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481 lines
20 KiB
R
481 lines
20 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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# Register at List of Prokaryotic names with Standing in Nomenclature (LPSN)
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# then got to https://lpsn.dsmz.de/downloads and download the latest CSV file.
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library(tidyverse)
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library(AMR)
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# these should still work after this update
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test_fullname <- microorganisms$fullname
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test_mo <- microorganisms$mo
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# Helper functions --------------------------------------------------------
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get_author_year <- function(ref) {
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# Only keep first author, e.g. transform 'Smith, Jones, 2011' to 'Smith et al., 2011'
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authors2 <- iconv(ref, from = "UTF-8", to = "ASCII//TRANSLIT")
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authors2 <- gsub(" ?\\(Approved Lists [0-9]+\\) ?", " () ", authors2)
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authors2 <- gsub(" [)(]+ $", "", authors2)
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# remove leading and trailing brackets
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authors2 <- trimws(gsub("^[(](.*)[)]$", "\\1", authors2))
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# only take part after brackets if there's a name
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authors2 <- ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
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gsub(".*[)] (.*)", "\\1", authors2),
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authors2)
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# get year from last 4 digits
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lastyear = as.integer(gsub(".*([0-9]{4})$", "\\1", authors2))
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# can never be later than now
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lastyear = ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
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NA,
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lastyear)
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# get authors without last year
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authors <- gsub("(.*)[0-9]{4}$", "\\1", authors2)
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# remove nonsense characters from names
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authors <- gsub("[^a-zA-Z,'& -]", "", authors)
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# remove trailing and leading spaces
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authors <- trimws(authors)
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# only keep first author and replace all others by 'et al'
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authors <- gsub("(,| and| et| &| ex| emend\\.?) .*", " et al.", authors)
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# et al. always with ending dot
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authors <- gsub(" et al\\.?", " et al.", authors)
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authors <- gsub(" ?,$", "", authors)
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# don't start with 'sensu' or 'ehrenb'
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authors <- gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE)
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# no initials, only surname
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authors <- gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE)
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# combine author and year if year is available
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ref <- ifelse(!is.na(lastyear),
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paste0(authors, ", ", lastyear),
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authors)
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# fix beginning and ending
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ref <- gsub(", $", "", ref)
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ref <- gsub("^, ", "", ref)
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ref <- gsub("^(emend|et al.,?)", "", ref)
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ref <- trimws(ref)
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ref <- gsub("'", "", ref)
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# a lot start with a lowercase character - fix that
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ref[!grepl("^d[A-Z]", ref)] <- gsub("^([a-z])", "\\U\\1", ref[!grepl("^d[A-Z]", ref)], perl = TRUE)
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# specific one for the French that are named dOrbigny
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ref[grepl("^d[A-Z]", ref)] <- gsub("^d", "d'", ref[grepl("^d[A-Z]", ref)])
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ref <- gsub(" +", " ", ref)
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ref
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}
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df_remove_nonASCII <- function(df) {
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# Remove non-ASCII characters (these are not allowed by CRAN)
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df %>%
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mutate_if(is.character, iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
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# also remove invalid characters
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mutate_if(is.character, ~gsub("[\"'`]+", "", .)) %>%
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AMR:::dataset_UTF8_to_ASCII()
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}
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abbreviate_mo <- function(x, minlength = 5, prefix = "", ...) {
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# keep a starting Latin ae
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suppressWarnings(
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gsub("^ae", "\u00E6\u00E6", x, ignore.case = TRUE) %>%
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abbreviate(minlength = minlength,
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use.classes = TRUE,
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method = "both.sides", ...) %>%
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paste0(prefix, .) %>%
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toupper() %>%
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gsub("(\u00C6|\u00E6)+", "AE", .)
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)
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}
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# Read data ---------------------------------------------------------------
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taxonomy <- read_csv("~/Downloads/taxonomy.csv")
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# Create synonyms ---------------------------------------------------------
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new_synonyms <- taxonomy %>%
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left_join(taxonomy,
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by = c("record_lnk" = "record_no"),
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suffix = c("", ".new")) %>%
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filter(!is.na(record_lnk)) %>%
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mutate_all(~ifelse(is.na(.), "", .)) %>%
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transmute(fullname = trimws(paste(genus_name, sp_epithet, subsp_epithet)),
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fullname_new = trimws(paste(genus_name.new, sp_epithet.new, subsp_epithet.new)),
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ref = get_author_year(authors),
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prevalence = 0) %>%
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distinct(fullname, .keep_all = TRUE) %>%
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filter(fullname != fullname_new) %>%
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# this part joins this table to itself to correct for entries that had >1 renames,
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# such as:
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# Bacteroides tectum -> Bacteroides tectus
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# Bacteroides tectus -> Bacteroides pyogenes
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left_join(., .,
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by = c("fullname_new" = "fullname"),
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suffix = c("", ".2")) %>%
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mutate(fullname_new = ifelse(!is.na(fullname_new.2), fullname_new.2, fullname_new),
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ref = ifelse(!is.na(ref.2), ref.2, ref)) %>%
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select(-ends_with(".2"))
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mo_became_synonym <- microorganisms %>%
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filter(fullname %in% new_synonyms$fullname)
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updated_microorganisms <- taxonomy %>%
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filter(is.na(record_lnk)) %>%
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mutate_all(~ifelse(is.na(.), "", .)) %>%
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transmute(mo = "",
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fullname = trimws(paste(genus_name, sp_epithet, subsp_epithet)),
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kingdom = "Bacteria",
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phylum = "",
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class = "",
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order = "",
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family = "",
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genus = trimws(genus_name),
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species = trimws(replace_na(sp_epithet, "")),
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subspecies = trimws(replace_na(subsp_epithet, "")),
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rank = case_when(subspecies == "" & species == "" ~ "genus",
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subspecies == "" ~ "species",
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TRUE ~ "subsp."),
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ref = get_author_year(authors),
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species_id = as.character(record_no),
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source = "LPSN",
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prevalence = 0,
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snomed = NA)
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new_microorganisms <- updated_microorganisms %>%
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filter(!fullname %in% microorganisms$fullname)
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genera_with_mo_code <- updated_microorganisms %>%
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filter(genus %in% (microorganisms %>% filter(kingdom == "Bacteria", rank == "genus") %>% pull(genus))) %>%
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distinct(genus) %>%
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left_join(microorganisms %>% filter(kingdom == "Bacteria", rank == "genus") %>% select(mo, genus),
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by = "genus")
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genera_without_mo_code <- updated_microorganisms %>%
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filter(!genus %in% genera_with_mo_code$genus) %>%
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pull(genus) %>%
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unique()
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genera_without_mo_code_abbr <- genera_without_mo_code %>%
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abbreviate_mo(5, prefix = "B_")
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genera_without_mo_code_abbr[genera_without_mo_code_abbr %in% microorganisms$mo] <- abbreviate_mo(genera_without_mo_code[genera_without_mo_code_abbr %in% microorganisms$mo], 6, prefix = "B_")
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genera_without_mo_code_abbr[genera_without_mo_code_abbr %in% microorganisms$mo] <- abbreviate_mo(genera_without_mo_code[genera_without_mo_code_abbr %in% microorganisms$mo], 7, prefix = "B_")
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# all unique??
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sum(genera_without_mo_code_abbr %in% microorganisms$mo) == 0
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genus_abb <- tibble(genus = genera_without_mo_code,
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abbr = genera_without_mo_code_abbr) %>%
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bind_rows(microorganisms %>%
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filter(kingdom == "Bacteria", rank == "genus", !genus %in% genera_without_mo_code) %>%
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transmute(genus, abbr = as.character(mo))) %>%
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arrange(genus)
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# Update taxonomy ---------------------------------------------------------
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# fill in the taxonomy of new genera
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updated_taxonomy <- tibble(phylum = character(0),
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class = character(0),
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order = character(0),
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family = character(0),
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genus = character(0))
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for (page in LETTERS) {
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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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rvest::html_node(".main-list") %>%
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# evety list element with a set <id> attribute
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rvest::html_nodes("li[id]")
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for (i in seq_len(length(x))) {
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txt <- x %>%
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magrittr::extract2(i) %>%
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rvest::html_text() %>%
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gsub("\\[[A-Za-z]+, no [a-z]+\\]", "NA", .) %>%
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gsub("Candidatus ", "", ., fixed = TRUE) %>%
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gsub("[ \t\r\n\"]+", "|", .) %>%
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gsub("\\|ShowHide.*", "", .) %>%
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gsub("[\\[\\]]", "", ., fixed = TRUE) %>%
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gsub("^\\|", "", .) %>%
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strsplit("|", fixed = TRUE) %>%
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unlist()
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txt[txt == "NA"] <- ""
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txt <- gsub("[^A-Za-z]+", "", txt)
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updated_taxonomy <- updated_taxonomy %>%
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bind_rows(tibble(phylum = txt[2],
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class = txt[3],
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order = txt[4],
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family = txt[5],
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genus = txt[6]))
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}
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message(length(x), " entries (total ", nrow(updated_taxonomy), ")")
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}
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# Create new microorganisms -----------------------------------------------
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new_microorganisms <- new_microorganisms %>%
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left_join(genus_abb, by = "genus") %>%
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group_by(genus) %>%
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mutate(species_abb = abbreviate_mo(species, 4)) %>%
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group_by(genus, species) %>%
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mutate(subspecies_abb = abbreviate_mo(subspecies, 4)) %>%
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ungroup() %>%
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mutate(mo = paste(abbr, species_abb, subspecies_abb, sep = "_"),
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mo = gsub("_+$", "", mo)) %>%
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select(-matches("abb"))
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# add taxonomy new microorganisms
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MOs <- microorganisms %>%
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mutate(mo = as.character(mo)) %>%
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bind_rows(new_microorganisms) %>%
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arrange(fullname)
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# unique MO codes
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MOs$mo[which(duplicated(MOs$mo))] <- paste0(MOs$mo[which(duplicated(MOs$mo))], 1)
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# all unique?
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!any(duplicated(MOs$mo))
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MOs <- MOs %>%
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# remove entries that are now a synonym
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filter(!fullname %in% new_synonyms$fullname) %>%
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# update the taxonomy
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left_join(updated_taxonomy, by = "genus", suffix = c("", ".new")) %>%
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mutate(phylum = ifelse(!is.na(phylum.new), phylum.new, phylum),
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class = ifelse(!is.na(class.new), class.new, class),
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order = ifelse(!is.na(order.new), order.new, order),
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family = ifelse(!is.na(family.new), family.new, family)) %>%
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select(-ends_with(".new")) %>%
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# update prevalence based on taxonomy (Berends et al., 2021)
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mutate(prevalence = case_when(
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class == "Gammaproteobacteria"
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| genus %in% c("Enterococcus", "Staphylococcus", "Streptococcus")
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~ 1,
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kingdom %in% c("Archaea", "Bacteria", "Chromista", "Fungi")
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& (phylum %in% c("Proteobacteria",
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"Firmicutes",
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"Actinobacteria",
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"Sarcomastigophora")
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| genus %in% c("Absidia", "Acremonium", "Actinotignum", "Alternaria", "Anaerosalibacter", "Apophysomyces",
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"Arachnia", "Aspergillus", "Aureobacterium", "Aureobasidium", "Bacteroides", "Basidiobolus",
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"Beauveria", "Blastocystis", "Branhamella", "Calymmatobacterium", "Candida", "Capnocytophaga",
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"Catabacter", "Chaetomium", "Chryseobacterium", "Chryseomonas", "Chrysonilia", "Cladophialophora",
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"Cladosporium", "Conidiobolus", "Cryptococcus", "Curvularia", "Exophiala", "Exserohilum",
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"Flavobacterium", "Fonsecaea", "Fusarium", "Fusobacterium", "Hendersonula", "Hypomyces",
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"Koserella", "Lelliottia", "Leptosphaeria", "Leptotrichia", "Malassezia", "Malbranchea",
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"Mortierella", "Mucor", "Mycocentrospora", "Mycoplasma", "Nectria", "Ochroconis",
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"Oidiodendron", "Phoma", "Piedraia", "Pithomyces", "Pityrosporum", "Prevotella", "Pseudallescheria",
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"Rhizomucor", "Rhizopus", "Rhodotorula", "Scolecobasidium", "Scopulariopsis", "Scytalidium",
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"Sporobolomyces", "Stachybotrys", "Stomatococcus", "Treponema", "Trichoderma", "Trichophyton",
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"Trichosporon", "Tritirachium", "Ureaplasma")
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| rank %in% c("kingdom", "phylum", "class", "order", "family"))
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~ 2,
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TRUE ~ 3
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))
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# add all mssing genera, families and orders
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MOs <- MOs %>%
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bind_rows(
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MOs %>%
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arrange(genus, species) %>%
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distinct(genus, .keep_all = TRUE) %>%
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filter(rank == "species", source != "manually added") %>%
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mutate(mo = gsub("^([A-Z]_[A-Z]+)_.*", "\\1", mo),
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fullname = genus,
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species = "",
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subspecies = "",
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rank = "genus",
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species_id = "",
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snomed = NA,
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ref = NA_character_),
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MOs %>%
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group_by(family) %>%
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filter(!any(rank == "family") & n() > 1) %>%
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ungroup() %>%
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arrange(family) %>%
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distinct(family, .keep_all = TRUE) %>%
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filter(!family %in% c("", NA), source != "manually added") %>%
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mutate(mo = paste0(substr(kingdom, 1, 1), "_[FAM]_",
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abbreviate(family,
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minlength = 8,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)),
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mo = toupper(mo),
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fullname = family,
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genus = "",
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species = "",
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subspecies = "",
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rank = "family",
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species_id = "",
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snomed = NA,
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ref = NA_character_),
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MOs %>%
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group_by(order) %>%
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filter(!any(rank == "order") & n() > 1) %>%
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ungroup() %>%
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arrange(order) %>%
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distinct(order, .keep_all = TRUE) %>%
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filter(!order %in% c("", NA), source != "manually added") %>%
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mutate(mo = paste0(substr(kingdom, 1, 1), "_[ORD]_",
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abbreviate(order,
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minlength = 8,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)),
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mo = toupper(mo),
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fullname = order,
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family = "",
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genus = "",
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species = "",
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subspecies = "",
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rank = "order",
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species_id = "",
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snomed = NA,
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ref = NA_character_)
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) %>%
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arrange(fullname)
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# clean up
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MOs <- MOs %>%
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df_remove_nonASCII()
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# Merge synonyms ----------------------------------------------------------
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# remove synonyms that are now valid names
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MOs.old <- microorganisms.old %>%
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# add new synonyms
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bind_rows(new_synonyms) %>%
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filter(!fullname %in% MOs$fullname) %>%
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arrange(fullname) %>%
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distinct(fullname, fullname_new, .keep_all = TRUE) %>%
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# add prevalence to old taxonomic names
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select(-prevalence) %>%
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left_join(MOs %>% select(fullname, prevalence), by = c("fullname_new" = "fullname")) %>%
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# clean up
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df_remove_nonASCII()
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# Keep old codes for translation ------------------------------------------
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# add removed microbial IDs to the internal translation table so old package versions keep working
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MOs.translation <- microorganisms %>%
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filter(!mo %in% MOs$mo) %>%
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select(mo, fullname) %>%
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left_join(new_synonyms) %>%
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left_join(MOs %>% transmute(fullname_new = fullname, mo2 = as.character(mo))) %>%
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select(mo_old = mo, mo_new = mo2) %>%
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distinct()
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MOs.translation <- AMR:::microorganisms.translation %>%
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left_join(MOs.translation %>% select(mo_new_update = mo_new, mo_new = mo_old)) %>%
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mutate(mo_new = as.character(ifelse(!is.na(mo_new_update), mo_new_update, mo_new))) %>%
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select(-mo_new_update) %>%
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bind_rows(
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# old IDs used in microorganisms.codes must put in here as well
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microorganisms.codes %>%
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filter(!mo %in% MOs$mo) %>%
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transmute(mo_old = mo, fullname = mo_name(mo)) %>%
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left_join(MOs.old %>%
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select(fullname, fullname_new)) %>%
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left_join(MOs %>%
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select(mo_new = mo, fullname_new = fullname)) %>%
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transmute(mo_old = as.character(mo_old), mo_new)) %>%
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arrange(mo_old) %>%
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filter(mo_old != mo_new,
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!mo_old %in% MOs$mo) %>%
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left_join(., .,
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by = c("mo_new" = "mo_old"),
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suffix = c("", ".2")) %>%
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mutate(mo_new = ifelse(!is.na(mo_new.2), mo_new.2, mo_new)) %>%
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distinct(mo_old, mo_new) %>%
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# clean up
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df_remove_nonASCII()
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message("microorganisms new: ", sum(!MOs$fullname %in% c(microorganisms$fullname, MOs.old$fullname)))
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message("microorganisms renamed: ", sum(!MOs.old$fullname %in% microorganisms.old$fullname))
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# Save --------------------------------------------------------------------
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# class <mo>
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class(MOs$mo) <- c("mo", "character")
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class(MOs.translation$mo_new) <- c("mo", "character")
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microorganisms <- MOs
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microorganisms.old <- MOs.old
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microorganisms.translation <- MOs.translation
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# on the server, do:
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usethis::use_data(microorganisms, overwrite = TRUE, version = 2, compress = "xz")
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usethis::use_data(microorganisms.old, overwrite = TRUE, version = 2)
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saveRDS(microorganisms.translation, file = "data-raw/microorganisms.translation.rds", version = 2)
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rm(microorganisms)
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rm(microorganisms.old)
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rm(microorganisms.translation)
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# to save microorganisms.translation internally to the package
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devtools::load_all(".")
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source("data-raw/_internals.R")
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# load new data sets
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devtools::load_all(".")
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# reset previously changed mo codes
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rsi_translation$mo <- as.mo(rsi_translation$mo, language = NULL)
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usethis::use_data(rsi_translation, overwrite = TRUE, version = 2)
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rm(rsi_translation)
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|
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microorganisms.codes$mo <- as.mo(microorganisms.codes$mo, language = NULL)
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usethis::use_data(microorganisms.codes, overwrite = TRUE, version = 2)
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rm(microorganisms.codes)
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|
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example_isolates$mo <- as.mo(example_isolates$mo, language = NULL)
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usethis::use_data(example_isolates, overwrite = TRUE, version = 2)
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rm(example_isolates)
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|
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intrinsic_resistant$microorganism <- suppressMessages(mo_name(intrinsic_resistant$microorganism))
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usethis::use_data(intrinsic_resistant, overwrite = TRUE, version = 2)
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|
rm(intrinsic_resistant)
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|
|
|
# load new data sets again
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|
devtools::load_all(".")
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|
source("data-raw/_internals.R")
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devtools::load_all(".")
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|
|
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# Test updates ------------------------------------------------------------
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# and check: these codes should not be missing (will otherwise throw a unit test error):
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AMR::microorganisms.codes %>% filter(!mo %in% MOs$mo)
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AMR::rsi_translation %>% filter(!mo %in% MOs$mo)
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AMR:::microorganisms.translation %>% filter(!mo_new %in% MOs$mo)
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AMR::example_isolates %>% filter(!mo %in% MOs$mo)
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|
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# Don't forget to add SNOMED codes! (data-raw/snomed.R)
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|
|
|
# run the unit tests
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|
Sys.setenv(NOT_CRAN = "true")
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|
testthat::test_file("tests/testthat/test-data.R")
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|
testthat::test_file("tests/testthat/test-mo.R")
|
|
testthat::test_file("tests/testthat/test-mo_property.R")
|