Implementation of the gradient-based t-SNE attribution method described in our GLBIO oral presentation: 'Towards Computing Attributions for Dimensionality Reduction Techniques'
Project description
Interpretable t-SNE
interpretable_tsne
is an implementation of our gradient-based attributiont technique described in our GLBIO oral presentation: 'Towards Computing Attributions for Dimensionality Reduction Techniques'.
To replicate the experiments we performed in our presentation, go to: MattScicluna/interpretable_tsne_experiment
Synthetic data experiment demonstrating that our t-SNE attributions can identify the ground truth significant features. See our pre-print for more details.
Installation
interpretable_tsne
requires Python 3.8 or higher to run. It is only available on Linux operating systems.
PyPi
This code can be installed via pip using the following command:
pip install interpretable_tsne
Source
You can install interpretable_tsne
from source. Just clone this repository and run the following line in the root directory:
pip install .
Run the unittests
python -m unittest -v tests/*.py
Citation
If you use interpretable_tsne
in your work, please cite us:
@article {Scicluna2023.05.12.540592,
author = {Matthew Crispin Scicluna and Jean-Christophe Grenier and Raphael Poujol and Sebastien Lemieux and Julie Hussin},
title = {Towards Computing Attributions for Dimensionality Reduction Techniques},
elocation-id = {2023.05.12.540592},
year = {2023},
doi = {10.1101/2023.05.12.540592},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2023/05/14/2023.05.12.540592},
eprint = {https://www.biorxiv.org/content/early/2023/05/14/2023.05.12.540592.full.pdf},
journal = {bioRxiv}
}
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