combatlearn 1.2.0
Batch-effect harmonization for machine learning frameworks.
License: MIT
Requires Python: >=3.10
Keywords: machine-learning, harmonization, combat, preprocessing

# **combatlearn**

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<div align="center">
<p><img src="https://raw.githubusercontent.com/EttoreRocchi/combatlearn/main/docs/source/_static/logo.png" alt="combatlearn logo" width="350" /></p>
</div>

**combatlearn** makes the popular _ComBat_ (and _CovBat_) batch-effect correction algorithm available for use into machine learning frameworks. It lets you harmonise high-dimensional data inside a scikit-learn `Pipeline`, so that cross-validation and grid-search automatically take batch structure into account, **without data leakage**.

**Three methods**:
- `method="johnson"` - classic ComBat (Johnson _et al._, 2007)
- `method="fortin"` - neuroComBat (Fortin _et al._, 2018)
- `method="chen"` - CovBat (Chen _et al._, 2022)

## Installation

```bash
pip install combatlearn
```

## Documentation

**Full documentation is available at [combatlearn.readthedocs.io](https://combatlearn.readthedocs.io)**

The documentation includes:
- [Methods Guide](https://combatlearn.readthedocs.io/en/latest/methods.html)
- [API Reference](https://combatlearn.readthedocs.io/en/latest/api.html)

## Quick start

```python
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from combatlearn import ComBat

df = pd.read_csv("data.csv", index_col=0)
X, y = df.drop(columns="y"), df["y"]

batch = pd.read_csv("batch.csv", index_col=0, squeeze=True)
diag = pd.read_csv("diagnosis.csv", index_col=0) # categorical
age = pd.read_csv("age.csv", index_col=0) # continuous

pipe = Pipeline([
    ("combat", ComBat(
        batch=batch,
        discrete_covariates=diag,
        continuous_covariates=age,
        method="fortin", # or "johnson" or "chen"
        parametric=True
    )),
    ("scaler", StandardScaler()),
    ("clf", LogisticRegression())
])

param_grid = {
    "combat__mean_only": [True, False],
    "clf__C": [0.01, 0.1, 1, 10],
}

grid = GridSearchCV(
    estimator=pipe,
    param_grid=param_grid,
    cv=5,
    scoring="roc_auc",
)

grid.fit(X, y)

print("Best parameters:", grid.best_params_)
print(f"Best CV AUROC: {grid.best_score_:.3f}")
```

For a full example of how to use **combatlearn** see the [notebook demo](https://github.com/EttoreRocchi/combatlearn/blob/main/docs/source/demo/combatlearn_demo.ipynb)

## `ComBat` parameters

The following section provides a detailed explanation of all parameters available in the scikit-learn-compatible `ComBat` class. For complete API documentation, see the [API Reference](https://combatlearn.readthedocs.io/en/latest/api/).

### Main Parameters

| Parameter | Type | Default | Description |
| --- | ---  | --- | --- |
| `batch` | array-like or pd.Series | **required** | Vector indicating batch assignment for each sample. This is used to estimate and remove batch effects. |
| `discrete_covariates` | array-like, pd.Series, or pd.DataFrame | `None` | Optional categorical covariates (e.g., sex, site). Only used in `"fortin"` and `"chen"` methods. |
| `continuous_covariates` | array-like, pd.Series or pd.DataFrame | `None` | Optional continuous covariates (e.g., age). Only used in `"fortin"` and `"chen"` methods. |

### Algorithm Options

| Parameter | Type | Default | Description |
| --- | --- | --- | --- |
| `method` | str | `"johnson"` | ComBat method to use: <ul><li>`"johnson"` - Classical ComBat (_Johnson et al. 2007_)</li><li>`"fortin"` - ComBat with covariates (_Fortin et al. 2018_)</li><li>`"chen"` - CovBat, PCA-based correction (_Chen et al. 2022_)</li></ul> |
| `parametric` | bool | `True` | Whether to use the **parametric empirical Bayes** formulation. If `False`, a non-parametric iterative scheme is used. |
| `mean_only` | bool | `False` | If `True`, only the **mean** is corrected, while variances are left unchanged. Useful for preserving variance structure in the data. |
| `reference_batch` | str or `None` | `None` | If specified, acts as a reference batch - other batches will be corrected to match this one. |
| `covbat_cov_thresh` | float, int | `0.9` | For `"chen"` method only: Cumulative variance threshold $]0,1[$ to retain PCs in PCA space (e.g., 0.9 = retain 90% explained variance). If an integer is provided, it represents the number of principal components to use. |
| `eps` | float | `1e-8` | Small jitter value added to variances to prevent divide-by-zero errors during standardization. |


### Batch Effect Correction Visualization

The `plot_transformation` method allows to visualize the **ComBat** transformation effect using dimensionality reduction, showing the before/after comparison of data transformed by `ComBat` using PCA, t-SNE, or UMAP to reduce dimensions for visualization.

For further details see the [API Reference](https://combatlearn.readthedocs.io/en/latest/api/) and the [notebook demo](https://github.com/EttoreRocchi/combatlearn/blob/main/docs/source/demo/combatlearn_demo.ipynb).

### Batch Effect Metrics

The `compute_batch_metrics` method provides quantitative assessment of batch correction quality. It computes metrics including Silhouette coefficient, Davies-Bouldin index, kBET, LISI, and variance ratio for batch effect quantification, as well as k-NN preservation and distance correlation for structure preservation.

For further details see the [API Reference](https://combatlearn.readthedocs.io/en/latest/api/) and the [notebook demo](https://github.com/EttoreRocchi/combatlearn/blob/main/docs/source/demo/combatlearn_demo.ipynb).

## Contributing

Pull requests, bug reports, and feature ideas are welcome: feel free to open a PR!

## Author

[**Ettore Rocchi**](https://github.com/ettorerocchi) @ University of Bologna

[Google Scholar](https://scholar.google.com/citations?user=MKHoGnQAAAAJ) | [Scopus](https://www.scopus.com/authid/detail.uri?authorId=57220152522)


## Citation

If **combatlearn** is useful in your research, please cite the paper introducing this Python package:

> Rocchi, E., Nicitra, E., Calvo, M. et al. Combining mass spectrometry and machine learning models for predicting Klebsiella pneumoniae antimicrobial resistance: a multicenter experience from clinical isolates in Italy. BMC Microbiol (2026). https://doi.org/10.1186/s12866-025-04657-2

```bibtex
@article{Rocchi2026,
  author    = {Rocchi, Ettore and Nicitra, Emanuele and Calvo, Maddalena and Cento, Valeria and Peiretti, Laura and Asif, Zian and Menchinelli, Giulia and Posteraro, Brunella and Sala, Claudia and Colosimo, Claudia and Cricca, Monica and Sambri, Vittorio and Sanguinetti, Maurizio and Castellani, Gastone and Stefani, Stefania},
  title     = {Combining mass spectrometry and machine learning models for predicting Klebsiella pneumoniae antimicrobial resistance: a multicenter experience from clinical isolates in Italy},
  journal   = {BMC Microbiology},
  year      = {2026},
  doi       = {10.1186/s12866-025-04657-2},
  url       = {https://doi.org/10.1186/s12866-025-04657-2}
}
```

## Acknowledgements

This project builds on the excellent work of the ComBat family of harmonisation methods.
Please consider citing the original papers:

- [**ComBat**](https://rdrr.io/bioc/sva/man/ComBat.html) - Johnson WE, Li C, Rabinovic A. _Biostatistics_. 2007. doi: [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistics/kxj037)

- [**neuroCombat**](https://github.com/Jfortin1/neuroCombat) - Fortin JP et al. _Neuroimage_. 2018. doi: [10.1016/j.neuroimage.2017.11.024](https://doi.org/10.1016/j.neuroimage.2017.11.024)

- [**CovBat**](https://github.com/andy1764/CovBat_Harmonization) - Chen AA et al. _Hum Brain Mapp_. 2022. doi: [10.1002/hbm.25688](https://doi.org/10.1002/hbm.25688)