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updated taxonomy
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 1.8.2.9061
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Date: 2022-12-11
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Version: 1.8.2.9062
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Date: 2022-12-12
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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data analysis and to work with microbial and antimicrobial properties by
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@ -52,5 +52,5 @@ BugReports: https://github.com/msberends/AMR/issues
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License: GPL-2 | file LICENSE
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Encoding: UTF-8
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LazyData: true
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RoxygenNote: 7.2.2
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RoxygenNote: 7.2.3
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Roxygen: list(markdown = TRUE)
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3
NEWS.md
3
NEWS.md
@ -1,4 +1,4 @@
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# AMR 1.8.2.9061
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# AMR 1.8.2.9062
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This version will eventually become v2.0! We're happy to reach a new major milestone soon!
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@ -45,6 +45,7 @@ This version will eventually become v2.0! We're happy to reach a new major miles
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* Support for using antibiotic selectors in scoped `dplyr` verbs (with or without `vars()`), such as in: `... %>% summarise_at(aminoglycosides(), resistance)`, see `resistance()`
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### Changes
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* Updated the microbiological taxonomy using the latest GBIF backbone (November 2022) and latest LPSN records (11 December 2022)
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* For `as.rsi()`:
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* Fixed certain EUCAST breakpoints for MIC values
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* Allow `NA` values (e.g. `as.rsi(as.disk(NA), ...)`)
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@ -72,12 +72,12 @@ EUCAST_VERSION_EXPERT_RULES <- list(
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TAXONOMY_VERSION <- list(
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GBIF = list(
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accessed_date = as.Date("2022-09-12"),
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citation = "GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset \\doi{10.15468/39omei}.",
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accessed_date = as.Date("2022-12-11"),
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citation = "GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset \\doi{10.15468/39omei}.",
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url = "https://www.gbif.org"
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),
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LPSN = list(
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accessed_date = as.Date("2022-09-12"),
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accessed_date = as.Date("2022-12-11"),
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citation = "Parte, AC *et al.* (2020). **List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.** International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \\doi{10.1099/ijsem.0.004332}.",
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url = "https://lpsn.dsmz.de"
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),
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@ -182,7 +182,7 @@ join_microorganisms <- function(type, x, by, suffix, ...) {
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}
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if (type %like% "full|left|right|inner" && NROW(joined) > NROW(x)) {
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warning_("in `", type, "_join()`: the newly joined data set contains ", nrow(joined) - nrow(x), " rows more than the number of rows of `x`.")
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warning_("in `", type, "_microorganisms()`: the newly joined data set contains ", nrow(joined) - nrow(x), " rows more than the number of rows of `x`.")
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}
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as_original_data_class(joined, class(x.bak))
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@ -207,7 +207,12 @@ create_MO_lookup <- function() {
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# all the rest
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MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
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if (length(MO_FULLNAME_LOWER) != nrow(MO_lookup)) {
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packageStartupMessage("fullname_lower not same size - applied tolower(), update sysdata.rda!")
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MO_lookup$fullname_lower <- tolower(MO_lookup$fullname)
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} else {
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MO_lookup$fullname_lower <- MO_FULLNAME_LOWER
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}
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MO_lookup$full_first <- substr(MO_lookup$fullname_lower, 1, 1)
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MO_lookup$species_first <- substr(MO_lookup$species, 1, 1)
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@ -8,7 +8,7 @@ This work was published in the Journal of Statistical Software (Volume 104(3); [
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`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. It is currently being used in over 175 countries.
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After installing this package, R knows ~49,000 distinct microbial species and all ~570 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish, and Ukrainian.
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After installing this package, R knows ~48,000 distinct microbial species and all ~600 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish, and Ukrainian.
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This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice Foundation and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
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@ -50,13 +50,17 @@ int_resis2 <- int_resis[, sapply(int_resis, function(x) any(!is.rsi(x) | x == "R
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# remove lab drugs
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untreatable <- antibiotics[which(antibiotics$name %like% "-high|EDTA|polysorbate|macromethod|screening|/nacubactam"), "ab", drop = TRUE]
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int_resis2 <- int_resis2 %>%
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filter(!ab %in% untreatable) %>%
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arrange(mo, ab)
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# takes ages with filter()..., weird
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int_resis3 <- int_resis2[which(!int_resis2$ab %in% untreatable), ]
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class(int_resis3$ab) <- c("ab", "character")
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int_resis3
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intrinsic_resistant <- as.data.frame(int_resis2, stringsAsFactors = FALSE)
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all(int_resis3$mo %in% microorganisms$mo)
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all(int_resis3$ab %in% antibiotics$ab)
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intrinsic_resistant <- df_remove_nonASCII(int_resis3)
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usethis::use_data(intrinsic_resistant, internal = FALSE, overwrite = TRUE, version = 2, compress = "xz")
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rm(intrinsic_resistant)
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# AFTER THIS:
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# DO NOT FORGET TO UPDATE THE VERSION NUMBER IN mo_is_intrinsic_resistant()
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# DO NOT FORGET TO UPDATE THE VERSION NUMBER IN mo_is_intrinsic_resistant() AND R/data.R
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@ -31,10 +31,10 @@
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# (at least 10 GB will be used by the R session for the size of the files)
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# 1. Go to https://doi.org/10.15468/39omei and find the download link for the
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# latest GBIF backbone taxonony ZIP file - unpack Taxon.tsv from it (~2.2 GB)
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# latest GBIF backbone taxonony under "Endpoints" and unpack Taxon.tsv from it (~2.2 GB)
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# ALSO BE SURE to get the date of release and update R/aa_globals.R later!
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# 2. Go to https://lpsn.dsmz.de/downloads (register first) and download the latest
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# CSV file. It should be (re)named "taxonomy.csv". Their API unfortunately does
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# CSV file (~12,5 MB) as "taxonomy.csv". Their API unfortunately does
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# not include the full taxonomy and is currently (2022) pretty worthless.
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# 3. Set this folder_location to the path where these two files are:
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folder_location <- "~/Downloads/backbone/"
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@ -47,7 +47,9 @@ if (!file.exists(file_gbif)) stop("GBIF file not found")
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if (!file.exists(file_lpsn)) stop("LPSN file not found")
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library(dplyr)
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library(vroom)
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library(vroom) # to import files
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library(rvest) # to scape LPSN website
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library(progress) # to show progress bars
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library(AMR)
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# also requires 'rvest' and 'progress' packages
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@ -158,15 +160,15 @@ abbreviate_mo <- function(x, minlength = 5, prefix = "", hyphen_as_space = FALSE
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# to retrieve LPSN and authors from LPSN website
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get_lpsn_and_author <- function(rank, name) {
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url <- paste0("https://lpsn.dsmz.de/", tolower(rank), "/", tolower(name))
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page_txt <- tryCatch(rvest::read_html(url), error = function(e) NULL)
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page_txt <- tryCatch(read_html(url), error = function(e) NULL)
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if (is.null(page_txt)) {
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warning("No LPSN found for ", tolower(rank), " '", name, "'")
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lpsn <- NA_character_
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ref <- NA_character_
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} else {
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page_txt <- page_txt %>%
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rvest::html_element("#detail-page") %>%
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rvest::html_text()
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html_element("#detail-page") %>%
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html_text()
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lpsn <- gsub(".*Record number:[\r\n\t ]*([0-9]+).*", "\\1", page_txt, perl = FALSE)
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ref <- page_txt %>%
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gsub(".*?Name: (.*[0-9]{4}?).*", "\\1", ., perl = FALSE) %>%
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@ -204,6 +206,23 @@ get_lpsn_and_author <- function(rank, name) {
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# Read GBIF data ----------------------------------------------------------
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taxonomy_gbif.bak <- vroom(file_gbif)
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include_fungal_orders <- c(
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"Eurotiales", "Microascales", "Mucorales", "Saccharomycetales",
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"Schizosaccharomycetales", "Tremellales", "Onygenales", "Pneumocystales"
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)
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# get latest taxonomic names of these fungal orders
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include_fungal_orders_ids <- taxonomy_gbif.bak %>%
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filter(order %in% include_fungal_orders)
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include_fungal_orders <- taxonomy_gbif.bak %>%
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filter(taxonID %in% c(include_fungal_orders_ids$taxonID, include_fungal_orders_ids$acceptedNameUsageID)) %>%
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distinct(order) %>%
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pull(order)
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# check some columns to validate below filters
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sort(table(taxonomy_gbif.bak$taxonomicStatus))
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sort(table(taxonomy_gbif.bak$taxonRank))
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taxonomy_gbif <- taxonomy_gbif.bak %>%
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# immediately filter rows we really never want
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filter(
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@ -273,10 +292,15 @@ taxonomy_gbif <- taxonomy_gbif.bak %>%
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sort(table(taxonomy_gbif$rank))
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sort(table(taxonomy_gbif$status))
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taxonomy_gbif
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# Read LPSN data ----------------------------------------------------------
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taxonomy_lpsn.bak <- vroom(file_lpsn)
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# check some columns to validate below filters
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sort(table(is.na(taxonomy_lpsn.bak$record_lnk))) # accepted = TRUE
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taxonomy_lpsn <- taxonomy_lpsn.bak %>%
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transmute(
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genus = genus_name,
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@ -306,22 +330,23 @@ taxonomy_lpsn_missing <- tibble(
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genus = character(0)
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)
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for (page in LETTERS) {
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message("Downloading page ", page, appendLF = FALSE)
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# this will not alter `taxonomy_lpsn` yet
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message("Downloading page ", page, "...", appendLF = FALSE)
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url <- paste0("https://lpsn.dsmz.de/genus?page=", page)
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x <- xml2::read_html(url) %>%
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x <- read_html(url) %>%
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# class "main-list" is the main table
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rvest::html_element(".main-list") %>%
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html_element(".main-list") %>%
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# get every list element with a set <id> attribute
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rvest::html_elements("li[id]")
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html_elements("li[id]")
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for (i in seq_len(length(x))) {
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if (i %% 25 == 0) {
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message(".", appendLF = FALSE)
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}
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elements <- x[[i]] %>% rvest::html_elements("a")
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hrefs <- elements %>% rvest::html_attr("href")
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elements <- x[[i]] %>% html_elements("a")
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hrefs <- elements %>% html_attr("href")
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ranks <- hrefs %>% gsub(".*/(.*?)/.*", "\\1", .)
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names <- elements %>%
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rvest::html_text() %>%
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html_text() %>%
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gsub('"', "", ., fixed = TRUE)
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# no species, this must be until genus level
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hrefs <- hrefs[ranks != "species"]
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@ -332,7 +357,7 @@ for (page in LETTERS) {
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df <- names %>%
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tibble() %>%
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t() %>%
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as_tibble() %>%
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as_tibble(.name_repair = "unique") %>%
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setNames(ranks) %>%
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# no candidates please
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filter(genus %unlike% "^(Candidatus|\\[)")
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@ -342,6 +367,7 @@ for (page in LETTERS) {
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}
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message(length(x), " entries incl. candidates (cleaned total: ", nrow(taxonomy_lpsn_missing), ")")
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}
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taxonomy_lpsn_missing
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taxonomy_lpsn <- taxonomy_lpsn %>%
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left_join(taxonomy_lpsn_missing, by = "genus") %>%
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@ -350,8 +376,8 @@ taxonomy_lpsn <- taxonomy_lpsn %>%
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mutate_all(function(x) ifelse(x %like_case% " no ", NA_character_, x))
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taxonomy_lpsn.bak2 <- taxonomy_lpsn
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# add family
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pb <- progress::progress_bar$new(total = length(unique(taxonomy_lpsn$family)))
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# download family directly from LPSN website using scraping
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pb <- progress_bar$new(total = length(unique(taxonomy_lpsn$family)))
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for (f in unique(taxonomy_lpsn$family)) {
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pb$tick()
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if (is.na(f)) next
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@ -370,8 +396,8 @@ for (f in unique(taxonomy_lpsn$family)) {
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ref = unname(tax_info["ref"])
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))
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}
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# add order
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pb <- progress::progress_bar$new(total = length(unique(taxonomy_lpsn$order)))
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# download order directly from LPSN website using scraping
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pb <- progress_bar$new(total = length(unique(taxonomy_lpsn$order)))
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for (o in unique(taxonomy_lpsn$order)) {
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pb$tick()
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if (is.na(o)) next
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@ -389,8 +415,8 @@ for (o in unique(taxonomy_lpsn$order)) {
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ref = unname(tax_info["ref"])
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))
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}
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# add class
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pb <- progress::progress_bar$new(total = length(unique(taxonomy_lpsn$class)))
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# download class directly from LPSN website using scraping
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pb <- progress_bar$new(total = length(unique(taxonomy_lpsn$class)))
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for (cc in unique(taxonomy_lpsn$class)) {
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pb$tick()
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if (is.na(cc)) next
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@ -407,8 +433,8 @@ for (cc in unique(taxonomy_lpsn$class)) {
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ref = unname(tax_info["ref"])
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))
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}
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# add phylum
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pb <- progress::progress_bar$new(total = length(unique(taxonomy_lpsn$phylum)))
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# download phylum directly from LPSN website using scraping
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pb <- progress_bar$new(total = length(unique(taxonomy_lpsn$phylum)))
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for (p in unique(taxonomy_lpsn$phylum)) {
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pb$tick()
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if (is.na(p)) next
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@ -424,8 +450,8 @@ for (p in unique(taxonomy_lpsn$phylum)) {
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ref = unname(tax_info["ref"])
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))
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}
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# add kingdom
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pb <- progress::progress_bar$new(total = length(unique(taxonomy_lpsn$kingdom)))
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# download kingdom directly from LPSN website using scraping
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pb <- progress_bar$new(total = length(unique(taxonomy_lpsn$kingdom)))
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for (k in unique(taxonomy_lpsn$kingdom)) {
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pb$tick()
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if (is.na(k)) next
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@ -450,7 +476,7 @@ sort(table(taxonomy_lpsn$status))
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saveRDS(taxonomy_gbif, "data-raw/taxonomy_gbif.rds", version = 2)
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saveRDS(taxonomy_lpsn, "data-raw/taxonomy_lpsn.rds", version = 2)
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# this allows to always get back to this point by simply loading the files from data-raw/.
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# Add full names ----------------------------------------------------------
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@ -468,7 +494,7 @@ taxonomy_gbif <- taxonomy_gbif %>%
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) %>%
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# keep only one GBIF taxon ID per full name
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arrange(fullname, gbif) %>%
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distinct(kingdom, fullname, .keep_all = TRUE)
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distinct(kingdom, rank, fullname, .keep_all = TRUE)
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taxonomy_lpsn <- taxonomy_lpsn %>%
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# clean NAs and add fullname
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@ -484,7 +510,7 @@ taxonomy_lpsn <- taxonomy_lpsn %>%
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) %>%
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# keep only one LPSN record ID per full name
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arrange(fullname, lpsn) %>%
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distinct(kingdom, fullname, .keep_all = TRUE)
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distinct(kingdom, rank, fullname, .keep_all = TRUE)
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# set parent LPSN IDs, requires full name
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taxonomy_lpsn$lpsn_parent[taxonomy_lpsn$rank == "phylum"] <- taxonomy_lpsn$lpsn[match(taxonomy_lpsn$kingdom[taxonomy_lpsn$rank == "phylum"], taxonomy_lpsn$fullname)]
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@ -513,6 +539,7 @@ taxonomy <- taxonomy %>%
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filter(fullname != "")
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# fix rank
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table(taxonomy$rank, useNA = "always")
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taxonomy <- taxonomy %>%
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mutate(rank = case_when(
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subspecies != "" ~ "subspecies",
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@ -525,7 +552,6 @@ taxonomy <- taxonomy %>%
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kingdom != "" ~ "kingdom",
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TRUE ~ NA_character_
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))
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table(taxonomy$rank, useNA = "always")
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# get the latest upper taxonomy from LPSN to update the GBIF data
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@ -579,6 +605,7 @@ taxonomy <- taxonomy %>%
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)
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# 2 = phylum ... 6 = genus
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taxonomy_all_missing <- NULL
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for (i in 2:6) {
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i_name <- colnames(taxonomy)[i + 1]
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message("Adding missing: ", i_name, "... ", appendLF = FALSE)
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@ -592,68 +619,60 @@ for (i in 2:6) {
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status = "accepted",
|
||||
source = "manually added"
|
||||
) %>%
|
||||
filter(!paste(kingdom, .[[ncol(.) - 4]], rank) %in% paste(taxonomy$kingdom, taxonomy[[i + 1]], taxonomy$rank)) # %>%
|
||||
filter(!paste(kingdom, .[[ncol(.) - 4]], rank) %in% paste(taxonomy$kingdom, taxonomy[[i + 1]], taxonomy$rank)) %>%
|
||||
# get GBIF identifier where available
|
||||
# left_join(current_gbif %>%
|
||||
# select(kingdom, all_of(i_name), rank = taxonRank, ref = scientificNameAuthorship, gbif = taxonID, gbif_parent = parentNameUsageID),
|
||||
# by = c("kingdom", "rank", i_name)
|
||||
# ) %>%
|
||||
# mutate(source = ifelse(!is.na(gbif), "GBIF", source))
|
||||
left_join(current_gbif %>%
|
||||
select(kingdom, all_of(i_name), rank = taxonRank, ref = scientificNameAuthorship, gbif = taxonID, gbif_parent = parentNameUsageID),
|
||||
by = c("kingdom", "rank", i_name)
|
||||
) %>%
|
||||
mutate(source = ifelse(!is.na(gbif), "GBIF", source))
|
||||
message("n = ", nrow(to_add))
|
||||
taxonomy <- taxonomy %>%
|
||||
if (is.null(taxonomy_all_missing)) {
|
||||
taxonomy_all_missing <- to_add
|
||||
} else {
|
||||
taxonomy_all_missing <- taxonomy_all_missing %>%
|
||||
bind_rows(to_add)
|
||||
}
|
||||
|
||||
# FIX LATER: added missings after finding out still some taxonomic levels were missing
|
||||
# this should not be needed - it was the only part that was required after last update
|
||||
# can now be removed? Check with next update!
|
||||
new_df <- AMR::microorganisms[0, ]
|
||||
for (tax in c("phylum", "class", "order", "family", "genus")) {
|
||||
print(tax)
|
||||
out <- AMR::microorganisms %>%
|
||||
pull(tax) %>%
|
||||
unique()
|
||||
missing <- vapply(FUN.VALUE = logical(1), out, function(x) length(which(AMR::microorganisms[[tax]] == x & AMR::microorganisms$rank == tax)) == 0)
|
||||
missing <- names(missing)[which(missing == TRUE & names(missing) != "" & names(missing) %unlike% "unknown")]
|
||||
out <- microorganisms %>%
|
||||
filter(.[[tax]] %in% missing) %>%
|
||||
distinct(.[[tax]], .keep_all = TRUE) %>%
|
||||
mutate_at(vars((which(colnames(.) == tax) + 1):subspecies), ~"") %>%
|
||||
mutate_at(vars(lpsn:gbif_renamed_to), ~NA_character_) %>%
|
||||
mutate(
|
||||
rank = tax,
|
||||
ref = NA_character_,
|
||||
status = "accepted",
|
||||
fullname = .[[tax]],
|
||||
source = "manually added",
|
||||
snomed = rep(list(character(0)), nrow(.))
|
||||
)
|
||||
new_df <- bind_rows(new_df, out)
|
||||
if (".[[tax]]" %in% colnames(new_df)) {
|
||||
new_df <- new_df %>% select(-`.[[tax]]`)
|
||||
}
|
||||
}
|
||||
new_df <- new_df %>%
|
||||
mutate(mo = as.character(mo))
|
||||
taxonomy_all_missing %>% View()
|
||||
|
||||
new_mo <- new_df %>%
|
||||
filter(rank == "family") %>%
|
||||
mutate(
|
||||
mo_rank_new8 = abbreviate_mo(family, minlength = 8, prefix = "[FAM]_"),
|
||||
mo_rank_new9 = abbreviate_mo(family, minlength = 9, prefix = "[FAM]_"),
|
||||
mo_rank_new = mo_rank_new8,
|
||||
mo_duplicated = duplicated(mo_rank_new),
|
||||
mo_rank_new = ifelse(mo_duplicated, mo_rank_new9, mo_rank_new),
|
||||
mo_duplicated = duplicated(mo_rank_new)
|
||||
) %>%
|
||||
transmute(fullname, mo_rank_new = paste0(gsub("_.*", "_", as.character(mo)), mo_rank_new))
|
||||
any(new_mo$mo_rank_new %in% microorganisms$mo)
|
||||
new_df[which(new_df$fullname %in% new_mo$fullname), "mo"] <- new_mo$mo_rank_new
|
||||
|
||||
|
||||
# species (requires combination with genus)
|
||||
taxonomy <- taxonomy %>%
|
||||
bind_rows(taxonomy %>%
|
||||
bind_rows(taxonomy_all_missing)
|
||||
|
||||
# we need to fix parent GBIF identifiers
|
||||
taxonomy$gbif_parent[taxonomy$rank == "phylum" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(taxonomy$kingdom[taxonomy$rank == "phylum" & !is.na(taxonomy$gbif)], taxonomy$fullname)]
|
||||
taxonomy$gbif_parent[taxonomy$rank == "class" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(taxonomy$phylum[taxonomy$rank == "class" & !is.na(taxonomy$gbif)], taxonomy$fullname)]
|
||||
taxonomy$gbif_parent[taxonomy$rank == "order" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(taxonomy$class[taxonomy$rank == "order" & !is.na(taxonomy$gbif)], taxonomy$fullname)]
|
||||
taxonomy$gbif_parent[taxonomy$rank == "family" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(taxonomy$order[taxonomy$rank == "family" & !is.na(taxonomy$gbif)], taxonomy$fullname)]
|
||||
taxonomy$gbif_parent[taxonomy$rank == "genus" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(taxonomy$family[taxonomy$rank == "genus" & !is.na(taxonomy$gbif)], taxonomy$fullname)]
|
||||
taxonomy$gbif_parent[taxonomy$rank == "species" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(taxonomy$genus[taxonomy$rank == "species" & !is.na(taxonomy$gbif)], taxonomy$fullname)]
|
||||
taxonomy$gbif_parent[taxonomy$rank == "subspecies" & !is.na(taxonomy$gbif)] <- taxonomy$gbif[match(paste(taxonomy$genus[taxonomy$rank == "subspecies" & !is.na(taxonomy$gbif)], taxonomy$species[taxonomy$rank == "subspecies" & !is.na(taxonomy$gbif)]), taxonomy$fullname)]
|
||||
|
||||
# these still have no record in our data set:
|
||||
all(taxonomy$lpsn_parent %in% taxonomy$lpsn)
|
||||
all(taxonomy$gbif_parent %in% taxonomy$gbif)
|
||||
|
||||
# fix for duplicate fullnames within a kingdom (such as Nitrospira which is the name of the genus AND its class)
|
||||
taxonomy <- taxonomy %>%
|
||||
mutate(rank_index = case_when(rank == "subspecies" ~ 1,
|
||||
rank == "species" ~ 2,
|
||||
rank == "genus" ~ 3,
|
||||
rank == "family" ~ 4,
|
||||
rank == "order" ~ 5,
|
||||
rank == "class" ~ 6,
|
||||
TRUE ~ 7),
|
||||
fullname_rank = paste0(fullname, " {", rank, "}")) %>%
|
||||
arrange(kingdom, fullname, rank_index) %>%
|
||||
group_by(kingdom, fullname) %>%
|
||||
mutate(fullname = if_else(row_number() > 1, fullname_rank, fullname)) %>%
|
||||
ungroup() %>%
|
||||
select(-fullname_rank) %>%
|
||||
arrange(fullname)
|
||||
|
||||
# now also add missing species (requires combination with genus)
|
||||
taxonomy <- taxonomy %>%
|
||||
bind_rows(
|
||||
taxonomy %>%
|
||||
filter(species != "") %>%
|
||||
distinct(kingdom, genus, species, .keep_all = TRUE) %>%
|
||||
select(kingdom:species) %>%
|
||||
@ -669,7 +688,8 @@ taxonomy <- taxonomy %>%
|
||||
select(kingdom, genus, species = specificEpithet, rank = taxonRank, ref = scientificNameAuthorship, gbif = taxonID, gbif_parent = parentNameUsageID),
|
||||
by = c("kingdom", "rank", "genus", "species")
|
||||
) %>%
|
||||
mutate(source = ifelse(!is.na(gbif), "GBIF", source)))
|
||||
mutate(source = ifelse(!is.na(gbif), "GBIF", source))
|
||||
)
|
||||
|
||||
|
||||
# remove NAs from taxonomy again, and keep unique full names
|
||||
@ -678,15 +698,16 @@ taxonomy <- taxonomy %>%
|
||||
distinct(kingdom, fullname, .keep_all = TRUE) %>%
|
||||
filter(kingdom != "")
|
||||
|
||||
|
||||
# Save intermediate results -----------------------------------------------
|
||||
|
||||
saveRDS(taxonomy, "data-raw/taxonomy.rds")
|
||||
saveRDS(taxonomy, "data-raw/taxonomy1.rds")
|
||||
|
||||
|
||||
# Get previously manually added entries -----------------------------------
|
||||
|
||||
manually_added <- AMR::microorganisms %>%
|
||||
filter(source == "manually added", !fullname %in% taxonomy$fullname) %>%
|
||||
filter(source == "manually added", !paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname)) %>%
|
||||
select(fullname:subspecies, ref, source, rank)
|
||||
|
||||
# get latest taxonomy for those entries
|
||||
@ -703,15 +724,14 @@ for (cc in unique(manually_added$class[manually_added$class != "" & manually_add
|
||||
manually_added$phylum[which(manually_added$class == cc)] <- taxonomy$phylum[which(taxonomy$class == cc & is.na(taxonomy$lpsn))][1]
|
||||
}
|
||||
|
||||
# previously required:
|
||||
taxonomy$ref[which(taxonomy$genus == "Streptococcus" & taxonomy$species %like% "group")] <- "Lancefield, 1933"
|
||||
|
||||
taxonomy <- taxonomy %>%
|
||||
bind_rows(manually_added %>%
|
||||
manually_added <- manually_added %>%
|
||||
mutate(
|
||||
status = "accepted",
|
||||
rank = ifelse(fullname %like% "unknown", "(unknown rank)", rank)
|
||||
)) %>%
|
||||
)
|
||||
|
||||
taxonomy <- taxonomy %>%
|
||||
bind_rows(manually_added) %>%
|
||||
arrange(fullname)
|
||||
|
||||
table(taxonomy$rank, useNA = "always")
|
||||
@ -741,11 +761,63 @@ taxonomy <- taxonomy %>%
|
||||
"Actinobacteria", # old, now Actinomycetota
|
||||
"Actinomycetota"
|
||||
) |
|
||||
genus %in% MO_PREVALENT_GENERA)
|
||||
genus %in% AMR:::MO_PREVALENT_GENERA)
|
||||
~ 2,
|
||||
TRUE ~ 3
|
||||
))
|
||||
table(taxonomy$prevalence, useNA = "always")
|
||||
# (a lot will be removed further below)
|
||||
|
||||
|
||||
# Add old entries that must be kept ---------------------------------------
|
||||
|
||||
# these are bacteria now removed, but have an renamed-to identifier to a current record, so add them
|
||||
old_to_keep <- microorganisms %>%
|
||||
filter(!paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname),
|
||||
kingdom == "Bacteria",
|
||||
(gbif_renamed_to %in% taxonomy$gbif & !is.na(gbif_renamed_to)) | (lpsn_renamed_to %in% taxonomy$lpsn & !is.na(lpsn_renamed_to)),
|
||||
rank %in% c("genus", "species", "subspecies")) %>%
|
||||
select(-mo, -snomed)
|
||||
|
||||
taxonomy <- taxonomy %>%
|
||||
bind_rows(old_to_keep) %>%
|
||||
arrange(fullname)
|
||||
|
||||
# and these had prevalence = 1, why do they miss now?
|
||||
old_to_keep2 <- microorganisms %>%
|
||||
filter(!paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname),
|
||||
prevalence == 1,
|
||||
!is.na(gbif_parent) & gbif_parent %in% taxonomy$gbif,
|
||||
rank %in% c("genus", "species", "subspecies")) %>%
|
||||
select(-mo, -snomed)
|
||||
|
||||
taxonomy <- taxonomy %>%
|
||||
bind_rows(old_to_keep2) %>%
|
||||
arrange(fullname)
|
||||
|
||||
# strangly, Trichomonas is no longer in GBIF?
|
||||
old_to_keep3 <- microorganisms %>%
|
||||
filter(fullname %like% "^trichomona") %>%
|
||||
select(-mo, -snomed)
|
||||
|
||||
taxonomy <- taxonomy %>%
|
||||
filter(!fullname %in% old_to_keep3$fullname) %>%
|
||||
bind_rows(old_to_keep3) %>%
|
||||
arrange(fullname)
|
||||
|
||||
# fix rank
|
||||
taxonomy <- taxonomy %>%
|
||||
mutate(rank = case_when(
|
||||
subspecies != "" ~ "subspecies",
|
||||
species != "" ~ "species",
|
||||
genus != "" ~ "genus",
|
||||
family != "" ~ "family",
|
||||
order != "" ~ "order",
|
||||
class != "" ~ "class",
|
||||
phylum != "" ~ "phylum",
|
||||
kingdom != "" ~ "kingdom",
|
||||
TRUE ~ NA_character_
|
||||
))
|
||||
|
||||
|
||||
# Add microbial IDs -------------------------------------------------------
|
||||
@ -1050,12 +1122,29 @@ taxonomy <- taxonomy %>%
|
||||
arrange(fullname)
|
||||
|
||||
# now check these - e.g. Nitrospira is the name of a genus AND its class
|
||||
taxonomy %>% filter(fullname %in% .[duplicated(fullname), "fullname", drop = TRUE])
|
||||
taxonomy %>% filter(fullname %in% .[duplicated(fullname), "fullname", drop = TRUE]) %>% View()
|
||||
taxonomy <- taxonomy %>%
|
||||
distinct(fullname, .keep_all = TRUE)
|
||||
mutate(rank_index = case_when(kingdom == "Bacteria" ~ 1,
|
||||
kingdom == "Fungi" ~ 2,
|
||||
kingdom == "Protozoa" ~ 3,
|
||||
kingdom == "Archaea" ~ 4,
|
||||
TRUE ~ 5)) %>%
|
||||
arrange(fullname, rank_index) %>%
|
||||
distinct(fullname, .keep_all = TRUE) %>%
|
||||
select(-rank_index) %>%
|
||||
filter(mo != "")
|
||||
|
||||
# This must not exist:
|
||||
taxonomy %>% filter(mo %like% "__")
|
||||
# this must not exist:
|
||||
taxonomy %>% filter(mo %like% "__") %>% View()
|
||||
taxonomy <- taxonomy %>% filter(mo %unlike% "__")
|
||||
|
||||
# this must be empty of course
|
||||
taxonomy %>% filter(mo %in% .[duplicated(mo), "mo", drop = TRUE]) %>% View()
|
||||
taxonomy <- taxonomy %>% distinct(mo, .keep_all = TRUE)
|
||||
|
||||
# Save intermediate results -----------------------------------------------
|
||||
|
||||
saveRDS(taxonomy, "data-raw/taxonomy2.rds")
|
||||
|
||||
|
||||
# Remove unwanted taxonomic entries from Protoza/Fungi --------------------
|
||||
@ -1067,15 +1156,33 @@ taxonomy <- taxonomy %>%
|
||||
!(phylum %in% c("Choanozoa", "Mycetozoa") & prevalence == 3),
|
||||
# Fungi:
|
||||
!(phylum %in% c("Ascomycota", "Zygomycota", "Basidiomycota") & prevalence == 3),
|
||||
!(genus %in% c("Phoma", "Leptosphaeria") & rank %in% c("species", "subspecies")), # only genus of this rare fungus, with resp. 1300 and 800 species
|
||||
!(genus %in% c("Phoma", "Leptosphaeria", "Physarum") & rank %in% c("species", "subspecies")), # only genus of this rare fungus, with resp. 1300 and 800 species
|
||||
# (leave Alternaria in there, part of human mycobiome and opportunistic pathogen)
|
||||
# Animalia:
|
||||
!genus %in% c("Lucilia", "Lumbricus"),
|
||||
!(class == "Insecta" & rank %in% c("species", "subspecies")), # keep only genus of insects
|
||||
!(genus == "Amoeba" & kingdom == "Animalia"),
|
||||
!(genus %in% c("Aedes", "Anopheles") & rank %in% c("species", "subspecies")), # only genus of the many hundreds of mosquitoes species
|
||||
kingdom != "Plantae"
|
||||
) # this kingdom only contained Curvularia and Hymenolepis, which have coincidental twin names with Fungi
|
||||
|
||||
message("\nCongratulations! The new taxonomic table will contain ", format(nrow(taxonomy), big.mark = ","), " rows.\n")
|
||||
# no ghost families, orders classes, phyla
|
||||
taxonomy <- taxonomy %>%
|
||||
group_by(kingdom, family) %>% filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
|
||||
group_by(kingdom, order) %>% filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
|
||||
group_by(kingdom, class) %>% filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
|
||||
group_by(kingdom, phylum) %>% filter(n() > 1 | fullname %like% "unknown" | rank == "kingdom") %>%
|
||||
ungroup()
|
||||
|
||||
|
||||
message("\nCongratulations! The new taxonomic table will contain ", format(nrow(taxonomy), big.mark = ","), " rows.\n",
|
||||
"This was ", format(nrow(microorganisms), big.mark = ","), " rows.\n")
|
||||
|
||||
# these are the new ones:
|
||||
taxonomy %>% filter(!paste(kingdom, fullname) %in% paste(microorganisms$kingdom, microorganisms$fullname)) %>% View()
|
||||
# these were removed:
|
||||
microorganisms %>% filter(!paste(kingdom, fullname) %in% paste(taxonomy$kingdom, taxonomy$fullname)) %>% View()
|
||||
microorganisms %>% filter(!fullname %in% taxonomy$fullname) %>% View()
|
||||
|
||||
|
||||
# Add SNOMED CT -----------------------------------------------------------
|
||||
@ -1121,15 +1228,13 @@ taxonomy <- taxonomy %>%
|
||||
# set class <mo>
|
||||
class(taxonomy$mo) <- c("mo", "character")
|
||||
|
||||
# Moraxella catarrhalis was named Branhamella catarrhalis (Catlin, 1970), but this is unaccepted in clinical microbiology
|
||||
# we keep them both
|
||||
taxonomy$status[which(taxonomy$fullname == "Moraxella catarrhalis")]
|
||||
taxonomy$lpsn_renamed_to[which(taxonomy$fullname == "Moraxella catarrhalis")]
|
||||
taxonomy$status[which(taxonomy$fullname == "Moraxella catarrhalis")] <- "accepted"
|
||||
taxonomy$lpsn_renamed_to[which(taxonomy$fullname == "Moraxella catarrhalis")] <- NA_character_
|
||||
|
||||
taxonomy <- taxonomy %>%
|
||||
AMR:::dataset_UTF8_to_ASCII()
|
||||
### this was previously needed?? Since 2022 M. catarrhalis seems to be "accepted" again
|
||||
# # Moraxella catarrhalis was named Branhamella catarrhalis (Catlin, 1970), but this is unaccepted in clinical microbiology
|
||||
# # we keep them both
|
||||
# taxonomy$status[which(taxonomy$fullname == "Moraxella catarrhalis")]
|
||||
# taxonomy$lpsn_renamed_to[which(taxonomy$fullname == "Moraxella catarrhalis")]
|
||||
# taxonomy$status[which(taxonomy$fullname == "Moraxella catarrhalis")] <- "accepted"
|
||||
# taxonomy$lpsn_renamed_to[which(taxonomy$fullname == "Moraxella catarrhalis")] <- NA_character_
|
||||
|
||||
|
||||
# Save to package ---------------------------------------------------------
|
||||
@ -1138,7 +1243,7 @@ microorganisms <- taxonomy
|
||||
usethis::use_data(microorganisms, overwrite = TRUE, version = 2, compress = "xz")
|
||||
rm(microorganisms)
|
||||
|
||||
# DON'T FORGET TO UPDATE R/globals.R!
|
||||
# DON'T FORGET TO UPDATE R/_globals.R!
|
||||
|
||||
|
||||
# Test updates ------------------------------------------------------------
|
||||
@ -1154,29 +1259,34 @@ devtools::load_all(".")
|
||||
|
||||
|
||||
# reset previously changed mo codes
|
||||
if (!identical(rsi_translation$mo, as.mo(rsi_translation$mo, language = NULL))) {
|
||||
rsi_translation$mo <- as.mo(rsi_translation$mo, language = NULL)
|
||||
usethis::use_data(rsi_translation, overwrite = TRUE, version = 2, compress = "xz")
|
||||
rm(rsi_translation)
|
||||
}
|
||||
|
||||
if (!identical(microorganisms.codes$mo, as.mo(microorganisms.codes$mo, language = NULL))) {
|
||||
microorganisms.codes <- microorganisms.codes %>% filter(mo %in% microorganisms$mo)
|
||||
microorganisms.codes$mo <- as.mo(microorganisms.codes$mo, language = NULL)
|
||||
# new NAs introduced?
|
||||
any(is.na(microorganisms.codes$mo))
|
||||
usethis::use_data(microorganisms.codes, overwrite = TRUE, version = 2, compress = "xz")
|
||||
rm(microorganisms.codes)
|
||||
}
|
||||
|
||||
if (!identical(example_isolates$mo, as.mo(example_isolates$mo, language = NULL))) {
|
||||
example_isolates$mo <- as.mo(example_isolates$mo, language = NULL)
|
||||
usethis::use_data(example_isolates, overwrite = TRUE, version = 2)
|
||||
rm(example_isolates)
|
||||
|
||||
intrinsic_resistant$microorganism <- suppressMessages(mo_name(intrinsic_resistant$microorganism))
|
||||
usethis::use_data(intrinsic_resistant, overwrite = TRUE, version = 2)
|
||||
rm(intrinsic_resistant)
|
||||
}
|
||||
|
||||
# load new data sets again
|
||||
devtools::load_all(".")
|
||||
source("data-raw/_pre_commit_hook.R")
|
||||
devtools::load_all(".")
|
||||
|
||||
if (!identical(intrinsic_resistant$mo, as.mo(intrinsic_resistant$mo, language = NULL))) {
|
||||
stop("Run data-raw/reproduction_of_intrinsic_resistant.R again")
|
||||
}
|
||||
|
||||
|
||||
# run the unit tests
|
||||
Sys.setenv(NOT_CRAN = "true")
|
||||
|
||||
Binary file not shown.
BIN
data-raw/taxonomy1.rds
Normal file
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index.md
2
index.md
@ -20,7 +20,7 @@ The `AMR` package is a [free and open-source](#copyright) R package with [zero d
|
||||
|
||||
This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
|
||||
|
||||
After installing this package, R knows [**~49,000 distinct microbial species**](./reference/microorganisms.html) and all [**~600 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl), and is being [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
|
||||
After installing this package, R knows [**~48,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl), and is being [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
|
||||
|
||||
##### Used in 175 countries, translated to 16 languages
|
||||
|
||||
|
||||
@ -32,7 +32,7 @@ Welcome to the \code{AMR} package.
|
||||
|
||||
This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
|
||||
|
||||
After installing this package, \R knows ~49,000 distinct microbial species and all ~600 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
|
||||
After installing this package, \R knows ~48,000 distinct microbial species and all ~600 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
|
||||
|
||||
This package is fully independent of any other \R package and works on Windows, macOS and Linux with all versions of \R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This \R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
|
||||
|
||||
|
||||
@ -120,8 +120,8 @@ The coercion rules consider the prevalence of microorganisms in humans grouped i
|
||||
\item Becker K \emph{et al.} (2020). \strong{Emergence of coagulase-negative staphylococci} \emph{Expert Rev Anti Infect Ther.} 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
|
||||
\item Lancefield RC (1933). \strong{A serological differentiation of human and other groups of hemolytic streptococci}. \emph{J Exp Med.} 57(4): 571-95; \doi{10.1084/jem.57.4.571}
|
||||
\item Berends MS \emph{et al.} (2022). \strong{Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019} \emph{Microorganisms} 10(9), 1801; \doi{10.3390/microorganisms10091801}
|
||||
\item Parte, AC \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 12 September, 2022.
|
||||
\item GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 12 September, 2022.
|
||||
\item Parte, AC \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 11 December, 2022.
|
||||
\item GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 11 December, 2022.
|
||||
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
|
||||
}
|
||||
}
|
||||
|
||||
@ -74,7 +74,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
|
||||
\strong{Note:} This function does not translate MIC values to RSI values. Use \code{\link[=as.rsi]{as.rsi()}} for that. \cr
|
||||
\strong{Note:} When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance. \cr
|
||||
|
||||
The file containing all EUCAST rules is located here: \url{https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv}. \strong{Note:} Old taxonomic names are replaced with the current taxonomy where applicable. For example, \emph{Ochrobactrum anthropi} was renamed to \emph{Brucella anthropi} in 2020; the original EUCAST rules v3.1 and v3.2 did not yet contain this new taxonomic name. The \code{AMR} package contains the full microbial taxonomy updated until 12 September, 2022, see \link{microorganisms}.
|
||||
The file containing all EUCAST rules is located here: \url{https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv}. \strong{Note:} Old taxonomic names are replaced with the current taxonomy where applicable. For example, \emph{Ochrobactrum anthropi} was renamed to \emph{Brucella anthropi} in 2020; the original EUCAST rules v3.1 and v3.2 did not yet contain this new taxonomic name. The \code{AMR} package contains the full microbial taxonomy updated until 11 December, 2022, see \link{microorganisms}.
|
||||
\subsection{Custom Rules}{
|
||||
|
||||
Custom rules can be created using \code{\link[=custom_eucast_rules]{custom_eucast_rules()}}, e.g.:
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
\alias{intrinsic_resistant}
|
||||
\title{Data Set with Bacterial Intrinsic Resistance}
|
||||
\format{
|
||||
A \link[tibble:tibble]{tibble} with 134,659 observations and 2 variables:
|
||||
A \link[tibble:tibble]{tibble} with 206,832 observations and 2 variables:
|
||||
\itemize{
|
||||
\item \code{mo}\cr Microorganism ID
|
||||
\item \code{ab}\cr Antibiotic ID
|
||||
|
||||
@ -3,9 +3,9 @@
|
||||
\docType{data}
|
||||
\name{microorganisms}
|
||||
\alias{microorganisms}
|
||||
\title{Data Set with 48,883 Microorganisms}
|
||||
\title{Data Set with 48,050 Microorganisms}
|
||||
\format{
|
||||
A \link[tibble:tibble]{tibble} with 48,883 observations and 22 variables:
|
||||
A \link[tibble:tibble]{tibble} with 48,050 observations and 22 variables:
|
||||
\itemize{
|
||||
\item \code{mo}\cr ID of microorganism as used by this package
|
||||
\item \code{fullname}\cr Full name, like \code{"Escherichia coli"}. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.
|
||||
@ -26,8 +26,8 @@ A \link[tibble:tibble]{tibble} with 48,883 observations and 22 variables:
|
||||
}
|
||||
\source{
|
||||
\itemize{
|
||||
\item Parte, AC \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 12 September, 2022.
|
||||
\item GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 12 September, 2022.
|
||||
\item Parte, AC \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 11 December, 2022.
|
||||
\item GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 11 December, 2022.
|
||||
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
|
||||
}
|
||||
}
|
||||
@ -35,7 +35,7 @@ A \link[tibble:tibble]{tibble} with 48,883 observations and 22 variables:
|
||||
microorganisms
|
||||
}
|
||||
\description{
|
||||
A data set containing the full microbial taxonomy (\strong{last updated: 12 September, 2022}) of five kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this \code{AMR} package. MO codes can be looked up using \code{\link[=as.mo]{as.mo()}}.
|
||||
A data set containing the full microbial taxonomy (\strong{last updated: 11 December, 2022}) of five kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this \code{AMR} package. MO codes can be looked up using \code{\link[=as.mo]{as.mo()}}.
|
||||
}
|
||||
\details{
|
||||
Please note that entries are only based on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
|
||||
@ -47,10 +47,10 @@ For example, \emph{Staphylococcus pettenkoferi} was described for the first time
|
||||
Included taxonomic data are:
|
||||
\itemize{
|
||||
\item All ~34,000 (sub)species from the kingdoms of Archaea and Bacteria
|
||||
\item ~7,400 (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histoplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
|
||||
\item ~5,000 (sub)species from the kingdom of Protozoa
|
||||
\item ~1,500 (sub)species from ~50 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia})
|
||||
\item All ~9,400 previously accepted names of all included (sub)species (these were taxonomically renamed)
|
||||
\item ~6,900 (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histoplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
|
||||
\item ~4,400 (sub)species from the kingdom of Protozoa
|
||||
\item ~1,100 (sub)species from ~40 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia})
|
||||
\item All ~9,100 previously accepted names of all included (sub)species (these were taxonomically renamed)
|
||||
\item The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
|
||||
\item The identifier of the parent taxons
|
||||
\item The year and first author of the related scientific publication
|
||||
|
||||
@ -348,8 +348,8 @@ All matches are sorted descending on their matching score and for all user input
|
||||
\item Becker K \emph{et al.} (2020). \strong{Emergence of coagulase-negative staphylococci} \emph{Expert Rev Anti Infect Ther.} 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
|
||||
\item Lancefield RC (1933). \strong{A serological differentiation of human and other groups of hemolytic streptococci}. \emph{J Exp Med.} 57(4): 571-95; \doi{10.1084/jem.57.4.571}
|
||||
\item Berends MS \emph{et al.} (2022). \strong{Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019} \emph{Microorganisms} 10(9), 1801; \doi{10.3390/microorganisms10091801}
|
||||
\item Parte, AC \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 12 September, 2022.
|
||||
\item GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 12 September, 2022.
|
||||
\item Parte, AC \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 11 December, 2022.
|
||||
\item GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 11 December, 2022.
|
||||
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
|
||||
}
|
||||
}
|
||||
|
||||
@ -29,6 +29,18 @@
|
||||
# ==================================================================== #
|
||||
*/
|
||||
|
||||
/* fix some footer issues with the logos */
|
||||
@media (min-width: 576px) {
|
||||
footer .pkgdown-footer-right {
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width: 300px;
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}
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}
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@media (max-width: 575.98px) {
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footer .pkgdown-footer-right {
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text-align: center;
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||||
}
|
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}
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|
||||
/* replace 'Developers' with 'Maintainers' */
|
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.developers h2 {
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display: none;
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Before Width: | Height: | Size: 350 KiB After Width: | Height: | Size: 343 KiB |
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