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133 lines
7.8 KiB
R
Executable File
133 lines
7.8 KiB
R
Executable File
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
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# AMR: An R Package for Working with Antimicrobial Resistance Data. #
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# Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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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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#' Calculate the Matching Score for Microorganisms
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#'
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#' This algorithm is used by [as.mo()] and all the [`mo_*`][mo_property()] functions to determine the most probable match of taxonomic records based on user input.
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#' @param x Any user input value(s)
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#' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
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#' @note This algorithm was originally developed in 2018 and subsequently described in: Berends MS *et al.* (2022). **AMR: An R Package for Working with Antimicrobial Resistance Data**. *Journal of Statistical Software*, 104(3), 1-31; \doi{10.18637/jss.v104.i03}.
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#'
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#' Later, the work of Bartlett A *et al.* about bacterial pathogens infecting humans (2022, \doi{10.1099/mic.0.001269}) was incorporated, and optimalisations to the algorithm were made.
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#' @section Matching Score for Microorganisms:
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#' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, is calculated as:
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#'
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#' \ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
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#'
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#' \ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
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#'
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#' where:
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#'
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#' * \eqn{x} is the user input;
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#' * \eqn{n} is a taxonomic name (genus, species, and subspecies);
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#' * \eqn{l_n} is the length of \eqn{n};
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#' * \eqn{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance) (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \eqn{x} into \eqn{n};
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#' * \eqn{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
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#' * \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.
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#'
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#' The grouping into human pathogenic prevalence \eqn{p} is based on recent work from Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:
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#'
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#' - **Established**, if a taxonomic species has infected at least three persons in three or more references. These records have `prevalence = 1.15` in the [microorganisms] data set;
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#' - **Putative**, if a taxonomic species has fewer than three known cases. These records have `prevalence = 1.25` in the [microorganisms] data set.
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#'
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#' Furthermore,
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#'
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#' - Genera from the World Health Organization's (WHO) Priority Pathogen List have `prevalence = 1.0` in the [microorganisms] data set;
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#' - Any genus present in the **established** list also has `prevalence = 1.15` in the [microorganisms] data set;
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#' - Any other genus present in the **putative** list has `prevalence = 1.25` in the [microorganisms] data set;
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#' - Any other species or subspecies of which the genus is present in the two aforementioned groups, has `prevalence = 1.5` in the [microorganisms] data set;
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#' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.25` in the [microorganisms] data set: `r vector_or(MO_RELEVANT_GENERA, quotes = "*")`;
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#' - All other records have `prevalence = 2.0` in the [microorganisms] data set.
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#'
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#' When calculating the matching score, all characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.
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#'
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#' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Escherichia coli"), 3)`}, a highly prevalent microorganism found in humans) and not *Entamoeba coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Entamoeba coli"), 3)`}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
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#' @export
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#' @inheritSection AMR Reference Data Publicly Available
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#' @examples
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#' mo_reset_session()
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#'
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#' as.mo("E. coli")
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#' mo_uncertainties()
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#'
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#' mo_matching_score(
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#' x = "E. coli",
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#' n = c("Escherichia coli", "Entamoeba coli")
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#' )
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mo_matching_score <- function(x, n) {
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meet_criteria(x, allow_class = c("character", "data.frame", "list"))
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meet_criteria(n, allow_class = "character")
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add_MO_lookup_to_AMR_env()
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x <- parse_and_convert(x)
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# no dots and other non-whitespace characters
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x <- gsub("[^a-zA-Z0-9 \\(\\)]+", "", x)
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# only keep one space
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x <- gsub(" +", " ", x)
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# force a capital letter, so this conversion will not count as a substitution
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substr(x, 1, 1) <- toupper(substr(x, 1, 1))
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# n is always a taxonomically valid full name
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if (length(n) == 1) {
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n <- rep(n, length(x))
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}
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if (length(x) == 1) {
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x <- rep(x, length(n))
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}
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# length of fullname
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l_n <- nchar(n)
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lev <- double(length = length(x))
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l_n.lev <- double(length = length(x))
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# get Levenshtein distance
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lev <- unlist(Map(f = function(a, b) {
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as.double(utils::adist(a, b,
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ignore.case = FALSE,
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fixed = TRUE,
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costs = c(insertions = 1, deletions = 2, substitutions = 2),
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counts = FALSE
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))
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}, x, n, USE.NAMES = FALSE))
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l_n.lev[l_n < lev] <- l_n[l_n < lev]
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l_n.lev[lev < l_n] <- lev[lev < l_n]
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l_n.lev[lev == l_n] <- lev[lev == l_n]
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# human pathogenic prevalence (1 to 3), see ?as.mo
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p_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "prevalence", drop = TRUE]
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# kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
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k_n <- AMR_env$MO_lookup[match(n, AMR_env$MO_lookup$fullname), "kingdom_index", drop = TRUE]
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# matching score:
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(l_n - 0.5 * l_n.lev) / (l_n * p_n * k_n)
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}
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