R/identify_metabolites_for_multiple_database.R
In jaspershen/metID: Metabolite identification based on MS1 and MS2 spectra
Defines functions
identify_metabolites_params
identify_metabolite_all
Documented in
identify_metabolite_all
identify_metabolites_params
#' @title Identify metabolites using multiple databases one time
#' @description Identify metabolites using multiple databases one time.
#' \lifecycle{maturing}
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param ms1.data The name of ms1 peak table (csv format). Column 1 is "name", column 2 is
#' "mz" and column 3 is "rt" (second).
#' @param ms2.data MS2 data, must be mgf, msp or mzXML format. For example, ms2.data = c("test.mgf", "test2.msp").
#' @param parameter.list A list contains paramters for each processing.
#' The parameter must get using metIdentifyParam or mzIdentifyParam.
#' @param path Work directory.
#' @return A list containing mzIdentifyClass object.
#' @export
#' @importFrom magrittr %>%
#' @seealso The example and demo data of this function can be found
#' https://jaspershen.github.io/metID/articles/multiple_databases.html
#' @examples
#' \dontrun{
#' ##creat a folder nameed as example
#' path <- file.path(".", "example")
#' dir.create(path = path, showWarnings = FALSE)
#'
#' ##get MS1 peak table from metID
#' ms1_peak <- system.file("ms1_peak", package = "metID")
#' file.copy(
#' from = file.path(ms1_peak, "ms1.peak.table.csv"),
#' to = path,
#' overwrite = TRUE,
#' recursive = TRUE
#' )
#'
#' ##get MS2 data from metID
#' ms2_data <- system.file("ms2_data", package = "metID")
#' file.copy(
#' from = file.path(ms2_data, "QC1_MSMS_NCE25.mgf"),
#' to = path,
#' overwrite = TRUE,
#' recursive = TRUE
#' )
#'
#' ##get databases from metID
#' database <- system.file("ms2_database", package = "metID")
#'
#' file.copy(
#' from = file.path(
#' database,
#' c(
#' "msDatabase_rplc0.0.2",
#' "orbitrapDatabase0.0.1",
#' "hmdbMS1Database0.0.1"
#' )
#' ),
#' to = path,
#' overwrite = TRUE,
#' recursive = TRUE
#' )
#' param1 <-
#' identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "msDatabase_rplc0.0.2"
#' )
#'
#' param2 <- identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "orbitrapDatabase0.0.1"
#' )
#'
#' param3 <- identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "hmdbMS1Database0.0.1"
#' )
#' result <- identify_metabolite_all(
#' ms1.data = "ms1.peak.table.csv",
#' ms2.data = "QC1_MSMS_NCE25.mgf",
#' parameter.list = c(param1, param2, param3),
#' path = path
#' )
#' result[[1]]
#' result[[2]]
#' result[[3]]
#' }
# tinytools::setwd_project()
# setwd("example")
# param1 <-
# identify_metabolites_params(
# ms1.match.ppm = 15,
# rt.match.tol = 15,
# polarity = "positive",
# ce = "all",
# column = "rp",
# total.score.tol = 0.5,
# candidate.num = 3,
# threads = 3,
# database = "msDatabase_rplc0.0.2"
# )
#
# param2 <- identify_metabolites_params(
# ms1.match.ppm = 15,
# rt.match.tol = 15,
# polarity = "positive",
# ce = "all",
# column = "rp",
# total.score.tol = 0.5,
# candidate.num = 3,
# threads = 3,
# database = "orbitrapDatabase0.0.1"
# )
#
# identify_metabolite_all(
# ms1.data = "ms1.peak.table.csv",
# ms2.data = "QC1_MSMS_NCE25.mgf",
# parameter.list = c(param1, param2)
# )
identify_metabolite_all = function(ms1.data,
ms2.data,
parameter.list,
path = ".") {
dir.create(path = path, showWarnings = FALSE)
old.path <- path
path <- file.path(path, "Result")
dir.create(path = path, showWarnings = FALSE)
threads = parameter.list[[1]]$threads
ms1.data.name <- ms1.data
ms2.data.name <- ms2.data
intermediate_path <-
file.path(path, "intermediate_data")
dir.create(intermediate_path, showWarnings = FALSE)
file <- dir(intermediate_path)
if (all(c("ms1.info", "ms2.info") %in% file)) {
cat(crayon::yellow("Use old data\n"))
load(file.path(intermediate_path, "ms1.info"))
load(file.path(intermediate_path, "ms2.info"))
} else{
##read MS2 data
cat(crayon::green("Reading MS2 data...\n"))
temp.ms2.type <-
stringr::str_split(string = ms2.data.name,
pattern = "\\.")[[1]]
temp.ms2.type <- temp.ms2.type[length(temp.ms2.type)]
if (temp.ms2.type %in% c("mzXML", "mzML")) {
ms2.data <-
read_mzxml(file = file.path(old.path, ms2.data.name),
threads = threads)
} else{
ms2.data <- lapply(ms2.data.name, function(temp.ms2.data) {
temp.ms2.type <- stringr::str_split(string = temp.ms2.data,
pattern = "\\.")[[1]]
temp.ms2.type <-
temp.ms2.type[length(temp.ms2.type)]
if (!temp.ms2.type %in% c("mgf", "msp"))
stop("We only support mgf or msp.\n")
if (temp.ms2.type == "msp") {
temp.ms2.data <- readMSP(file = file.path(old.path, temp.ms2.data))
} else{
temp.ms2.data <- read_mgf(file = file.path(old.path, temp.ms2.data))
}
temp.ms2.data
})
names(ms2.data) <- ms2.data.name
###prepare data for metIdentification function
cat(crayon::green("Preparing MS2 data for identification...\n"))
ms2.data <-
mapply(
FUN = function(temp.ms2.data, temp.ms2.data.name) {
temp.ms2.data <- lapply(temp.ms2.data, function(x) {
info <- x$info
info <-
data.frame(
name = paste("mz", info[1], "rt", info[2], sep = ""),
"mz" = info[1],
"rt" = info[2],
"file" = temp.ms2.data.name,
stringsAsFactors = FALSE
)
rownames(info) <- NULL
x$info <- info
x
})
temp.ms2.data
},
temp.ms2.data = ms2.data,
temp.ms2.data.name = ms2.data.name
)
if (class(ms2.data) == "matrix") {
ms2.data <- ms2.data[, 1]
} else{
ms2.data <- do.call(what = c, args = ms2.data)
}
}
ms1.info <- lapply(ms2.data, function(x) {
x[[1]]
})
ms2.info <- lapply(ms2.data, function(x) {
x[[2]]
})
ms1.info <- do.call(what = rbind, args = ms1.info)
ms1.info <- as.data.frame(ms1.info)
rownames(ms1.info) <- NULL
duplicated.name <-
unique(ms1.info$name[duplicated(ms1.info$name)])
if (length(duplicated.name) > 0) {
lapply(duplicated.name, function(x) {
ms1.info$name[which(ms1.info$name == x)] <-
paste(x, c(1:sum(ms1.info$name == x)), sep = "_")
})
}
names(ms2.info) <- ms1.info$name
##save intermediate data
save(ms1.info,
file = file.path(intermediate_path, "ms1.info"),
compress = "xz")
save(ms2.info,
file = file.path(intermediate_path, "ms2.info"),
compress = "xz")
}
database_class =
purrr::map(parameter.list, function(x) {
class(x$database)
}) %>%
unlist()
database.name <-
unlist(lapply(parameter.list, function(x) {
if (class(x$database) != "databaseClass") {
x$database
} else{
paste(x$database@database.info$Source,
x$database@database.info$Version,
sep = "_")
}
}))
##check databases with same names
database.name = make.unique(names = database.name, sep = "_")
##output database information to intermediate_data path
database_info =
data.frame(database.name,
database_class,
parameter = 1:length(database.name))
write.csv(
database_info,
file = file.path(intermediate_path, "database_info.csv"),
row.names = FALSE
)
if (!all(database.name[database_class != "databaseClass"] %in% dir(old.path))) {
stop(
"The database: ",
paste(database.name[!database.name %in% dir(old.path)], collapse = ", "),
"\n",
" you want to use are not in you directory: \n",
old.path
)
}
identification.result <-
vector(mode = "list", length = length(database.name))
names(identification.result) <- database.name
for (i in 1:length(database.name)) {
cat(crayon::yellow("-------------------------------\n"))
cat(crayon::yellow('Database', i, ":", database.name[i], "\n"))
cat(crayon::yellow("-------------------------------\n"))
new.path <-
file.path(path, paste(database.name[i], "Result", sep = '_'))
dir.create(new.path, showWarnings = FALSE)
if (any(dir(new.path) == "result")) {
load(file.path(new.path, "result"))
identification.result[[i]] <- result
rm(list = "result")
next()
}
if (class(parameter.list[[i]]$database) == "databaseClass") {
temp_database =
parameter.list[[i]]$database
} else{
temp_database <-
load(file.path(old.path, parameter.list[[i]]$database))
temp_database <-
get(temp_database)
}
if (length(temp_database@spectra.data) == 0) {
rm(list = parameter.list[[i]]$database)
result <- mzIdentify(
ms1.data = ms1.data.name,
ms1.match.ppm = parameter.list[[i]]$ms1.match.ppm,
rt.match.tol = parameter.list[[i]]$rt.match.tol,
polarity = parameter.list[[i]]$polarity,
column = parameter.list[[i]]$column,
path = old.path,
candidate.num = parameter.list[[i]]$candidate.num,
database = parameter.list[[i]]$database,
threads = parameter.list[[i]]$threads,
silence.deprecated = TRUE
)
} else{
# rm(list = parameter.list[[i]]$database)
result <- identify_metabolites(
ms1.data = ms1.data.name,
ms2.data = ms2.data.name,
ms1.ms2.match.mz.tol = parameter.list[[i]]$ms1.ms2.match.mz.tol,
ms1.ms2.match.rt.tol = parameter.list[[i]]$ms1.ms2.match.rt.tol,
ms1.match.ppm = parameter.list[[i]]$ms1.match.ppm,
ms2.match.ppm = parameter.list[[i]]$ms2.match.ppm,
mz.ppm.thr = parameter.list[[i]]$mz.ppm.thr,
ms2.match.tol = parameter.list[[i]]$ms2.match.tol,
fraction.weight = parameter.list[[i]]$fraction.weight,
dp.forward.weight = parameter.list[[i]]$dp.forward.weight,
dp.reverse.weight = parameter.list[[i]]$dp.reverse.weight,
rt.match.tol = parameter.list[[i]]$rt.match.tol,
polarity = parameter.list[[i]]$polarity,
ce = parameter.list[[i]]$ce,
column = parameter.list[[i]]$column,
ms1.match.weight = parameter.list[[i]]$ms1.match.weight,
rt.match.weight = parameter.list[[i]]$rt.match.weight,
ms2.match.weight = parameter.list[[i]]$ms2.match.weight,
path = old.path,
total.score.tol = parameter.list[[i]]$total.score.tol,
candidate.num = parameter.list[[i]]$candidate.num,
database = parameter.list[[i]]$database,
threads = parameter.list[[i]]$threads
)
}
# unlink(x = new.path, recursive = TRUE, force = TRUE)
identification.result[[i]] <- result
save(result, file = file.path(new.path, "result"))
rm(list = "result")
}
invisible(identification.result)
}
#' @title Generate the parameter list for identify_metabolites function
#' @description Generate the parameter list for metIdentify function.
#' \lifecycle{maturing}
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param ms1.ms2.match.mz.tol MS1 peak and MS2 spectrum matching m/z tolerance. Default is 25 pm.
#' @param ms1.ms2.match.rt.tol MS1 peak and MS2 spectrum matching RT tolerance. Default is 10 s.
#' @param ms1.match.ppm Precursor match ppm tolerance.
#' @param ms2.match.ppm Fragment ion match ppm tolerance.
#' @param mz.ppm.thr Accurate mass tolerance for m/z error calculation.
#' @param ms2.match.tol MS2 match (MS2 similarity) tolerance.
#' @param fraction.weight The weight for matched fragments.
#' @param dp.forward.weight Forward dot product weight.
#' @param dp.reverse.weight Reverse dot product weight.
#' @param rt.match.tol RT match tolerance.
#' @param polarity The polarity of data, "positive"or "negative".
#' @param ce Collision energy. Please confirm the CE values in your database. Default is "all".
#' @param column "hilic" (HILIC column) or "rp" (reverse phase).
#' @param ms1.match.weight The weight of MS1 match for total score calculation.
#' @param rt.match.weight The weight of RT match for total score calculation.
#' @param ms2.match.weight The weight of MS2 match for total score calculation.
#' @param total.score.tol Total score tolerance. The total score are refering to MS-DIAL.
#' @param candidate.num The number of candidate.
#' @param database MS2 database name or MS2 database.
#' @param threads Number of threads
#' @return A metIdentifyClass object.
#' @export
#' @seealso The example and demo data of this function can be found
#' https://jaspershen.github.io/metID/articles/multiple_databases.html
#' @examples
#' param1 <-
#' identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "msDatabase_rplc0.0.2"
#' )
#' param1
identify_metabolites_params = function(ms1.ms2.match.mz.tol = 25,
ms1.ms2.match.rt.tol = 10,
ms1.match.ppm = 25,
ms2.match.ppm = 30,
mz.ppm.thr = 400,
ms2.match.tol = 0.5,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
rt.match.tol = 30,
polarity = c("positive", "negative"),
ce = "all",
column = c("hilic", "rp"),
ms1.match.weight = 0.25,
rt.match.weight = 0.25,
ms2.match.weight = 0.5,
total.score.tol = 0.5,
candidate.num = 3,
database,
threads = 3) {
if (missing(database)) {
stop("The database or database name must be provided.\n")
}
# if(class(database) != "metIdentifyClass"){
# stop("database must be metIdentifyClass object\n")
# }
polarity <- match.arg(polarity)
column <- match.arg(column)
param <-
list(
ms1.ms2.match.mz.tol = ms1.ms2.match.mz.tol,
ms1.ms2.match.rt.tol = ms1.ms2.match.rt.tol,
ms1.ms2.match.mz.tol = ms1.ms2.match.mz.tol,
ms1.ms2.match.rt.tol = ms1.ms2.match.rt.tol,
ms1.match.ppm = ms1.match.ppm,
ms2.match.ppm = ms2.match.ppm,
mz.ppm.thr = mz.ppm.thr,
ms2.match.tol = ms2.match.tol,
fraction.weight = fraction.weight,
dp.forward.weight = dp.forward.weight,
dp.reverse.weight = dp.reverse.weight,
rt.match.tol = rt.match.tol,
polarity = polarity,
ce = ce,
column = column,
ms1.match.weight = ms1.match.weight,
rt.match.weight = rt.match.weight,
ms2.match.weight = ms2.match.weight,
total.score.tol = total.score.tol,
candidate.num = candidate.num,
database = database,
threads = threads
)
list("metIdentifyParam" = param)
}
jaspershen/metID documentation built on July 31, 2022, 11:31 p.m.
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Defines functions identify_metabolites_params identify_metabolite_all
Documented in identify_metabolite_all identify_metabolites_params
#' @title Identify metabolites using multiple databases one time
#' @description Identify metabolites using multiple databases one time.
#' \lifecycle{maturing}
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param ms1.data The name of ms1 peak table (csv format). Column 1 is "name", column 2 is
#' "mz" and column 3 is "rt" (second).
#' @param ms2.data MS2 data, must be mgf, msp or mzXML format. For example, ms2.data = c("test.mgf", "test2.msp").
#' @param parameter.list A list contains paramters for each processing.
#' The parameter must get using metIdentifyParam or mzIdentifyParam.
#' @param path Work directory.
#' @return A list containing mzIdentifyClass object.
#' @export
#' @importFrom magrittr %>%
#' @seealso The example and demo data of this function can be found
#' https://jaspershen.github.io/metID/articles/multiple_databases.html
#' @examples
#' \dontrun{
#' ##creat a folder nameed as example
#' path <- file.path(".", "example")
#' dir.create(path = path, showWarnings = FALSE)
#'
#' ##get MS1 peak table from metID
#' ms1_peak <- system.file("ms1_peak", package = "metID")
#' file.copy(
#' from = file.path(ms1_peak, "ms1.peak.table.csv"),
#' to = path,
#' overwrite = TRUE,
#' recursive = TRUE
#' )
#'
#' ##get MS2 data from metID
#' ms2_data <- system.file("ms2_data", package = "metID")
#' file.copy(
#' from = file.path(ms2_data, "QC1_MSMS_NCE25.mgf"),
#' to = path,
#' overwrite = TRUE,
#' recursive = TRUE
#' )
#'
#' ##get databases from metID
#' database <- system.file("ms2_database", package = "metID")
#'
#' file.copy(
#' from = file.path(
#' database,
#' c(
#' "msDatabase_rplc0.0.2",
#' "orbitrapDatabase0.0.1",
#' "hmdbMS1Database0.0.1"
#' )
#' ),
#' to = path,
#' overwrite = TRUE,
#' recursive = TRUE
#' )
#' param1 <-
#' identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "msDatabase_rplc0.0.2"
#' )
#'
#' param2 <- identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "orbitrapDatabase0.0.1"
#' )
#'
#' param3 <- identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "hmdbMS1Database0.0.1"
#' )
#' result <- identify_metabolite_all(
#' ms1.data = "ms1.peak.table.csv",
#' ms2.data = "QC1_MSMS_NCE25.mgf",
#' parameter.list = c(param1, param2, param3),
#' path = path
#' )
#' result[[1]]
#' result[[2]]
#' result[[3]]
#' }
# tinytools::setwd_project()
# setwd("example")
# param1 <-
# identify_metabolites_params(
# ms1.match.ppm = 15,
# rt.match.tol = 15,
# polarity = "positive",
# ce = "all",
# column = "rp",
# total.score.tol = 0.5,
# candidate.num = 3,
# threads = 3,
# database = "msDatabase_rplc0.0.2"
# )
#
# param2 <- identify_metabolites_params(
# ms1.match.ppm = 15,
# rt.match.tol = 15,
# polarity = "positive",
# ce = "all",
# column = "rp",
# total.score.tol = 0.5,
# candidate.num = 3,
# threads = 3,
# database = "orbitrapDatabase0.0.1"
# )
#
# identify_metabolite_all(
# ms1.data = "ms1.peak.table.csv",
# ms2.data = "QC1_MSMS_NCE25.mgf",
# parameter.list = c(param1, param2)
# )
identify_metabolite_all = function(ms1.data,
ms2.data,
parameter.list,
path = ".") {
dir.create(path = path, showWarnings = FALSE)
old.path <- path
path <- file.path(path, "Result")
dir.create(path = path, showWarnings = FALSE)
threads = parameter.list[[1]]$threads
ms1.data.name <- ms1.data
ms2.data.name <- ms2.data
intermediate_path <-
file.path(path, "intermediate_data")
dir.create(intermediate_path, showWarnings = FALSE)
file <- dir(intermediate_path)
if (all(c("ms1.info", "ms2.info") %in% file)) {
cat(crayon::yellow("Use old data\n"))
load(file.path(intermediate_path, "ms1.info"))
load(file.path(intermediate_path, "ms2.info"))
} else{
##read MS2 data
cat(crayon::green("Reading MS2 data...\n"))
temp.ms2.type <-
stringr::str_split(string = ms2.data.name,
pattern = "\\.")[[1]]
temp.ms2.type <- temp.ms2.type[length(temp.ms2.type)]
if (temp.ms2.type %in% c("mzXML", "mzML")) {
ms2.data <-
read_mzxml(file = file.path(old.path, ms2.data.name),
threads = threads)
} else{
ms2.data <- lapply(ms2.data.name, function(temp.ms2.data) {
temp.ms2.type <- stringr::str_split(string = temp.ms2.data,
pattern = "\\.")[[1]]
temp.ms2.type <-
temp.ms2.type[length(temp.ms2.type)]
if (!temp.ms2.type %in% c("mgf", "msp"))
stop("We only support mgf or msp.\n")
if (temp.ms2.type == "msp") {
temp.ms2.data <- readMSP(file = file.path(old.path, temp.ms2.data))
} else{
temp.ms2.data <- read_mgf(file = file.path(old.path, temp.ms2.data))
}
temp.ms2.data
})
names(ms2.data) <- ms2.data.name
###prepare data for metIdentification function
cat(crayon::green("Preparing MS2 data for identification...\n"))
ms2.data <-
mapply(
FUN = function(temp.ms2.data, temp.ms2.data.name) {
temp.ms2.data <- lapply(temp.ms2.data, function(x) {
info <- x$info
info <-
data.frame(
name = paste("mz", info[1], "rt", info[2], sep = ""),
"mz" = info[1],
"rt" = info[2],
"file" = temp.ms2.data.name,
stringsAsFactors = FALSE
)
rownames(info) <- NULL
x$info <- info
x
})
temp.ms2.data
},
temp.ms2.data = ms2.data,
temp.ms2.data.name = ms2.data.name
)
if (class(ms2.data) == "matrix") {
ms2.data <- ms2.data[, 1]
} else{
ms2.data <- do.call(what = c, args = ms2.data)
}
}
ms1.info <- lapply(ms2.data, function(x) {
x[[1]]
})
ms2.info <- lapply(ms2.data, function(x) {
x[[2]]
})
ms1.info <- do.call(what = rbind, args = ms1.info)
ms1.info <- as.data.frame(ms1.info)
rownames(ms1.info) <- NULL
duplicated.name <-
unique(ms1.info$name[duplicated(ms1.info$name)])
if (length(duplicated.name) > 0) {
lapply(duplicated.name, function(x) {
ms1.info$name[which(ms1.info$name == x)] <-
paste(x, c(1:sum(ms1.info$name == x)), sep = "_")
})
}
names(ms2.info) <- ms1.info$name
##save intermediate data
save(ms1.info,
file = file.path(intermediate_path, "ms1.info"),
compress = "xz")
save(ms2.info,
file = file.path(intermediate_path, "ms2.info"),
compress = "xz")
}
database_class =
purrr::map(parameter.list, function(x) {
class(x$database)
}) %>%
unlist()
database.name <-
unlist(lapply(parameter.list, function(x) {
if (class(x$database) != "databaseClass") {
x$database
} else{
paste(x$database@database.info$Source,
x$database@database.info$Version,
sep = "_")
}
}))
##check databases with same names
database.name = make.unique(names = database.name, sep = "_")
##output database information to intermediate_data path
database_info =
data.frame(database.name,
database_class,
parameter = 1:length(database.name))
write.csv(
database_info,
file = file.path(intermediate_path, "database_info.csv"),
row.names = FALSE
)
if (!all(database.name[database_class != "databaseClass"] %in% dir(old.path))) {
stop(
"The database: ",
paste(database.name[!database.name %in% dir(old.path)], collapse = ", "),
"\n",
" you want to use are not in you directory: \n",
old.path
)
}
identification.result <-
vector(mode = "list", length = length(database.name))
names(identification.result) <- database.name
for (i in 1:length(database.name)) {
cat(crayon::yellow("-------------------------------\n"))
cat(crayon::yellow('Database', i, ":", database.name[i], "\n"))
cat(crayon::yellow("-------------------------------\n"))
new.path <-
file.path(path, paste(database.name[i], "Result", sep = '_'))
dir.create(new.path, showWarnings = FALSE)
if (any(dir(new.path) == "result")) {
load(file.path(new.path, "result"))
identification.result[[i]] <- result
rm(list = "result")
next()
}
if (class(parameter.list[[i]]$database) == "databaseClass") {
temp_database =
parameter.list[[i]]$database
} else{
temp_database <-
load(file.path(old.path, parameter.list[[i]]$database))
temp_database <-
get(temp_database)
}
if (length(temp_database@spectra.data) == 0) {
rm(list = parameter.list[[i]]$database)
result <- mzIdentify(
ms1.data = ms1.data.name,
ms1.match.ppm = parameter.list[[i]]$ms1.match.ppm,
rt.match.tol = parameter.list[[i]]$rt.match.tol,
polarity = parameter.list[[i]]$polarity,
column = parameter.list[[i]]$column,
path = old.path,
candidate.num = parameter.list[[i]]$candidate.num,
database = parameter.list[[i]]$database,
threads = parameter.list[[i]]$threads,
silence.deprecated = TRUE
)
} else{
# rm(list = parameter.list[[i]]$database)
result <- identify_metabolites(
ms1.data = ms1.data.name,
ms2.data = ms2.data.name,
ms1.ms2.match.mz.tol = parameter.list[[i]]$ms1.ms2.match.mz.tol,
ms1.ms2.match.rt.tol = parameter.list[[i]]$ms1.ms2.match.rt.tol,
ms1.match.ppm = parameter.list[[i]]$ms1.match.ppm,
ms2.match.ppm = parameter.list[[i]]$ms2.match.ppm,
mz.ppm.thr = parameter.list[[i]]$mz.ppm.thr,
ms2.match.tol = parameter.list[[i]]$ms2.match.tol,
fraction.weight = parameter.list[[i]]$fraction.weight,
dp.forward.weight = parameter.list[[i]]$dp.forward.weight,
dp.reverse.weight = parameter.list[[i]]$dp.reverse.weight,
rt.match.tol = parameter.list[[i]]$rt.match.tol,
polarity = parameter.list[[i]]$polarity,
ce = parameter.list[[i]]$ce,
column = parameter.list[[i]]$column,
ms1.match.weight = parameter.list[[i]]$ms1.match.weight,
rt.match.weight = parameter.list[[i]]$rt.match.weight,
ms2.match.weight = parameter.list[[i]]$ms2.match.weight,
path = old.path,
total.score.tol = parameter.list[[i]]$total.score.tol,
candidate.num = parameter.list[[i]]$candidate.num,
database = parameter.list[[i]]$database,
threads = parameter.list[[i]]$threads
)
}
# unlink(x = new.path, recursive = TRUE, force = TRUE)
identification.result[[i]] <- result
save(result, file = file.path(new.path, "result"))
rm(list = "result")
}
invisible(identification.result)
}
#' @title Generate the parameter list for identify_metabolites function
#' @description Generate the parameter list for metIdentify function.
#' \lifecycle{maturing}
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param ms1.ms2.match.mz.tol MS1 peak and MS2 spectrum matching m/z tolerance. Default is 25 pm.
#' @param ms1.ms2.match.rt.tol MS1 peak and MS2 spectrum matching RT tolerance. Default is 10 s.
#' @param ms1.match.ppm Precursor match ppm tolerance.
#' @param ms2.match.ppm Fragment ion match ppm tolerance.
#' @param mz.ppm.thr Accurate mass tolerance for m/z error calculation.
#' @param ms2.match.tol MS2 match (MS2 similarity) tolerance.
#' @param fraction.weight The weight for matched fragments.
#' @param dp.forward.weight Forward dot product weight.
#' @param dp.reverse.weight Reverse dot product weight.
#' @param rt.match.tol RT match tolerance.
#' @param polarity The polarity of data, "positive"or "negative".
#' @param ce Collision energy. Please confirm the CE values in your database. Default is "all".
#' @param column "hilic" (HILIC column) or "rp" (reverse phase).
#' @param ms1.match.weight The weight of MS1 match for total score calculation.
#' @param rt.match.weight The weight of RT match for total score calculation.
#' @param ms2.match.weight The weight of MS2 match for total score calculation.
#' @param total.score.tol Total score tolerance. The total score are refering to MS-DIAL.
#' @param candidate.num The number of candidate.
#' @param database MS2 database name or MS2 database.
#' @param threads Number of threads
#' @return A metIdentifyClass object.
#' @export
#' @seealso The example and demo data of this function can be found
#' https://jaspershen.github.io/metID/articles/multiple_databases.html
#' @examples
#' param1 <-
#' identify_metabolites_params(
#' ms1.match.ppm = 15,
#' rt.match.tol = 15,
#' polarity = "positive",
#' ce = "all",
#' column = "rp",
#' total.score.tol = 0.5,
#' candidate.num = 3,
#' threads = 3,
#' database = "msDatabase_rplc0.0.2"
#' )
#' param1
identify_metabolites_params = function(ms1.ms2.match.mz.tol = 25,
ms1.ms2.match.rt.tol = 10,
ms1.match.ppm = 25,
ms2.match.ppm = 30,
mz.ppm.thr = 400,
ms2.match.tol = 0.5,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
rt.match.tol = 30,
polarity = c("positive", "negative"),
ce = "all",
column = c("hilic", "rp"),
ms1.match.weight = 0.25,
rt.match.weight = 0.25,
ms2.match.weight = 0.5,
total.score.tol = 0.5,
candidate.num = 3,
database,
threads = 3) {
if (missing(database)) {
stop("The database or database name must be provided.\n")
}
# if(class(database) != "metIdentifyClass"){
# stop("database must be metIdentifyClass object\n")
# }
polarity <- match.arg(polarity)
column <- match.arg(column)
param <-
list(
ms1.ms2.match.mz.tol = ms1.ms2.match.mz.tol,
ms1.ms2.match.rt.tol = ms1.ms2.match.rt.tol,
ms1.ms2.match.mz.tol = ms1.ms2.match.mz.tol,
ms1.ms2.match.rt.tol = ms1.ms2.match.rt.tol,
ms1.match.ppm = ms1.match.ppm,
ms2.match.ppm = ms2.match.ppm,
mz.ppm.thr = mz.ppm.thr,
ms2.match.tol = ms2.match.tol,
fraction.weight = fraction.weight,
dp.forward.weight = dp.forward.weight,
dp.reverse.weight = dp.reverse.weight,
rt.match.tol = rt.match.tol,
polarity = polarity,
ce = ce,
column = column,
ms1.match.weight = ms1.match.weight,
rt.match.weight = rt.match.weight,
ms2.match.weight = ms2.match.weight,
total.score.tol = total.score.tol,
candidate.num = candidate.num,
database = database,
threads = threads
)
list("metIdentifyParam" = param)
}
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