Single-cell perturbation prediction stops chasing the average: scDisentangle keeps each cell's identity in its counterfactuals
Single-cell perturbation-response prediction has struggled to beat simple mean baselines, and population-average metrics cannot show whether a prediction keeps the source cell's identity. scDisentangle splits each cell's expression into attribute embeddings and a sequence of residual factors extracted one after another, then generates counterfactuals at inference by replacing only the attribute embeddings while holding cell-specific factors fixed. On interferon stimulation, liver infection and unseen combinatorial genetic perturbations it beat the models and mean baselines compared in each benchmark, and in a myocarditis cohort it predicted donor-specific heart-infiltrating CD8+ T cell states from peripheral blood.