In ClorisZhou2000/MDRClassifier: Multi-Drug Resistance Classifier

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MDRClassifier

The MDRClassifer is the tool for predicting and classifying 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. It is improved since we compare drug resistance predicted from the previous antimicrobial-resistant genes against the criteria from the European Center for Disease Prevention and Control (ECDC) to better view the level of multi-drug resistance of isolates. 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")

Overview

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

MDRClassifier is an R package developed to analyze and classify multidrug resistance (MDR) of protein sequences. The package is targeted for bioinformatics exploring mutidrug resistance of pathogens. Three categories define by European Centre for Disease Prevention and Control: MDR, XDR, PDR, are used for classifying the mutidrug resistance level of protein sequences.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 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 (2020). 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/