interpretable-tsne 0.0.1

Implementation of the gradient-based t-SNE sttribution method described in our GLBIO oral presentation: 'Towards Computing Attributions for Dimensionality Reduction Techniques'

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.

https://github.com/MattScicluna/interpretable_tsne/assets/19255250/789c075c-44fd-48ef-9264-e7dca60a59ce

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Installation

interpretable_tsne requires Python 3.8 or higher to run. It is only available on Linux operating systems.

TestPyPi

This code can be installed via TestPyPi using the following command:

pip install -i https://test.pypi.org/simple/ 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

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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}
    }