Please download the pbta-gene-expression-kallisto.stranded.rds
data from OpenPBTA to data/
directory. Instructions on how to download the dataset are here.
How to run the code.
- Run
src/create_raw_files.py
file to generate node and edge_list files. - Run
src/main.py
to generate the node embedding. This code is a based on metapath2vec paper.pytorch_geometric
has incorporated the methodology here.metapath2vec
is an approach based on unweighted edges. Our proposed approach generalizes this to weighted networks.
- Please use the
--use_weight
flag to run weighted Deep Walk. Weighted deep walk is slow. Therefore, the ideal approach is to run the unweighted approach first and then fine tune the embedding by using the weighted approach. - Please use the
--help
flag to see the command line arguments available.
- To visualize the generated embedding please use projetor.tensorflow.org.