https://github.com/theislab/disent

Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data
https://github.com/theislab/disent

Last synced: 10 months ago
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Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

• Host: GitHub
• URL: https://github.com/theislab/disent
• Owner: theislab
• License: bsd-3-clause
• Created: 2019-11-07T15:33:05.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-05-08T12:21:52.000Z (about 5 years ago)
• Last Synced: 2024-12-30T12:46:52.467Z (over 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 30.1 MB
• Stars: 6
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

The pdf for the paper can be found [here](https://github.com/theislab/disent/raw/master/paper.pdf).

### Usage

Reproducing results from Jupyter Notebooks:

* JN1_train.ipynb - loads, normalizes and trains the data

* JN2_kang_analysis.ipynb - further analyses the trained model for Kang dataset. It calculates both types of disentanglement scores and also visualizes the latent space as well as gene-feature space plots.

* JN3_dentate_analysis.ipynb - further analyses the trained model for Dentate Gyrus dataset.

* JN4_Out of Distribution Prediction.ipynb - implements OOD Prediction and analyses the results further

* Model Comparisons folder provides the notebooks to reproduce the model architecture comparison plots.

### Misc

* The **environment.yml** file specifies the conda environment in which the project was run.

* Data can be accessed from [here](https://drive.google.com/open?id=1ywXG0K-_nuqnFL8u3x4klsPNOrEdeg27)

## Reference

please consider citing

```

@inproceedings{lotfollahi2020out,

  title={Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data},

  author={Lotfollahi, Mohammad and Dony, Leander and Agarwala, Harshita and Theis, Fabian},

  booktitle={ICML 2020 Workshop on Computational Biology (WCB) Proceedings Paper},

  volume={37},

  year={2020}

}