In msberends/AMR: Antimicrobial Resistance Data Analysis

knitr::opts_chunk$set(
  warning = FALSE,
  collapse = TRUE,
  comment = "#>",
  fig.width = 7.5,
  fig.height = 5
)

Introduction

The AMR package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the AMR Python Package Index.

This Python package is a wrapper round the AMR R package. It uses the rpy2 package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.

Install

1. Since the Python package is available on the official Python Package Index, you can just run:

   bash pip install AMR

2. Make sure you have R installed. There is no need to install the AMR R package, as it will be installed automatically.

   For Linux:

   ```bash

   Ubuntu / Debian

   sudo apt install r-base

   Fedora:

   sudo dnf install R

   CentOS/RHEL

   sudo yum install R ```

   For macOS (using Homebrew):

   bash brew install r

   For Windows, visit the CRAN download page to download and install R.

Examples of Usage

Cleaning Taxonomy

Here’s an example that demonstrates how to clean microorganism and drug names using the AMR Python package:

import pandas as pd
import AMR

# Sample data
data = {
    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
}
df = pd.DataFrame(data)

# Use AMR functions to clean microorganism and drug names
df['MO_clean'] = AMR.mo_name(df['MOs'])
df['Drug_clean'] = AMR.ab_name(df['Drug'])

# Display the results
print(df)

| MOs | Drug | MO_clean | Drug_clean | |-------------|-----------|--------------------|---------------| | E. coli | Cipro | Escherichia coli | Ciprofloxacin | | ESCCOL | CIP | Escherichia coli | Ciprofloxacin | | esco | J01MA02 | Escherichia coli | Ciprofloxacin | | Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |

Explanation

• mo_name: This function standardises microorganism names. Here, different variations of Escherichia coli (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".

• ab_name: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".

Calculating AMR

import AMR
import pandas as pd

df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result)

[0.59555556]

Generating Antibiograms

One of the core functions of the AMR package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:

result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
print(result2a)

| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam | |-----------------|-----------------|-----------------|--------------------------| | CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) | | E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) | | K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) | | P. aeruginosa | 0% (0/30) | 100% (30/30) | None | | P. mirabilis | None | 94% (34/36) | None | | S. aureus | 6% (8/131) | 90% (171/191) | None | | S. epidermidis | 1% (1/91) | 64% (87/136) | None | | S. hominis | None | 80% (56/70) | None | | S. pneumoniae | 100% (112/112) | None | 100% (112/112) |

result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
print(result2b)

| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam | |----------------|-----------------|------------------|--------------------------| | Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) | | Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |

In this example, we generate an antibiogram by selecting various antibiotics.

Taxonomic Data Sets Now in Python!

As a Python user, you might like that the most important data sets of the AMR R package, microorganisms, antibiotics, clinical_breakpoints, and example_isolates, are now available as regular Python data frames:

AMR.microorganisms

| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence | |--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------| | B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 | | B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 | | B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 | | B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 | | B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 | | ... | ... | ... | ... | ... | ... | ... | ... | | B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 | | B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 | | B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 | | B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 | | F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |

AMR.antibiotics

| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units | |-----|-------------|----------------------|----------------------------|----------|------------|--------|----------| | AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None | | ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None | | ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None | | ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None | | AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g | | ... | ... | ... | ... | ... | ... | ... | ... | | VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None | | VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g | | XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None | | ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None | | ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |

Conclusion

With the AMR Python package, Python users can now effortlessly call R functions from the AMR R package. This eliminates the need for complex rpy2 configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.

By just running import AMR, users can seamlessly integrate the robust features of the R AMR package into Python workflows.

Whether you're cleaning data or analysing resistance patterns, the AMR Python package makes it easy to work with AMR data in Python.

msberends/AMR documentation built on March 5, 2025, 2:28 p.m.