Single-Cell Genomics

CellDISECT (Cell DISentangled Experts for Covariate counTerfactuals) is a powerful causal generative model that enhances single-cell analysis by disentangling variations, making counterfactual predictions, and achieving flexible fairness.

CellDISECT models how cell states change across covariates and perturbations, making it easier to ask counterfactual questions in single-cell biology.

CPA is a deep generative framework to learn effects of perturbations at the single-cell level. It performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.