bpca

bpca#

Tests Integration Tests codecov Documentation

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 release of bpca from PyPI:

pip install bpca

  1. Or 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

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