A deep generative framework for disentangling known and unknown attributes in single-cell data.
Project description
biolord - biological representation disentanglement
A deep generative framework for disentangling known and unknown attributes in single-cell data.
We assume partial supervision over known attributes (categorical or ordered) along with single-cell measurements. Given the partial supervision biolord finds a decomposed latent space, and provides a generative model to obtain single-cell measurements for different cell states.
For more details read the preprint.
Getting started
Please refer to the documentation.
Installation
There are several alternative options to install biolord:
-
Install the latest release of biolord from PyPI:
pip install biolord
-
Install the latest development version:
pip install git+https://github.com/nitzanlab/biolord.git@main
Release notes
See the changelog.
Contact
Feel free to contact us by mail. If you found a bug, please use the issue tracker.
Citation
@article{piran2023biological,
title={Biological representation disentanglement of single-cell data},
author={Piran, Zoe and Cohen, Niv and Hoshen, Yedid and Nitzan, Mor},
journal={bioRxiv},
pages={2023--03},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
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