Metadata-Version: 2.4
Name: maldisuite
Version: 0.1.0
Summary: MaldiSuite: a Python ecosystem for MALDI-TOF spectral processing and analysis in antimicrobial resistance research
Project-URL: Homepage, https://ettorerocchi.github.io/MaldiSuite/
Project-URL: Documentation, https://ettorerocchi.github.io/MaldiSuite/
Project-URL: Repository, https://github.com/EttoreRocchi/MaldiSuite
Project-URL: Bug Tracker, https://github.com/EttoreRocchi/MaldiSuite/issues
Project-URL: MaldiAMRKit, https://github.com/EttoreRocchi/MaldiAMRKit
Project-URL: MaldiBatchKit, https://github.com/EttoreRocchi/MaldiBatchKit
Project-URL: MaldiDeepKit, https://github.com/EttoreRocchi/MaldiDeepKit
Author: Ettore Rocchi
License: MIT License
        
        Copyright (c) 2026 Ettore Rocchi
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: AMR,MALDI-TOF,antimicrobial resistance,batch effect correction,clinical microbiology,deep learning,machine learning,mass spectrometry
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.10
Requires-Dist: maldiamrkit[batch]>=0.14.0
Requires-Dist: maldibatchkit[viz]>=0.1.0
Requires-Dist: maldideepkit>=0.1.0
Description-Content-Type: text/markdown

# MaldiSuite

[![Website](https://img.shields.io/badge/website-online-1e3a8a)](https://ettorerocchi.github.io/MaldiSuite/)
[![PyPI meta-package](https://img.shields.io/pypi/v/maldisuite.svg?color=ea580c)](https://pypi.org/project/maldisuite/)
[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)

<p align="center">
  <img src="assets/maldi_suite_logo.png" alt="MaldiSuite" width="320"/>
</p>

<p align="center">
  <strong>A Python ecosystem for MALDI-TOF spectral processing and analysis in antimicrobial resistance research</strong>
</p>

**MaldiSuite** combines preprocessing, batch effect correction, and deep learning classifiers in a single sklearn-compatible workflow for clinical microbiology and computational biology research.

> **Landing page:** [MaldiSuite](<https://ettorerocchi.github.io/MaldiSuite/>)

---

## What this repository contains

This repository serves two purposes:

1. **Landing page** for the website.
2. **Meta-package**: a minimal PyPI package (`maldisuite`) that installs the three
   packages of the suite with pinned compatible versions.

This repository **does not** contain the source code of the three packages.
Each package lives in its own repository, with its own issues, documentation,
and release cycle.

## The three packages

MaldiAMRKit owns the data model, preprocessing, evaluation, and biomarker / drift modules; MaldiBatchKit re-exports every corrector (ComBat variants, Limma, Harmony, MALDI-specific harmonisers) at the top level; MaldiDeepKit ships scikit-learn-compatible deep classifiers (`MaldiMLPClassifier`, `MaldiCNNClassifier`, `MaldiResNetClassifier`, `MaldiTransformerClassifier`).

| Logo | Package | Purpose | Repository |
|:---:|---|---|---|
| <img src="assets/maldiamrkit_logo.png" alt="MaldiAMRKit" width="80"> | **MaldiAMRKit** | Preprocessing, evaluation, biomarkers, drift monitoring | [MaldiAMRKit](https://github.com/EttoreRocchi/MaldiAMRKit) |
| <img src="assets/maldibatchkit_logo.png" alt="MaldiBatchKit" width="80"> | **MaldiBatchKit** | Batch effect correction for mass spectra | [MaldiBatchKit](https://github.com/EttoreRocchi/MaldiBatchKit) |
| <img src="assets/maldideepkit_logo.png" alt="MaldiDeepKit" width="80"> | **MaldiDeepKit** | Deep learning classifiers for MALDI-TOF spectra | [MaldiDeepKit](https://github.com/EttoreRocchi/MaldiDeepKit) |

## Installation

Install the full suite with a single command:

```bash
pip install maldisuite
```

This installs `maldiamrkit`, `maldibatchkit`, and `maldideepkit` at compatible versions.

Alternatively, install individual packages separately:

```bash
pip install maldiamrkit     # preprocessing, biomarkers, drift
pip install maldibatchkit   # batch effect correction
pip install maldideepkit    # deep learning classifiers
```

## Quick start

```python
from maldiamrkit import MaldiSet
from maldiamrkit.evaluation import CaseGroupedKFold, vme_scorer
from maldibatchkit import BatchAwareWarping, ComBat
from maldideepkit import MaldiMLPClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score

data = MaldiSet.from_directory("driams/", "meta.csv", bin_width=3)
batch = data.meta["batch"]

pipe = Pipeline([
    ("warp",   BatchAwareWarping(batch=batch)),
    ("combat", ComBat(batch=batch, method="fortin")),
    ("clf",    MaldiMLPClassifier(input_dim=data.X.shape[1], random_state=0)),
])

scores = cross_val_score(
    pipe, data.X, data.get_y_single("Drug"),
    scoring=vme_scorer,
    cv=CaseGroupedKFold(n_splits=5),
    groups=data.meta["patient_id"],
)
```

## Citation

Citation will be available soon.

## License

MIT License - see [`LICENSE`](LICENSE) for the full text.

## Acknowledgments

MaldiSuite builds on the work of the mass spectrometry and clinical microbiology communities. In particular, we acknowledge:

- MALDIquant (Gibb & Strimmer, 2012, *Bioinformatics*)
- The Borgwardt Lab and the DRIAMS benchmark (Weis et al., 2022, *Nature Medicine*), which established Python-based MALDI-TOF AMR prediction as a research area