antibioticR

is an R package to estimate antibiotic resistance cutoff values from ZD and MIC distributions of environmental samples

The package aims to improve the accessibility to statistical methods for analyzing populations of resistant and non-resistant bacteria from an environmental, i.e. non-clinical perspective. The methods are intended to describe sensitivity, tolerance and resistance on a sub-acute level in order to compare populations of different origin on gradual scales.

We assume that environmental populations are composed of different geno- and phenotypes, so that quantitative data from standard methods like disc diffusion zone diameters (ZD) or minimum inhibitory concentration (MIC) values will yield multi-modal univariate mixture distributions when tested against single antibiotics.

The package relies on existing packages, especially packages evmix [@Hu2018] for boundary corrected density estimation and package bbmle [@Bolker2017] for maximum likelihood estimation. The package will be amended by visualization tools and interactive web-applications using R’s base graphics and statistics packages [RCore2015], packages ggplot2 [@Wickham2016] and shiny [@Chang2018].

Please note: The package and this document are in an early stage of development (pre-alpha). It comes without warranty and is not intended for clinical applications. Its functions and classes are likely to change and may contain mistakes and errors. Comments are welcome.

Methods

The package supports currently three methods:

  1. Kernel density smoothing for getting mean values and multiple modes from the distributions,
  2. An R implementation of the ECOFFinder algorithm [@Turnidge2006] with automatic start value estimation and a shiny app for interactive use,
  3. Maximum likelihood estimation of multi-modal normal and exponential-normal mixtures.

Download and Installation

Development version

Install with package devtools:

install.packages("devtools")
library(devtools)
install_github("tpetzoldt/antibioticR")

Release version

The package is not yet released.

Demo

A live demo of the ECOFFinder approach can be found at:

https://weblab.hydro.tu-dresden.de/ecoffinder/

Documentation

The documentation is still to be written. An initial version is found here:

https://tpetzoldt.github.io/antibioticR/articles/Introduction.html

Original author

tpetzoldt