Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks

This repository contains the Python implementation for GraphSCI. Further details about GraphSCI can be found in our paper:

Jiahua Rao, Xiang Zhou, Yutong Lu, Huiying Zhao, Yuedong Yang. Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks

Requirements

=================

• TensorFlow (1.0 or later)
• python 3.6
• scikit-learn
• scipy
• scanpy

Overview

Tutorial

see our manuscript and tutorial for more details.

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Preprocess

Preprocess the gene expression matrix and construct the input of gene-to-gene relationships

Training

python train.py --adata ./splatter_data/counts_simulated_dataset3_3000x3000_dropout0.30.h5ad --adj  --learning_rate 1e-3 --epochs 100 --hidden1 32 --hidden2 64 --batch_size 50 --dropout 0.1

Evaluation

t-SNE

Clustermap

Run the demo

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python train.py

Citation

If you want to use our codes and datasets in your research, please cite our paper:

@article{rao2020imputing,
  title={Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks},
  author={Rao, Jiahua and Zhou, Xiang and Lu, Yutong and Zhao, Huiying and Yang, Yuedong},
  journal={biorxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}