# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # SOURCE # # https://gitlab.com/msberends/AMR # # # # LICENCE # # (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # # # # This R package is free software; you can freely use and distribute # # it for both personal and commercial purposes under the terms of the # # GNU General Public License version 2.0 (GNU GPL-2), as published by # # the Free Software Foundation. # # # # This R package was created for academic research and was publicly # # released in the hope that it will be useful, but it comes WITHOUT # # ANY WARRANTY OR LIABILITY. # # Visit our website for more info: https://msberends.gitab.io/AMR. # # ==================================================================== # #' Transform to microorganism ID #' #' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms \emph{Bacteria}, \emph{Fungi} and \emph{Protozoa} (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples. #' @param x a character vector or a \code{data.frame} with one or two columns #' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1]. #' #' This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS". #' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. #' #' This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D. #' @param allow_uncertain a logical to indicate whether the input should be checked for less possible results, see Details #' @param reference_df a \code{data.frame} to use for extra reference when translating \code{x} to a valid \code{mo}. See \code{\link{set_mo_source}} and \code{\link{get_mo_source}} to automate the usage of your own codes (e.g. used in your analysis or organisation). #' @rdname as.mo #' @aliases mo #' @keywords mo Becker becker Lancefield lancefield guess #' @details #' A microbial ID from this package (class: \code{mo}) typically looks like these examples:\cr #' \preformatted{ #' Code Full name #' --------------- -------------------------------------- #' B_KLBSL Klebsiella #' B_KLBSL_PNE Klebsiella pneumoniae #' B_KLBSL_PNE_RHI Klebsiella pneumoniae rhinoscleromatis #' | | | | #' | | | | #' | | | ----> subspecies, a 3-4 letter acronym #' | | ----> species, a 3-4 letter acronym #' | ----> genus, a 5-7 letter acronym, mostly without vowels #' ----> taxonomic kingdom: A (Archaea), B (Bacteria), C (Chromista), #' F (Fungi), P (Protozoa) or V (Viruses) #' } #' #' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples. #' #' This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order: #' \itemize{ #' \item{Taxonomic kingdom: it first searches in Bacteria, then Fungi, then Protozoa} #' \item{Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones (see section \emph{Microbial prevalence of pathogens in humans})} #' \item{Valid MO codes and full names: it first searches in already valid MO code and known genus/species combinations} #' \item{Breakdown of input values: from here it starts to breakdown input values to find possible matches} #' } #' #' A couple of effects because of these rules: #' \itemize{ #' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first} #' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae} for the same reason} #' \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}} #' \item{Something like \code{"stau"} or \code{"S aur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}} #' } #' This means that looking up human pathogenic microorganisms takes less time than looking up human \strong{non}-pathogenic microorganisms. #' #' \strong{UNCERTAIN RESULTS} \cr #' When using \code{allow_uncertain = TRUE} (which is the default setting), it will use additional rules if all previous AI rules failed to get valid results. These are: #' \itemize{ #' \item{It tries to look for previously accepted (but now invalid) taxonomic names} #' \item{It strips off values between brackets and the brackets itself, and re-evaluates the input with all previous rules} #' \item{It strips off words from the end one by one and re-evaluates the input with all previous rules} #' \item{It strips off words from the start one by one and re-evaluates the input with all previous rules} #' \item{It tries to look for some manual changes which are not yet published to the Catalogue of Life (like \emph{Propionibacterium} not yet being \emph{Cutibacterium})} #' } #' #' Examples: #' \itemize{ #' \item{\code{"Streptococcus group B (known as S. agalactiae)"}. The text between brackets will be removed and a warning will be thrown that the result \emph{Streptococcus group B} (\code{B_STRPT_GRB}) needs review.} #' \item{\code{"S. aureus - please mind: MRSA"}. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result \emph{Staphylococcus aureus} (\code{B_STPHY_AUR}) needs review.} #' \item{\code{"Fluoroquinolone-resistant Neisseria gonorrhoeae"}. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result \emph{Neisseria gonorrhoeae} (\code{B_NESSR_GON}) needs review.} #' } #' #' Use \code{mo_failures()} to get a vector with all values that could not be coerced to a valid value. #' #' Use \code{mo_uncertainties()} to get a vector with all values that were coerced to a valid value, but with uncertainty. #' #' Use \code{mo_renamed()} to get a vector with all values that could be coerced based on an old, previously accepted taxonomic name. #' #' @section Microbial prevalence of pathogens in humans: #' The artificial intelligence takes into account microbial prevalence of pathogens in humans. It uses three groups and every (sub)species is in the group it matches first. These groups are: #' \itemize{ #' \item{1 (most prevalent): class is Gammaproteobacteria \strong{or} genus is one of: \emph{Enterococcus}, \emph{Staphylococcus}, \emph{Streptococcus}.} #' \item{2: phylum is one of: Proteobacteria, Firmicutes, Actinobacteria, Sarcomastigophora \strong{or} genus is one of: \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton}, \emph{Ureaplasma}.} #' \item{3 (least prevalent): all others.} #' } #' #' Group 1 contains all common Gram negatives, like all Enterobacteriaceae and e.g. \emph{Pseudomonas} and \emph{Legionella}. #' #' Group 2 probably contains all microbial pathogens ever found in humans. #' @inheritSection catalogue_of_life Catalogue of Life # (source as a section, so it can be inherited by other man pages) #' @section Source: #' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13} #' #' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571} #' #' [3] Catalogue of Life: Annual Checklist (public online database), \url{www.catalogueoflife.org}. #' @export #' @return Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}. #' @seealso \code{\link{microorganisms}} for the \code{data.frame} that is being used to determine ID's. \cr #' The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code. #' @inheritSection AMR Read more on our website! #' @examples #' # These examples all return "B_STPHY_AUR", the ID of S. aureus: #' as.mo("stau") #' as.mo("STAU") #' as.mo("staaur") #' as.mo("S. aureus") #' as.mo("S aureus") #' as.mo("Staphylococcus aureus") #' as.mo("Staphylococcus aureus (MRSA)") #' as.mo("MRSA") # Methicillin Resistant S. aureus #' as.mo("VISA") # Vancomycin Intermediate S. aureus #' as.mo("VRSA") # Vancomycin Resistant S. aureus #' #' as.mo("Streptococcus group A") #' as.mo("GAS") # Group A Streptococci #' as.mo("GBS") # Group B Streptococci #' #' as.mo("S. epidermidis") # will remain species: B_STPHY_EPI #' as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CNS #' #' as.mo("S. pyogenes") # will remain species: B_STRPT_PYO #' as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRA #' #' # Use mo_* functions to get a specific property based on `mo` #' Ecoli <- as.mo("E. coli") # returns `B_ESCHR_COL` #' mo_genus(Ecoli) # returns "Escherichia" #' mo_gramstain(Ecoli) # returns "Gram negative" #' # but it uses as.mo internally too, so you could also just use: #' mo_genus("E. coli") # returns "Escherichia" #' #' #' \dontrun{ #' df$mo <- as.mo(df$microorganism_name) #' #' # the select function of tidyverse is also supported: #' library(dplyr) #' df$mo <- df %>% #' select(microorganism_name) %>% #' as.mo() #' #' # and can even contain 2 columns, which is convenient for genus/species combinations: #' df$mo <- df %>% #' select(genus, species) %>% #' as.mo() #' # although this works easier and does the same: #' df <- df %>% #' mutate(mo = as.mo(paste(genus, species))) #' } as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE, reference_df = get_mo_source()) { if (all(x %in% AMR::microorganisms$mo) & isFALSE(Becker) & isFALSE(Lancefield) & is.null(reference_df)) { y <- x } else if (all(x %in% AMR::microorganisms$fullname) & isFALSE(Becker) & isFALSE(Lancefield) & is.null(reference_df)) { # we need special treatment for very prevalent full names, they are likely! # e.g. as.mo("Staphylococcus aureus") y <- microorganismsDT[prevalence == 1][data.table(fullname = x), on = "fullname", "mo"][[1]] if (any(is.na(y))) { y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(fullname = x[is.na(y)]), on = "fullname", "mo"][[1]] } if (any(is.na(y))) { y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(fullname = x[is.na(y)]), on = "fullname", "mo"][[1]] } } else { # will be checked for mo class in validation and uses exec_as.mo internally if necessary y <- mo_validate(x = x, property = "mo", Becker = Becker, Lancefield = Lancefield, allow_uncertain = allow_uncertain, reference_df = reference_df) } structure(.Data = y, class = "mo") } #' @rdname as.mo #' @export is.mo <- function(x) { identical(class(x), "mo") } #' @importFrom dplyr %>% pull left_join n_distinct progress_estimated filter #' @importFrom data.table data.table as.data.table setkey #' @importFrom crayon magenta red blue silver italic has_color exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE, reference_df = get_mo_source(), property = "mo", clear_options = TRUE) { if (!"AMR" %in% base::.packages()) { library("AMR") # check onLoad() in R/zzz.R: data tables are created there. } if (clear_options == TRUE) { options(mo_failures = NULL) options(mo_uncertainties = NULL) options(mo_renamed = NULL) } if (NCOL(x) == 2) { # support tidyverse selection like: df %>% select(colA, colB) # paste these columns together x_vector <- vector("character", NROW(x)) for (i in 1:NROW(x)) { x_vector[i] <- paste(pull(x[i,], 1), pull(x[i,], 2), sep = " ") } x <- x_vector } else { if (NCOL(x) > 2) { stop('`x` can be 2 columns at most', call. = FALSE) } x[is.null(x)] <- NA # support tidyverse selection like: df %>% select(colA) if (!is.vector(x) & !is.null(dim(x))) { x <- pull(x, 1) } } notes <- character(0) uncertainties <- character(0) failures <- character(0) x_input <- x # already strip leading and trailing spaces x <- trimws(x, which = "both") # only check the uniques, which is way faster x <- unique(x) # remove empty values (to later fill them in again with NAs) x <- x[!is.na(x) & !is.null(x) & !identical(x, "")] # conversion of old MO codes from v0.5.0 (ITIS) to later versions (Catalogue of Life) if (any(x %like% "^[BFP]_[A-Z]{3,7}") & !all(x %in% microorganisms$mo)) { leftpart <- gsub("^([BFP]_[A-Z]{3,7}).*", "\\1", x) if (any(leftpart %in% names(mo_codes_v0.5.0))) { rightpart <- gsub("^[BFP]_[A-Z]{3,7}(.*)", "\\1", x) leftpart <- mo_codes_v0.5.0[leftpart] x[!is.na(leftpart)] <- paste0(leftpart[!is.na(leftpart)], rightpart[!is.na(leftpart)]) } } # defined df to check for if (!is.null(reference_df)) { if (!is.data.frame(reference_df) | NCOL(reference_df) < 2) { stop('`reference_df` must be a data.frame with at least two columns.', call. = FALSE) } if (!"mo" %in% colnames(reference_df)) { stop("`reference_df` must contain a column `mo` with values from the 'microorganisms' data set.", call. = FALSE) } reference_df <- reference_df %>% filter(!is.na(mo)) # # remove factors, just keep characters suppressWarnings( reference_df[] <- lapply(reference_df, as.character) ) } # all empty if (all(identical(trimws(x_input), "") | is.na(x_input))) { if (property == "mo") { return(structure(rep(NA_character_, length(x_input)), class = "mo")) } else { return(rep(NA_character_, length(x_input))) } } else if (all(x %in% reference_df[, 1]) & all(reference_df[, "mo"] %in% AMR::microorganisms$mo)) { # all in reference df colnames(reference_df)[1] <- "x" suppressWarnings( x <- data.frame(x = x, stringsAsFactors = FALSE) %>% left_join(reference_df, by = "x") %>% left_join(AMR::microorganisms, by = "mo") %>% pull(property) ) } else if (all(x %in% AMR::microorganisms$mo)) { # existing mo codes when not looking for property "mo", like mo_genus("B_ESCHR_COL") y <- microorganismsDT[prevalence == 1][data.table(mo = x), on = "mo", ..property][[1]] if (any(is.na(y))) { y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(mo = x[is.na(y)]), on = "mo", ..property][[1]] } if (any(is.na(y))) { y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(mo = x[is.na(y)]), on = "mo", ..property][[1]] } x <- y } else if (all(x %in% AMR::microorganisms$fullname)) { # we need special treatment for very prevalent full names, they are likely! # e.g. as.mo("Staphylococcus aureus") y <- microorganismsDT[prevalence == 1][data.table(fullname = x), on = "fullname", ..property][[1]] if (any(is.na(y))) { y[is.na(y)] <- microorganismsDT[prevalence == 2][data.table(fullname = x[is.na(y)]), on = "fullname", ..property][[1]] } if (any(is.na(y))) { y[is.na(y)] <- microorganismsDT[prevalence == 3][data.table(fullname = x[is.na(y)]), on = "fullname", ..property][[1]] } x <- y } else if (all(toupper(x) %in% AMR::microorganisms.codes$code)) { # commonly used MO codes y <- as.data.table(AMR::microorganisms.codes)[data.table(code = toupper(x)), on = "code", ] x <- microorganismsDT[data.table(mo = y[["mo"]]), on = "mo", ..property][[1]] } else if (!all(x %in% AMR::microorganisms[, property])) { x_backup <- x # remove spp and species x <- trimws(gsub(" +(spp.?|ssp.?|sp.? |ss ?.?|subsp.?|subspecies|biovar |serovar |species)", " ", x_backup, ignore.case = TRUE), which = "both") x_species <- paste(x, "species") # translate to English for supported languages of mo_property x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x, ignore.case = TRUE) # remove 'empty' genus and species values x <- gsub("(no MO)", "", x, fixed = TRUE) # remove non-text in case of "E. coli" except dots and spaces x <- gsub("[^.a-zA-Z0-9/ \\-]+", "", x) # replace minus by a space x <- gsub("-+", " ", x) # replace hemolytic by haemolytic x <- gsub("ha?emoly", "haemoly", x) # place minus back in streptococci x <- gsub("(alpha|beta|gamma) ha?emoly", "\\1-haemoly", x) # remove genus as first word x <- gsub("^Genus ", "", x) # but spaces before and after should be omitted x <- trimws(x, which = "both") x_trimmed <- x x_trimmed_species <- paste(x_trimmed, "species") x_trimmed_without_group <- gsub(" group$", "", x_trimmed, ignore.case = TRUE) # remove last part from "-" or "/" x_trimmed_without_group <- gsub("(.*)[-/].*", "\\1", x_trimmed_without_group) # replace space and dot by regex sign x_withspaces <- gsub("[ .]+", ".* ", x) x <- gsub("[ .]+", ".*", x) # add start en stop regex x <- paste0('^', x, '$') x_withspaces_start_only <- paste0('^', x_withspaces) x_withspaces_end_only <- paste0(x_withspaces, '$') x_withspaces_start_end <- paste0('^', x_withspaces, '$') # cat(paste0('x "', x, '"\n')) # cat(paste0('x_species "', x_species, '"\n')) # cat(paste0('x_withspaces_start_only "', x_withspaces_start_only, '"\n')) # cat(paste0('x_withspaces_end_only "', x_withspaces_end_only, '"\n')) # cat(paste0('x_withspaces_start_end "', x_withspaces_start_end, '"\n')) # cat(paste0('x_backup "', x_backup, '"\n')) # cat(paste0('x_trimmed "', x_trimmed, '"\n')) # cat(paste0('x_trimmed_species "', x_trimmed_species, '"\n')) # cat(paste0('x_trimmed_without_group "', x_trimmed_without_group, '"\n')) progress <- progress_estimated(n = length(x), min_time = 3) for (i in 1:length(x)) { progress$tick()$print() found <- microorganismsDT[mo == toupper(x_backup[i]), ..property][[1]] # is a valid MO code if (length(found) > 0) { x[i] <- found[1L] next } if (tolower(x_trimmed[i]) %in% c("", "xxx", "other", "none", "unknown")) { # empty and nonsense values, ignore without warning ("xxx" is WHONET code for 'no growth') x[i] <- NA_character_ next } if (nchar(gsub("[^a-zA-Z]", "", x_trimmed[i])) < 3) { # check if search term was like "A. species", then return first genus found with ^A if (x_backup[i] %like% "[a-z]+ species" | x_backup[i] %like% "[a-z] spp[.]?") { # get mo code of first hit found <- microorganismsDT[fullname %like% x_withspaces_start_only[i], mo] if (length(found) > 0) { mo_code <- found[1L] %>% strsplit("_") %>% unlist() %>% .[1:2] %>% paste(collapse = "_") found <- microorganismsDT[mo == mo_code, ..property][[1]] # return first genus that begins with x_trimmed, e.g. when "E. spp." if (length(found) > 0) { x[i] <- found[1L] next } } } # fewer than 3 chars and not looked for species, add as failure x[i] <- NA_character_ failures <- c(failures, x_backup[i]) next } if (x_trimmed[i] %like% "virus") { # there is no fullname like virus, so don't try to coerce it x[i] <- NA_character_ failures <- c(failures, x_backup[i]) next } # translate known trivial abbreviations to genus + species ---- if (!is.na(x_trimmed[i])) { if (toupper(x_trimmed[i]) %in% c('MRSA', 'MSSA', 'VISA', 'VRSA')) { x[i] <- microorganismsDT[mo == 'B_STPHY_AUR', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %in% c('MRSE', 'MSSE')) { x[i] <- microorganismsDT[mo == 'B_STPHY_EPI', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) == "VRE" | x_trimmed[i] %like% '(enterococci|enterokok|enterococo)[a-z]*?$') { x[i] <- microorganismsDT[mo == 'B_ENTRC', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %in% c("EHEC", "EPEC", "EIEC", "STEC", "ATEC")) { x[i] <- microorganismsDT[mo == 'B_ESCHR_COL', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) == 'MRPA') { # multi resistant P. aeruginosa x[i] <- microorganismsDT[mo == 'B_PSDMN_AER', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) == 'CRS' | toupper(x_trimmed[i]) == 'CRSM') { # co-trim resistant S. maltophilia x[i] <- microorganismsDT[mo == 'B_STNTR_MAL', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) { # peni I, peni R, vanco I, vanco R: S. pneumoniae x[i] <- microorganismsDT[mo == 'B_STRPT_PNE', ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %like% '^G[ABCDFGHK]S$') { # Streptococci, like GBS = Group B Streptococci (B_STRPT_GRB) x[i] <- microorganismsDT[mo == gsub("G([ABCDFGHK])S", "B_STRPT_GR\\1", x_trimmed[i], ignore.case = TRUE), ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %like% '(streptococc|streptokok).* [ABCDFGHK]$') { # Streptococci in different languages, like "estreptococos grupo B" x[i] <- microorganismsDT[mo == gsub(".*(streptococ|streptokok|estreptococ).* ([ABCDFGHK])$", "B_STRPT_GR\\2", x_trimmed[i], ignore.case = TRUE), ..property][[1]][1L] next } if (toupper(x_trimmed[i]) %like% 'group [ABCDFGHK] (streptococ|streptokok|estreptococ)') { # Streptococci in different languages, like "Group A Streptococci" x[i] <- microorganismsDT[mo == gsub(".*group ([ABCDFGHK]) (streptococ|streptokok|estreptococ).*", "B_STRPT_GR\\1", x_trimmed[i], ignore.case = TRUE), ..property][[1]][1L] next } # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ---- if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]' | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]' | tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') { # coerce S. coagulase negative x[i] <- microorganismsDT[mo == 'B_STPHY_CNS', ..property][[1]][1L] next } if (tolower(x[i]) %like% '[ck]oagulas[ea] positie?[vf]' | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] positie?[vf]' | tolower(x[i]) %like% '[ck]o?ps[^a-z]?$') { # coerce S. coagulase positive x[i] <- microorganismsDT[mo == 'B_STPHY_CPS', ..property][[1]][1L] next } if (tolower(x[i]) %like% 'gram[ -]?neg.*' | tolower(x_trimmed[i]) %like% 'gram[ -]?neg.*') { # coerce S. coagulase positive x[i] <- microorganismsDT[mo == 'B_GRAMN', ..property][[1]][1L] next } if (tolower(x[i]) %like% 'gram[ -]?pos.*' | tolower(x_trimmed[i]) %like% 'gram[ -]?pos.*') { # coerce S. coagulase positive x[i] <- microorganismsDT[mo == 'B_GRAMP', ..property][[1]][1L] next } if (grepl("[sS]almonella [A-Z][a-z]+ ?.*", x_trimmed[i])) { if (x_trimmed[i] %like% "Salmonella group") { # Salmonella Group A to Z, just return S. species for now x[i] <- microorganismsDT[mo == 'B_SLMNL', ..property][[1]][1L] notes <- c(notes, magenta(paste0("Note: ", italic("Salmonella"), " ", trimws(gsub("Salmonella", "", x_trimmed[i])), " was considered ", italic("Salmonella species"), " (B_SLMNL)"))) } else { # Salmonella with capital letter species like "Salmonella Goettingen" - they're all S. enterica x[i] <- microorganismsDT[mo == 'B_SLMNL_ENT', ..property][[1]][1L] notes <- c(notes, magenta(paste0("Note: ", italic("Salmonella"), " ", trimws(gsub("Salmonella", "", x_trimmed[i])), " was considered a subspecies of ", italic("Salmonella enterica"), " (B_SLMNL_ENT)"))) } next } } # FIRST TRY FULLNAMES AND CODES # if only genus is available, return only genus if (all(!c(x[i], x_trimmed[i]) %like% " ")) { found <- microorganismsDT[tolower(fullname) %in% tolower(c(x_species[i], x_trimmed_species[i])), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } if (nchar(x_trimmed[i]) >= 6) { found <- microorganismsDT[tolower(fullname) %like% paste0(x_withspaces_start_only[i], "[a-z]+ species"), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] next } } # rest of genus only is in allow_uncertain part. } # TRY OTHER SOURCES ---- if (toupper(x_backup[i]) %in% AMR::microorganisms.codes[, 1]) { mo_found <- AMR::microorganisms.codes[toupper(x_backup[i]) == AMR::microorganisms.codes[, 1], "mo"][1L] if (length(mo_found) > 0) { x[i] <- microorganismsDT[mo == mo_found, ..property][[1]][1L] next } } if (!is.null(reference_df)) { if (x_backup[i] %in% reference_df[, 1]) { ref_mo <- reference_df[reference_df[, 1] == x_backup[i], "mo"] if (ref_mo %in% microorganismsDT[, mo]) { x[i] <- microorganismsDT[mo == ref_mo, ..property][[1]][1L] next } else { warning("Value '", x_backup[i], "' was found in reference_df, but '", ref_mo, "' is not a valid MO code.", call. = FALSE) } } } check_per_prevalence <- function(data_to_check, a.x_backup, b.x_trimmed, c.x_trimmed_without_group, d.x_withspaces_start_end, e.x_withspaces_start_only, f.x_withspaces_end_only) { found <- data_to_check[tolower(fullname) %in% tolower(c(a.x_backup, b.x_trimmed)), ..property][[1]] # most probable: is exact match in fullname if (length(found) > 0) { return(found[1L]) } found <- data_to_check[tolower(fullname) == tolower(c.x_trimmed_without_group), ..property][[1]] if (length(found) > 0) { return(found[1L]) } # try any match keeping spaces ---- found <- data_to_check[fullname %like% d.x_withspaces_start_end, ..property][[1]] if (length(found) > 0 & nchar(b.x_trimmed) >= 6) { return(found[1L]) } # try any match keeping spaces, not ending with $ ---- found <- data_to_check[fullname %like% paste0(trimws(e.x_withspaces_start_only), " "), ..property][[1]] if (length(found) > 0) { return(found[1L]) } found <- data_to_check[fullname %like% e.x_withspaces_start_only, ..property][[1]] if (length(found) > 0 & nchar(b.x_trimmed) >= 6) { return(found[1L]) } # try any match keeping spaces, not start with ^ ---- found <- data_to_check[fullname %like% paste0(" ", trimws(f.x_withspaces_end_only)), ..property][[1]] if (length(found) > 0) { return(found[1L]) } found <- data_to_check[fullname %like% f.x_withspaces_end_only, ..property][[1]] if (length(found) > 0 & nchar(b.x_trimmed) >= 6) { return(found[1L]) } # try splitting of characters in the middle and then find ID ---- # only when text length is 6 or lower # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus, staaur = S. aureus if (nchar(b.x_trimmed) <= 6) { x_length <- nchar(b.x_trimmed) x_split <- paste0("^", b.x_trimmed %>% substr(1, x_length / 2), '.* ', b.x_trimmed %>% substr((x_length / 2) + 1, x_length)) found <- data_to_check[fullname %like% x_split, ..property][[1]] if (length(found) > 0) { return(found[1L]) } } # try fullname without start and without nchar limit of >= 6 ---- # like "K. pneu rhino" >> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH found <- data_to_check[fullname %like% e.x_withspaces_start_only, ..property][[1]] if (length(found) > 0) { return(found[1L]) } # didn't found any return(NA_character_) } # FIRST TRY VERY PREVALENT IN HUMAN INFECTIONS ---- x[i] <- check_per_prevalence(data_to_check = microorganismsDT[prevalence == 1], a.x_backup = x_backup[i], b.x_trimmed = x_trimmed[i], c.x_trimmed_without_group = x_trimmed_without_group[i], d.x_withspaces_start_end = x_withspaces_start_end[i], e.x_withspaces_start_only = x_withspaces_start_only[i], f.x_withspaces_end_only = x_withspaces_end_only[i]) if (!is.na(x[i])) { next } # THEN TRY PREVALENT IN HUMAN INFECTIONS ---- x[i] <- check_per_prevalence(data_to_check = microorganismsDT[prevalence == 2], a.x_backup = x_backup[i], b.x_trimmed = x_trimmed[i], c.x_trimmed_without_group = x_trimmed_without_group[i], d.x_withspaces_start_end = x_withspaces_start_end[i], e.x_withspaces_start_only = x_withspaces_start_only[i], f.x_withspaces_end_only = x_withspaces_end_only[i]) if (!is.na(x[i])) { next } # THEN UNPREVALENT IN HUMAN INFECTIONS ---- x[i] <- check_per_prevalence(data_to_check = microorganismsDT[prevalence == 3], a.x_backup = x_backup[i], b.x_trimmed = x_trimmed[i], c.x_trimmed_without_group = x_trimmed_without_group[i], d.x_withspaces_start_end = x_withspaces_start_end[i], e.x_withspaces_start_only = x_withspaces_start_only[i], f.x_withspaces_end_only = x_withspaces_end_only[i]) if (!is.na(x[i])) { next } # MISCELLANEOUS ---- # look for old taxonomic names ---- found <- microorganisms.oldDT[tolower(fullname) == tolower(x_backup[i]) | fullname %like% x_withspaces_start_end[i],] if (NROW(found) > 0) { col_id_new <- found[1, col_id_new] # when property is "ref" (which is the case in mo_ref, mo_authors and mo_year), return the old value, so: # mo_ref("Chlamydia psittaci") = "Page, 1968" (with warning) # mo_ref("Chlamydophila psittaci") = "Everett et al., 1999" if (property == "ref") { x[i] <- found[1, ref] } else { x[i] <- microorganismsDT[col_id == found[1, col_id_new], ..property][[1]] } was_renamed(name_old = found[1, fullname], name_new = microorganismsDT[col_id == found[1, col_id_new], fullname], ref_old = found[1, ref], ref_new = microorganismsDT[col_id == found[1, col_id_new], ref], mo = microorganismsDT[col_id == found[1, col_id_new], mo]) next } # check for uncertain results ---- if (allow_uncertain == TRUE) { uncertain_fn <- function(a.x_backup, b.x_trimmed, c.x_withspaces_start_end, d.x_withspaces_start_only) { # (1) look for genus only, part of name ---- if (nchar(b.x_trimmed) > 4 & !b.x_trimmed %like% " ") { if (!grepl("^[A-Z][a-z]+", b.x_trimmed, ignore.case = FALSE)) { # not when input is like Genustext, because then Neospora would lead to Actinokineospora found <- microorganismsDT[tolower(fullname) %like% paste(b.x_trimmed, "species"), ..property][[1]] if (length(found) > 0) { x[i] <- found[1L] uncertainties <<- c(uncertainties, paste0("'", a.x_backup, "' >> ", microorganismsDT[mo == found[1L], fullname][[1]], " (", found[1L], ")")) return(x) } } } # (2) look again for old taxonomic names, now for G. species ---- found <- microorganisms.oldDT[fullname %like% c.x_withspaces_start_end | fullname %like% d.x_withspaces_start_only] if (NROW(found) > 0 & nchar(b.x_trimmed) >= 6) { if (property == "ref") { # when property is "ref" (which is the case in mo_ref, mo_authors and mo_year), return the old value, so: # mo_ref("Chlamydia psittaci) = "Page, 1968" (with warning) # mo_ref("Chlamydophila psittaci) = "Everett et al., 1999" x <- found[1, ref] } else { x <- microorganismsDT[col_id == found[1, col_id_new], ..property][[1]] } was_renamed(name_old = found[1, fullname], name_new = microorganismsDT[col_id == found[1, col_id_new], fullname], ref_old = found[1, ref], ref_new = microorganismsDT[col_id == found[1, col_id_new], ref], mo = microorganismsDT[col_id == found[1, col_id_new], mo]) uncertainties <<- c(uncertainties, paste0("'", a.x_backup, "' >> ", found[1, fullname], " (Catalogue of Life ID ", found[1, col_id], ")")) return(x) } # (3) strip values between brackets ---- a.x_backup_stripped <- gsub("( [(].*[)])", "", a.x_backup) a.x_backup_stripped <- trimws(gsub(" ", " ", a.x_backup_stripped, fixed = TRUE)) found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup_stripped, clear_options = FALSE, allow_uncertain = FALSE))) if (!is.na(found) & nchar(b.x_trimmed) >= 6) { found_result <- found found <- microorganismsDT[mo == found, ..property][[1]] uncertainties <<- c(uncertainties, paste0("'", a.x_backup, "' >> ", microorganismsDT[mo == found_result[1L], fullname][[1]], " (", found_result[1L], ")")) return(found[1L]) } # (4) try to strip off one element from end and check the remains ---- x_strip <- a.x_backup %>% strsplit(" ") %>% unlist() if (length(x_strip) > 1 & nchar(b.x_trimmed) >= 6) { for (i in 1:(length(x_strip) - 1)) { x_strip_collapsed <- paste(x_strip[1:(length(x_strip) - i)], collapse = " ") found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, clear_options = FALSE, allow_uncertain = FALSE))) if (!is.na(found)) { found_result <- found found <- microorganismsDT[mo == found, ..property][[1]] uncertainties <<- c(uncertainties, paste0("'", a.x_backup, "' >> ", microorganismsDT[mo == found_result[1L], fullname][[1]], " (", found_result[1L], ")")) return(found[1L]) } } } # (5) try to strip off one element from start and check the remains ---- x_strip <- a.x_backup %>% strsplit(" ") %>% unlist() if (length(x_strip) > 1 & nchar(b.x_trimmed) >= 6) { for (i in 2:(length(x_strip))) { x_strip_collapsed <- paste(x_strip[i:length(x_strip)], collapse = " ") found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, clear_options = FALSE, allow_uncertain = FALSE))) if (!is.na(found)) { found_result <- found found <- microorganismsDT[mo == found, ..property][[1]] uncertainties <<- c(uncertainties, paste0("'", a.x_backup, "' >> ", microorganismsDT[mo == found_result[1L], fullname][[1]], " (", found_result[1L], ")")) return(found[1L]) } } } # (6) not yet implemented taxonomic changes in Catalogue of Life ---- found <- suppressMessages(suppressWarnings(exec_as.mo(TEMPORARY_TAXONOMY(b.x_trimmed), clear_options = FALSE, allow_uncertain = FALSE))) if (!is.na(found)) { found_result <- found found <- microorganismsDT[mo == found, ..property][[1]] warning(silver(paste0('Guessed with uncertainty: "', a.x_backup, '" >> ', italic(microorganismsDT[mo == found_result[1L], fullname][[1]]), " (", found_result[1L], ")")), call. = FALSE, immediate. = FALSE) uncertainties <<- c(uncertainties, paste0('"', a.x_backup, '" >> ', microorganismsDT[mo == found_result[1L], fullname][[1]], " (", found_result[1L], ")")) return(found[1L]) } # didn't found in uncertain results too return(NA_character_) } x[i] <- uncertain_fn(x_backup[i], x_trimmed[i], x_withspaces_start_end[i], x_withspaces_start_only[i]) if (!is.na(x[i])) { next } } # not found ---- x[i] <- NA_character_ failures <- c(failures, x_backup[i]) } } # failures failures <- failures[!failures %in% c(NA, NULL, NaN)] if (length(failures) > 0) { options(mo_failures = sort(unique(failures))) plural <- c("value", "it") if (n_distinct(failures) > 1) { plural <- c("values", "them") } total_failures <- length(x_input[x_input %in% failures & !x_input %in% c(NA, NULL, NaN)]) total_n <- length(x_input[!x_input %in% c(NA, NULL, NaN)]) msg <- paste0("\n", n_distinct(failures), " unique ", plural[1], " (^= ", percent(total_failures / total_n, round = 1, force_zero = TRUE), ") could not be coerced to a valid MO code") if (n_distinct(failures) <= 10) { msg <- paste0(msg, ": ", paste('"', unique(failures), '"', sep = "", collapse = ', ')) } msg <- paste0(msg, ". Use mo_failures() to review ", plural[2], ".") warning(red(msg), call. = FALSE, immediate. = TRUE) # thus will always be shown, even if >= warnings } # uncertainties if (length(uncertainties) > 0) { options(mo_uncertainties = sort(unique(uncertainties))) plural <- c("value", "it") if (n_distinct(failures) > 1) { plural <- c("values", "them") } msg <- paste0("\nResults of ", n_distinct(uncertainties), " input ", plural[1], " guessed with uncertainty. Use mo_uncertainties() to review ", plural[2], ".") warning(red(msg), call. = FALSE, immediate. = TRUE) # thus will always be shown, even if >= warnings } # Becker ---- if (Becker == TRUE | Becker == "all") { # See Source. It's this figure: # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/ MOs_staph <- microorganismsDT[genus == "Staphylococcus"] setkey(MOs_staph, species) CoNS <- MOs_staph[species %in% c("arlettae", "auricularis", "capitis", "caprae", "carnosus", "cohnii", "condimenti", "devriesei", "epidermidis", "equorum", "fleurettii", "gallinarum", "haemolyticus", "hominis", "jettensis", "kloosii", "lentus", "lugdunensis", "massiliensis", "microti", "muscae", "nepalensis", "pasteuri", "petrasii", "pettenkoferi", "piscifermentans", "rostri", "saccharolyticus", "saprophyticus", "sciuri", "stepanovicii", "simulans", "succinus", "vitulinus", "warneri", "xylosus"), ..property][[1]] CoPS <- MOs_staph[species %in% c("simiae", "agnetis", "chromogenes", "delphini", "felis", "lutrae", "hyicus", "intermedius", "pseudintermedius", "pseudointermedius", "schleiferi"), ..property][[1]] x[x %in% CoNS] <- microorganismsDT[mo == 'B_STPHY_CNS', ..property][[1]][1L] x[x %in% CoPS] <- microorganismsDT[mo == 'B_STPHY_CPS', ..property][[1]][1L] if (Becker == "all") { x[x == microorganismsDT[mo == 'B_STPHY_AUR', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STPHY_CPS', ..property][[1]][1L] } } # Lancefield ---- if (Lancefield == TRUE | Lancefield == "all") { # group A - S. pyogenes x[x == microorganismsDT[mo == 'B_STRPT_PYO', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPT_GRA', ..property][[1]][1L] # group B - S. agalactiae x[x == microorganismsDT[mo == 'B_STRPT_AGA', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPT_GRB', ..property][[1]][1L] # group C S_groupC <- microorganismsDT %>% filter(genus == "Streptococcus", species %in% c("equisimilis", "equi", "zooepidemicus", "dysgalactiae")) %>% pull(property) x[x %in% S_groupC] <- microorganismsDT[mo == 'B_STRPT_GRC', ..property][[1]][1L] if (Lancefield == "all") { # all Enterococci x[x %like% "^(Enterococcus|B_ENTRC)"] <- microorganismsDT[mo == 'B_STRPT_GRD', ..property][[1]][1L] } # group F - S. anginosus x[x == microorganismsDT[mo == 'B_STRPT_ANG', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPT_GRF', ..property][[1]][1L] # group H - S. sanguinis x[x == microorganismsDT[mo == 'B_STRPT_SAN', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPT_GRH', ..property][[1]][1L] # group K - S. salivarius x[x == microorganismsDT[mo == 'B_STRPT_SAL', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPT_GRK', ..property][[1]][1L] } # Wrap up ---------------------------------------------------------------- # comply to x, which is also unique and without empty values x_input_unique_nonempty <- unique(x_input[!is.na(x_input) & !is.null(x_input) & !identical(x_input, "")]) # left join the found results to the original input values (x_input) df_found <- data.frame(input = as.character(x_input_unique_nonempty), found = as.character(x), stringsAsFactors = FALSE) df_input <- data.frame(input = as.character(x_input), stringsAsFactors = FALSE) x <- df_input %>% left_join(df_found, by = "input") %>% pull(found) if (property == "mo") { class(x) <- "mo" } if (length(mo_renamed()) > 0) { if (has_color()) { notes <- getOption("mo_renamed") } else { notes <- mo_renamed() } notes <- sort(notes) for (i in 1:length(notes)) { base::message(blue(paste("Note:", notes[i]))) } } x } TEMPORARY_TAXONOMY <- function(x) { x[x %like% 'Cutibacterium'] <- gsub('Cutibacterium', 'Propionibacterium', x[x %like% 'Cutibacterium']) x } #' @importFrom crayon italic was_renamed <- function(name_old, name_new, ref_old = "", ref_new = "", mo = "") { if (!is.na(ref_old)) { ref_old <- paste0(" (", ref_old, ")") } else { ref_old <- "" } if (!is.na(ref_new)) { ref_new <- paste0(" (", ref_new, ")") } else { ref_new <- "" } if (!is.na(mo)) { mo <- paste0(" (", mo, ")") } else { mo <- "" } msg <- paste0(italic(name_old), ref_old, " was renamed ", italic(name_new), ref_new, mo) msg <- gsub("et al.", italic("et al."), msg) options(mo_renamed = sort(msg)) } #' @exportMethod print.mo #' @export #' @noRd print.mo <- function(x, ...) { cat("Class 'mo'\n") x_names <- names(x) x <- as.character(x) names(x) <- x_names print.default(x, quote = FALSE) } #' @exportMethod summary.mo #' @export #' @noRd summary.mo <- function(object, ...) { # unique and top 1-3 x <- object top_3 <- unname(top_freq(freq(x), 3)) c("Class" = "mo", "" = length(x[is.na(x)]), "Unique" = dplyr::n_distinct(x[!is.na(x)]), "#1" = top_3[1], "#2" = top_3[2], "#3" = top_3[3]) } #' @exportMethod as.data.frame.mo #' @export #' @noRd as.data.frame.mo <- function (x, ...) { # same as as.data.frame.character but with removed stringsAsFactors, since it will be class "mo" nm <- paste(deparse(substitute(x), width.cutoff = 500L), collapse = " ") if (!"nm" %in% names(list(...))) { as.data.frame.vector(x, ..., nm = nm) } else { as.data.frame.vector(x, ...) } } #' @exportMethod pull.mo #' @export #' @importFrom dplyr pull #' @noRd pull.mo <- function(.data, ...) { pull(as.data.frame(.data), ...) } #' @rdname as.mo #' @export mo_failures <- function() { getOption("mo_failures") } #' @rdname as.mo #' @export mo_uncertainties <- function() { getOption("mo_uncertainties") } #' @rdname as.mo #' @export mo_renamed <- function() { strip_style(gsub("was renamed", ">>", getOption("mo_renamed"), fixed = TRUE)) }