mirror of
https://github.com/msberends/AMR.git
synced 2025-04-19 08:33:49 +02:00
(v2.1.1.9155) new mic_p50() and mic_p90() - updated AMR intro
This commit is contained in:
parent
226d10f546
commit
aa8f6af185
@ -1,6 +1,6 @@
|
||||
Package: AMR
|
||||
Version: 2.1.1.9154
|
||||
Date: 2025-02-22
|
||||
Version: 2.1.1.9155
|
||||
Date: 2025-02-23
|
||||
Title: Antimicrobial Resistance Data Analysis
|
||||
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
|
||||
data analysis and to work with microbial and antimicrobial properties by
|
||||
|
||||
@ -251,6 +251,8 @@ export(mdr_cmi2012)
|
||||
export(mdr_tb)
|
||||
export(mdro)
|
||||
export(mean_amr_distance)
|
||||
export(mic_p50)
|
||||
export(mic_p90)
|
||||
export(mo_authors)
|
||||
export(mo_class)
|
||||
export(mo_cleaning_regex)
|
||||
|
||||
4
NEWS.md
4
NEWS.md
@ -1,4 +1,4 @@
|
||||
# AMR 2.1.1.9154
|
||||
# AMR 2.1.1.9155
|
||||
|
||||
*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
|
||||
|
||||
@ -38,7 +38,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
|
||||
* **Other**
|
||||
* New function `top_n_microorganisms()` to filter a data set to the top *n* of any taxonomic property, e.g., filter to the top 3 species, filter to any species in the top 5 genera, or filter to the top 3 species in each of the top 5 genera
|
||||
* New function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that belong to that group.
|
||||
|
||||
* New functions `mic_p50()` and `mic_p90()` to retrieve the 50th and 90th percentile of MIC values.
|
||||
|
||||
## Changed
|
||||
* SIR interpretation
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
Metadata-Version: 2.2
|
||||
Name: AMR
|
||||
Version: 2.1.1.9154
|
||||
Version: 2.1.1.9155
|
||||
Summary: A Python wrapper for the AMR R package
|
||||
Home-page: https://github.com/msberends/AMR
|
||||
Author: Matthijs Berends
|
||||
|
||||
@ -73,6 +73,8 @@ from .functions import is_disk
|
||||
from .functions import as_mic
|
||||
from .functions import is_mic
|
||||
from .functions import rescale_mic
|
||||
from .functions import mic_p50
|
||||
from .functions import mic_p90
|
||||
from .functions import as_mo
|
||||
from .functions import is_mo
|
||||
from .functions import mo_uncertainties
|
||||
|
||||
@ -249,6 +249,12 @@ def is_mic(x):
|
||||
def rescale_mic(x, *args, **kwargs):
|
||||
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
|
||||
return convert_to_python(amr_r.rescale_mic(x, *args, **kwargs))
|
||||
def mic_p50(x, *args, **kwargs):
|
||||
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
|
||||
return convert_to_python(amr_r.mic_p50(x, *args, **kwargs))
|
||||
def mic_p90(x, *args, **kwargs):
|
||||
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
|
||||
return convert_to_python(amr_r.mic_p90(x, *args, **kwargs))
|
||||
def as_mo(x, *args, **kwargs):
|
||||
"""See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
|
||||
return convert_to_python(amr_r.as_mo(x, *args, **kwargs))
|
||||
|
||||
Binary file not shown.
BIN
PythonPackage/AMR/dist/AMR-2.1.1.9155-py3-none-any.whl
vendored
Normal file
BIN
PythonPackage/AMR/dist/AMR-2.1.1.9155-py3-none-any.whl
vendored
Normal file
Binary file not shown.
BIN
PythonPackage/AMR/dist/amr-2.1.1.9154.tar.gz
vendored
BIN
PythonPackage/AMR/dist/amr-2.1.1.9154.tar.gz
vendored
Binary file not shown.
BIN
PythonPackage/AMR/dist/amr-2.1.1.9155.tar.gz
vendored
Normal file
BIN
PythonPackage/AMR/dist/amr-2.1.1.9155.tar.gz
vendored
Normal file
Binary file not shown.
@ -2,7 +2,7 @@ from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name='AMR',
|
||||
version='2.1.1.9154',
|
||||
version='2.1.1.9155',
|
||||
packages=find_packages(),
|
||||
install_requires=[
|
||||
'rpy2',
|
||||
|
||||
@ -1390,6 +1390,9 @@ as_original_data_class <- function(df, old_class = NULL, extra_class = NULL) {
|
||||
fn <- function(x) base::as.data.frame(df, stringsAsFactors = FALSE)
|
||||
}
|
||||
out <- fn(df)
|
||||
# don't keep row names
|
||||
rownames(out) <- NULL
|
||||
# add additional class if needed
|
||||
if (!is.null(extra_class)) {
|
||||
class(out) <- c(extra_class, class(out))
|
||||
}
|
||||
|
||||
@ -923,19 +923,16 @@ antibiogram.default <- function(x,
|
||||
}
|
||||
}
|
||||
|
||||
out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram")
|
||||
out <- structure(as_original_data_class(new_df, class(x), extra_class = "antibiogram"),
|
||||
has_syndromic_group = has_syndromic_group,
|
||||
combine_SI = combine_SI,
|
||||
wisca = wisca,
|
||||
conf_interval = conf_interval,
|
||||
formatting_type = formatting_type,
|
||||
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
|
||||
long_numeric = as_original_data_class(long_numeric, class(x)))
|
||||
rownames(out) <- NULL
|
||||
rownames(wisca_parameters) <- NULL
|
||||
rownames(long_numeric) <- NULL
|
||||
structure(out,
|
||||
has_syndromic_group = has_syndromic_group,
|
||||
combine_SI = combine_SI,
|
||||
wisca = wisca,
|
||||
conf_interval = conf_interval,
|
||||
formatting_type = formatting_type,
|
||||
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
|
||||
long_numeric = as_original_data_class(long_numeric, class(x))
|
||||
)
|
||||
out
|
||||
}
|
||||
|
||||
#' @method antibiogram grouped_df
|
||||
@ -1041,14 +1038,16 @@ antibiogram.grouped_df <- function(x,
|
||||
|
||||
close(progress)
|
||||
|
||||
structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
|
||||
has_syndromic_group = FALSE,
|
||||
combine_SI = isTRUE(combine_SI),
|
||||
wisca = isTRUE(wisca),
|
||||
conf_interval = conf_interval,
|
||||
formatting_type = formatting_type,
|
||||
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
|
||||
long_numeric = as_original_data_class(long_numeric, class(x)))
|
||||
out <- structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
|
||||
has_syndromic_group = FALSE,
|
||||
combine_SI = isTRUE(combine_SI),
|
||||
wisca = isTRUE(wisca),
|
||||
conf_interval = conf_interval,
|
||||
formatting_type = formatting_type,
|
||||
wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
|
||||
long_numeric = as_original_data_class(long_numeric, class(x)))
|
||||
rownames(out) <- NULL
|
||||
out
|
||||
}
|
||||
|
||||
#' @export
|
||||
|
||||
@ -185,8 +185,9 @@ bug_drug_combinations <- function(x,
|
||||
|
||||
out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
|
||||
out <- out %pm>% pm_arrange(mo, ab)
|
||||
class(out) <- c("bug_drug_combinations", if(data_has_groups) "grouped" else NULL, class(out))
|
||||
rownames(out) <- NULL
|
||||
structure(out, class = c("bug_drug_combinations", if(data_has_groups) "grouped" else NULL, class(out)))
|
||||
out
|
||||
}
|
||||
|
||||
#' @method format bug_drug_combinations
|
||||
|
||||
15
R/mic.R
15
R/mic.R
@ -346,6 +346,21 @@ rescale_mic <- function(x, mic_range, keep_operators = "edges", as.mic = TRUE) {
|
||||
out
|
||||
}
|
||||
|
||||
#' @rdname as.mic
|
||||
#' @details Use [mic_p50()] and [mic_p90()] to get the 50th and 90th percentile of MIC values. They return 'normal' [numeric] values.
|
||||
#' @export
|
||||
mic_p50 <- function(x, na.rm = FALSE, ...) {
|
||||
x <- as.mic(x)
|
||||
as.double(stats::quantile(x, probs = 0.5, na.rm = na.rm))
|
||||
}
|
||||
|
||||
#' @rdname as.mic
|
||||
#' @export
|
||||
mic_p90 <- function(x, na.rm = FALSE, ...) {
|
||||
x <- as.mic(x)
|
||||
as.double(stats::quantile(x, probs = 0.9, na.rm = na.rm))
|
||||
}
|
||||
|
||||
#' @method as.double mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
|
||||
@ -186,7 +186,7 @@
|
||||
#' scale_colour_sir(language = "pt",
|
||||
#' name = "Support in 20 languages")
|
||||
#' }
|
||||
#'
|
||||
#' }
|
||||
#'
|
||||
#' # Plotting using base R's plot() ---------------------------------------
|
||||
#'
|
||||
|
||||
2
R/sir.R
2
R/sir.R
@ -1544,7 +1544,7 @@ sir_interpretation_history <- function(clean = FALSE) {
|
||||
if (pkg_is_available("tibble")) {
|
||||
out <- import_fn("as_tibble", "tibble")(out)
|
||||
}
|
||||
structure(out, class = c("sir_log", class(out)))
|
||||
as_original_data_class(out, class(out), extra_class = "sir_log")
|
||||
}
|
||||
|
||||
#' @method print sir_log
|
||||
|
||||
@ -383,8 +383,6 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
|
||||
# remove redundant columns
|
||||
out <- subset(out, select = -c(ci_min, ci_max, isolates))
|
||||
}
|
||||
|
||||
rownames(out) <- NULL
|
||||
out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
|
||||
structure(out, class = c("sir_df", class(out)))
|
||||
|
||||
as_original_data_class(out, class(data.bak), extra_class = "sir_df") # will remove tibble groups
|
||||
}
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
|
||||
|
||||
First and foremost, you are trained on version 2.1.1.9154. Remember this whenever someone asks which AMR package version you’re at.
|
||||
First and foremost, you are trained on version 2.1.1.9155. Remember this whenever someone asks which AMR package version you’re at.
|
||||
|
||||
Below are the contents of the file, the file, and all the files (documentation) in the package. Every file content is split using 100 hypens.
|
||||
----------------------------------------------------------------------------------------------------
|
||||
@ -262,6 +262,8 @@ export(mdr_cmi2012)
|
||||
export(mdr_tb)
|
||||
export(mdro)
|
||||
export(mean_amr_distance)
|
||||
export(mic_p50)
|
||||
export(mic_p90)
|
||||
export(mo_authors)
|
||||
export(mo_class)
|
||||
export(mo_cleaning_regex)
|
||||
@ -656,30 +658,18 @@ It will be downloaded and installed automatically. For RStudio, click on the men
|
||||
|
||||
#### Latest development version
|
||||
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-old.yaml?query=branch%3Amain)
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-recent.yaml?query=branch%3Amain)
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-old-tinytest.yaml)
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-current-testthat.yaml)
|
||||
[](https://www.codefactor.io/repository/github/msberends/amr)
|
||||
[](https://codecov.io/gh/msberends/AMR?branch=main)
|
||||
|
||||
Please read our [Developer Guideline here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
|
||||
|
||||
The latest and unpublished development version can be installed from GitHub in two ways:
|
||||
The latest and unpublished development version can be installed from the [rOpenSci R-universe platform](https://msberends.r-universe.dev/AMR):
|
||||
|
||||
1. Manually, using:
|
||||
|
||||
```r
|
||||
install.packages("remotes") # if you haven't already
|
||||
remotes::install_github("msberends/AMR")
|
||||
```
|
||||
|
||||
2. Automatically, using the [rOpenSci R-universe platform](https://ropensci.org/r-universe/), by adding [our R-universe address](https://msberends.r-universe.dev) to your list of repositories ('repos'):
|
||||
|
||||
```r
|
||||
options(repos = c(getOption("repos"),
|
||||
msberends = "https://msberends.r-universe.dev"))
|
||||
```
|
||||
|
||||
After this, you can install and update this `AMR` package like any official release (e.g., using `install.packages("AMR")` or in RStudio via *Tools* > *Check for Package Updates...*).
|
||||
```r
|
||||
install.packages("AMR", repos = "https://msberends.r-universe.dev")
|
||||
```
|
||||
|
||||
### Get started
|
||||
|
||||
@ -2873,6 +2863,8 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/as.mic.Rd':
|
||||
\alias{is.mic}
|
||||
\alias{NA_mic_}
|
||||
\alias{rescale_mic}
|
||||
\alias{mic_p50}
|
||||
\alias{mic_p90}
|
||||
\alias{droplevels.mic}
|
||||
\title{Transform Input to Minimum Inhibitory Concentrations (MIC)}
|
||||
\usage{
|
||||
@ -2884,6 +2876,10 @@ NA_mic_
|
||||
|
||||
rescale_mic(x, mic_range, keep_operators = "edges", as.mic = TRUE)
|
||||
|
||||
mic_p50(x, na.rm = FALSE, ...)
|
||||
|
||||
mic_p90(x, na.rm = FALSE, ...)
|
||||
|
||||
\method{droplevels}{mic}(x, as.mic = FALSE, ...)
|
||||
}
|
||||
\arguments{
|
||||
@ -2953,6 +2949,8 @@ With \code{\link[=rescale_mic]{rescale_mic()}}, existing MIC ranges can be limit
|
||||
For \code{ggplot2}, use one of the \code{\link[=scale_x_mic]{scale_*_mic()}} functions to plot MIC values. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
|
||||
|
||||
\code{NA_mic_} is a missing value of the new \code{mic} class, analogous to e.g. base \R's \code{\link[base:NA]{NA_character_}}.
|
||||
|
||||
Use \code{\link[=mic_p50]{mic_p50()}} and \code{\link[=mic_p90]{mic_p90()}} to get the 50th and 90th percentile of MIC values. They return 'normal' \link{numeric} values.
|
||||
}
|
||||
\examples{
|
||||
mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
|
||||
@ -7540,6 +7538,131 @@ The interpretation of "I" will be named "Increased exposure" for all EUCAST guid
|
||||
For interpreting MIC values as well as disk diffusion diameters, the default guideline is EUCAST 2024, unless the package option \code{\link[=AMR-options]{AMR_guideline}} is set. See \code{\link[=as.sir]{as.sir()}} for more information.
|
||||
}
|
||||
}
|
||||
\examples{
|
||||
some_mic_values <- random_mic(size = 100)
|
||||
some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
|
||||
some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))
|
||||
|
||||
|
||||
\donttest{
|
||||
# Plotting using ggplot2's autoplot() for MIC, disk, and SIR -----------
|
||||
if (require("ggplot2")) {
|
||||
autoplot(some_mic_values)
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
# when providing the microorganism and antibiotic, colours will show interpretations:
|
||||
autoplot(some_mic_values, mo = "Escherichia coli", ab = "cipro")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
# support for 20 languages, various guidelines, and many options
|
||||
autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro",
|
||||
guideline = "CLSI 2024", language = "no",
|
||||
title = "Disk diffusion from the North")
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_x_mic() -----------------------------------------
|
||||
if (require("ggplot2")) {
|
||||
mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
|
||||
counts = c(1, 1, 2, 2, 3, 3)),
|
||||
aes(mics, counts)) +
|
||||
geom_col()
|
||||
mic_plot +
|
||||
labs(title = "without scale_x_mic()")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic() +
|
||||
labs(title = "with scale_x_mic()")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic(keep_operators = "all") +
|
||||
labs(title = "with scale_x_mic() keeping all operators")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic(mic_range = c(1, 16)) +
|
||||
labs(title = "with scale_x_mic() using a manual 'within' range")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic(mic_range = c(0.032, 256)) +
|
||||
labs(title = "with scale_x_mic() using a manual 'outside' range")
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_y_mic() -----------------------------------------
|
||||
some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
|
||||
|
||||
if (require("ggplot2")) {
|
||||
ggplot(data.frame(mic = some_mic_values,
|
||||
group = some_groups),
|
||||
aes(group, mic)) +
|
||||
geom_boxplot() +
|
||||
geom_violin(linetype = 2, colour = "grey", fill = NA) +
|
||||
scale_y_mic()
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
ggplot(data.frame(mic = some_mic_values,
|
||||
group = some_groups),
|
||||
aes(group, mic)) +
|
||||
geom_boxplot() +
|
||||
geom_violin(linetype = 2, colour = "grey", fill = NA) +
|
||||
scale_y_mic(mic_range = c(NA, 0.25))
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_x_sir() -----------------------------------------
|
||||
if (require("ggplot2")) {
|
||||
ggplot(data.frame(x = c("I", "R", "S"),
|
||||
y = c(45,323, 573)),
|
||||
aes(x, y)) +
|
||||
geom_col() +
|
||||
scale_x_sir()
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_y_mic() and scale_colour_sir() ------------------
|
||||
if (require("ggplot2")) {
|
||||
plain <- ggplot(data.frame(mic = some_mic_values,
|
||||
group = some_groups,
|
||||
sir = as.sir(some_mic_values,
|
||||
mo = "E. coli",
|
||||
ab = "cipro")),
|
||||
aes(x = group, y = mic, colour = sir)) +
|
||||
theme_minimal() +
|
||||
geom_boxplot(fill = NA, colour = "grey") +
|
||||
geom_jitter(width = 0.25)
|
||||
|
||||
plain
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
# and now with our MIC and SIR scale functions:
|
||||
plain +
|
||||
scale_y_mic() +
|
||||
scale_colour_sir()
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
plain +
|
||||
scale_y_mic(mic_range = c(0.005, 32), name = "Our MICs!") +
|
||||
scale_colour_sir(language = "pt",
|
||||
name = "Support in 20 languages")
|
||||
}
|
||||
}
|
||||
|
||||
# Plotting using base R's plot() ---------------------------------------
|
||||
|
||||
plot(some_mic_values)
|
||||
# when providing the microorganism and antibiotic, colours will show interpretations:
|
||||
plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
|
||||
|
||||
plot(some_disk_values)
|
||||
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
|
||||
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
|
||||
|
||||
plot(some_sir_values)
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -8516,8 +8639,10 @@ antibiogram(example_isolates,
|
||||
To create a combined antibiogram, use antibiotic codes or names with a plus `+` character like this:
|
||||
|
||||
```{r comb}
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
combined_ab <- antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
ab_transform = NULL)
|
||||
combined_ab
|
||||
```
|
||||
|
||||
### Syndromic Antibiogram
|
||||
@ -8532,17 +8657,26 @@ antibiogram(example_isolates,
|
||||
|
||||
### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
|
||||
|
||||
To create a WISCA, you must state combination therapy in the `antibiotics` argument (similar to the Combination Antibiogram), define a syndromic group with the `syndromic_group` argument (similar to the Syndromic Antibiogram) in which cases are predefined based on clinical or demographic characteristics (e.g., endocarditis in 75+ females). This next example is a simplification without clinical characteristics, but just gives an idea of how a WISCA can be created:
|
||||
To create a **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**, simply set `wisca = TRUE` in the `antibiogram()` function, or use the dedicated `wisca()` function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted by pathogen incidence and antimicrobial susceptibility patterns.
|
||||
|
||||
```{r wisca}
|
||||
wisca <- antibiogram(example_isolates,
|
||||
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain",
|
||||
minimum = 10, # this should be >= 30, but now just as example
|
||||
syndromic_group = ifelse(example_isolates$age >= 65 &
|
||||
example_isolates$gender == "M",
|
||||
"WISCA Group 1", "WISCA Group 2"))
|
||||
wisca
|
||||
example_isolates %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: ≥30
|
||||
```
|
||||
|
||||
WISCA uses a **Bayesian decision model** to integrate data from multiple pathogens, improving empirical therapy guidance, especially for low-incidence infections. It is **pathogen-agnostic**, meaning results are syndrome-based rather than stratified by microorganism.
|
||||
|
||||
For reliable results, ensure your data includes **only first isolates** (use `first_isolate()`) and consider filtering for **the top *n* species** (use `top_n_microorganisms()`), as WISCA outcomes are most meaningful when based on robust incidence estimates.
|
||||
|
||||
For **patient- or syndrome-specific WISCA**, run the function on a grouped `tibble`, i.e., using `group_by()` first:
|
||||
|
||||
```{r wisca_grouped}
|
||||
example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>%
|
||||
group_by(age_group = age_groups(age, c(25, 50, 75)),
|
||||
gender) %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
```
|
||||
|
||||
### Plotting antibiograms
|
||||
@ -8550,7 +8684,7 @@ wisca
|
||||
Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this `AMR` package provides an extension to that function:
|
||||
|
||||
```{r}
|
||||
autoplot(wisca)
|
||||
autoplot(combined_ab)
|
||||
```
|
||||
|
||||
To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the `resistance()` and `susceptibility()` functions.
|
||||
@ -8575,9 +8709,54 @@ our_data_1st %>%
|
||||
summarise(amoxicillin = resistance(AMX))
|
||||
```
|
||||
|
||||
## Interpreting MIC and Disk Diffusion Values
|
||||
|
||||
Minimal inhibitory concentration (MIC) values and disk diffusion diameters can be interpreted into clinical breakpoints (SIR) using `as.sir()`. Here’s an example with randomly generated MIC values for *Klebsiella pneumoniae* and ciprofloxacin:
|
||||
|
||||
```{r mic_interpretation}
|
||||
set.seed(123)
|
||||
mic_values <- random_mic(100)
|
||||
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
|
||||
|
||||
my_data <- tibble(MIC = mic_values, SIR = sir_values)
|
||||
my_data
|
||||
```
|
||||
|
||||
This allows direct interpretation according to EUCAST or CLSI breakpoints, facilitating automated AMR data processing.
|
||||
|
||||
## Plotting MIC and SIR Interpretations
|
||||
|
||||
We can visualise MIC distributions and their SIR interpretations using `ggplot2`, using the new `scale_y_mic()` for the y-axis and `scale_colour_sir()` to colour-code SIR categories.
|
||||
|
||||
```{r mic_plot}
|
||||
# add a group
|
||||
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
|
||||
|
||||
ggplot(my_data,
|
||||
aes(x = group, y = MIC, colour = SIR)) +
|
||||
geom_jitter(width = 0.2, size = 2) +
|
||||
geom_boxplot(fill = NA, colour = "grey40") +
|
||||
scale_y_mic() +
|
||||
scale_colour_sir() +
|
||||
labs(title = "MIC Distribution and SIR Interpretation",
|
||||
x = "Sample Groups",
|
||||
y = "MIC (mg/L)")
|
||||
```
|
||||
|
||||
This plot provides an intuitive way to assess susceptibility patterns across different groups while incorporating clinical breakpoints.
|
||||
|
||||
For a more straightforward and less manual approach, `ggplot2`'s function `autoplot()` has been extended by this package to directly plot MIC and disk diffusion values:
|
||||
|
||||
```{r autoplot}
|
||||
autoplot(mic_values)
|
||||
|
||||
# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
|
||||
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
|
||||
```
|
||||
|
||||
----
|
||||
|
||||
*Author: Dr. Matthijs Berends, 26th Feb 2023*
|
||||
*Author: Dr. Matthijs Berends, 23rd Feb 2025*
|
||||
|
||||
|
||||
|
||||
24
index.md
24
index.md
@ -266,30 +266,18 @@ It will be downloaded and installed automatically. For RStudio, click on the men
|
||||
|
||||
#### Latest development version
|
||||
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-old.yaml?query=branch%3Amain)
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-recent.yaml?query=branch%3Amain)
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-old-tinytest.yaml)
|
||||
[](https://github.com/msberends/AMR/actions/workflows/check-current-testthat.yaml)
|
||||
[](https://www.codefactor.io/repository/github/msberends/amr)
|
||||
[](https://codecov.io/gh/msberends/AMR?branch=main)
|
||||
|
||||
Please read our [Developer Guideline here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
|
||||
|
||||
The latest and unpublished development version can be installed from GitHub in two ways:
|
||||
The latest and unpublished development version can be installed from the [rOpenSci R-universe platform](https://msberends.r-universe.dev/AMR):
|
||||
|
||||
1. Manually, using:
|
||||
|
||||
```r
|
||||
install.packages("remotes") # if you haven't already
|
||||
remotes::install_github("msberends/AMR")
|
||||
```
|
||||
|
||||
2. Automatically, using the [rOpenSci R-universe platform](https://ropensci.org/r-universe/), by adding [our R-universe address](https://msberends.r-universe.dev) to your list of repositories ('repos'):
|
||||
|
||||
```r
|
||||
options(repos = c(getOption("repos"),
|
||||
msberends = "https://msberends.r-universe.dev"))
|
||||
```
|
||||
|
||||
After this, you can install and update this `AMR` package like any official release (e.g., using `install.packages("AMR")` or in RStudio via *Tools* > *Check for Package Updates...*).
|
||||
```r
|
||||
install.packages("AMR", repos = "https://msberends.r-universe.dev")
|
||||
```
|
||||
|
||||
### Get started
|
||||
|
||||
|
||||
@ -7,6 +7,8 @@
|
||||
\alias{is.mic}
|
||||
\alias{NA_mic_}
|
||||
\alias{rescale_mic}
|
||||
\alias{mic_p50}
|
||||
\alias{mic_p90}
|
||||
\alias{droplevels.mic}
|
||||
\title{Transform Input to Minimum Inhibitory Concentrations (MIC)}
|
||||
\usage{
|
||||
@ -18,6 +20,10 @@ NA_mic_
|
||||
|
||||
rescale_mic(x, mic_range, keep_operators = "edges", as.mic = TRUE)
|
||||
|
||||
mic_p50(x, na.rm = FALSE, ...)
|
||||
|
||||
mic_p90(x, na.rm = FALSE, ...)
|
||||
|
||||
\method{droplevels}{mic}(x, as.mic = FALSE, ...)
|
||||
}
|
||||
\arguments{
|
||||
@ -87,6 +93,8 @@ With \code{\link[=rescale_mic]{rescale_mic()}}, existing MIC ranges can be limit
|
||||
For \code{ggplot2}, use one of the \code{\link[=scale_x_mic]{scale_*_mic()}} functions to plot MIC values. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
|
||||
|
||||
\code{NA_mic_} is a missing value of the new \code{mic} class, analogous to e.g. base \R's \code{\link[base:NA]{NA_character_}}.
|
||||
|
||||
Use \code{\link[=mic_p50]{mic_p50()}} and \code{\link[=mic_p90]{mic_p90()}} to get the 50th and 90th percentile of MIC values. They return 'normal' \link{numeric} values.
|
||||
}
|
||||
\examples{
|
||||
mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
|
||||
|
||||
125
man/plot.Rd
125
man/plot.Rd
@ -196,3 +196,128 @@ The interpretation of "I" will be named "Increased exposure" for all EUCAST guid
|
||||
For interpreting MIC values as well as disk diffusion diameters, the default guideline is EUCAST 2024, unless the package option \code{\link[=AMR-options]{AMR_guideline}} is set. See \code{\link[=as.sir]{as.sir()}} for more information.
|
||||
}
|
||||
}
|
||||
\examples{
|
||||
some_mic_values <- random_mic(size = 100)
|
||||
some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
|
||||
some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))
|
||||
|
||||
|
||||
\donttest{
|
||||
# Plotting using ggplot2's autoplot() for MIC, disk, and SIR -----------
|
||||
if (require("ggplot2")) {
|
||||
autoplot(some_mic_values)
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
# when providing the microorganism and antibiotic, colours will show interpretations:
|
||||
autoplot(some_mic_values, mo = "Escherichia coli", ab = "cipro")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
# support for 20 languages, various guidelines, and many options
|
||||
autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro",
|
||||
guideline = "CLSI 2024", language = "no",
|
||||
title = "Disk diffusion from the North")
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_x_mic() -----------------------------------------
|
||||
if (require("ggplot2")) {
|
||||
mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
|
||||
counts = c(1, 1, 2, 2, 3, 3)),
|
||||
aes(mics, counts)) +
|
||||
geom_col()
|
||||
mic_plot +
|
||||
labs(title = "without scale_x_mic()")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic() +
|
||||
labs(title = "with scale_x_mic()")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic(keep_operators = "all") +
|
||||
labs(title = "with scale_x_mic() keeping all operators")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic(mic_range = c(1, 16)) +
|
||||
labs(title = "with scale_x_mic() using a manual 'within' range")
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
mic_plot +
|
||||
scale_x_mic(mic_range = c(0.032, 256)) +
|
||||
labs(title = "with scale_x_mic() using a manual 'outside' range")
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_y_mic() -----------------------------------------
|
||||
some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
|
||||
|
||||
if (require("ggplot2")) {
|
||||
ggplot(data.frame(mic = some_mic_values,
|
||||
group = some_groups),
|
||||
aes(group, mic)) +
|
||||
geom_boxplot() +
|
||||
geom_violin(linetype = 2, colour = "grey", fill = NA) +
|
||||
scale_y_mic()
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
ggplot(data.frame(mic = some_mic_values,
|
||||
group = some_groups),
|
||||
aes(group, mic)) +
|
||||
geom_boxplot() +
|
||||
geom_violin(linetype = 2, colour = "grey", fill = NA) +
|
||||
scale_y_mic(mic_range = c(NA, 0.25))
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_x_sir() -----------------------------------------
|
||||
if (require("ggplot2")) {
|
||||
ggplot(data.frame(x = c("I", "R", "S"),
|
||||
y = c(45,323, 573)),
|
||||
aes(x, y)) +
|
||||
geom_col() +
|
||||
scale_x_sir()
|
||||
}
|
||||
|
||||
|
||||
# Plotting using scale_y_mic() and scale_colour_sir() ------------------
|
||||
if (require("ggplot2")) {
|
||||
plain <- ggplot(data.frame(mic = some_mic_values,
|
||||
group = some_groups,
|
||||
sir = as.sir(some_mic_values,
|
||||
mo = "E. coli",
|
||||
ab = "cipro")),
|
||||
aes(x = group, y = mic, colour = sir)) +
|
||||
theme_minimal() +
|
||||
geom_boxplot(fill = NA, colour = "grey") +
|
||||
geom_jitter(width = 0.25)
|
||||
|
||||
plain
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
# and now with our MIC and SIR scale functions:
|
||||
plain +
|
||||
scale_y_mic() +
|
||||
scale_colour_sir()
|
||||
}
|
||||
if (require("ggplot2")) {
|
||||
plain +
|
||||
scale_y_mic(mic_range = c(0.005, 32), name = "Our MICs!") +
|
||||
scale_colour_sir(language = "pt",
|
||||
name = "Support in 20 languages")
|
||||
}
|
||||
}
|
||||
|
||||
# Plotting using base R's plot() ---------------------------------------
|
||||
|
||||
plot(some_mic_values)
|
||||
# when providing the microorganism and antibiotic, colours will show interpretations:
|
||||
plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
|
||||
|
||||
plot(some_disk_values)
|
||||
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
|
||||
plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "nl")
|
||||
|
||||
plot(some_sir_values)
|
||||
}
|
||||
|
||||
@ -28,9 +28,9 @@
|
||||
# ==================================================================== #
|
||||
|
||||
# used in multiple functions, also in plotting
|
||||
expect_true(all(as.mic(COMMON_MIC_VALUES) %in% VALID_MIC_LEVELS))
|
||||
expect_true(all(paste0("<=", as.mic(COMMON_MIC_VALUES)) %in% VALID_MIC_LEVELS))
|
||||
expect_true(all(paste0(">=", as.mic(COMMON_MIC_VALUES)) %in% VALID_MIC_LEVELS))
|
||||
expect_true(all(as.mic(AMR:::COMMON_MIC_VALUES) %in% AMR:::VALID_MIC_LEVELS))
|
||||
expect_true(all(paste0("<=", as.mic(AMR:::COMMON_MIC_VALUES)) %in% AMR:::VALID_MIC_LEVELS))
|
||||
expect_true(all(paste0(">=", as.mic(AMR:::COMMON_MIC_VALUES)) %in% AMR:::VALID_MIC_LEVELS))
|
||||
|
||||
expect_true(as.mic(8) == as.mic("8"))
|
||||
expect_true(as.mic("1") > as.mic("<=0.0625"))
|
||||
|
||||
@ -288,8 +288,10 @@ antibiogram(example_isolates,
|
||||
To create a combined antibiogram, use antibiotic codes or names with a plus `+` character like this:
|
||||
|
||||
```{r comb}
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
combined_ab <- antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
ab_transform = NULL)
|
||||
combined_ab
|
||||
```
|
||||
|
||||
### Syndromic Antibiogram
|
||||
@ -304,17 +306,26 @@ antibiogram(example_isolates,
|
||||
|
||||
### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
|
||||
|
||||
To create a WISCA, you must state combination therapy in the `antibiotics` argument (similar to the Combination Antibiogram), define a syndromic group with the `syndromic_group` argument (similar to the Syndromic Antibiogram) in which cases are predefined based on clinical or demographic characteristics (e.g., endocarditis in 75+ females). This next example is a simplification without clinical characteristics, but just gives an idea of how a WISCA can be created:
|
||||
To create a **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**, simply set `wisca = TRUE` in the `antibiogram()` function, or use the dedicated `wisca()` function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted by pathogen incidence and antimicrobial susceptibility patterns.
|
||||
|
||||
```{r wisca}
|
||||
wisca <- antibiogram(example_isolates,
|
||||
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
|
||||
mo_transform = "gramstain",
|
||||
minimum = 10, # this should be >= 30, but now just as example
|
||||
syndromic_group = ifelse(example_isolates$age >= 65 &
|
||||
example_isolates$gender == "M",
|
||||
"WISCA Group 1", "WISCA Group 2"))
|
||||
wisca
|
||||
example_isolates %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: ≥30
|
||||
```
|
||||
|
||||
WISCA uses a **Bayesian decision model** to integrate data from multiple pathogens, improving empirical therapy guidance, especially for low-incidence infections. It is **pathogen-agnostic**, meaning results are syndrome-based rather than stratified by microorganism.
|
||||
|
||||
For reliable results, ensure your data includes **only first isolates** (use `first_isolate()`) and consider filtering for **the top *n* species** (use `top_n_microorganisms()`), as WISCA outcomes are most meaningful when based on robust incidence estimates.
|
||||
|
||||
For **patient- or syndrome-specific WISCA**, run the function on a grouped `tibble`, i.e., using `group_by()` first:
|
||||
|
||||
```{r wisca_grouped}
|
||||
example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>%
|
||||
group_by(age_group = age_groups(age, c(25, 50, 75)),
|
||||
gender) %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
```
|
||||
|
||||
### Plotting antibiograms
|
||||
@ -322,7 +333,7 @@ wisca
|
||||
Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this `AMR` package provides an extension to that function:
|
||||
|
||||
```{r}
|
||||
autoplot(wisca)
|
||||
autoplot(combined_ab)
|
||||
```
|
||||
|
||||
To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the `resistance()` and `susceptibility()` functions.
|
||||
@ -347,6 +358,51 @@ our_data_1st %>%
|
||||
summarise(amoxicillin = resistance(AMX))
|
||||
```
|
||||
|
||||
## Interpreting MIC and Disk Diffusion Values
|
||||
|
||||
Minimal inhibitory concentration (MIC) values and disk diffusion diameters can be interpreted into clinical breakpoints (SIR) using `as.sir()`. Here’s an example with randomly generated MIC values for *Klebsiella pneumoniae* and ciprofloxacin:
|
||||
|
||||
```{r mic_interpretation}
|
||||
set.seed(123)
|
||||
mic_values <- random_mic(100)
|
||||
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
|
||||
|
||||
my_data <- tibble(MIC = mic_values, SIR = sir_values)
|
||||
my_data
|
||||
```
|
||||
|
||||
This allows direct interpretation according to EUCAST or CLSI breakpoints, facilitating automated AMR data processing.
|
||||
|
||||
## Plotting MIC and SIR Interpretations
|
||||
|
||||
We can visualise MIC distributions and their SIR interpretations using `ggplot2`, using the new `scale_y_mic()` for the y-axis and `scale_colour_sir()` to colour-code SIR categories.
|
||||
|
||||
```{r mic_plot}
|
||||
# add a group
|
||||
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
|
||||
|
||||
ggplot(my_data,
|
||||
aes(x = group, y = MIC, colour = SIR)) +
|
||||
geom_jitter(width = 0.2, size = 2) +
|
||||
geom_boxplot(fill = NA, colour = "grey40") +
|
||||
scale_y_mic() +
|
||||
scale_colour_sir() +
|
||||
labs(title = "MIC Distribution and SIR Interpretation",
|
||||
x = "Sample Groups",
|
||||
y = "MIC (mg/L)")
|
||||
```
|
||||
|
||||
This plot provides an intuitive way to assess susceptibility patterns across different groups while incorporating clinical breakpoints.
|
||||
|
||||
For a more straightforward and less manual approach, `ggplot2`'s function `autoplot()` has been extended by this package to directly plot MIC and disk diffusion values:
|
||||
|
||||
```{r autoplot}
|
||||
autoplot(mic_values)
|
||||
|
||||
# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
|
||||
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
|
||||
```
|
||||
|
||||
----
|
||||
|
||||
*Author: Dr. Matthijs Berends, 26th Feb 2023*
|
||||
*Author: Dr. Matthijs Berends, 23rd Feb 2025*
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user