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metabolomics_data_dictionary.R
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metabolomics_data_dictionary.R
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# All about the RefMet data dictionary
#' @title Get and Validate the Entire RefMet Database from Metabolomics Workbench
#'
#' @description This function fetches and validates the Metabolomics Data Dictionary
#' from the Metabolomics Workbench. It provides options to remove duplicates.
#'
#' @param remove_duplications Logical; if `TRUE`, removes duplicate entries based on
#' the `refmet_name` column.
#' @param verbose Logical; if `TRUE` (default), displays progress messages and warnings
#' during the function execution.
#'
#' @return Returns a data frame with the following columns:
#' \describe{
#' \item{\code{refmet_name}}{Character; the name standarized refmet name}
#' \item{\code{pubchem_cid}}{Character; the PubChem compound ID.}
#' \item{\code{lm_id}}{Character; the LIPID MAPS ID.}
#' \item{\code{inchi_key}}{Character; the International Chemical Identifier Key.}
#' \item{\code{exactmass}}{Numeric; the exact mass of the metabolite.}
#' \item{\code{formula}}{Character; the chemical formula of the metabolite.}
#' \item{\code{super_class}}{Character; the superclass category of the metabolite.}
#' \item{\code{main_class}}{Character; the main class category of the metabolite.}
#' \item{\code{sub_class}}{Character; the subclass category of the metabolite.}
#' \item{\code{hmdb_id}}{Character; the Human Metabolome Database ID.}
#' \item{\code{kegg_id}}{Character; the Kyoto Encyclopedia of Genes and Genomes ID.}
#' }
#' Each row of the data frame represents a unique metabolite entry from the
#' Metabolomics Workbench Data Dictionary.
#'
#' @details This function downloads the entire RefMet database from the Metabolomics
#' Workbench using their REST API. The data is initially fetched in JSON format and
#' then converted to a data frame. The function checks for the presence of a 'name'
#' column in the data frame, renaming it to 'refmet_name' for consistency. It also
#' provides an option to remove duplicate entries based on the 'refmet_name' column.
#' If duplicates are found and \code{remove_duplications} is `FALSE`, the function will
#' list the duplicated IDs but will not remove them. This can be helpful for reviewing
#' the data quality and consistency.
#'
#' @examples
#' \dontrun{
#' refmet <- get_and_validate_mdd(remove_duplications = TRUE, verbose = TRUE)
#' head(refmet)
#' }
#'
#' @export
get_and_validate_mdd <- function(remove_duplications = FALSE, verbose = TRUE){
name = NULL
if(verbose) message("- Warning: Downloading data from Metabolomics Workbench. This might take a few minutes.")
# REST metabolomics workbench data dictionary
# Previous REST versions
# refmetjson <- jsonlite::fromJSON("https://www.metabolomicsworkbench.org/rest/refmet/motrpac")
# refmetjson <- jsonlite::fromJSON("https://www.metabolomicsworkbench.org/rest/refmet/all/")
refmetjson <- jsonlite::fromJSON("https://www.metabolomicsworkbench.org/rest/refmet/all_ids/")
df <- purrr::map_dfr(refmetjson, ~ as.data.frame(.x), .id = "id")
if( !("name" %in% colnames(df)) ){
stop("`refmet_name` column not found in the Metabolomics Workbench data dictionary")
}else{
df <- rename(df, refmet_name = name)
}
# CHECK DUPLICATIONS
if(any(duplicated(df$refmet_name))){
duplications <- df[duplicated(df$refmet_name),]
if(verbose) message("Duplicated ids: ", length(duplications$refmet_name))
if(verbose) message("IDS: ", paste(duplications$refmet_name, collapse = ", "))
}
refmet <- subset(df, select = -c(id))
if(remove_duplications){
# REMOVE DUPLICATIONS
if( any(duplicated(refmet$refmet_name)) ){
duplirefmets <- length(refmet$refmet_name[(duplicated(refmet$refmet_name))])
# message("WARNING: [ ",duplirefmets, " ] DUPLICATION(s) found in data dictionary!")
refmet <- refmet[!(duplicated(refmet$refmet_name)),]
}
}
return(refmet)
}
#' @title Validate refmet_name
#'
#' @description Validate the refment_name using the Metabolomics Workbench API
#' @param dataf (data.frame) Data frame with the refmet_name column
#' @param verbose (logical) `TRUE` (default) shows messages)
#' @return (numeric) number of refmet_name ids not available in RefMet
#' @export
validate_refmetname <- function(dataf, verbose){
irm <- 0
for(i in 1:dim(dataf)[1]){
rn <- dataf$refmet_name[i]
tosearch <- "_hp_|_rp_|_rn_|_in_|_lp_|_ln_"
if(grepl(tosearch, rn)){
# message("\tMulti-isotope detected: ", rn, appendLF = FALSE)
rn <- gsub("(.*)(_\\w{2}_\\w{1})", "\\1", rn)
# message(" > ", rn)
}
# Remove the standard label
standards <- "\\[.*\\]"
if(grepl(standards, rn)){
# message("\tStandard detected: ", rn, appendLF = FALSE)
rn <- gsub("(.*)(\\s+\\[iSTD\\])", "\\1", rn)
# message(" > ", rn)
}
search_api <- paste0("https://www.metabolomicsworkbench.org/rest/refmet/match/",URLencode(rn),"/name/")
here <- jsonlite::fromJSON(search_api)
if(here$refmet_name == "-"){
if(verbose) message(paste0(" (-) `refmet_name` [`", rn, "`] not available in RefMet. Please, contact MW/BIC (Error RN1)"))
irm <- irm + 1
}else{
if(here$refmet_name != rn){
if(verbose) message(paste0(" (-) `refmet_name` [`", rn, "`] must be modified to the RefMet Standarized name: \"", here$refmet_name, "\" (Error RN2)"))
irm <- irm + 1
}
}
}
if(irm > 0){
if(verbose) message(" (-) Total number of missed ids on MW: ", irm)
}
return(irm)
}