#' @title Create edges spectra
#'
#' @description This function create edges
#' based on fragmentation spectra similarity
#'
#' @include create_edges.R
#' @include get_params.R
#' @include get_spectra_ids.R
#' @include import_spectra.R
#'
#' @param input Query file containing spectra. Currently an '.mgf' file
#' @param output Output file.
#' @param name_source Name of the source features column
#' @param name_target Name of the target features column
#' @param method Similarity method
#' @param threshold Minimal similarity to report
#' @param ppm Relative ppm tolerance to be used
#' @param dalton Absolute Dalton tolerance to be used
#' @param qutoff Intensity under which ms2 fragments will be removed.
#'
#' @return The path to the created spectral edges
#'
#' @export
#'
#' @examples
#' \dontrun{
#' copy_backbone()
#' go_to_cache()
#' get_file(
#' url = get_default_paths()$urls$examples$spectra_mini,
#' export = get_params(step = "create_edges_spectra")$files$spectral$raw
#' )
#' create_edges_spectra()
#' unlink("data", recursive = TRUE)
#' }
create_edges_spectra <- function(
input = get_params(step = "create_edges_spectra")$files$spectral$raw,
output = get_params(
step = "create_edges_spectra"
)$files$networks$spectral$edges$raw,
name_source = get_params(step = "create_edges_spectra")$names$source,
name_target = get_params(step = "create_edges_spectra")$names$target,
method = get_params(step = "create_edges_spectra")$similarities$methods$edges,
threshold = get_params(
step = "create_edges_spectra"
)$similarities$thresholds$edges,
ppm = get_params(step = "create_edges_spectra")$ms$tolerances$mass$ppm$ms2,
dalton = get_params(
step = "create_edges_spectra"
)$ms$tolerances$mass$dalton$ms2,
qutoff = get_params(step = "create_edges_spectra")$ms$thresholds$ms2$intensity
) {
stopifnot("Your input file does not exist." = file.exists(input))
## Not checking for ppm and Da limits, everyone is free.
spectra <- input |>
import_spectra(
cutoff = qutoff,
dalton = dalton,
ppm = ppm
)
if (length(spectra) > 1) {
logger::log_trace("Performing spectral comparison")
logger::log_trace(
"As we do not limit the precursors delta,
expect a (relatively) long processing time."
)
logger::log_with_separator("Take yourself a break, you deserve it.")
nspecz <- length(spectra)
fragz <- spectra@backend@peaksData
precz <- spectra@backend@spectraData$precursorMz
edges <- create_edges(
frags = fragz,
nspecs = nspecz,
precs = precz,
method = method,
ms2_tolerance = dalton,
ppm_tolerance = ppm,
threshold = threshold
)
logger::log_trace("Calculating features' entropy")
entropy <- purrr::map(
.x = seq_along(1:nspecz),
.f = function(x, peaks = fragz) {
return(
peaks[[x]] |>
msentropy::calculate_spectral_entropy()
)
}
)
logger::log_trace("Counting features' number of peaks")
npeaks <- purrr::map(
.x = seq_along(1:nspecz),
.f = function(x, peaks = fragz) {
return(
peaks[[x]] |>
length()
)
}
)
rm(nspecz, fragz)
edges <- edges |>
tidytable::select(
!!as.name(name_source) := "feature_id",
!!as.name(name_target) := "target_id",
tidyselect::everything()
)
idz <- spectra |>
get_spectra_ids()
rm(spectra)
edges <- edges |>
tidytable::mutate(
name_source = idz[name_source],
name_target = idz[name_target]
)
entropy_df <- tidytable::tidytable(entropy) |>
tidyfst::rn_col(var = name_source) |>
tidytable::mutate(
name_source = idz[name_source],
feature_spectrum_entropy = as.character(entropy),
feature_spectrum_peaks = as.character(npeaks)
) |>
tidytable::mutate(
!!as.name(name_source) := as.integer(!!as.name(name_source))
) |>
tidytable::distinct(
!!as.name(name_source),
feature_spectrum_entropy,
feature_spectrum_peaks
)
rm(entropy, npeaks, idz)
edges <- edges |>
tidytable::select(tidyselect::any_of(
c(
name_source,
name_target,
"candidate_score_similarity" = "score",
"candidate_count_similarity_peaks_matched"
)
))
edges <- edges |>
tidytable::filter(candidate_score_similarity >= threshold)
edges <- edges |>
tidytable::full_join(entropy_df) |>
tidytable::mutate(
!!as.name(name_target) := tidytable::coalesce(
!!as.name(name_target),
!!as.name(name_source)
)
)
rm(entropy_df)
} else {
logger::log_warn(
"No spectra were found, returning an empty dataframe instead"
)
edges <- tidytable::tidytable(
!!as.name(name_source) := NA,
"feature_spectrum_entropy" = NA,
"feature_spectrum_peaks" = NA,
!!as.name(name_target) := NA,
"candidate_score_similarity" = NA,
"candidate_count_similarity_peaks_matched" = NA
)
}
export_params(
parameters = get_params(step = "create_edges_spectra"),
step = "create_edges_spectra"
)
export_output(x = edges, file = output[[1]])
rm(edges)
return(output[[1]])
}
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