REGINA: Regularized Encoder with Latent Cycle-GAN for In-vitro Neural Cell Perturbation Approximation.

Counterfactual inference from unpaired observational data is a fundamental challenge in machine learning. A prominent example arises in single-cell biology, where predicting cellular responses to genetic perturbations is inherently unpaired, as each cell is destroyed during measurement. Existing approaches address this challenge using heuristic assumptions, such as restricting the input space to highly variable genes or incorporating external biological priors. However, these methods frequently optimize for mean point-estimates, resulting in catastrophic output variance collapse and a failure to capture biological heterogeneity. We introduce REGINA, a fully data-driven framework that formulates perturbation prediction as an unpaired distribution matching problem in a learned latent space. By combining structured latent representation learning with adversarial, cycle-consistent mappings between control and perturbed populations, REGINA captures non-linear, population-level transformations without requiring paired observations. A prompt-based latent conditioning mechanism further enables robust compositional generalization to novel perturbation combinations. Empirically, REGINA maintains competitive local prediction accuracy on key differentially expressed genes while generating biologically realistic single-cell distributions (evaluated via MMD and Wasserstein distance).