mirror of https://github.com/msberends/AMR.git
77 lines
4.4 KiB
R
Executable File
77 lines
4.4 KiB
R
Executable File
# ==================================================================== #
|
|
# TITLE #
|
|
# Antimicrobial Resistance (AMR) Analysis #
|
|
# #
|
|
# SOURCE #
|
|
# https://github.com/msberends/AMR #
|
|
# #
|
|
# LICENCE #
|
|
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
|
# #
|
|
# This R package is free software; you can freely use and distribute #
|
|
# it for both personal and commercial purposes under the terms of the #
|
|
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
|
# the Free Software Foundation. #
|
|
# #
|
|
# We created this package for both routine data analysis and academic #
|
|
# research and it was publicly released in the hope that it will be #
|
|
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
|
|
# Visit our website for more info: https://msberends.github.io/AMR. #
|
|
# ==================================================================== #
|
|
|
|
#' Calculate the matching score for microorganisms
|
|
#'
|
|
#' This helper function is used by [as.mo()] to determine the most probable match of taxonomic records, based on user input.
|
|
#' @param x Any user input value(s)
|
|
#' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
|
|
#' @section Matching score for microorganisms:
|
|
#' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, is calculated as:
|
|
#'
|
|
#' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}
|
|
#'
|
|
#' where:
|
|
#'
|
|
#' * \eqn{x} is the user input;
|
|
#' * \eqn{n} is a taxonomic name (genus, species and subspecies) as found in [`microorganisms$fullname`][microorganisms];
|
|
#' * \eqn{l_{n}}{l_n} is the length of \eqn{n};
|
|
#' * \eqn{\operatorname{lev}}{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance);
|
|
#' * \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see *Details* in `?as.mo`), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
|
|
#' * \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
|
|
#'
|
|
#' This means that the user input `x = "E. coli"` gets for *Escherichia coli* a matching score of `r percentage(mo_matching_score("E. coli", "Escherichia coli"), 1)` and for *Entamoeba coli* a matching score of `r percentage(mo_matching_score("E. coli", "Entamoeba coli"), 1)`.
|
|
#'
|
|
#' All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
|
|
#' @export
|
|
#' @examples
|
|
#' as.mo("E. coli")
|
|
#' mo_uncertainties()
|
|
#'
|
|
#' mo_matching_score("E. coli", "Escherichia coli")
|
|
mo_matching_score <- function(x, n) {
|
|
# n is always a taxonomically valid full name
|
|
levenshtein <- double(length = length(x))
|
|
if (length(n) == 1) {
|
|
n <- rep(n, length(x))
|
|
}
|
|
if (length(x) == 1) {
|
|
x <- rep(x, length(n))
|
|
}
|
|
for (i in seq_len(length(x))) {
|
|
# determine Levenshtein distance, but maximise to nchar of n
|
|
levenshtein[i] <- min(as.double(utils::adist(x[i], n[i], ignore.case = FALSE)),
|
|
nchar(n[i]))
|
|
}
|
|
|
|
# F = length of fullname
|
|
var_F <- nchar(n)
|
|
# L = modified Levenshtein distance
|
|
var_L <- levenshtein
|
|
# P = prevalence (1 to 3), see ?as.mo
|
|
var_P <- MO_lookup[match(n, MO_lookup$fullname), "prevalence", drop = TRUE]
|
|
# K = kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
|
|
var_K <- MO_lookup[match(n, MO_lookup$fullname), "kingdom_index", drop = TRUE]
|
|
|
|
# matching score:
|
|
(var_F - 0.5 * var_L) / (var_F * var_P * var_K)
|
|
}
|