In Cloris2000/MDRClassifier: Multi-Drug Resistance Classifier

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

MDRClassifier

The MDRClassifer is the tool for classifying and analyzing the multi-drug resistance (MDR) of pathogen isolates. Three categories define by European Centre for Disease Prevention and Control: MDR, XDR, PDR, are used for classifying the MDR of isolates. This package provides users with a better understanding of the level of multi-drug resistance of isolates by categorize multi-drug resistance from antimicrobial agents. Two criteria are available for different user input. If all the antimicrobial agents are tested, this tool classifies the MDR categories by the criterion of European Center for Disease Prevention and Control (ECDC). Otherwise, a more specific criterion from ECDC's subdivision is used. The R version is R 4.1.1 and the platfrom is macOs Big Sur.

Installation

You can install the released version of MDRClassifier from CRAN with:

require("devtools")
devtools::install_github("Cloris2000/MDRClassifier", build_vignettes = TRUE)
library("MDRClassifier")

To run the Shiny app:

runMDRClassifier()

Overview

ls("package:MDRClassifier") 
data(package = "MDRClassifier")

MDRClassifier is an R package developed to classify and analyze multi-drug resistance (MDR) of bacteria isolates. The package is targeted for bioinformatics exploring multi-drug resistance of pathogens. Three categories define by European Centre for Disease Prevention and Control: MDR, XDR, PDR, are used for classifying the multi-drug resistance level of .MDR stands for non-susceptibility to at least one agent in three or more antimicrobial categories. XDR represents that the isolate is non-susceptibility to at least one agent in all but less than or equal to 2 antimicrobial categories. PDR means the isolate is non-susceptibility to all agents in all antimicrobial categories. The main function classifyAllMDR classifies the multi-drug resistance level of isolates. The function classifyMDR returns the multi-drug resistance level of a specific isolate given the Sample ID. The function classifyMDRfromRSI generates the MDR category of specific sample from RSI table, which is commonly used in antibiotic resistance analysis. The function classifyallMDRfromRSI produce a dataframe with sample ID and their corresponding MDR categories from RSI table. The function MDRPlot plots the isolates by their categories of multidrug resistances. Function predictAMR is available to calculate PCA value of new isolates. Function plotPCA is to generate dimension reduction plot for clustering and predict relationships of new isolates. For more information, see details below.

browseVignettes("MDRClassifier")

An overview of the package is illustrated below.

Contributions

The author of the package is Xiaolin Zhou. The MDRPlot function makes use of the graphic R package. The classifyMDR function uses hash package generates a hash object for storing information. The predictAMR and plotPCA utilize factoextra pacakge to do principle component analysis.

References

Berends, M. S., Luz, C. F., Friedrich, A. W. et al. (2021). Data sets for download / own use. AMR(for R). https://msberends.github.io/AMR/articles/datasets.html

Kassambara, A. (2017). Principal Component Analysis in R: prcomp vs princomp. Articles - STHDA. http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/118-principal-component-analysis-in-r-prcomp-vs-princomp/

R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

BioRender. (2020). Image created by Zhou, X. Retrieved November 12, 2021, from https://app.biorender.com/

Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software. https://doi.org/10.21105/joss.01686

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.https://ggplot2.tidyverse.org.

Wickham, H. and Bryan, J. (2019). R Packages (2nd edition). Newton, Massachusetts: O’Reilly Media. https://r-pkgs.org/