RVAgene

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Recurrent Variational Auto gene encoder

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# RVAgene: modeling gene expression dynamics

### Overview
RVAgene models gene expression dynamics in single-cell or bulk data. Read the paper [here](https://academic.oup.com/bioinformatics/article/37/19/3252/6273572).

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

### Quickstart
 - Jupyter notebook demo of RVAgene [here](https://github.com/maclean-lab/RVAgene/blob/master/demo.ipynb)
 - Jupyter notebook demo of processing bulk Single cell data and running RVAgene [here](https://github.com/maclean-lab/RVAgene/blob/master/single_cell_demo.ipynb)
 - 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 
`single_cell_demo.ipynb` :demo of processing bulk Single cell data and running RVAgene

![alt text](https://github.com/maclean-lab/RVAgene/blob/master/figs/demo.png?raw=true)

### Model and training parameters:
`sequence_length`: length of the input sequence 
`number_of_features` : number of features per timepoint per gene i.e. 1 
`hidden_size`: hidden size of the RNN 
`hidden_layer_depth`: number of layers in RNN (1 is enough) 
`latent_length`: latent vector length 
`batch_size`: number of genes in a single batch. IMPORTANT: last batch will be dropped is it is not a divisor of number of training genes. 
`learning_rate`: the learning rate of the module 
`n_epochs`: Number of iterations/epochs 
`dropout_rate`: The probability of a node being dropped-out 
`optimizer`: ADAM/ SGD optimizer to reduce the loss function 
`loss`: SmoothL1Loss / MSELoss / ReconLoss / any custom loss which inherits from `_Loss` class 
 boolean `cuda`: to be run on GPU or not 
`print_every`: The number of iterations after which loss should be printed for each epoch 
boolean `clip`: Gradient clipping to overcome explosion 
`max_grad_norm`: The grad-norm to be clipped if using clipping 
`dload`: Download directory where models are to be dumped 
`log_file`: File to log training loss , default None i.e. STDOUT

### Datasets
scRNA seq Data used in the paper from : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65525 (Klein et. al. 2015)
Bulk RNA seq Data used in the paper from : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98622 (Liu. et. al. , 2017) 
### 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
<a href="https://github.com/maclean-lab/RVAgene/graphs/contributors">
 <img src="https://contributors-img.web.app/image?repo=maclean-lab/RVAgene" />
</a>

RVAgene

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