R/51_annotate_peaks_mz_rt_ms2.R
In tidymass/metid: Metabolite identification based on MS1 and MS2 spectra
Defines functions
annotate_peaks_mz_rt_ms2
Documented in
annotate_peaks_mz_rt_ms2
#' Annotate Peaks Based on MS1, Retention Time (RT), and MS2
#'
#' This function annotates metabolites by matching MS1, retention time (RT), and MS2 spectra against a provided database. It allows for the use of custom parameters such as mass tolerance, MS2 fragment matching criteria, and retention time tolerance.
#'
#' @param ms1.info A data frame containing MS1 peak information (m/z and RT). If `based_on` includes `"ms1"` or `"rt"`, this argument is required.
#' @param ms2.info A list containing MS2 spectra for each corresponding `ms2_spectrum_id`. If `based_on` includes `"ms2"`, this argument is required.
#' @param database A `databaseClass` object containing the reference database for metabolite annotation.
#' @param based_on Character vector. Specifies which criteria to base the matching on. Can include `"ms1"`, `"rt"`, and `"ms2"`. Default is `c("ms1", "rt", "ms2")`.
#' @param polarity Character. The ionization mode, either `"positive"` or `"negative"`. Default is `"positive"`.
#' @param ce Character. Collision energy used in MS2 spectra. Default is `"all"`.
#' @param column Character. The chromatographic column type, either `"hilic"` or `"rp"` (reversed-phase). Default is `"hilic"`.
#' @param adduct.table A data frame containing the adducts to use in the matching process. If `NULL`, a default table is loaded based on the `polarity` and `column`.
#' @param ms1.match.ppm Numeric. The mass tolerance in parts per million (ppm) for MS1 peak matching. Default is 25.
#' @param mz.ppm.thr Numeric. m/z threshold for ppm calculation. Default is 400.
#' @param rt.match.tol Numeric. Retention time matching tolerance in seconds. Default is 30.
#' @param ms2.match.ppm Numeric. The mass tolerance in ppm for MS2 peak matching. Default is 30.
#' @param ms2.match.tol Numeric. The retention time tolerance for MS2 fragment matching. Default is 0.5.
#' @param fraction.weight Numeric. Weight for the fraction of matched fragments in MS2 spectra. Default is 0.3.
#' @param dp.forward.weight Numeric. Weight for the forward dot product score in MS2 matching. Default is 0.6.
#' @param dp.reverse.weight Numeric. Weight for the reverse dot product score in MS2 matching. Default is 0.1.
#' @param remove_fragment_intensity_cutoff Numeric. Intensity cutoff for removing low-intensity MS2 fragments. Default is 0.
#' @param ms1.match.weight Numeric. Weight for MS1 matching score in the total score calculation. Default is 0.25.
#' @param rt.match.weight Numeric. Weight for RT matching score in the total score calculation. Default is 0.25.
#' @param ms2.match.weight Numeric. Weight for MS2 matching score in the total score calculation. Default is 0.5.
#' @param total.score.tol Numeric. Threshold for the total score. Only results with a score above this value are retained. Default is 0.5.
#' @param candidate.num Numeric. Maximum number of top candidate annotations to retain per metabolite. Default is 3.
#' @param threads Numeric. Number of threads to use for parallel processing. Default is 3.
#'
#' @return A data frame with annotated metabolites, including columns for matched m/z, retention time, MS2 spectra, and the calculated scores for each match.
#'
#' @details
#' The function uses a combination of MS1 peak information (m/z and retention time), MS2 spectra, and a reference database to annotate metabolites. The matching process can be customized by adjusting the mass tolerance, retention time tolerance, and MS2 fragment matching parameters.
#'
#' If `based_on` includes `"ms1"` or `"rt"`, the MS1 information is extracted from the `ms1.info` data frame. If `based_on` includes `"ms2"`, the function uses the provided MS2 spectra in `ms2.info` to perform fragment matching. The function calculates individual scores for m/z, retention time, and MS2 fragment matches, which are then combined into a total score. Annotations with total scores above `total.score.tol` are retained, and only the top `candidate.num` annotations are kept for each metabolite.
#'
#' @examples
#' \dontrun{
#' # Example MS1 and MS2 data
#' ms1_info <- data.frame(
#' variable_id = c("id1", "id2"),
#' mz = c(150.08, 180.12),
#' rt = c(12.5, 14.7)
#' )
#' ms2_info <- list(
#' id1 = matrix(c(75, 1000, 80, 2000),
#' ncol = 2, byrow = TRUE,
#' dimnames = list(NULL, c("mz", "intensity"))),
#' id2 = matrix(c(85, 3000, 90, 1500),
#' ncol = 2, byrow = TRUE,
#' dimnames = list(NULL, c("mz", "intensity")))
#' )
#'
#' # Example database
#' database <- load_database("path/to/database")
#'
#' # Annotate metabolites using MS1, RT, and MS2 data
#' annotations <- annotate_peaks_mz_rt_ms2(
#' ms1.info = ms1_info,
#' ms2.info = ms2_info,
#' database = database,
#' based_on = c("ms1", "rt", "ms2"),
#' polarity = "positive",
#' column = "rp",
#' ms1.match.ppm = 20,
#' rt.match.tol = 20,
#' candidate.num = 5,
#' threads = 4
#' )
#'
#' print(annotations)
#' }
#'
#'
#' @export
annotate_peaks_mz_rt_ms2 <-
function(ms1.info = NULL,
ms2.info = NULL,
database = NULL,
based_on = c("ms1", "rt", "ms2"),
polarity = c("positive", "negative"),
ce = "all",
column = c("hilic", "rp"),
adduct.table = NULL,
ms1.match.ppm = 25,
mz.ppm.thr = 400,
rt.match.tol = 30,
ms2.match.ppm = 30,
ms2.match.tol = 0.5,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
remove_fragment_intensity_cutoff = 0,
ms1.match.weight = 0.25,
rt.match.weight = 0.25,
ms2.match.weight = 0.5,
total.score.tol = 0.5,
candidate.num = 3,
threads = 3) {
options(warn = -1)
based_on <- match.arg(based_on, several.ok = TRUE)
if (is.null(database)) {
stop("No database is provided.\n")
}
##check ms1.file and ms2.file
if (!is(database, "databaseClass")) {
stop("database should be databaseClass object.\n")
}
###if based_on contains ms2, the ms1.info should contains the ms2_spectrum_id
###and file
##parameter specification
polarity <- match.arg(polarity)
column <- match.arg(column)
# ce <- match.arg(ce)
if (is.null(adduct.table)) {
adduct.table <-
load_adduct_table(polarity = polarity, column = column)
}
check_adduct_table(adduct.table)
database.name <-
extract_database_name(database = database)
####------------------------------------------------------------------
####ms1 and/or rt match
####------------------------------------------------------------------
if ("ms1" %in% based_on | "rt" %in% based_on) {
###Check data
if (is.null(ms1.info)) {
stop("No ms1.info is provided.\n")
}
###check ms.info
check_ms1_ms2_info(ms1.info = ms1.info,
ms2.info = ms2.info,
based_on = based_on)
ms1_database <-
extract_ms1_database(database = database)
###calculate the mz_rt_matrix for all the compound with all adducts
mz_matrix <-
seq_len(nrow(adduct.table)) %>%
purrr::map(function(i) {
temp_n <-
stringr::str_extract(string = adduct.table$adduct[i], pattern = "[0-9]{1}M") %>%
stringr::str_replace("M", "") %>%
as.numeric()
temp_n[is.na(temp_n)] <- 1
temp <-
data.frame(as.numeric(adduct.table$mz[i]) + temp_n * as.numeric(ms1_database$mz))
colnames(temp) <- i
temp
}) %>%
dplyr::bind_cols()
colnames(mz_matrix) <-
adduct.table$adduct
rownames(mz_matrix) <-
ms1_database$Lab.ID
mz_matrix <-
mz_matrix %>%
tibble::rownames_to_column(var = "Lab.ID") %>%
tidyr::pivot_longer(cols = -Lab.ID,
names_to = "Adduct",
values_to = "mz") %>%
as.data.frame()
mz_rt_matrix <-
mz_matrix %>%
dplyr::left_join(ms1_database[, c("Lab.ID", "RT")], by = "Lab.ID")
###mz match
# pb <- progress::progress_bar$new(total = nrow(ms1.info))
future::plan(strategy = future::multisession, workers = threads)
match_result <-
seq_len(nrow(ms1.info)) %>%
furrr::future_map(function(i) {
# pb$tick()
# cat(i, " ")
temp <-
mz_rt_matrix %>%
dplyr::mutate(variable_id = ms1.info$variable_id[i]) %>%
dplyr::mutate(
mz.error = abs(ms1.info$mz[i] - mz) * 10 ^ 6 / ifelse(ms1.info$mz[i] < 400, 400, ms1.info$mz[i])
) %>%
dplyr::mutate(RT.error = abs(ms1.info$rt[i] - RT)) %>%
dplyr::filter(mz.error < ifelse("ms1" %in% based_on, ms1.match.ppm, max(mz.error) + 100)) %>%
dplyr::select(variable_id, Lab.ID, Adduct, mz.error, RT.error) %>%
dplyr::arrange(mz.error, RT.error)
if ("rt" %in% based_on) {
temp <-
temp %>%
dplyr::filter(RT.error < rt.match.tol)
}
temp %>%
dplyr::arrange(mz.error, RT.error) %>%
head(candidate.num)
}, .progress = TRUE)
match_result <-
match_result %>%
dplyr::bind_rows() %>%
dplyr::mutate(Database = database.name) %>%
dplyr::select(variable_id, dplyr::everything())
if (nrow(match_result) == 0) {
message("No matched compounds.")
return(NULL)
}
match_result$mz.match.score <-
calculate_mz_match_score(mz.error = match_result$mz.error, ms1.match.ppm = ms1.match.ppm)
match_result$RT.match.score <-
calculate_rt_match_score(RT.error = match_result$RT.error, rt.match.tol = rt.match.tol)
match_result <-
match_result %>%
dplyr::left_join(ms1_database, by = "Lab.ID")
match_result <-
match_result %>%
dplyr::select(
"variable_id",
"Compound.name",
"CAS.ID",
"HMDB.ID",
"KEGG.ID",
"Lab.ID",
"Adduct",
"mz.error",
"mz.match.score",
"RT.error",
"RT.match.score",
"Database",
dplyr::everything()
)
####remove some impossible adducts
match_result <-
remove_impossible_annotations(match_result)
match_result <-
match_result %>%
dplyr::arrange(variable_id,
dplyr::desc(mz.match.score),
dplyr::desc(RT.match.score))
if (nrow(match_result) == 0) {
message("No matched compounds.")
return(match_result)
}
} else{
match_result <- NULL
}
####------------------------------------------------------------------
####ms2 and/or ms1/rt match
####------------------------------------------------------------------
if ("ms2" %in% based_on) {
if (missing(ms2.info)) {
stop("No ms2.info is provided.\n")
}
check_ms1_ms2_info(ms1.info = ms1.info,
ms2.info = ms2.info,
based_on = based_on)
##extract MS2 data
spectra.data <-
extract_ms2_database(database = database,
polarity = polarity,
ce = ce)
if (is.null(spectra.data)) {
stop("No MS2 spectra in the database.\n")
}
if (masstools::get_os() == "windows") {
bpparam = BiocParallel::SnowParam(workers = threads, progressbar = TRUE)
} else{
bpparam = BiocParallel::MulticoreParam(workers = threads, progressbar = TRUE)
}
###remove some metabolites from spectra.data according to ms1 and/or rt matching
if (!is.null(match_result)) {
match_result <-
match_result %>%
dplyr::left_join(ms1.info[, c("variable_id", "ms2_files_id", "ms2_spectrum_id")], by = "variable_id")
spectra.data <-
spectra.data[names(spectra.data) %in% match_result$Lab.ID]
ms2.info <-
ms2.info[which(names(ms2.info) %in% match_result$ms2_spectrum_id)]
}
if (length(ms2.info) == 0) {
return(NULL)
}
if (length(spectra.data) == 0) {
return(NULL)
}
###debug
# for (i in seq_len(length(ms2.info))) {
# cat(i, " ")
# match_ms2_temp(
# idx = i,
# ms2.info = ms2.info,
# pre_match_result = match_result,
# spectra.data = spectra.data,
# ms2.match.ppm = ms2.match.ppm,
# mz.ppm.thr = mz.ppm.thr,
# ms2.match.tol = ms2.match.tol,
# candidate.num = candidate.num,
# fraction.weight = fraction.weight,
# dp.forward.weight = dp.forward.weight,
# dp.reverse.weight = dp.reverse.weight,
# remove_fragment_intensity_cutoff = remove_fragment_intensity_cutoff
# )
# }
match_result_ms2 <-
suppressMessages(
BiocParallel::bplapply(
seq_len(length(ms2.info)),
FUN = match_ms2_temp,
BPPARAM = bpparam,
pre_match_result = match_result,
ms2.info = ms2.info,
spectra.data = spectra.data,
ms2.match.ppm = ms2.match.ppm,
mz.ppm.thr = mz.ppm.thr,
ms2.match.tol = ms2.match.tol,
candidate.num = candidate.num,
fraction.weight = fraction.weight,
dp.forward.weight = dp.forward.weight,
dp.reverse.weight = dp.reverse.weight,
remove_fragment_intensity_cutoff = remove_fragment_intensity_cutoff
)
)
names(match_result_ms2) <- names(ms2.info)
match_result_ms2 <-
match_result_ms2[which(!unlist(lapply(match_result_ms2, function(x)
all(is.null(x)))))]
match_result_ms2 <-
match_result_ms2 %>%
do.call(rbind, .) %>%
as.data.frame()
if (nrow(match_result_ms2) == 0) {
return(NULL)
}
rownames(match_result_ms2) <- NULL
if ("ms1" %in% based_on | "rt" %in% based_on) {
if (any(colnames(match_result) == "CE")) {
match_result <-
match_result %>%
dplyr::select(-CE)
}
match_result <-
match_result_ms2 %>%
dplyr::left_join(match_result, by = c("ms2_spectrum_id", "Lab.ID")) %>%
dplyr::select(
"variable_id",
"ms2_spectrum_id",
"ms2_files_id",
"Compound.name",
"CAS.ID",
"HMDB.ID",
"KEGG.ID",
"Lab.ID",
"Adduct",
"mz.error",
"mz.match.score",
"RT.error",
"RT.match.score",
"SS",
"Database",
dplyr::everything()
)
} else{
match_result_ms2$Database <- database.name
match_result <-
match_result_ms2 %>%
dplyr::left_join(ms1.info, by = "ms2_spectrum_id") %>%
dplyr::left_join(ms1_database, by = c("Lab.ID")) %>%
dplyr::mutate(
Adduct = NA,
mz.error = NA,
mz.match.score = NA,
RT.error = NA,
RT.match.score = NA,
SS = NA
) %>%
dplyr::select(
"variable_id",
"ms2_spectrum_id",
"ms2_files_id",
"Compound.name",
"CAS.ID",
"HMDB.ID",
"KEGG.ID",
"Lab.ID",
"Adduct",
"mz.error",
"mz.match.score",
"RT.error",
"RT.match.score",
"SS",
"Database",
dplyr::everything()
)
}
} else{
match_result <-
match_result %>%
dplyr::mutate(
ms2_spectrum_id = NA,
ms2_files_id = NA,
CE = NA,
SS = NA
)
}
###calculate the total score
Total.score <-
calculate_total_score(
mz.match.score = match_result$mz.match.score,
RT.match.score = match_result$RT.match.score,
SS = match_result$SS,
ms1.match.weight = ms1.match.weight,
rt.match.weight = rt.match.weight,
ms2.match.weight = ms2.match.weight,
based_on = based_on
)
match_result$Total.score <- Total.score
match_result <-
match_result %>%
dplyr::filter(Total.score > total.score.tol)
if (nrow(match_result) == 0) {
message("No matched compounds.")
return(NULL)
}
Level <-
calculate_confidence_level(annotation_result = match_result)
match_result$Level <- Level
match_result <-
match_result %>%
dplyr::arrange(
variable_id,
Level,
dplyr::desc(Total.score),
dplyr::desc(SS),
dplyr::desc(mz.match.score),
dplyr::desc(RT.match.score)
) %>%
dplyr::group_by(variable_id) %>%
dplyr::slice_head(n = candidate.num) %>%
dplyr::ungroup() %>%
as.data.frame()
return(match_result)
}
#' Match MS2 Spectra to Database Compounds
#'
#' This function matches MS2 spectra from experimental data against MS2 spectra in a database. It calculates MS2 matching scores for each compound in the database, and selects the best matches based on the provided matching parameters.
#'
#' @param idx Numeric. The index of the current MS2 spectrum in the `ms2.info` list.
#' @param ms2.info A list of MS2 spectra, where each element is a matrix with two columns (`mz` and `intensity`).
#' @param pre_match_result A data frame containing the preliminary match results from MS1 or RT matching, including compound identifiers (`Lab.ID`).
#' @param spectra.data A list of MS2 spectra from the database, where each element is a matrix with two columns (`mz` and `intensity`).
#' @param ms2.match.ppm Numeric. Mass tolerance in parts per million (ppm) for MS2 peak matching. Default is 30.
#' @param mz.ppm.thr Numeric. m/z threshold for ppm calculation. Default is 400.
#' @param ms2.match.tol Numeric. Score threshold for MS2 matches. Only matches with scores above this value are retained. Default is 0.5.
#' @param candidate.num Numeric. The number of top candidate annotations to retain per MS2 spectrum. Default is 3.
#' @param fraction.weight Numeric. Weight for the fraction of matched fragments in the total MS2 score calculation. Default is 0.3.
#' @param dp.forward.weight Numeric. Weight for the forward dot product score in the total MS2 score calculation. Default is 0.6.
#' @param dp.reverse.weight Numeric. Weight for the reverse dot product score in the total MS2 score calculation. Default is 0.1.
#' @param remove_fragment_intensity_cutoff Numeric. Intensity cutoff to remove low-intensity MS2 fragments from matching. Default is 0.
#' @param ... Additional arguments to pass to internal functions.
#'
#' @return A data frame containing the top candidate matches for the given MS2 spectrum. The output includes:
#' \describe{
#' \item{Lab.ID}{The database compound ID.}
#' \item{CE}{The collision energy used in the MS2 spectrum.}
#' \item{SS}{The matching score for the compound.}
#' \item{ms2_spectrum_id}{The ID of the experimental MS2 spectrum.}
#' }
#'
#' @details
#' The function takes an MS2 spectrum from the experimental data and matches it against MS2 spectra in a reference database. It computes matching scores based on the provided mass tolerance (`ms2.match.ppm`), fragment matching weight parameters (`fraction.weight`, `dp.forward.weight`, `dp.reverse.weight`), and intensity cutoff. The best-matching spectra from the database are retained and returned as candidates.
#'
#' If preliminary MS1/RT matches are provided (`pre_match_result`), the function only considers those compounds for MS2 matching. Otherwise, all compounds in the database are considered.
#'
#' @export
match_ms2_temp <-
function(idx,
ms2.info,
pre_match_result,
spectra.data,
ms2.match.ppm = 30,
mz.ppm.thr = 400,
ms2.match.tol = 0.5,
candidate.num = 3,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
remove_fragment_intensity_cutoff = 0,
masstplus_method_cutoff = 100,
...) {
peak_ms2_spectrum <-
as.data.frame(ms2.info[[idx]])
ms2_spectrum_id <- names(ms2.info)[idx]
rm(list = c("ms2.info"))
if (length(peak_ms2_spectrum) == 0) {
return(NA)
}
if (!is.null(pre_match_result)) {
library_compound_id <-
pre_match_result$Lab.ID[which(pre_match_result$ms2_spectrum_id == ms2_spectrum_id)]
} else{
library_compound_id <- names(spectra.data)
}
library_compound_id <-
library_compound_id[which(library_compound_id %in% names(spectra.data))]
###if no matched compounds with MS2 in spectra
if (length(library_compound_id) == 0) {
return(NULL)
}
if (length(library_compound_id) <= masstplus_method_cutoff) {
###MS2 spectra match
ms2_match_score <-
seq_len(length(spectra.data[library_compound_id])) %>%
purrr::map(function(i) {
temp_spectra_data <- spectra.data[library_compound_id][[i]]
score <-
lapply(temp_spectra_data, function(y) {
y <- as.data.frame(y)
y$mz <- as.numeric(y$mz)
y$intensity <- as.numeric(y$intensity)
calculate_ms2_matching_score(
experimental.spectrum = peak_ms2_spectrum,
library.spectrum = y,
ms2.match.ppm = ms2.match.ppm,
mz.ppm.thr = mz.ppm.thr,
fraction.weight = fraction.weight,
dp.forward.weight = dp.forward.weight,
dp.reverse.weight = dp.reverse.weight,
remove_fragment_intensity_cutoff = remove_fragment_intensity_cutoff
)
})
score <- score[which.max(unlist(score))]
score <- unlist(score)
data.frame(
Lab.ID = names(spectra.data[library_compound_id])[i],
"CE" = names(score),
"SS" = score,
stringsAsFactors = FALSE
)
}) %>%
dplyr::bind_rows()
rownames(ms2_match_score) <- NULL
ms2_match_score <-
ms2_match_score %>%
dplyr::mutate(ms2_spectrum_id = ms2_spectrum_id) %>%
dplyr::arrange(dplyr::desc(SS)) %>%
head(candidate.num) %>%
dplyr::filter(SS > ms2.match.tol)
} else{
}
# rm(list = c("ms2_match_score"))
if (nrow(ms2_match_score) == 0) {
return(NULL)
} else{
return(ms2_match_score)
}
}
# spectra_database <-
# purrr::map2(.x = names(spectra.data), .y = spectra.data, function(x, y) {
# names(y) <- paste(x, names(y), sep = "_")
# y
# }) %>%
# do.call(c, .)
#
# names(spectra_database) <-
# masstools::name_duplicated(names(spectra_database))
#
# save(query_ms2, file = "example/query_ms2")
# save(spectra_database, file = "example/spectra_database")
###load data first if you want to run this function
# load("example/query_ms2")
# load("example/spectra_database")
match_ms2_masstplus <-
function(query_ms2,
spectra_database,
remove_fragment_intensity_cutoff = 0,
ms2.match.ppm = 30,
mz.ppm.thr = 400) {
total_spectra_data <-
purrr::map2(.x = names(spectra_database), .y = spectra_database, function(x, y) {
y$intensity <- as.numeric(y$intensity) / max(as.numeric(y$intensity))
y$Lab.ID <- x
y
}) %>%
do.call(rbind, .) %>%
as.data.frame() %>%
dplyr::arrange(mz, intensity, Lab.ID) %>%
dplyr::filter(intensity > remove_fragment_intensity_cutoff) %>%
dplyr::mutate(idx = seq_len(nrow(.)))
all_spectra_ids <-
unique(total_spectra_data$Lab.ID)
query_ms2 <-
query_ms2 %>%
dplyr::mutate(intensity = as.numeric(intensity) / max(as.numeric(intensity))) %>%
dplyr::filter(intensity > remove_fragment_intensity_cutoff) %>%
remove_noise()
###match query and total spectra
match_matrix <-
purrr::map(seq_len(nrow(experimental.spectrum)), function(i) {
temp_mz <-
experimental.spectrum$mz[i]
mz_error <-
abs(temp_mz - total_spectra_data$mz) * 10 ^ 6 / ifelse(temp_mz < 400, 400, temp_mz)
idx <- total_spectra_data$idx[which(mz_error < ms2.match.ppm)]
return_intensity <- rep(0, length(all_spectra_ids))
names(return_intensity) <- all_spectra_ids
if (length(idx) > 0) {
duplicated_id <-
unique(total_spectra_data$Lab.ID[idx][duplicated(total_spectra_data$Lab.ID[idx])])
if (length(duplicated_id) > 0) {
idx <-
total_spectra_data[idx, ] %>%
dplyr::filter(Lab.ID %in% duplicated_id) %>%
dplyr::group_by(Lab.ID) %>%
dplyr::filter(intensity == max(intensity)) %>%
dplyr::ungroup() %>%
pull(idx)
}
return_intensity[total_spectra_data$Lab.ID[idx]] <-
total_spectra_data$intensity[idx]
return_intensity
} else{
return_intensity
}
}) %>%
do.call(cbind, .) %>%
t() %>%
as.data.frame()
###remove the spectra with no matched fragments
remove_idx <-
which(colSums(match_matrix) == 0)
if (length(remove_idx) > 0) {
match_matrix <- match_matrix[, -remove_idx, drop = FALSE]
}
if (ncol(match_matrix) == 0) {
return(NULL)
}
forward_dp <-
purrr::map(match_matrix, function(x) {
calculate_dotproduct(exp.int = query_ms2$intensity, lib.int = x)
}) %>%
unlist()
forward_dp2 <-
lapply(spectra_database, function(x) {
calculate_ms2_matching_score(x, query_ms2)
}) %>%
unlist()
x = forward_dp
y = forward_dp2[match(names(forward_dp), names(forward_dp2))]
plot(x, y)
}
tidymass/metid documentation built on Oct. 8, 2024, 10:32 p.m.
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Defines functions annotate_peaks_mz_rt_ms2
Documented in annotate_peaks_mz_rt_ms2
#' Annotate Peaks Based on MS1, Retention Time (RT), and MS2
#'
#' This function annotates metabolites by matching MS1, retention time (RT), and MS2 spectra against a provided database. It allows for the use of custom parameters such as mass tolerance, MS2 fragment matching criteria, and retention time tolerance.
#'
#' @param ms1.info A data frame containing MS1 peak information (m/z and RT). If `based_on` includes `"ms1"` or `"rt"`, this argument is required.
#' @param ms2.info A list containing MS2 spectra for each corresponding `ms2_spectrum_id`. If `based_on` includes `"ms2"`, this argument is required.
#' @param database A `databaseClass` object containing the reference database for metabolite annotation.
#' @param based_on Character vector. Specifies which criteria to base the matching on. Can include `"ms1"`, `"rt"`, and `"ms2"`. Default is `c("ms1", "rt", "ms2")`.
#' @param polarity Character. The ionization mode, either `"positive"` or `"negative"`. Default is `"positive"`.
#' @param ce Character. Collision energy used in MS2 spectra. Default is `"all"`.
#' @param column Character. The chromatographic column type, either `"hilic"` or `"rp"` (reversed-phase). Default is `"hilic"`.
#' @param adduct.table A data frame containing the adducts to use in the matching process. If `NULL`, a default table is loaded based on the `polarity` and `column`.
#' @param ms1.match.ppm Numeric. The mass tolerance in parts per million (ppm) for MS1 peak matching. Default is 25.
#' @param mz.ppm.thr Numeric. m/z threshold for ppm calculation. Default is 400.
#' @param rt.match.tol Numeric. Retention time matching tolerance in seconds. Default is 30.
#' @param ms2.match.ppm Numeric. The mass tolerance in ppm for MS2 peak matching. Default is 30.
#' @param ms2.match.tol Numeric. The retention time tolerance for MS2 fragment matching. Default is 0.5.
#' @param fraction.weight Numeric. Weight for the fraction of matched fragments in MS2 spectra. Default is 0.3.
#' @param dp.forward.weight Numeric. Weight for the forward dot product score in MS2 matching. Default is 0.6.
#' @param dp.reverse.weight Numeric. Weight for the reverse dot product score in MS2 matching. Default is 0.1.
#' @param remove_fragment_intensity_cutoff Numeric. Intensity cutoff for removing low-intensity MS2 fragments. Default is 0.
#' @param ms1.match.weight Numeric. Weight for MS1 matching score in the total score calculation. Default is 0.25.
#' @param rt.match.weight Numeric. Weight for RT matching score in the total score calculation. Default is 0.25.
#' @param ms2.match.weight Numeric. Weight for MS2 matching score in the total score calculation. Default is 0.5.
#' @param total.score.tol Numeric. Threshold for the total score. Only results with a score above this value are retained. Default is 0.5.
#' @param candidate.num Numeric. Maximum number of top candidate annotations to retain per metabolite. Default is 3.
#' @param threads Numeric. Number of threads to use for parallel processing. Default is 3.
#'
#' @return A data frame with annotated metabolites, including columns for matched m/z, retention time, MS2 spectra, and the calculated scores for each match.
#'
#' @details
#' The function uses a combination of MS1 peak information (m/z and retention time), MS2 spectra, and a reference database to annotate metabolites. The matching process can be customized by adjusting the mass tolerance, retention time tolerance, and MS2 fragment matching parameters.
#'
#' If `based_on` includes `"ms1"` or `"rt"`, the MS1 information is extracted from the `ms1.info` data frame. If `based_on` includes `"ms2"`, the function uses the provided MS2 spectra in `ms2.info` to perform fragment matching. The function calculates individual scores for m/z, retention time, and MS2 fragment matches, which are then combined into a total score. Annotations with total scores above `total.score.tol` are retained, and only the top `candidate.num` annotations are kept for each metabolite.
#'
#' @examples
#' \dontrun{
#' # Example MS1 and MS2 data
#' ms1_info <- data.frame(
#' variable_id = c("id1", "id2"),
#' mz = c(150.08, 180.12),
#' rt = c(12.5, 14.7)
#' )
#' ms2_info <- list(
#' id1 = matrix(c(75, 1000, 80, 2000),
#' ncol = 2, byrow = TRUE,
#' dimnames = list(NULL, c("mz", "intensity"))),
#' id2 = matrix(c(85, 3000, 90, 1500),
#' ncol = 2, byrow = TRUE,
#' dimnames = list(NULL, c("mz", "intensity")))
#' )
#'
#' # Example database
#' database <- load_database("path/to/database")
#'
#' # Annotate metabolites using MS1, RT, and MS2 data
#' annotations <- annotate_peaks_mz_rt_ms2(
#' ms1.info = ms1_info,
#' ms2.info = ms2_info,
#' database = database,
#' based_on = c("ms1", "rt", "ms2"),
#' polarity = "positive",
#' column = "rp",
#' ms1.match.ppm = 20,
#' rt.match.tol = 20,
#' candidate.num = 5,
#' threads = 4
#' )
#'
#' print(annotations)
#' }
#'
#'
#' @export
annotate_peaks_mz_rt_ms2 <-
function(ms1.info = NULL,
ms2.info = NULL,
database = NULL,
based_on = c("ms1", "rt", "ms2"),
polarity = c("positive", "negative"),
ce = "all",
column = c("hilic", "rp"),
adduct.table = NULL,
ms1.match.ppm = 25,
mz.ppm.thr = 400,
rt.match.tol = 30,
ms2.match.ppm = 30,
ms2.match.tol = 0.5,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
remove_fragment_intensity_cutoff = 0,
ms1.match.weight = 0.25,
rt.match.weight = 0.25,
ms2.match.weight = 0.5,
total.score.tol = 0.5,
candidate.num = 3,
threads = 3) {
options(warn = -1)
based_on <- match.arg(based_on, several.ok = TRUE)
if (is.null(database)) {
stop("No database is provided.\n")
}
##check ms1.file and ms2.file
if (!is(database, "databaseClass")) {
stop("database should be databaseClass object.\n")
}
###if based_on contains ms2, the ms1.info should contains the ms2_spectrum_id
###and file
##parameter specification
polarity <- match.arg(polarity)
column <- match.arg(column)
# ce <- match.arg(ce)
if (is.null(adduct.table)) {
adduct.table <-
load_adduct_table(polarity = polarity, column = column)
}
check_adduct_table(adduct.table)
database.name <-
extract_database_name(database = database)
####------------------------------------------------------------------
####ms1 and/or rt match
####------------------------------------------------------------------
if ("ms1" %in% based_on | "rt" %in% based_on) {
###Check data
if (is.null(ms1.info)) {
stop("No ms1.info is provided.\n")
}
###check ms.info
check_ms1_ms2_info(ms1.info = ms1.info,
ms2.info = ms2.info,
based_on = based_on)
ms1_database <-
extract_ms1_database(database = database)
###calculate the mz_rt_matrix for all the compound with all adducts
mz_matrix <-
seq_len(nrow(adduct.table)) %>%
purrr::map(function(i) {
temp_n <-
stringr::str_extract(string = adduct.table$adduct[i], pattern = "[0-9]{1}M") %>%
stringr::str_replace("M", "") %>%
as.numeric()
temp_n[is.na(temp_n)] <- 1
temp <-
data.frame(as.numeric(adduct.table$mz[i]) + temp_n * as.numeric(ms1_database$mz))
colnames(temp) <- i
temp
}) %>%
dplyr::bind_cols()
colnames(mz_matrix) <-
adduct.table$adduct
rownames(mz_matrix) <-
ms1_database$Lab.ID
mz_matrix <-
mz_matrix %>%
tibble::rownames_to_column(var = "Lab.ID") %>%
tidyr::pivot_longer(cols = -Lab.ID,
names_to = "Adduct",
values_to = "mz") %>%
as.data.frame()
mz_rt_matrix <-
mz_matrix %>%
dplyr::left_join(ms1_database[, c("Lab.ID", "RT")], by = "Lab.ID")
###mz match
# pb <- progress::progress_bar$new(total = nrow(ms1.info))
future::plan(strategy = future::multisession, workers = threads)
match_result <-
seq_len(nrow(ms1.info)) %>%
furrr::future_map(function(i) {
# pb$tick()
# cat(i, " ")
temp <-
mz_rt_matrix %>%
dplyr::mutate(variable_id = ms1.info$variable_id[i]) %>%
dplyr::mutate(
mz.error = abs(ms1.info$mz[i] - mz) * 10 ^ 6 / ifelse(ms1.info$mz[i] < 400, 400, ms1.info$mz[i])
) %>%
dplyr::mutate(RT.error = abs(ms1.info$rt[i] - RT)) %>%
dplyr::filter(mz.error < ifelse("ms1" %in% based_on, ms1.match.ppm, max(mz.error) + 100)) %>%
dplyr::select(variable_id, Lab.ID, Adduct, mz.error, RT.error) %>%
dplyr::arrange(mz.error, RT.error)
if ("rt" %in% based_on) {
temp <-
temp %>%
dplyr::filter(RT.error < rt.match.tol)
}
temp %>%
dplyr::arrange(mz.error, RT.error) %>%
head(candidate.num)
}, .progress = TRUE)
match_result <-
match_result %>%
dplyr::bind_rows() %>%
dplyr::mutate(Database = database.name) %>%
dplyr::select(variable_id, dplyr::everything())
if (nrow(match_result) == 0) {
message("No matched compounds.")
return(NULL)
}
match_result$mz.match.score <-
calculate_mz_match_score(mz.error = match_result$mz.error, ms1.match.ppm = ms1.match.ppm)
match_result$RT.match.score <-
calculate_rt_match_score(RT.error = match_result$RT.error, rt.match.tol = rt.match.tol)
match_result <-
match_result %>%
dplyr::left_join(ms1_database, by = "Lab.ID")
match_result <-
match_result %>%
dplyr::select(
"variable_id",
"Compound.name",
"CAS.ID",
"HMDB.ID",
"KEGG.ID",
"Lab.ID",
"Adduct",
"mz.error",
"mz.match.score",
"RT.error",
"RT.match.score",
"Database",
dplyr::everything()
)
####remove some impossible adducts
match_result <-
remove_impossible_annotations(match_result)
match_result <-
match_result %>%
dplyr::arrange(variable_id,
dplyr::desc(mz.match.score),
dplyr::desc(RT.match.score))
if (nrow(match_result) == 0) {
message("No matched compounds.")
return(match_result)
}
} else{
match_result <- NULL
}
####------------------------------------------------------------------
####ms2 and/or ms1/rt match
####------------------------------------------------------------------
if ("ms2" %in% based_on) {
if (missing(ms2.info)) {
stop("No ms2.info is provided.\n")
}
check_ms1_ms2_info(ms1.info = ms1.info,
ms2.info = ms2.info,
based_on = based_on)
##extract MS2 data
spectra.data <-
extract_ms2_database(database = database,
polarity = polarity,
ce = ce)
if (is.null(spectra.data)) {
stop("No MS2 spectra in the database.\n")
}
if (masstools::get_os() == "windows") {
bpparam = BiocParallel::SnowParam(workers = threads, progressbar = TRUE)
} else{
bpparam = BiocParallel::MulticoreParam(workers = threads, progressbar = TRUE)
}
###remove some metabolites from spectra.data according to ms1 and/or rt matching
if (!is.null(match_result)) {
match_result <-
match_result %>%
dplyr::left_join(ms1.info[, c("variable_id", "ms2_files_id", "ms2_spectrum_id")], by = "variable_id")
spectra.data <-
spectra.data[names(spectra.data) %in% match_result$Lab.ID]
ms2.info <-
ms2.info[which(names(ms2.info) %in% match_result$ms2_spectrum_id)]
}
if (length(ms2.info) == 0) {
return(NULL)
}
if (length(spectra.data) == 0) {
return(NULL)
}
###debug
# for (i in seq_len(length(ms2.info))) {
# cat(i, " ")
# match_ms2_temp(
# idx = i,
# ms2.info = ms2.info,
# pre_match_result = match_result,
# spectra.data = spectra.data,
# ms2.match.ppm = ms2.match.ppm,
# mz.ppm.thr = mz.ppm.thr,
# ms2.match.tol = ms2.match.tol,
# candidate.num = candidate.num,
# fraction.weight = fraction.weight,
# dp.forward.weight = dp.forward.weight,
# dp.reverse.weight = dp.reverse.weight,
# remove_fragment_intensity_cutoff = remove_fragment_intensity_cutoff
# )
# }
match_result_ms2 <-
suppressMessages(
BiocParallel::bplapply(
seq_len(length(ms2.info)),
FUN = match_ms2_temp,
BPPARAM = bpparam,
pre_match_result = match_result,
ms2.info = ms2.info,
spectra.data = spectra.data,
ms2.match.ppm = ms2.match.ppm,
mz.ppm.thr = mz.ppm.thr,
ms2.match.tol = ms2.match.tol,
candidate.num = candidate.num,
fraction.weight = fraction.weight,
dp.forward.weight = dp.forward.weight,
dp.reverse.weight = dp.reverse.weight,
remove_fragment_intensity_cutoff = remove_fragment_intensity_cutoff
)
)
names(match_result_ms2) <- names(ms2.info)
match_result_ms2 <-
match_result_ms2[which(!unlist(lapply(match_result_ms2, function(x)
all(is.null(x)))))]
match_result_ms2 <-
match_result_ms2 %>%
do.call(rbind, .) %>%
as.data.frame()
if (nrow(match_result_ms2) == 0) {
return(NULL)
}
rownames(match_result_ms2) <- NULL
if ("ms1" %in% based_on | "rt" %in% based_on) {
if (any(colnames(match_result) == "CE")) {
match_result <-
match_result %>%
dplyr::select(-CE)
}
match_result <-
match_result_ms2 %>%
dplyr::left_join(match_result, by = c("ms2_spectrum_id", "Lab.ID")) %>%
dplyr::select(
"variable_id",
"ms2_spectrum_id",
"ms2_files_id",
"Compound.name",
"CAS.ID",
"HMDB.ID",
"KEGG.ID",
"Lab.ID",
"Adduct",
"mz.error",
"mz.match.score",
"RT.error",
"RT.match.score",
"SS",
"Database",
dplyr::everything()
)
} else{
match_result_ms2$Database <- database.name
match_result <-
match_result_ms2 %>%
dplyr::left_join(ms1.info, by = "ms2_spectrum_id") %>%
dplyr::left_join(ms1_database, by = c("Lab.ID")) %>%
dplyr::mutate(
Adduct = NA,
mz.error = NA,
mz.match.score = NA,
RT.error = NA,
RT.match.score = NA,
SS = NA
) %>%
dplyr::select(
"variable_id",
"ms2_spectrum_id",
"ms2_files_id",
"Compound.name",
"CAS.ID",
"HMDB.ID",
"KEGG.ID",
"Lab.ID",
"Adduct",
"mz.error",
"mz.match.score",
"RT.error",
"RT.match.score",
"SS",
"Database",
dplyr::everything()
)
}
} else{
match_result <-
match_result %>%
dplyr::mutate(
ms2_spectrum_id = NA,
ms2_files_id = NA,
CE = NA,
SS = NA
)
}
###calculate the total score
Total.score <-
calculate_total_score(
mz.match.score = match_result$mz.match.score,
RT.match.score = match_result$RT.match.score,
SS = match_result$SS,
ms1.match.weight = ms1.match.weight,
rt.match.weight = rt.match.weight,
ms2.match.weight = ms2.match.weight,
based_on = based_on
)
match_result$Total.score <- Total.score
match_result <-
match_result %>%
dplyr::filter(Total.score > total.score.tol)
if (nrow(match_result) == 0) {
message("No matched compounds.")
return(NULL)
}
Level <-
calculate_confidence_level(annotation_result = match_result)
match_result$Level <- Level
match_result <-
match_result %>%
dplyr::arrange(
variable_id,
Level,
dplyr::desc(Total.score),
dplyr::desc(SS),
dplyr::desc(mz.match.score),
dplyr::desc(RT.match.score)
) %>%
dplyr::group_by(variable_id) %>%
dplyr::slice_head(n = candidate.num) %>%
dplyr::ungroup() %>%
as.data.frame()
return(match_result)
}
#' Match MS2 Spectra to Database Compounds
#'
#' This function matches MS2 spectra from experimental data against MS2 spectra in a database. It calculates MS2 matching scores for each compound in the database, and selects the best matches based on the provided matching parameters.
#'
#' @param idx Numeric. The index of the current MS2 spectrum in the `ms2.info` list.
#' @param ms2.info A list of MS2 spectra, where each element is a matrix with two columns (`mz` and `intensity`).
#' @param pre_match_result A data frame containing the preliminary match results from MS1 or RT matching, including compound identifiers (`Lab.ID`).
#' @param spectra.data A list of MS2 spectra from the database, where each element is a matrix with two columns (`mz` and `intensity`).
#' @param ms2.match.ppm Numeric. Mass tolerance in parts per million (ppm) for MS2 peak matching. Default is 30.
#' @param mz.ppm.thr Numeric. m/z threshold for ppm calculation. Default is 400.
#' @param ms2.match.tol Numeric. Score threshold for MS2 matches. Only matches with scores above this value are retained. Default is 0.5.
#' @param candidate.num Numeric. The number of top candidate annotations to retain per MS2 spectrum. Default is 3.
#' @param fraction.weight Numeric. Weight for the fraction of matched fragments in the total MS2 score calculation. Default is 0.3.
#' @param dp.forward.weight Numeric. Weight for the forward dot product score in the total MS2 score calculation. Default is 0.6.
#' @param dp.reverse.weight Numeric. Weight for the reverse dot product score in the total MS2 score calculation. Default is 0.1.
#' @param remove_fragment_intensity_cutoff Numeric. Intensity cutoff to remove low-intensity MS2 fragments from matching. Default is 0.
#' @param ... Additional arguments to pass to internal functions.
#'
#' @return A data frame containing the top candidate matches for the given MS2 spectrum. The output includes:
#' \describe{
#' \item{Lab.ID}{The database compound ID.}
#' \item{CE}{The collision energy used in the MS2 spectrum.}
#' \item{SS}{The matching score for the compound.}
#' \item{ms2_spectrum_id}{The ID of the experimental MS2 spectrum.}
#' }
#'
#' @details
#' The function takes an MS2 spectrum from the experimental data and matches it against MS2 spectra in a reference database. It computes matching scores based on the provided mass tolerance (`ms2.match.ppm`), fragment matching weight parameters (`fraction.weight`, `dp.forward.weight`, `dp.reverse.weight`), and intensity cutoff. The best-matching spectra from the database are retained and returned as candidates.
#'
#' If preliminary MS1/RT matches are provided (`pre_match_result`), the function only considers those compounds for MS2 matching. Otherwise, all compounds in the database are considered.
#'
#' @export
match_ms2_temp <-
function(idx,
ms2.info,
pre_match_result,
spectra.data,
ms2.match.ppm = 30,
mz.ppm.thr = 400,
ms2.match.tol = 0.5,
candidate.num = 3,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
remove_fragment_intensity_cutoff = 0,
masstplus_method_cutoff = 100,
...) {
peak_ms2_spectrum <-
as.data.frame(ms2.info[[idx]])
ms2_spectrum_id <- names(ms2.info)[idx]
rm(list = c("ms2.info"))
if (length(peak_ms2_spectrum) == 0) {
return(NA)
}
if (!is.null(pre_match_result)) {
library_compound_id <-
pre_match_result$Lab.ID[which(pre_match_result$ms2_spectrum_id == ms2_spectrum_id)]
} else{
library_compound_id <- names(spectra.data)
}
library_compound_id <-
library_compound_id[which(library_compound_id %in% names(spectra.data))]
###if no matched compounds with MS2 in spectra
if (length(library_compound_id) == 0) {
return(NULL)
}
if (length(library_compound_id) <= masstplus_method_cutoff) {
###MS2 spectra match
ms2_match_score <-
seq_len(length(spectra.data[library_compound_id])) %>%
purrr::map(function(i) {
temp_spectra_data <- spectra.data[library_compound_id][[i]]
score <-
lapply(temp_spectra_data, function(y) {
y <- as.data.frame(y)
y$mz <- as.numeric(y$mz)
y$intensity <- as.numeric(y$intensity)
calculate_ms2_matching_score(
experimental.spectrum = peak_ms2_spectrum,
library.spectrum = y,
ms2.match.ppm = ms2.match.ppm,
mz.ppm.thr = mz.ppm.thr,
fraction.weight = fraction.weight,
dp.forward.weight = dp.forward.weight,
dp.reverse.weight = dp.reverse.weight,
remove_fragment_intensity_cutoff = remove_fragment_intensity_cutoff
)
})
score <- score[which.max(unlist(score))]
score <- unlist(score)
data.frame(
Lab.ID = names(spectra.data[library_compound_id])[i],
"CE" = names(score),
"SS" = score,
stringsAsFactors = FALSE
)
}) %>%
dplyr::bind_rows()
rownames(ms2_match_score) <- NULL
ms2_match_score <-
ms2_match_score %>%
dplyr::mutate(ms2_spectrum_id = ms2_spectrum_id) %>%
dplyr::arrange(dplyr::desc(SS)) %>%
head(candidate.num) %>%
dplyr::filter(SS > ms2.match.tol)
} else{
}
# rm(list = c("ms2_match_score"))
if (nrow(ms2_match_score) == 0) {
return(NULL)
} else{
return(ms2_match_score)
}
}
# spectra_database <-
# purrr::map2(.x = names(spectra.data), .y = spectra.data, function(x, y) {
# names(y) <- paste(x, names(y), sep = "_")
# y
# }) %>%
# do.call(c, .)
#
# names(spectra_database) <-
# masstools::name_duplicated(names(spectra_database))
#
# save(query_ms2, file = "example/query_ms2")
# save(spectra_database, file = "example/spectra_database")
###load data first if you want to run this function
# load("example/query_ms2")
# load("example/spectra_database")
match_ms2_masstplus <-
function(query_ms2,
spectra_database,
remove_fragment_intensity_cutoff = 0,
ms2.match.ppm = 30,
mz.ppm.thr = 400) {
total_spectra_data <-
purrr::map2(.x = names(spectra_database), .y = spectra_database, function(x, y) {
y$intensity <- as.numeric(y$intensity) / max(as.numeric(y$intensity))
y$Lab.ID <- x
y
}) %>%
do.call(rbind, .) %>%
as.data.frame() %>%
dplyr::arrange(mz, intensity, Lab.ID) %>%
dplyr::filter(intensity > remove_fragment_intensity_cutoff) %>%
dplyr::mutate(idx = seq_len(nrow(.)))
all_spectra_ids <-
unique(total_spectra_data$Lab.ID)
query_ms2 <-
query_ms2 %>%
dplyr::mutate(intensity = as.numeric(intensity) / max(as.numeric(intensity))) %>%
dplyr::filter(intensity > remove_fragment_intensity_cutoff) %>%
remove_noise()
###match query and total spectra
match_matrix <-
purrr::map(seq_len(nrow(experimental.spectrum)), function(i) {
temp_mz <-
experimental.spectrum$mz[i]
mz_error <-
abs(temp_mz - total_spectra_data$mz) * 10 ^ 6 / ifelse(temp_mz < 400, 400, temp_mz)
idx <- total_spectra_data$idx[which(mz_error < ms2.match.ppm)]
return_intensity <- rep(0, length(all_spectra_ids))
names(return_intensity) <- all_spectra_ids
if (length(idx) > 0) {
duplicated_id <-
unique(total_spectra_data$Lab.ID[idx][duplicated(total_spectra_data$Lab.ID[idx])])
if (length(duplicated_id) > 0) {
idx <-
total_spectra_data[idx, ] %>%
dplyr::filter(Lab.ID %in% duplicated_id) %>%
dplyr::group_by(Lab.ID) %>%
dplyr::filter(intensity == max(intensity)) %>%
dplyr::ungroup() %>%
pull(idx)
}
return_intensity[total_spectra_data$Lab.ID[idx]] <-
total_spectra_data$intensity[idx]
return_intensity
} else{
return_intensity
}
}) %>%
do.call(cbind, .) %>%
t() %>%
as.data.frame()
###remove the spectra with no matched fragments
remove_idx <-
which(colSums(match_matrix) == 0)
if (length(remove_idx) > 0) {
match_matrix <- match_matrix[, -remove_idx, drop = FALSE]
}
if (ncol(match_matrix) == 0) {
return(NULL)
}
forward_dp <-
purrr::map(match_matrix, function(x) {
calculate_dotproduct(exp.int = query_ms2$intensity, lib.int = x)
}) %>%
unlist()
forward_dp2 <-
lapply(spectra_database, function(x) {
calculate_ms2_matching_score(x, query_ms2)
}) %>%
unlist()
x = forward_dp
y = forward_dp2[match(names(forward_dp), names(forward_dp2))]
plot(x, y)
}
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