amR/README.Rmd at main · JRaviLab/amR · GitHub

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---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

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

# amR: an R package suite for antimicrobial resistance prediction

<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
<!-- badges: end -->

**amR** is a metapackage that provides a single installation point for the amR suite of packages for antimicrobial resistance (AMR) prediction in bacterial pathogens.

```{r, echo=FALSE, fig.align='center', out.width='60%'}
knitr::include_graphics("man/figures/README-amR.png")
```

## The amR suite

The amR suite consists of three packages that work together:

| Package | Description | Repository |
|---------|-------------|------------|
| **amRdata** | Data curation and feature extraction from bacterial genomes | [JRaviLab/amRdata](https://github.com/JRaviLab/amRdata) |
| **amRml** | Machine learning models for AMR prediction | [JRaviLab/amRml](https://github.com/JRaviLab/amRml) |
| **amRshiny** | Interactive dashboard for exploring results | [JRaviLab/amRshiny](https://github.com/JRaviLab/amRshiny) |

## Installation

### Install the entire suite

```r
# Install amR metapackage
if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")

remotes::install_github("JRaviLab/amR")

# Then install all packages in the suite
library(amR)
installAMR()
```

### Install individual packages

You can also install packages individually:

```r
remotes::install_github("JRaviLab/amRdata")
remotes::install_github("JRaviLab/amRml")
remotes::install_github("JRaviLab/amRshiny")
```

## Quick start

```r
# Load all packages
library(amRdata)
library(amRml)
library(amRshiny)

# 1. Prepare data with amRdata
# features <- prepareFeatures(...)

# 2. Train ML models with amRml
# results <- runMLPipeline(...)

# 3. Explore results with amRshiny
# launchDashboard(...)
```

## Workflow overview

```
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  amRdata    │ --> │   amRml     │ --> │  amRshiny   │
│             │     │             │     │             │
│ - Genomes   │     │ - Train LR  │     │ - Dashboard │
│ - Features  │     │ - Evaluate  │     │ - Plots     │
│ - Metadata  │     │ - Top feats │     │ - Export    │
└─────────────┘     └─────────────┘     └─────────────┘
```

## Documentation

- [amRdata documentation](https://jravilab.github.io/amRdata)
- [amRml documentation](https://jravilab.github.io/amRml)
- [amRshiny documentation](https://jravilab.github.io/amRshiny)

## Citation

If you use the amR suite in your research, please cite:

```
Brenner EP^, Ghosh A^, Wolfe EP, Boyer EA, Vang CK, Lesiyon RL, Mayer DA, Ravi J. (2026).
amR: An R package suite for antimicrobial resistance prediction in bacterial pathogens.
https://github.com/JRaviLab/amR
```

Looking for a cool application of this amR prediction framework? Check out our recent work on predicting AMR in ESKAPE pathogens: [Ghosh^, Brenner^, Vang^, Wolfe^, _et al.,_ _bioRxiv_ 2025](https://doi.org/10.1101/2025.07.03.663053).


## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

BSD 3-Clause License. See [LICENSE](LICENSE) for details.

## Contact

**Corresponding author**: Janani Ravi (janani.ravi@cuanschutz.edu)

**Lab website**: https://jravilab.github.io

## Code of Conduct

Please note that amR is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.