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scDRP documentation
===================

This is a package to analyze single-cell perturbation data. 
It learns the latent factors that generates the observed gene profiles via sparsity-regularized disentangled VAE, 
which disentangles the latent space into perturbation-dependent and perturbation-invariant subspaces. 
Leveraging the disentangled latent factors, 
we can estimate individual treatment effects (ITE) and generate counterfactual samples via soft-conditional optimal transport. 
This is based on the idea that the effect of perturbation on those perturbation-dependent latent factors should be rank-preserved when conditioning on confounders.


.. toctree::
   :maxdepth: 2
   :caption: Contents:
   
   modules