# MaldiAMRKit
<p align="center">
<img src="docs/maldiamrkit.png" alt="MaldiAMRKit" width="250"/>
</p>
<p align="center">
<strong>Toolkit to read and preprocess MALDI-TOF mass-spectra for AMR analyses</strong>
</p>
## 🚀 Installation
```bash
pip install maldiamrkit
```
## 🏃 Quick Start
```python
from maldiamrkit.spectrum import MaldiSpectrum
from maldiamrkit.dataset import MaldiSet
from maldiamrkit.peak_detector import MaldiPeakDetector
# Load and preprocess a single spectrum
spec = MaldiSpectrum("data/1s.txt").preprocess() # smoothing, baseline removal, normalisation
spec.bin(3) # [optional] bin width 3 Da
spec.plot(binned=True) # plot
# Build a dataset from a directory of spectra + metadata CSV
data = MaldiSet.from_directory(
"data/", "data/metadata/metadata.csv",
aggregate_by=dict(antibiotic="Drug"),
bin_width=3
)
X, y = data.X, data.y["Drug"]
# Machine learning pipeline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
pipe = Pipeline([
("peaks", MaldiPeakDetector(binary=False, prominence=0.05)),
("scaler", StandardScaler()),
("clf", LogisticRegression(max_iter=500))
])
pipe.fit(X, y)
```
For further details please see the [quick guide](docs/quick_guide.ipynb).
## 🤝 Contributing
Pull requests, bug reports, and feature ideas are welcome: feel free to open a PR!
## 📝 License
This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgements
This pipeline is based on the methodology described in the following publication and aims to facilitate the application of similar approaches on MALDI-TOF spectra for AMR prediction in a machine learning context:
> **Weis, C., Cuénod, A., Rieck, B., et al.** (2022). *Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning*. **Nature Medicine**, 28, 164–174. [https://doi.org/10.1038/s41591-021-01619-9](https://doi.org/10.1038/s41591-021-01619-9)
Please consider citing this work if you find `MaldiAMRKit` useful.
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"description": "# MaldiAMRKit\n\n<p align=\"center\">\n <img src=\"docs/maldiamrkit.png\" alt=\"MaldiAMRKit\" width=\"250\"/>\n</p>\n<p align=\"center\">\n <strong>Toolkit to read and preprocess MALDI-TOF mass-spectra for AMR analyses</strong>\n</p>\n\n## \ud83d\ude80\u00a0Installation\n\n```bash\npip install maldiamrkit\n```\n\n## \ud83c\udfc3\u00a0Quick Start\n\n```python\nfrom maldiamrkit.spectrum import MaldiSpectrum\nfrom maldiamrkit.dataset import MaldiSet\nfrom maldiamrkit.peak_detector import MaldiPeakDetector\n\n# Load and preprocess a single spectrum\nspec = MaldiSpectrum(\"data/1s.txt\").preprocess() # smoothing, baseline removal, normalisation\nspec.bin(3) # [optional]\u00a0bin width 3 Da\nspec.plot(binned=True) # plot\n\n# Build a dataset from a directory of spectra + metadata CSV\ndata = MaldiSet.from_directory(\n \"data/\", \"data/metadata/metadata.csv\",\n aggregate_by=dict(antibiotic=\"Drug\"),\n bin_width=3\n)\nX, y = data.X, data.y[\"Drug\"]\n\n# Machine learning pipeline\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\n\npipe = Pipeline([\n (\"peaks\", MaldiPeakDetector(binary=False, prominence=0.05)),\n (\"scaler\", StandardScaler()),\n (\"clf\", LogisticRegression(max_iter=500))\n])\npipe.fit(X, y)\n```\nFor further details please see the [quick guide](docs/quick_guide.ipynb).\n\n## \ud83e\udd1d Contributing\n\nPull requests, bug reports, and feature ideas are welcome: feel free to open a PR!\n\n## \ud83d\udcdd\u00a0License\n\nThis project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgements\n\nThis pipeline is based on the methodology described in the following publication and aims to facilitate the application of similar approaches on MALDI-TOF spectra for AMR prediction in a machine learning context:\n\n> **Weis, C., Cu\u00e9nod, A., Rieck, B., et al.** (2022). *Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning*. **Nature Medicine**, 28, 164\u2013174. [https://doi.org/10.1038/s41591-021-01619-9](https://doi.org/10.1038/s41591-021-01619-9)\n\nPlease consider citing this work if you find `MaldiAMRKit` useful.\n",
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