MaldiAMRKit Documentation

A Python toolkit for MALDI-TOF mass spectrometry preprocessing for antimicrobial resistance (AMR) prediction. Scikit-learn compatible transformers for seamless integration into machine learning pipelines.

Installation

API Reference

Quickstart Guide

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Key Features

Preprocessing Pipeline

Composable transformers (smoothing, baseline, trimming, normalization), multiple binning strategies, and peak detection. Serializable to JSON/YAML.

quickstart.html#custom-preprocessing-pipeline

Sklearn Pipelines

Scikit-learn compatible transformers. Drop into any Pipeline, cross_val_score, or GridSearchCV workflow.

quickstart.html#building-ml-pipelines

Spectral Alignment

Shift, linear, piecewise, and DTW warping for both binned and raw full-resolution spectra.

api/alignment.html

AMR Evaluation

VME, ME, sensitivity, specificity, and classification reports following EUCAST conventions. Species-drug stratified and case-based splitting to prevent data leakage.

api/evaluation.html

DRIAMS Dataset Builder

Build and load DRIAMS-like dataset directories from raw spectra and metadata with year-based subfolders and custom processing handlers.

quickstart.html#building-driams-like-datasets

Composable Filters

SpeciesFilter, DrugFilter, QualityFilter, MetadataFilter combinable with &, |, ~ operators.

api/core.html#filters

Exploratory Plots

PCA, t-SNE, and UMAP scatter plots colored by species, resistance phenotype, or any metadata column.

api/visualization.html#exploratory-plots

CLI & Export

maldiamrkit preprocess, maldiamrkit quality, and maldiamrkit build for batch processing. Export to CSV/TXT.

cli.html

Batch Correction

Multi-site and multi-instrument correction via combatlearn.

quickstart.html#batch-effect-correction

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Quick Example

from maldiamrkit import MaldiSpectrum, MaldiSet
from maldiamrkit.alignment import Warping
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

# Load dataset (with parallel loading)
data = MaldiSet.from_directory(
    "spectra/", "metadata.csv",
    aggregate_by=dict(antibiotics="Ceftriaxone"),
    n_jobs=-1  # Use all cores
)

# Create pipeline (with parallel warping)
pipe = Pipeline([
    ("warp", Warping(method="shift", n_jobs=-1)),
    ("scaler", StandardScaler()),
    ("clf", RandomForestClassifier())
])

# Train and evaluate
pipe.fit(data.X, data.get_y_single())

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