bpca 0.0.1

Bayesian Principal Component Analysis

bpca

Bayesian Principal Component Analysis

Getting started

BPCA follows the standard scikit-learn syntax

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 development version:

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

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