MaldiAMRKit

A comprehensive toolkit for MALDI-TOF mass spectrometry data preprocessing for antimicrobial resistance (AMR) prediction purposes

Installation • Features • Quick Start • License • Contributing

Installation

pip install maldiamrkit

Features

• 📊 Spectrum Processing: Load, smooth, baseline correct, and normalize MALDI-TOF spectra
• 📦 Dataset Management: Process multiple spectra with metadata integration
• 🔍 Peak Detection: Automated peak finding with customizable parameters
• 📈 Spectral Alignment (Warping): Multiple alignment methods (shift, linear, piecewise, DTW)
• 🤖 ML-Ready: Direct integration with scikit-learn pipelines

Quick Start

Load and Preprocess a Single Spectrum

from maldiamrkit.spectrum import MaldiSpectrum

# Load spectrum from file
spec = MaldiSpectrum("data/spectrum.txt")

# Preprocess: smoothing, baseline removal, normalization
spec.preprocess()

# Optional: bin to reduce dimensions
spec.bin(bin_width=3)  # 3 Da bins

# Visualize
spec.plot(binned=True)

Build a Dataset from Multiple Spectra

from maldiamrkit.dataset import MaldiSet

# Load multiple spectra with metadata
data = MaldiSet.from_directory(
    spectra_dir="data/spectra/",
    metadata_path="data/metadata.csv",
    aggregate_by={"antibiotic": "Drug", "species": "Species"},
    bin_width=3
)

# Access features and labels
X = data.X  # Feature matrix
y = data.y["Drug"]  # Target labels

Machine Learning Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from maldiamrkit.peak_detector import MaldiPeakDetector

# Create ML pipeline
pipe = Pipeline([
    ("peaks", MaldiPeakDetector(binary=False, prominence=0.05)),
    ("scaler", StandardScaler()),
    ("clf", RandomForestClassifier(n_estimators=100, random_state=42))
])

# Train and predict
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)

Align spectra to correct for mass calibration drift:

from maldiamrkit.warping import Warping

# Create warping transformer with shift method
warper = Warping(
    method='shift',  # or 'linear', 'piecewise', 'dtw'
    reference='median',  # use median spectrum as reference
    max_shift=50
)

# Fit on training data and transform
warper.fit(X_train)
X_aligned = warper.transform(X_test)

# Visualize alignment results
fig, axes = warper.plot_alignment(
    X_original=X_test,
    X_aligned=X_aligned,
    indices=[0, 5, 10],  # plot multiple spectra
    xlim=(2000, 10000),  # zoom to m/z range
    show_peaks=True
)

Alignment Methods:

• shift: Global median shift (fast, simple)
• linear: Least-squares linear transformation
• piecewise: Local shifts across spectrum segments (most flexible)
• dtw: Dynamic Time Warping (best for non-linear drift)

For further details please see the quick guide notebook.

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 toolkit is inspired by and builds upon the methodology described in:

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.