Single-Cell Genomics

CellDISECT (Cell DISentangled Experts for Covariate counTerfactuals) is a causal generative model for disentangled single-cell representations and counterfactual perturbation prediction. Preprint on bioRxiv; under review at Nature Methods.

CellDISECT models how cell states change across covariates and perturbations for counterfactual questions in single-cell biology; the manuscript is under review at Nature Methods.

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.