https://github.com/danko-lab/clipnet_paper
Scripts and notebooks to reproduce figures used in the CLIPNET paper
https://github.com/danko-lab/clipnet_paper
Last synced: 3 months ago
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Scripts and notebooks to reproduce figures used in the CLIPNET paper
- Host: GitHub
- URL: https://github.com/danko-lab/clipnet_paper
- Owner: Danko-Lab
- License: mit
- Created: 2024-02-03T23:04:02.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-13T05:59:10.000Z (about 1 year ago)
- Last Synced: 2025-08-16T23:19:37.779Z (10 months ago)
- Language: Jupyter Notebook
- Size: 98 MB
- Stars: 2
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CLIPNET paper
Scripts and notebooks to reproduce figures in the CLIPNET paper (preprint [here](https://www.biorxiv.org/content/10.1101/2024.03.13.583868)). Code for generating new predictions and feature interpretions with CLIPNET is available in a [separate repo](https://github.com/Danko-Lab/clipnet/).
## Install dependencies
Dependencies used to produce plots are listed in `requirements.txt`. To install them, run:
```bash
# Either use conda/mamba or python venv to isolate installation
mamba create -n clipnet-paper -c conda-forge -c bioconda python=3.9
mamba activate clipnet-paper
pip install -r requirements.txt
```
## Code/Notebooks
Jupyter notebooks to reproduce figures used in the paper are organized by analysis type. While we provide some scripts/instructions to calculate predictions and feature interpretations (we haven't written many of these up, so please just raise an issue if you want clarification on something), many of these calculations will be very time/resource-intensive. As a result, for the purposes of reproducing the figures in the paper, we assume precalculated predictions and feature interpretations, which we have archived on Zenodo.
## Downloads
Training data and data to reproduce figures are available at [10.5281/zenodo.10597358](https://zenodo.org/doi/10.5281/zenodo.10597358). To preserve directory structure, we packaged the data into tar files, divided roughly by figure/analysis. For a description of these files, see the `DOWNLOADS_README.md` file.