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

The pdf for the paper can be found here.

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

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}
}