adamlmaclean / rvagene

Recurrent Variational Auto gene encoder

RVAgene: modeling gene expression dynamics

Overview

RVAgene models gene expression dynamics in single-cell or bulk data. Read the paper here.

Requirements

1. Python 3
2. numpy, matplotlib, pytorch, scikit-learn
3. GPU (optional)

Quickstart

• Jupyter notebook demo of RVAgene here
• Or on the command line
  • python gen_synthetic_data.py <dataset_name> e.g. python gen_synthetic_data.py demosim
  • python train_and_gen.py <dataset_name> e.g. python train_and_gen.py demosim

Contents

data : contains example synthetic gene expression time series data with 6 inherent clusters
rvagene : contains code for recurrent variational autoencoder
train_and_gen.py : code demonstrating training RVAgene, unsupervised clustering on latent space using K-means and Generating new gene expression data by sampling and decoding points from latent space.
gen_synthetic_data.py : code to generate synthetic data with cluster structure as described in the paper.
figs : contains figures generated by the demo code.
demo.ipynb : Demonstration on the whole synthetic data generation, RVAgene training, clustering on latent space and new cluster specific data generation process

Acknowledgments

1. Thanks to open source implementation of recurrent VAE at https://github.com/tejaslodaya/timeseries-clustering-vae
2. Relevant research works as cited in the work.

Contributors