You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

450 lines
16 KiB

# ==================================================================== #
# 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
#'
4 years ago
#' Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), 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.
#'
#' Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. These are:
4 years ago
#' \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}}
#' \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{"staaur"} 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
#' @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
#'
#' # 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:
4 years ago
#' library(dplyr)
#' df$mo <- df %>%
4 years ago
#' select(microorganism_name) %>%
#' guess_mo()
#'
#' # and can even contain 2 columns, which is convenient for genus/species combinations:
#' df$mo <- df %>%
4 years ago
#' select(genus, species) %>%
#' guess_mo()
4 years ago
#'
#' # same result:
4 years ago
#' df <- df %>%
#' mutate(mo = guess_mo(paste(genus, species)))
#' }
as.mo <- function(x, Becker = FALSE, Lancefield = FALSE) {
4 years ago
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)
}
4 years ago
# support tidyverse selection like: df %>% select(colA)
if (!is.vector(x)) {
x <- pull(x, 1)
}
}
MOs <- AMR::microorganisms %>% filter(!mo %like% '^_FAM') # dont search in those
failures <- character(0)
x_input <- x
# only check the uniques, which is way faster
x <- unique(x)
x_backup <- x
# remove dots and other non-text in case of "E. coli" except spaces
4 years ago
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)
# for species
x_species <- paste(x, 'species')
# add start en stop regex
x <- paste0('^', x, '$')
4 years ago
x_withspaces_all <- x_withspaces
x_withspaces <- paste0('^', x_withspaces, '$')
for (i in 1:length(x)) {
if (identical(x_trimmed[i], "")) {
# empty values
x[i] <- NA
failures <- c(failures, x_backup[i])
next
}
if (x_backup[i] %in% AMR::microorganisms$mo) {
4 years ago
# is already a valid MO code
x[i] <- x_backup[i]
next
}
if (x_trimmed[i] %in% AMR::microorganisms$mo) {
4 years ago
# is already a valid MO code
x[i] <- x_trimmed[i]
next
}
if (tolower(x[i]) == '^e.*coli$') {
# avoid detection of Entamoeba coli in case of E. coli
x[i] <- 'ESCCOL'
next
}
if (tolower(x[i]) == '^h.*influenzae$') {
# avoid detection of Haematobacter influenzae in case of H. influenzae
x[i] <- 'HAEINF'
next
}
if (tolower(x[i]) == '^st.*au$'
| tolower(x[i]) == '^stau$'
| tolower(x[i]) == '^staaur$') {
# avoid detection of Staphylococcus auricularis in case of S. aureus
x[i] <- 'STAAUR'
next
}
if (tolower(x[i]) == '^p.*aer$') {
# avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa
x[i] <- 'PSEAER'
next
}
if (tolower(x[i]) %like% 'coagulase negative'
4 years ago
| tolower(x[i]) %like% 'cns'
| tolower(x[i]) %like% 'cons') {
# coerce S. coagulase negative
x[i] <- 'STACNS'
next
}
if (tolower(x[i]) %like% 'coagulase positive'
| tolower(x[i]) %like% 'cps'
| tolower(x[i]) %like% 'cops') {
# coerce S. coagulase positive
x[i] <- 'STACPS'
next
}
# translate known trivial names 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] <- 'ENC'
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'
4 years ago
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 any match diregarding spaces
found <- MOs[which(MOs$fullname %like% x[i]),]$mo
if (length(found) > 0) {
x[i] <- found[1L]
next
}
# try exact match of only genus, with 'species' attached
# (this prevents Streptococcus from becoming Peptostreptococcus, since "p" < "s")
found <- MOs[which(MOs$fullname == x_species[i]),]$mo
if (length(found) > 0) {
x[i] <- found[1L]
next
}
# try any match of only genus, with 'species' attached
found <- MOs[which(MOs$fullname %like% x_species[i]),]$mo
if (length(found) > 0) {
x[i] <- found[1L]
next
}
4 years ago
# try fullname without start and stop regex, to also find subspecies, like "K. pneu rhino"
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])
}
4 years ago
failures <- failures[!failures %in% c(NA, NULL, NaN)]
if (length(failures) > 0) {
warning("These values could not be coerced to a valid mo: ",
4 years ago
paste('"', unique(failures), '"', sep = "", collapse = ', '),
".",
call. = FALSE)
}
4 years ago
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"
}
}
if (Lancefield == TRUE | Lancefield == "all") {
4 years ago
# 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
}
4 years ago
# group F
x[x == "STCANG"] <- "STCGRF" # S. anginosus
# group H
x[x == "STCSAN"] <- "STCGRH" # S. sanguis
# group K
x[x == "STCSAL"] <- "STCGRK" # S. salivarius
}
# 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"
4 years ago
attr(x, 'package') <- 'AMR'
x
}
4 years ago
#' @rdname as.mo
4 years ago
#' @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)
}
4 years ago
#' @exportMethod as.data.frame.mo
4 years ago
#' @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), ...)
4 years ago
}
#' @exportMethod print.bactid
#' @export
#' @noRd
print.bactid <- function(x, ...) {
cat("Class 'bactid'\n")
print.default(as.character(x), quote = FALSE)
}
#' @exportMethod as.data.frame.bactid
#' @export
#' @noRd
as.data.frame.bactid <- 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.bactid
#' @export
#' @importFrom dplyr pull
#' @noRd
pull.bactid <- function(.data, ...) {
pull(as.data.frame(.data), ...)
}