <- c(mzml_files, mzml_filex)
File_id <- c(File_id, paste("file_", nx, sep = ""))
}
input_table <- cbind(mzml_files, ResultFileNames, File_id)
write.csv(input_table, paste(output_dir, "/input_table.csv", sep = ""))
return(data.frame(input_table))
}
else{
stop("Your input_dir is incorrect. Please provide the directory where all your input files are stored. : ) Good Luck")
}
}
#' All spectra in mzML files preprocessing, return two outputs, pre-processed MS2 spectra and all precursor masses
# x is one mzML file
#' All spectra in mzML files preprocessing, return two outputs, pre-processed MS2 spectra and all precursor masses
# x is one mzML file
spec_Processing <- function(x, result_dir){
#x <- paste(input_dir, str_remove(x, "."), sep = "")
#result_dir <- paste(input_dir, str_remove(result_dir, "."), sep = "")
if (!dir.exists(result_dir)){
dir.create(result_dir)
}
if (file.exists(x) && substring(x, nchar(x)) == "L"){
# read the spectra
sps_all <- Spectra(x, backend = MsBackendMzR())
#' Change backend to a MsBackendDataFrame: load data into memory
#sps_all <- setBackend(sps_all, MsBackendDataFrame())
#' Filter Empty Spectra
sps_all <- filterEmptySpectra(sps_all)
#' Extract Precursor m/z(s) in each file
pre_mz <- unique(precursorMz(sps_all))
#' Remove any NAs
pre_mz <- na.omit(pre_mz)
if (!file.exists(paste(result_dir, "/processedSpectra.mzML", sep = ""))){
export(sps_all, backend = MsBackendMzR(), file = paste(result_dir, "/processedSpectra.mzML", sep = ""))
}
if (!file.exists(paste(result_dir, "/premz_list.txt", sep = ""))){
write.table(pre_mz, file = paste(result_dir, "/premz_list.txt", sep = ""), sep = "/t",row.names = FALSE, col.names = FALSE)
}
spsall_pmz <- list(sps_all, pre_mz)
return(spsall_pmz)
}
else{
stop("Are you sure x is an mzML input file?")
}
}
spec2_Processing <- function(z, obj, spec = "spec_all", ppmx = 15){
if (spec == "spec_all"){
#' Subset the dataset to MS2 spectra matching the m/z
sps <- filterPrecursorMzValues(obj, mz = z + ppm(c(-z, z), 10))
} else if (spec == "gnps"){
#gnps spectra that contains precursor mass
has_mz <- containsMz(obj, mz = z, ppm = ppmx)
#' Subset the GNPS Spectra
sps <- obj[has_mz]
} else if (spec == "hmdb"){
#hmdb spectra that contains precursor mass
has_mz <- containsMz(obj, mz = z, ppm = ppmx)
#' Subset the HMDB Spectra
sps <- obj[has_mz]
} else if (spec == "mbank"){
has_mz <- containsMz(obj, mz = z, ppm = ppmx)
#' Subset the MB Spectra
sps <- obj[has_mz]
}
#wrong input error message
else if (!grepl(db, databases, fixed = TRUE)){
stop("Wrong db input. Following inputs apply: gnps, hmdb, mbank or all")
}
if (length(sps)>0){
#' Apply the function to filter the spectra
sps <- filterIntensity(sps, intensity = low_int)
#' *Apply* the function to the data
sps <- addProcessing(sps, norm_int)
# cleaning peaks that are heavier or equal to the precursor mass
pkd <- peaksData(sps)@listData
#obtain the list of peaks that are higher or equal to precursor mass
# y is peaksData from spectra
removePrecursorPeaks <- function(m){
m <- m[m[, "mz"] <= z, ]
}
# use lapply to apply the function to the list of peaksData
pkd <- lapply(pkd, removePrecursorPeaks)
#store the indices of spectra with 0 peaks
store_i <- c()
for (i in 1:length(pkd)){
if (is.null(nrow(pkd[[i]]))){
#convert the object of one peak into a matrix
mz <- pkd[[i]][[1]]
intensity <- pkd[[i]][[2]]
mat <- cbind(mz, intensity)
pkd[[i]] <- mat
}else if(nrow(pkd[[i]])==0){
#store indices with 0 peaks
store_i <- c(store_i, i)
}
}
# if 0 peaks, remove the relevant spectra from gnps or hmdb or mbank
if (!(is.null(store_i))){
pkd <- pkd[-(store_i)]
sps <- sps[-(store_i)]
peaksData(sps@backend)<- pkd
}else {
peaksData(sps@backend)<- pkd
}
return(sps)
}
else {
sps <- NULL
return(sps)
}
}
##-----------------------------------------------------------------
## Extract peaksdata in a dataframe
##-----------------------------------------------------------------
#' obtain peaksData for each spectral matching between query and database spectra
#inputs a is best match from Database, b is best match from query spectra
peakdf <- function(a, b, ppmx){
#' obtain peaklists for both query spectra and best matched spectra from MassBank
z<- peaksData(a)[[1]] #from GNPS/HMDB
y <- peaksData(b)[[1]] #from query
if (!(nrow(z)==0)){
#' Since we used 15 ppm, so to find the range, calculate the mass error range
range <- (ppmx/1000000)*y[,"mz"]
y <- cbind(y, range)
low_range <- y[,"mz"]-y[,"range"] # low range of m/z
high_range <- y[,"mz"]+y[,"range"] # high range of m/z
y <- cbind(y, low_range, high_range)
#from GNPS/HMDB/MassBank spectra
mz.z <- c()
intensity.z <- c()
#from query spectra
mz.y <- c()
intensity.y <- c()
#difference between their intensity
diff <- c()
#' for all rows of y
for (m in 1:nrow(y)){
#' for all rows of z
for(j in 1:nrow(z)){
###################################################################
## IFELSE Statement no.2 -- LOOP 1.1.1.1
#' if the m/z of MB Spectra is within the 20 ppm range, save difference between intensities
if (y[m,"low_range"] <= z[j, "mz"] && z[j, "mz"] <= y[m,"high_range"]){
#GNPS/HMDB
mz_z <- as.numeric(z[j, "mz"])
mz.z <- c(mz.z, mz_z)
intensity_z <- as.numeric(z[j, "intensity"])
intensity.z <- c(intensity.z, intensity_z)
#QUERY
mz_y <- as.numeric(y[m, "mz"])
mz.y <- c(mz.y, mz_y)
intensity_y <- as.numeric(y[m, "intensity"])
intensity.y <- c(intensity.y, intensity_y)
#Difference between intensities
difference <- as.numeric(abs(z[j, "intensity"]-y[m, "intensity"]))
diff <- c(diff, difference)
}
}
}
df_peaklists <- cbind(mz.y, intensity.y, mz.z, intensity.z, diff)
return(df_peaklists)
}
else{
df_peaklists <- NULL
return(df_peaklists)
}
#output is a dataframe with mz and intensity from db spectra and query spectra and their difference
}
##-----------------------------------------------------------------
## Plotting Mirror Spectra
##-----------------------------------------------------------------
#' Specifying a function to draw peak labels
#label_fun <- function(x) {
#ints <- unlist(intensity(x))
#mzs <- format(unlist(mz(x)), digits = 4)
#mzs[ints < 5] <- ""
#mzs
#}
spec_dereplication_file <- function(mzml_file, pre_tbl, proc_mzml, db, result_dir, file_id, no_of_candidates = 30, ppmx, error = TRUE){
# if the database selected is HMDB or all
# if the database selected is GNPS or all
len_of_str = length(unlist(strsplit(mzml_file, "/")))
rem <- unlist(strsplit(mzml_file, "/"))[len_of_str]
input_dir <- str_remove(mzml_file, rem)
if (db == "all" || db =="gnps"){
# if (file.exists(paste(input_dir,"gnps.rda", sep = ""))){
# # load the gnps spectral database
# load(file = paste(input_dir,"gnps.rda", sep = ""))
# }
# else if (file.exists("gnps.rda")){
# # load the gnps spectral database
# load("gnps.rda")
# }
load(gnps_file)
}
# if the database selected is HMDB or all
if (db == "all" || db =="hmdb"){
# if (file.exists(paste(input_dir,"hmdb.rda", sep = ""))){
# # load the hmdb spectral database
# load(file = paste(input_dir,"hmdb.rda", sep = ""))
# }
# else if (file.exists("hmdb.rda")){
# # load the gnps spectral database
# load("hmdb.rda")
# }
load(hmdb_file)
}
# if the database selected is HMDB or all
if (db == "all" || db == "mbank"){
# if (file.exists(paste(input_dir,"mbankNIST.rda", sep = ""))){
# # load the mbank spectral database
# load(file = paste(input_dir,"mbankNIST.rda", sep = ""))
# }
# else if (file.exists("mbankNIST.rda")){
# # load the gnps spectral database
# load("mbankNIST.rda")
# }
load(mbank_file)
}
# read spectra object
sps_all <- Spectra(proc_mzml, source = MsBackendMzR())
# extract precursor m/z
tbl <- read.table(pre_tbl)
pre_mz <- tbl[[1]]
# common feature information
id_X <- c() # id
# ft_id <- c() # scan number
premz <- c() # precursor mz
rtmin <- c() # stores rtmin
rtmax <- c() # stores rtmax
rtmed <- c() # stores calculated median of rtmin and rtmax
rtmean <- c() # stores calculated mean of rtmin and rtmax
col_eng <- c() # stores collision energy
pol <- c() # stores polarity
int <- c() # store intensity
source_file <- c() # source file
nx <- 0 # numbering the ids
pre_mzs <- listenv() # list for holding pre_mz futures
# for each pre mass
for (x in pre_mz){
print(x)
# to name the file
nx <- nx+1
# filter spectra based on precusror m/z
# this is done to extract all common information for id_X
spsrt <- filterPrecursorMzRange(sps_all, x)
# id based on file id,
id_Xx <- paste(file_id, "M", as.character(round(x, digits = 0)),
"R", as.character(round(median(spsrt$rtime, na.rm = TRUE), digits = 0)),
"ID", as.character(nx), sep = '')
id_X <- c(id_X, id_Xx)
# if (ftid){
# ft_id1 <- spsrt$spectrumId
# ft_id <- c(ft_id, ft_id1)
# }
# pre_mas
pre <- x
premz <- c(premz, pre)
# rt min
rti <- min(spsrt$rtime)
rtmin <- c(rtmin, rti)
#rt max
rtx <- max(spsrt$rtime)
rtmax <- c(rtmax, rtx)
#rt median
rtmd <- median(spsrt$rtime, na.rm = TRUE)
rtmed <- c(rtmed, rtmd)
#rt mean
rtmn <- mean(spsrt$rtime, na.rm = TRUE)
rtmean <- c(rtmean, rtmn)
#collision energy
ce <- max(spsrt$collisionEnergy)
col_eng <- c(col_eng, ce)
#polarity
pl <- max(spsrt$polarity)
if (pl == 1){
px <- 'pos'
pol <- c(pol, px)
}
else {
px <- 'neg'
pol <- c(pol, px)
}
#int
ints <- max(spsrt$precursorIntensity)
int <- c(int, ints)
#mzmlfile
source_file <- c(source_file, mzml_file)
# after all the common infromation is stored,
# move to extracting matching candidates with input spectra
pre_mzs[[x]] <- future({
sps <- spec2_Processing(x, sps_all, spec = "spec_all")
####-------------------------------------------------------------
#### Dereplication with all or GNPS ----
####-------------------------------------------------------------
# define variables for result dataframe
# if the database selected is GNPS or all
f_gnps <- future(
if (db == "all" || db =="gnps"){
GNPSmax_similarity <- c() # dot product score
GNPSmzScore <- c() # similar m/z score
GNPSintScore <- c() # similar int score
GQMatchingPeaks <- c() # matching peaks between gnps candidate and input spectra
GNPSTotalPeaks <- c() # total peaks in gnps candidate
gQueryTotalPeaks<- c() # total peaks in input spectra
GNPSSMILES <- c() # smiles of gnps candidate
#GNPSspectrumID <- c() # spectrum id of gnps candidate
GNPScompound_name <- c() # compound name of gnps candidate
#GNPSmirrorSpec <- c() # path for mirror spectra between gnps candidate and input of gnps candidate
GNPSinstrument <- c()
GNPSformula <- c()
Source <- c() # GNPS as source of result
#### GNPS spec with pre_mz
gnps_with_mz <- spec2_Processing(x, gnpsdb, spec = "gnps", ppmx) # change here later
# define the directoyr name to store all GNPS results
dir_name <- paste(result_dir, "/spectral_dereplication/GNPS/", sep = "")
if (!file.exists(dir_name)){
dir.create(dir_name, recursive = TRUE)
}
if (length(sps) != 0 && length(gnps_with_mz) !=0){
#' Compare experimental spectra against GNPS
res <- compareSpectra(sps, gnps_with_mz, ppm = 15, FUN = MsCoreUtils::gnps, MAPFUN = joinPeaksGnps)
# first condition for GNPS
# if more input spectra and more candidates have been extracted from GNPS
if (length(sps) > 1 && length(gnps_with_mz) >1){
# given threshold of 0.85 for GNPS, extract top candidates
res_top <- which(res > res[res>0.85], arr.ind = TRUE)
# if there are some compounds from GNPS detected
if (length(res_top) > 0){
res_topdf <- data.frame(res_top)
# to store the scores to add to res_topdf
gnps_scores <- c()
# for all rows and columns in res_topdf
for (i in 1:nrow(res_topdf)){
# store the scores
gnps_scores <- c(gnps_scores, res[(res_topdf[i, "row"]), (res_topdf[i, "col"])])
}
if (length(gnps_scores)>0){
# add the score column to res_top
gnps_res <- cbind(res_top, gnps_scores)
gnps_res <- data.frame(gnps_res)
# sort in descending order
ordered_gnps_res <- gnps_res[order(-gnps_res[,"gnps_scores"]),]
df_ord_gnps_res <- data.frame(ordered_gnps_res)
if (nrow(df_ord_gnps_res)>no_of_candidates){
df_ord_gnps_res <- df_ord_gnps_res[1:no_of_candidates,]
}
#for each candidate from GNPS
for (k in 1:nrow(df_ord_gnps_res)){
# take each component from df_ord_gnps_res
idv <- df_ord_gnps_res[k,]
df_peaklists <- peakdf(gnps_with_mz[idv[[2]]], sps[idv[[1]]], ppmx)
if (!(is.null(df_peaklists))){
GNPSscore <- idv[1, "gnps_scores"]
GNPSmax_similarity <- c(GNPSmax_similarity, GNPSscore)
GNPSmz <- (nrow(df_peaklists)*2)/(nrow(peaksData(gnps_with_mz[idv[[2]]])[[1]])+nrow(peaksData(sps[idv[[1]]])[[1]]))
GNPSmzScore <- c(GNPSmzScore, GNPSmz)
GNPSint <- mean(1-(df_peaklists[,"diff"]/100))
GNPSintScore <- c(GNPSintScore, GNPSint)
GQMatPeaks <- nrow(df_peaklists)
GQMatchingPeaks <- c(GQMatchingPeaks, GQMatPeaks)
GNPSTPeaks <- nrow(peaksData(gnps_with_mz[idv[[2]]])[[1]])
GNPSTotalPeaks <- c(GNPSTotalPeaks, GNPSTPeaks)
gQTPeaks<- nrow(peaksData(sps[idv[[1]]])[[1]])
gQueryTotalPeaks <- c(gQueryTotalPeaks, gQTPeaks)
GNPS_SMILES <- gnps_with_mz[idv[[2]]]$smiles
GNPSSMILES <- c(GNPSSMILES, GNPS_SMILES)
#GNPSID <- gnps_with_mz[idv[[2]]]$SPECTRUMID
#GNPSspectrumID <- c(GNPSspectrumID, GNPSID)
GNPSname <- gnps_with_mz[idv[[2]]]$name
GNPScompound_name <- c(GNPScompound_name, GNPSname)
GNPSinstru <- gnps_with_mz[idv[[2]]]$INSTRUMENTTYPE
GNPSinstrument <- c(GNPSinstrument, GNPSinstru)
GNPSfor <- gnps_with_mz[idv[[2]]]$formula
GNPSformula <- c(GNPSformula, GNPSfor)
Src <- "GNPS"
Source <- c(Source, Src)
}# if df_peaklists isnt empty
}# for each candidate
}# gnps_score exists
}# if res_top has some good candidates
}# first condition
# if only one sepctrum from input and more candidates from GNPS
else if (length(sps) == 1 && length(gnps_with_mz) >1){
# given threshold of 0.85 for GNPS, extract top candidates
res_top <- which(res > res[res>0.85], arr.ind = TRUE)
# if there are candidates with good score
if (length(res_top) > 0){
res_topdf <- data.frame(res_top)
# top store the scores to add to res_topdf
gnps_scores <- c()
# for all rows and columns in res_topdf
for (i in 1:nrow(res_topdf)){
# store the scores
gnps_scores <- c(gnps_scores, res[(res_topdf[i, "res_topdf"])])
}
if (length(gnps_scores)>0){
# add the score column to res_top
gnps_res <- cbind(res_top, gnps_scores)
gnps_res <- data.frame(gnps_res)
# sort in descending order
ordered_gnps_res <- gnps_res[order(-gnps_res[,"gnps_scores"]),]
df_ord_gnps_res <- data.frame(ordered_gnps_res)
if (nrow(df_ord_gnps_res)>no_of_candidates){
df_ord_gnps_res <- df_ord_gnps_res[1:no_of_candidates,]
}
# for each candidate
for (k in 1:nrow(df_ord_gnps_res)){
# take each candidate
idv <- df_ord_gnps_res[k,]
df_peaklists <- peakdf(gnps_with_mz[idv[[1]]], sps, ppmx)
# if there are matchingpeaks
if (!(is.null(df_peaklists))){
GNPSscore <- idv[1, "gnps_scores"]
GNPSmax_similarity <- c(GNPSmax_similarity, GNPSscore)
GNPSmz <- (nrow(df_peaklists)*2)/(nrow(peaksData(gnps_with_mz[idv[[1]]])[[1]])+nrow(peaksData(sps)[[1]]))
GNPSmzScore <- c(GNPSmzScore, GNPSmz)
GNPSint <- mean(1-(df_peaklists[,"diff"]/100))
GNPSintScore <- c(GNPSintScore, GNPSint)
GQMatPeaks <- nrow(df_peaklists)
GQMatchingPeaks <- c(GQMatchingPeaks, GQMatPeaks)
GNPSTPeaks <- nrow(peaksData(gnps_with_mz[idv[[1]]])[[1]])
GNPSTotalPeaks <- c(GNPSTotalPeaks, GNPSTPeaks)
gQTPeaks<- nrow(peaksData(sps)[[1]])
gQueryTotalPeaks <- c(gQueryTotalPeaks, gQTPeaks)
GNPS_SMILES <- gnps_with_mz[idv[[1]]]$smiles
GNPSSMILES <- c(GNPSSMILES, GNPS_SMILES)
#GNPSID <- gnps_with_mz[idv[[1]]]$SPECTRUMID
#GNPSspectrumID <- c(GNPSspectrumID, GNPSID)
GNPSname <- gnps_with_mz[idv[[1]]]$name
GNPScompound_name <- c(GNPScompound_name, GNPSname)
GNPSinstru <- gnps_with_mz[idv[[1]]]$INSTRUMENTTYPE
GNPSinstrument <- c(GNPSinstrument, GNPSinstru)
GNPSfor <- gnps_with_mz[idv[[1]]]$formula
GNPSformula <- c(GNPSformula, GNPSfor)
Src <- "GNPS"
Source <- c(Source, Src)
}# if df_peaklists isnt empty
}# for each candidate
}# gnps_score exists
}# if res_top has some good candidates
}# second condition
# if there are more input spectra and one candidate from GNPS
else if (length(sps) > 1 && length(gnps_with_mz) == 1){
# given threshold of 0.85 for GNPS, extract top candidates
res_top <- which(res > res[res>0.85], arr.ind = TRUE)
# if there are good matching candidates
if (length(res_top) > 0){
res_topdf <- data.frame(res_top)
# top store the scores to add to res_topdf
gnps_scores <- c()
# for all rows and columns in res_topdf
# for all candidates
for (i in 1:nrow(res_topdf)){
# store the scores
gnps_scores <- c(gnps_scores, res[(res_topdf[i, "res_topdf"])])
}
if (length(gnps_scores)>0){
# add the score column to res_top
gnps_res <- cbind(res_top, gnps_scores)
gnps_res <- data.frame(gnps_res)
# sort in descending order
ordered_gnps_res <- gnps_res[order(-gnps_res[,"gnps_scores"]),]
df_ord_gnps_res <- data.frame(ordered_gnps_res)
if (nrow(df_ord_gnps_res)>no_of_candidates){
df_ord_gnps_res <- df_ord_gnps_res[1:no_of_candidates,]
}
# for each candidate match
for (k in 1:nrow(df_ord_gnps_res)){
# take each candidate
idv <- df_ord_gnps_res[k,]
df_peaklists <- peakdf(gnps_with_mz, sps[idv[[1]]], ppmx)
# if there are matching peaks
if (!(is.null(df_peaklists))){
GNPSscore <- idv[1, "gnps_scores"]
GNPSmax_similarity <- c(GNPSmax_similarity, GNPSscore)
GNPSmz <- (nrow(df_peaklists)*2)/(nrow(peaksData(gnps_with_mz)[[1]])+nrow(peaksData(sps[idv[[1]]])[[1]]))
GNPSmzScore <- c(GNPSmzScore, GNPSmz)
GNPSint <- mean(1-(df_peaklists[,"diff"]/100))
GNPSintScore <- c(GNPSintScore, GNPSint)
GQMatPeaks <- nrow(df_peaklists)
GQMatchingPeaks <- c(GQMatchingPeaks, GQMatPeaks)
GNPSTPeaks <- nrow(peaksData(gnps_with_mz)[[1]])
GNPSTotalPeaks <- c(GNPSTotalPeaks, GNPSTPeaks)
gQTPeaks<- nrow(peaksData(sps[idv[[1]]])[[1]])
gQueryTotalPeaks <- c(gQueryTotalPeaks, gQTPeaks)
GNPS_SMILES <- gnps_with_mz$smiles
GNPSSMILES <- c(GNPSSMILES, GNPS_SMILES)
#GNPSID <- gnps_with_mz$SPECTRUMID
#GNPSspectrumID <- c(GNPSspectrumID, GNPSID)
GNPSname <- gnps_with_mz$name
GNPScompound_name <- c(GNPScompound_name, GNPSname)
GNPSinstru <- gnps_with_mz$INSTRUMENTTYPE
GNPSinstrument <- c(GNPSinstrument, GNPSinstru)
GNPSfor <- gnps_with_mz$formula
GNPSformula <- c(GNPSformula, GNPSfor)
Src <- "GNPS"
Source <- c(Source, Src)
}# if df_peaklists isnt empty
}# for each candidate
}# gnps_score exists
}# if res_top has some good candidates
}# third condition
else if (length(sps) == 1 && length(gnps_with_mz) == 1){
if (res>= 0.85){
#take that one candidate
gnps_best_match <- gnps_with_mz
df_peaklists <- peakdf(gnps_best_match, sps, ppmx)
# if there are matching peaks
if (!(is.null(df_peaklists))){
GNPSscore <- max(res)
GNPSmax_similarity <- c(GNPSmax_similarity, GNPSscore)
GNPSmz <- (nrow(df_peaklists)*2)/(nrow(peaksData(gnps_best_match)[[1]])+nrow(peaksData(sps)[[1]]))
GNPSmzScore <- c(GNPSmzScore, GNPSmz)
GNPSint <- mean(1-(df_peaklists[,"diff"]/100))
GNPSintScore <- c(GNPSintScore, GNPSint)
GQMatPeaks <- NA
GQMatchingPeaks <- c(GQMatchingPeaks, GQMatPeaks)
GNPSTPeaks <- nrow(peaksData(gnps_best_match)[[1]])
GNPSTotalPeaks <- c(GNPSTotalPeaks, GNPSTPeaks)
gQTPeaks<- nrow(peaksData(sps)[[1]])
gQueryTotalPeaks <- c(gQueryTotalPeaks, gQTPeaks)
GNPS_SMILES <- gnps_best_match$smiles
GNPSSMILES <- c(GNPSSMILES, GNPS_SMILES)
GNPSname <- gnps_best_match$name
GNPScompound_name <- c(GNPScompound_name, GNPSname)
GNPSinstru <- gnps_best_match$INSTRUMENTTYPE
GNPSinstrument <- c(GNPSinstrument, GNPSinstru)
GNPSfor <- gnps_best_match$formula
GNPSformula <- c(GNPSformula, GNPSfor)
#GNPSID <- gnps_best_match$SPECTRUMID
#GNPSspectrumID <- c(GNPSspectrumID, GNPSID)
Src <- "GNPS"
Source <- c(Source, Src)
}# if candidate exixts
}# if res_top has some good candidates
}# fourth condition
}# if sps and gnps has some candidate
gnps_x <- data.frame(cbind(GNPSmax_similarity, GNPSmzScore,
GNPSintScore, GQMatchingPeaks,
GNPSTotalPeaks, gQueryTotalPeaks,
GNPSSMILES, GNPScompound_name, GNPSformula, GNPSinstrument, Source))
write.csv(gnps_x, file = paste(dir_name, "/gnps_results_for_", id_Xx, ".csv", sep = ""))
})# gnps ends
####-------------------------------------------------------------
#### Dereplication with all or HMDB ----
####-------------------------------------------------------------
# if the database selected is HMDB or all
f_hmdb <- future(
if (db == "all" || db =="hmdb"){
# hmdb
HMDBmax_similarity <- c()
HMDBmzScore <- c()
HMDBintScore <- c()
HQMatchingPeaks <- c()
HMDBTotalPeaks <- c()
hQueryTotalPeaks<- c()
HMDBcompoundID <- c()
HMDBinstrument <- c()
HMDBSMILES <- c()
HMDBformula <- c()
HMDBcompound_name<- c()
HMDBcollision <- c()
Source <- c()
#### HMDB spec with pre_mz
hmdb_with_mz <- spec2_Processing(x, hmdb, spec = "hmdb", ppmx) # change here later
# directory name for HMDB results
dir_name <- paste(result_dir, "/spectral_dereplication/HMDB/", sep = "")
if (!file.exists(dir_name)){
dir.create(dir_name, recursive = TRUE)
}
if (length(sps) != 0 && length(hmdb_with_mz) !=0){
#' Compare experimental spectra against HMDB
res <- compareSpectra(sps, hmdb_with_mz, ppm = 15)
# if there are more input spectra and more candidates from GNPS
if (length(sps) > 1 && length(hmdb_with_mz) >1){
# given threshold of 0.70 for HMDB, extract top candidates
res_top <- which(res > res[res>0.70], arr.ind = TRUE)
if (length(res_top) > 0){
res_topdf <- data.frame(res_top)
# to store the scores to add to res_topdf
hmdb_scores <- c()
# for all rows and columns in res_topdf
for (i in 1:nrow(res_topdf)){
# store the scores
hmdb_scores <- c(hmdb_scores, res[(res_topdf[i, "row"]), (res_topdf[i, "col"])])
}
if (length(hmdb_scores)>0){
# add the score column to res_top
hmdb_res <- cbind(res_top, hmdb_scores)
hmdb_res <- data.frame(hmdb_res)
# sort in descending order
ordered_hmdb_res <- hmdb_res[order(-hmdb_res[,"hmdb_scores"]),]
df_ord_hmdb_res <- data.frame(ordered_hmdb_res)
if (nrow(df_ord_hmdb_res)>no_of_candidates){
df_ord_hmdb_res <- df_ord_hmdb_res[1:no_of_candidates,]
}
for (k in 1:nrow(df_ord_hmdb_res)){
idv <- df_ord_hmdb_res[k,]
df_peaklists <- peakdf(hmdb_with_mz[idv[[2]]], sps[idv[[1]]], ppmx)
if (!(is.null(df_peaklists))){
HMDBscore <- idv[1, "hmdb_scores"]
HMDBmax_similarity <- c(HMDBmax_similarity, HMDBscore)
HMDBmz <- (nrow(df_peaklists)*2)/(nrow(peaksData(hmdb_with_mz[idv[[2]]])[[1]])+nrow(peaksData(sps[idv[[1]]])[[1]]))
HMDBmzScore <- c(HMDBmzScore, HMDBmz)
HMDBint <- mean(1-(df_peaklists[,"diff"]/100))
HMDBintScore <- c(HMDBintScore, HMDBint)
HQMatPeaks <- nrow(df_peaklists)
HQMatchingPeaks <- c(HQMatchingPeaks, HQMatPeaks)
HMDBTPeaks <- nrow(peaksData(hmdb_with_mz[idv[[2]]])[[1]])
HMDBTotalPeaks <- c(HMDBTotalPeaks, HMDBTPeaks)
hQTPeaks<- nrow(peaksData(sps[idv[[1]]])[[1]])
hQueryTotalPeaks<- c(hQueryTotalPeaks, hQTPeaks)
HMDBID <- hmdb_with_mz[idv[[2]]]$compound_id
HMDBcompoundID <- c(HMDBcompoundID, HMDBID)
HMDBinstru <- hmdb_with_mz[idv[[2]]]$instrument_type
HMDBinstrument <- c(HMDBinstrument, HMDBinstru)
hsmiles <- hmdb_with_mz[idv[[2]]]$smiles
HMDBSMILES <- c(HMDBSMILES, hsmiles)
HMDBfor <- hmdb_with_mz[idv[[2]]]$formula
HMDBformula <- c(HMDBformula, HMDBfor)
HMDBname <- hmdb_with_mz[idv[[2]]]$name
HMDBcompound_name<- c(HMDBcompound_name, HMDBname)
HMDBcol <- hmdb_with_mz[idv[[2]]]$collisionEnergy
HMDBcollision <- c(HMDBcollision, HMDBcol)
Src <- "HMDB"
Source <- c(Source, Src)
}# if df_peaklists is not empty
}# for each candidate
}# if hmdb_scores exist
}# if there are top candidadates with good scores
}#first condition ends
else if (length(sps) == 1 && length(hmdb_with_mz) >1){
# given threshold of 0.70 for HMDB, extract top candidates
res_top <- which(res > res[res>0.70], arr.ind = TRUE)
if (length(res_top) > 0){
res_topdf <- data.frame(res_top)
# top store the scores to add to res_topdf
hmdb_scores <- c()
# for all rows and columns in res_topdf
for (i in 1:nrow(res_topdf)){
# store the scores
hmdb_scores <- c(hmdb_scores, res[(res_topdf[i, "res_topdf"])])
}
if (length(hmdb_scores > 0)){
# add the score column to res_top
hmdb_res <- cbind(res_top, hmdb_scores)
hmdb_res <- data.frame(hmdb_res)
# sort in descending order
ordered_hmdb_res <- hmdb_res[order(-hmdb_res[,"hmdb_scores"]),]
df_ord_hmdb_res <- data.frame(ordered_hmdb_res)
if (nrow(df_ord_hmdb_res)>no_of_candidates){
df_ord_hmdb_res <- df_ord_hmdb_res[1:no_of_candidates,]
}
for (k in 1:nrow(df_ord_hmdb_res)){
idv <- df_ord_hmdb_res[k,]
df_peaklists <- peakdf(hmdb_with_mz[idv[[1]]], sps, ppmx)
if (!(is.null(df_peaklists))){
HMDBscore <- idv[1, "hmdb_scores"]
HMDBmax_similarity <- c(HMDBmax_similarity, HMDBscore)
HMDBmz <- (nrow(df_peaklists)*2)/(nrow(peaksData(hmdb_with_mz[idv[[1]]])[[1]])+nrow(peaksData(sps)[[1]]))
HMDBmzScore <- c(HMDBmzScore, HMDBmz)
HMDBint <- mean(1-(df_peaklists[,"diff"]/100))
HMDBintScore <- c(HMDBintScore, HMDBint)
HQMatPeaks <- nrow(df_peaklists)
HQMatchingPeaks <- c(HQMatchingPeaks, HQMatPeaks)
HMDBTPeaks <- nrow(peaksData(hmdb_with_mz[idv[[1]]])[[1]])
HMDBTotalPeaks <- c(HMDBTotalPeaks, HMDBTPeaks)