MaldiAMRKit 0.2.1

Toolkit to read and preprocess MALDI-TOF mass-spectra for AMR analyses.

MaldiAMRKit

Toolkit to read and preprocess MALDI-TOF mass-spectra for AMR analyses

🚀 Installation

pip install maldiamrkit

🏃 Quick Start

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.

🤝 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 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

Please consider citing this work if you find MaldiAMRKit useful.