Sparse VAE

This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''.

Data Sources

The datasets used in this paper were downloaded from the following sites.

• MovieLens 25M (Harper and Konstan, 2015): https://grouplens.org/datasets/movielens/25m/

• PeerRead (Kang et al., 2018): https://github.com/allenai/PeerRead

• Zeisel (Zeisel et al., 2015): https://storage.googleapis.com/linnarsson-lab-www-blobs/blobs/cortex/expression_mRNA_17-Aug-2014.txt

Requirements

The code has been tested on Python 3.7.6 with the following packages:

bottleneck==1.3.2 
conda==4.9.2
nltk==3.6.1 
numpy==1.20.2
pandas==1.2.4
scikit-learn==0.24.1
scipy==1.6.2
torch==1.8.1

The R functions have been tested on R version 4.0.2 with the following packages:

preprocessCore
ggplot2
reshape2
ggpubr
Rtsne

Instructions

You can run the Sparse VAE on the simulated dataset with:

python -m experiment.run_experiment --model=spikeslab

For a description of the list of flags and their default values, run:

python -m experiment.run_experiment --help

References

• D. Kang, W. Ammar, B. Dalvi, M. van Zuylen, S. Kohlmeier, E. Hovy, and R. Schwartz. A dataset of peer reviews (PeerRead): Collection, insights and NLP applications. arXiv preprint arXiv:1804.09635, 2018
• F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872
• Zeisel, A., Muñoz-Manchado, A.B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C. and Rolny, C., 2015. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347(6226), pp.1138-1142.