Interpretable Modeling of Single-cell perturbation Responses to Novel
  Drugs Using Cycle Consistence Learning
Wei Huang Aichun Zhu Hui Liu
Phenotype-based screening has attracted much attention for identifying
cell-active compounds. Transcriptional and proteomic profiles of cell
population or single cells are informative phenotypic measures of cellular
responses to perturbations. In this paper, we proposed a deep learning
framework based on encoder-decoder architecture that maps the initial cellular
states to a latent space, in which we assume the effects of drug perturbation
on cellular states follow linear additivity. Next, we introduced the cycle
consistency constraints to enforce that initial cellular state subjected to
drug perturbations would produce the perturbed cellular responses, and,
conversely, removal of drug perturbation from the perturbed cellular states
would restore the initial cellular states. The cycle consistency constraints
and linear modeling in latent space enable to learn interpretable and
transferable drug perturbation representations, so that our model can predict
cellular response to unseen drugs. We validated our model on three different
types of datasets, including bulk transcriptional responses, bulk proteomic
responses, and single-cell transcriptional responses to drug perturbations. The
experimental results show that our model achieves better performance than
existing state-of-the-art methods.