MaldiAMRKit 0.6.2

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

MaldiAMRKit

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

Installation • Features • Documentation • License

Installation

pip install maldiamrkit

Development Installation

git clone https://github.com/EttoreRocchi/MaldiAMRKit.git
cd MaldiAMRKit
pip install -e .[dev]

Features

• Spectrum Processing: Load, smooth, baseline correct, and normalize MALDI-TOF spectra
• Dataset Management: Process multiple spectra with metadata integration
• Peak Detection: Local maxima and persistent homology methods
• Spectral Alignment (Warping): Multiple alignment methods (shift, linear, piecewise, DTW)
• Raw Spectra Warping: Full m/z resolution alignment before binning
• Quality Metrics: SNR estimation, comprehensive quality reports, and alignment assessment
• Parallel Processing: Multi-core support via n_jobs parameter for faster processing
• ML-Ready: Direct integration with scikit-learn pipelines

Quick Start

Load and Preprocess a Single Spectrum

from maldiamrkit 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 import MaldiSet

# Load multiple spectra with metadata
data = MaldiSet.from_directory(
    spectra_dir="data/spectra/",
    meta_file="data/metadata.csv",
    aggregate_by=dict(antibiotics="Drug", species="Species"),
    bin_width=3
)

# Access features and labels
X = data.X  # Feature matrix
y = data.get_y_single("Drug")  # Target labels

Binning Methods

MaldiAMRKit supports multiple binning strategies:

from maldiamrkit import MaldiSpectrum

spec = MaldiSpectrum("data/spectrum.txt").preprocess()

# Uniform binning (default)
spec.bin(bin_width=3)

# Logarithmic binning (width scales with m/z)
spec.bin(bin_width=3, method="logarithmic")

# Adaptive binning (smaller bins in peak-dense regions)
spec.bin(method="adaptive", adaptive_min_width=1.0, adaptive_max_width=10.0)

# Custom binning (user-defined edges)
spec.bin(method="custom", custom_edges=[2000, 5000, 10000, 15000, 20000])

# Access bin metadata
print(spec.bin_metadata.head())
#    bin_index  bin_start  bin_end  bin_width
# 0          0     2000.0   2003.0        3.0
# 1          1     2003.0   2006.0        3.0

Binning Methods:

• uniform: Fixed width bins (default)
• logarithmic: Bin width scales with m/z (matches instrument resolution)
• adaptive: Smaller bins where peaks are dense, larger bins elsewhere
• custom: User-defined bin edges for domain-specific analysis

Machine Learning Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from maldiamrkit import MaldiPeakDetector, Warping

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

# Cross-validation (recommended over train accuracy)
scores = cross_val_score(pipe, X, y, cv=5, scoring="accuracy")
print(f"CV Accuracy: {scores.mean():.3f} +/- {scores.std():.3f}")

Spectral Alignment

Align spectra to correct for mass calibration drift:

from maldiamrkit import Warping

# Create warping transformer
warper = Warping(
    method='piecewise',  # or 'shift', 'linear', 'dtw'
    reference='median',
    n_segments=5
)

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

# Check alignment quality
quality = warper.get_alignment_quality(X_test, X_aligned)
print(f"Mean improvement: {quality['improvement'].mean():.4f}")

# Visualize
warper.plot_alignment(X_test, X_aligned, indices=[0], show_peaks=True)

Raw Spectra Warping

For higher precision, use RawWarping which operates at full m/z resolution:

from maldiamrkit import RawWarping, create_raw_input

# Create input DataFrame from spectrum files
X_raw = create_raw_input("data/spectra/")

# Raw warping loads original files for warping
warper = RawWarping(
    method="piecewise",
    bin_width=3,
    max_shift_da=10.0,
    n_jobs=-1  # Parallel processing
)

# Outputs binned data for pipeline compatibility
warper.fit(X_raw)
X_aligned = warper.transform(X_raw)

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)

Quality Assessment

from maldiamrkit import estimate_snr, SpectrumQuality, MaldiSpectrum

# Estimate signal-to-noise ratio
spec = MaldiSpectrum("spectrum.txt").preprocess()
snr = estimate_snr(spec.preprocessed)
print(f"SNR: {snr:.1f}")

# Comprehensive quality report
qc = SpectrumQuality()  # Uses high m/z region (19500-20000) by default
report = qc.assess(spec.preprocessed)
print(f"SNR: {report.snr:.1f}")
print(f"Peak count: {report.peak_count}")
print(f"Dynamic range: {report.dynamic_range:.2f}")

Parallel Processing

Use n_jobs parameter for multi-core processing:

from maldiamrkit import MaldiSet, MaldiPeakDetector, Warping

# Parallel dataset loading
data = MaldiSet.from_directory("spectra/", "meta.csv", n_jobs=-1)

# Parallel peak detection
detector = MaldiPeakDetector(prominence=0.01, n_jobs=-1)
peaks = detector.fit_transform(X)

# Parallel alignment
warper = Warping(method="piecewise", n_jobs=-1)
X_aligned = warper.fit_transform(X)

Project Structure

maldiamrkit/
├── core/           # Core data structures (MaldiSpectrum, MaldiSet)
├── preprocessing/  # Preprocessing functions (pipeline, binning, quality)
├── alignment/      # Warping transformers (Warping, RawWarping)
├── detection/      # Peak detection (MaldiPeakDetector)
├── io/             # File I/O utilities
└── utils/          # Validation and plotting helpers

Tutorials

For more detailed examples, see the notebooks:

• Quick Start - Loading, preprocessing, binning, and quality assessment
• Peak Detection - Local maxima and persistent homology methods
• Alignment - Warping methods and alignment quality

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.