Metadata-Version: 2.4
Name: bpca
Version: 0.0.1
Summary: Bayesian Principal Component Analysis
Project-URL: Documentation, https://bpca.readthedocs.io/
Project-URL: Homepage, https://github.com/lucas-diedrich/bpca
Project-URL: Source, https://github.com/lucas-diedrich/bpca
Author: lucas-diedrich
Maintainer-email: lucas-diedrich <diedrich@biochem.mpg.de>
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        BPCA - Bayesian Principal Component Analysis in Python
        Copyright (C) 2025 Lucas Diedrich
        
        Based on the reference BPCA implementation by Stacklies, W., Redestig, H., Scholz, M., Walther, D. and Selbig, J., 2007
        (GNU-Public License v2)
        
        This program is free software; you can redistribute it and/or modify
        it under the terms of the GNU General Public License as published by
        the Free Software Foundation; either version 2 of the License, or
        (at your option) any later version.
        
        This program is distributed in the hope that it will be useful,
        but WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
        GNU General Public License for more details.
        
        You should have received a copy of the GNU General Public License
        along with this program; if not, write to the Free Software
        Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
        
        This General Public License does not permit incorporating your program into
        proprietary programs. If your program is a subroutine library, you may
        consider it more useful to permit linking proprietary applications with the
        library. If this is what you want to do, use the GNU Library General
        Public License instead of this License.
License-File: LICENSE
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: >=3.11
Requires-Dist: anndata
Requires-Dist: session-info2
Description-Content-Type: text/markdown

# bpca

[![Tests][badge-tests]][tests]
[![Integration Tests][badge-integration-tests]][integration-tests]
[![codecov](https://codecov.io/gh/lucas-diedrich/bpca/graph/badge.svg?token=SIA7YSWCET)](https://codecov.io/gh/lucas-diedrich/bpca)
[![Documentation][badge-docs]][documentation]

[badge-tests]: https://img.shields.io/github/actions/workflow/status/lucas-diedrich/bpca/test.yaml?branch=main
[badge-integration-tests]: https://github.com/lucas-diedrich/bpca/actions/workflows/integration-test.yaml/badge.svg
[badge-docs]: https://img.shields.io/readthedocs/bpca

Bayesian Principal Component Analysis

## Getting started
BPCA follows the standard scikit-learn syntax

```python
from bpca import BPCA
from sklearn.datasets import load_iris

iris_dataset = load_iris()
X = iris_dataset["data"]

# Fit + Extract information
bpca = BPCA(n_components=2)
usage = bpca.fit_transform(X)
loadings = bpca.components_
explained_variance_ratio = bpca.explained_variance_ratio_
```

Please refer to the [documentation][], in particular, the [API documentation][].


## Installation

You need to have Python 3.11 or newer installed on your system.

<!--
1) Install the latest release of `bpca` from [PyPI][]:

```bash
pip install bpca
```
-->

1. Install the latest development version:

```bash
pip install git+https://github.com/lucas-diedrich/bpca.git@main
```

## Release notes

See the [Release Notes][].

## Contact

For questions and help requests, you can reach out in the [scverse discourse][].
If you found a bug, please use the [issue tracker][].

## Citation

This package implements the algorithm proposed by Oba, 2003 and is built on the reference implementation by Stacklies et al, 2008 **Please cite the original authors**

> Oba, S. et al. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19, 2088 - 2096 (2003).

> Stacklies, W., Redestig, H., Scholz, M., Walther, D. & Selbig, J. pcaMethods—a bioconductor package providing PCA methods for incomplete data. Bioinformatics 23, 1164 - 1167 (2007).

Generative model proposed by Bishop, 1998:
> Bishop, C. Bayesian PCA. in Advances in Neural Information Processing Systems vol. 11 (MIT Press, 1998).

If you find this implementation useful, consider giving it a star on GitHub and [cite this implementation](https://github.com/lucas-diedrich/bpca/blob/main/CITATION.cff)

> Diedrich, L. bpca [Computer software]. https://github.com/lucas-diedrich/bpca.git

[uv]: https://github.com/astral-sh/uv
[scverse discourse]: https://discourse.scverse.org/
[issue tracker]: https://github.com/lucas-diedrich/bpca/issues
[tests]: https://github.com/lucas-diedrich/bpca/actions/workflows/test.yaml
[integration-tests]: https://github.com/lucas-diedrich/bpca/actions/workflows/integration-test.yaml
[documentation]: https://bpca.readthedocs.io
[Release Notes]: https://github.com/lucas-diedrich/bpca/releases
[api documentation]: https://bpca.readthedocs.io/en/latest/api.html
[pypi]: https://pypi.org/project/bpca
