MDWGAN-GP: Data Augmentation for GeneExpression Data based on Multiple Discriminator WGAN-GP

Architecture

Data sources

Download this small data, run the experiment. E_coli:https://github.com/rvinas/adversarial-gene-expression/tree/master/data/E_coli_v4_Build_6

TCGA and GTEx dataset:https://github.com/mskcc/RNAseqDB/tree/master/data/normalized

PPI

HumanNet V3:https://staging2.inetbio.org/humannetv3/download.php

String :https://cn.string-db.org/cgi/download?sessionId=b9sL0FbgoiPo

Homo sapiens reference genome database pipeline GRCh38 GRCh38 (https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/ 106/)

Genes (ENSG), transcripts (ENST) and proteins (ENSP), interconversion

Runing Processes

1.conda create -n your_env_name python=3.8.0

2.conda activate your_env_name

3. Note You do not need to install all packages. Select the required packages.

#conda install --yes --file requirements.txt

python main.py

References

• Thanks to the following authors for their papers and codes.

  [1] VIAS R, ANDRS-TERR H, LI P, et al. Adversarial generation of gene expression data [J]. Bioinformatics, 2022, 38(3): 730-7.

  [2] TRAN N-T, TRAN V-H, NGUYEN N-B, et al. On data augmentation for gan training [J]. IEEE Transactions on Image Processing, 2021, 30: 1882-97.

  [3] WANG J, MA A, CHANG Y, et al. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses [J]. Nature communications, 2021, 12(1): 1-11.