# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # AUTHORS # # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # # # # LICENCE # # This program is free software; you can redistribute it and/or modify # # it under the terms of the GNU General Public License version 2.0, # # as published by the Free Software Foundation. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # ==================================================================== # #' Transform to microorganism ID #' #' Use this function to determine a valid ID based on a genus (and species). Determination is done using Artificial Intelligence (AI), 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, i.e. \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. #' @rdname as.mo #' @aliases mo #' @keywords mo Becker becker Lancefield lancefield guess #' @details \code{guess_mo} is an alias of \code{as.mo}. #' #' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples. #' #' Thus function uses Artificial Intelligence (AI) to help getting more logical results, based on type of input and known prevalence of human pathogens. For example: #' \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}} #' } #' Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr #' For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}. #' @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} #' @export #' @importFrom dplyr %>% pull left_join arrange #' @return Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}. #' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's. #' @examples #' # These examples all return "STAAUR", 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("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 #' #' # guess_mo is an alias of as.mo and works the same #' guess_mo("S. epidermidis") # will remain species: STAEPI #' guess_mo("S. epidermidis", Becker = TRUE) # will not remain species: STACNS #' #' guess_mo("S. pyogenes") # will remain species: STCPYO #' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: STCGRA #' #' # Use mo_* functions to get a specific property based on `mo` #' Ecoli <- as.mo("E. coli") # returns `ESCCOL` #' mo_genus(Ecoli) # returns "Escherichia" #' mo_gramstain(Ecoli) # returns "Negative rods" #' #' \dontrun{ #' df$mo <- as.mo(df$microorganism_name) #' #' # the select function of tidyverse is also supported: #' library(dplyr) #' df$mo <- df %>% #' select(microorganism_name) %>% #' guess_mo() #' #' # and can even contain 2 columns, which is convenient for genus/species combinations: #' df$mo <- df %>% #' select(genus, species) %>% #' guess_mo() #' #' # same result: #' df <- df %>% #' mutate(mo = guess_mo(paste(genus, species))) #' } as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) { 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)) { x <- pull(x, 1) } } MOs <- AMR::microorganisms %>% arrange(prevalence) %>% # more expected result on multiple findings filter(!mo %like% '^_FAM', # don't search in those (nchar(mo) > 3 | mo %in% c("GNR", "GPR", "GNC", "GPC"))) # no genera failures <- character(0) x_input <- x # only check the uniques, which is way faster x <- unique(x) x_backup <- x # translate to English for supported languages of mo_property x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x) # remove 'empty' genus and species values x <- gsub("(no MO)", "", x, fixed = TRUE) # remove dots and other non-text in case of "E. coli" except spaces x <- gsub("[^a-zA-Z0-9/ \\-]+", "", x) # but spaces before and after should be omitted x <- trimws(x, which = "both") x_trimmed <- x # replace space by regex sign x_withspaces <- gsub(" ", ".* ", x, fixed = TRUE) x <- gsub(" ", ".*", x, fixed = TRUE) # add start en stop regex x <- paste0('^', x, '$') x_withspaces_all <- x_withspaces x_withspaces_start <- paste0('^', x_withspaces) x_withspaces <- paste0('^', x_withspaces, '$') # cat(paste0('x "', x, '"\n')) # cat(paste0('x_withspaces_all "', x_withspaces_all, '"\n')) # cat(paste0('x_withspaces_start "', x_withspaces_start, '"\n')) # cat(paste0('x_withspaces "', x_withspaces, '"\n')) # cat(paste0('x_backup "', x_backup, '"\n')) for (i in 1:length(x)) { if (identical(x_trimmed[i], "")) { # empty values x[i] <- NA next } if (toupper(x_backup[i]) %in% AMR::microorganisms$mo) { # is already a valid MO code x[i] <- toupper(x_backup[i]) next } if (toupper(x_trimmed[i]) %in% AMR::microorganisms$mo) { # is already a valid MO code x[i] <- toupper(x_trimmed[i]) next } if (tolower(x_backup[i]) %in% tolower(AMR::microorganisms$fullname)) { # is exact match in fullname x[i] <- AMR::microorganisms[which(AMR::microorganisms$fullname == x_backup[i]), ]$mo[1L] next } # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ---- if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]' | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]' | tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') { # coerce S. coagulase negative x[i] <- 'STACNS' 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]?$') { # coerce S. coagulase positive x[i] <- 'STACPS' next } # translate known trivial abbreviations to genus + species ---- if (!is.na(x_trimmed[i])) { if (toupper(x_trimmed[i]) == 'MRSA' | toupper(x_trimmed[i]) == 'VISA' | toupper(x_trimmed[i]) == 'VRSA') { x[i] <- 'STAAUR' next } if (toupper(x_trimmed[i]) == 'MRSE') { x[i] <- 'STAEPI' next } if (toupper(x_trimmed[i]) == 'VRE') { x[i] <- 'ENCSPP' next } if (toupper(x_trimmed[i]) == 'MRPA') { # multi resistant P. aeruginosa x[i] <- 'PSEAER' next } if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) { # peni I, peni R, vanco I, vanco R: S. pneumoniae x[i] <- 'STCPNE' next } if (toupper(x_trimmed[i]) %like% '^G[ABCDFHK]S$') { x[i] <- gsub("G([ABCDFHK])S", "STCGR\\1", x_trimmed[i]) next } } # try any match keeping spaces ---- found <- MOs[which(MOs$fullname %like% x_withspaces[i]),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try the same, now based on genus + species ---- found <- MOs[which(paste(MOs$genus, MOs$species) %like% x_withspaces[i]),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try any match with genus, keeping spaces, not ending with $ ---- found <- MOs[which(MOs$genus %like% x_withspaces_start[i] & MOs$mo %like% 'SPP$'),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try any match keeping spaces, not ending with $ ---- found <- MOs[which(MOs$fullname %like% x_withspaces_start[i]),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try any match diregarding spaces ---- found <- MOs[which(MOs$fullname %like% x[i]),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try fullname without start and stop regex, to also find subspecies ---- # like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH found <- MOs[which(gsub("[\\(\\)]", "", MOs$fullname) %like% x_withspaces_all[i]),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # search for GLIMS code ---- found <- AMR::microorganisms.umcg[which(toupper(AMR::microorganisms.umcg$umcg) == toupper(x_trimmed[i])),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try splitting of characters and then find ID ---- # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus x_split <- x x_length <- nchar(x_trimmed[i]) x_split[i] <- paste0(x_trimmed[i] %>% substr(1, x_length / 2) %>% trimws(), '.* ', x_trimmed[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws()) found <- MOs[which(MOs$fullname %like% paste0('^', x_split[i])),]$mo if (length(found) > 0) { x[i] <- found[1L] next } # try any match with text before and after original search string ---- # so "negative rods" will be "GNR" if (x_trimmed[i] %like% "^Gram") { x_trimmed[i] <- gsub("^Gram", "", x_trimmed[i], ignore.case = TRUE) # remove leading and trailing spaces again x_trimmed[i] <- trimws(x_trimmed[i], which = "both") } if (!is.na(x_trimmed[i])) { found <- MOs[which(MOs$fullname %like% x_trimmed[i]),]$mo if (length(found) > 0) { x[i] <- found[1L] next } } # not found ---- x[i] <- NA_character_ failures <- c(failures, x_backup[i]) } failures <- failures[!failures %in% c(NA, NULL, NaN)] if (length(failures) > 0) { warning("These ", length(failures) , " values could not be coerced to a valid mo: ", paste('"', unique(failures), '"', sep = "", collapse = ', '), ".", call. = FALSE) } # Becker ---- if (Becker == TRUE | Becker == "all") { # See Source. It's this figure: # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/ CoNS <- MOs %>% filter(genus == "Staphylococcus", 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")) %>% pull(mo) CoPS <- MOs %>% filter(genus == "Staphylococcus", species %in% c("simiae", "agnetis", "chromogenes", "delphini", "felis", "lutrae", "hyicus", "intermedius", "pseudintermedius", "pseudointermedius", "schleiferi")) %>% pull(mo) x[x %in% CoNS] <- "STACNS" x[x %in% CoPS] <- "STACPS" if (Becker == "all") { x[x == "STAAUR"] <- "STACPS" } } # Lancefield ---- if (Lancefield == TRUE | Lancefield == "all") { # group A x[x == "STCPYO"] <- "STCGRA" # S. pyogenes # group B x[x == "STCAGA"] <- "STCGRB" # S. agalactiae # group C S_groupC <- MOs %>% filter(genus == "Streptococcus", species %in% c("equisimilis", "equi", "zooepidemicus", "dysgalactiae")) %>% pull(mo) x[x %in% S_groupC] <- "STCGRC" # S. agalactiae if (Lancefield == "all") { x[substr(x, 1, 3) == "ENC"] <- "STCGRD" # all Enterococci } # group F x[x == "STCANG"] <- "STCGRF" # S. anginosus # group H x[x == "STCSAN"] <- "STCGRH" # S. sanguis # group K x[x == "STCSAL"] <- "STCGRK" # S. salivarius } # for the returned genera without species, add species ---- # like "ESC" -> "ESCSPP", but only where the input contained it indices <- nchar(unique(x)) == 3 & !x %like% "[A-Z]{3}SPP" & !x %in% c("GNR", "GPR", "GNC", "GPC", "GNS", "GPS", "GNK", "GPK") indices <- indices[!is.na(indices)] x[indices] <- paste0(x[indices], 'SPP') # left join the found results to the original input values (x_input) df_found <- data.frame(input = as.character(unique(x_input)), found = 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) class(x) <- "mo" attr(x, 'package') <- 'AMR' x } #' @rdname as.mo #' @export is.mo <- function(x) { # bactid for older releases # remove when is.bactid will be removed identical(class(x), "mo") | identical(class(x), "bactid") } #' @rdname as.mo #' @export guess_mo <- as.mo #' @exportMethod print.mo #' @export #' @noRd print.mo <- function(x, ...) { cat("Class 'mo'\n") print.default(as.character(x), quote = FALSE) } #' @exportMethod as.data.frame.mo #' @export #' @noRd as.data.frame.mo <- function (x, ...) { # same as as.data.frame.character but with removed stringsAsFactors 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), ...) }