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# ==================================================================== #
# 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()) {
# will be checked for mo class in validation
mo_validate(x = x, property = "mo",
Becker = Becker, Lancefield = Lancefield,
allow_uncertain = allow_uncertain, reference_df = reference_df)
}
#' @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",
"<NA>" = 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))
}
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