GRGNN -- Gene Regulatory Graph Neural Network

About

Gene regulatory graph neural network (GRGNN): an end-to-end approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework.

Preprocessing script is provided, readers can use the data directly or generate the data by downloading the DREAM5 challenge data from https://www.synapse.org/#!Synapse:syn3130840

Requirements

Tested with Python 3.7.3, Pytorch 1.12.0 on Ubuntu 16.04

Required python libraries: gensim and scipy; all python libraries required by pytorch_DGCNN are networkx, tqdm, sklearn etc.

If you want to enable embeddings for link prediction, please install the network embedding software 'node2vec' in "software" (if the included one does not work).

Installation

Type

bash install.sh

to install the required software and libraries. Node2vec and DGCNN are included in software folder.

Usages

1. Unzip DREAM5 data

   cd data/dream

   unzip dreamdata.zip

   cd ../../

2. (Optional): Preprocessing DREAM5 data

   cd preprocessing

   python Preprocessing_DREAM5.py 3

   python Preprocessing_DREAM5.py 4

3. In this program, for simple, data3 means E.coli dataset, data4 means S. cerevisae dataset Train S. cerevisae and test on E. coli with default parameters, Type:

   python Main_inductive_ensemble.py --traindata-name data4 --testdata-name data3

Train S. cerevisae and test on E. coli with hop 1 and embedding, Type:

python Main_inductive_ensemble.py  --traindata-name data4 --testdata-name data3 --hop 1 --use-embedding

Train E. coli and test on S. cerevisae with hop 1 and embedding, Type:

python Main_inductive_ensemble.py  --traindata-name data3 --testdata-name data4 --hop 1 --use-embedding

References:

1. SEAL code: https://github.com/muhanzhang/SEAL
2. Dream data: http://dreamchallenges.org/project/dream-5-network-inference-challenge/