- Pre train steps:
- Use any latent space representation model (such as pca) to obtain z_pca
- Generate graph based on z_pca space and project it to batch space and group space
- GRASP training :
- input : raw data and two graphs in batch space and group space
- model :
- Encode raw data to z_mix
- Capture batch effect z_batch using GNN(z_mix,batch space graph)
- Capture group effect z_group using GNN(z_mix,group space graph)
- Isolate z_unknown from FCN (z_mix, [z_batch + z_group])
- Reconstruct data using z_batch + z_group + z_unknown
- Discriminator learning for batch and group effect