HiGCN: a hierarchical graph convolution network for representation learning of gene expression data
CONTACT: For questions or comments about the code please contact: kwtan0909@qq.com / cskwtan93@mail.scut.edu.cn / sbdong@scut.edu.cn
- NumPy (1.17.4) http://www.numpy.org/
- Pandas (0.25.3) https://pandas.pydata.org/
- Scikit-learn (0.22) http://scikit-learn.org/stable/install.html
- pytorch (1.3.1) https://pytorch.org/
- snfpy (0.2.1) https://github.com/rmarkello/snfpy
train.py: main script for classification
model.py: contains PyTorch model definitions for HiGCN
sparsegcn_layer.py: contains PyTorch definitions for Sparse GCN
featureweightedgcn_layer.py: contains PyTorch definitions for Feature-weighted GCN
until.py: contains definitions for Sample Similarity Graph construction, C-index, data loader, etc
cox/train.py: main script for Cox.
cox/utils.py: contains definitions for Sample Similarity Graph construction, figure plotting, etc
simulation/crimmix: (SimData1). It's generated from Crimmix (ten datasets in total)
simulation/crimmix/omic1: sample-feature matrix of the first dataset
simulation/crimmix/omic1_gene_A: gene interaction graph of the first dataset
simulation/crimmix/omic1_positive: true signals of the first dataset
simulation/gedfn: (SimData2). It's generated based on GEDFN (ten datasets in total)
simulation/gedfn/gedfn1_x: sample-feature matrix of the first dataset
simulation/gedfn/gedfn1_y: labels of the first dataset
simulation/gedfn/gedfn1_gene_A: gene interaction graph of the first dataset
simulation/gedfn/gedfn1_position: true signals of the first dataset
By default, you can directly run python ./train.py to get the result of HiGCN on SimData1. The train_portions in train.py is used to set Training Percentage (Default: 1%).
If you want to classify other dataset (i.e., SimData2). You can directly comment out the loading code of SimData1 and add code to load your own dataset. (The code to load SimData2 can be finded in train.py)
Run python ./cox/train.py. The survival analysis data we used is too large to upload. If you need them, please contact us by email or you can download Kidney and Pancancer data from:
https://xenabrowser.net/datapages/?hub=https://tcga.xenahubs.net:443
This code is inspired by GEDFN and AffinityNet, code base structure was inspired by GCN