# Single-cell perturbation response prediction

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.

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.

Single-cell perturbation atlases cover only a fraction of possible experimental combinations, and existing generative models map measured control cells to perturbed cells without a reference for how good a prediction can be. GENESIS instead generates single-cell expression distributions directly from covariates such as cell line or donor, drug or cytokine, dose and batch, training a lightweight multilayer perceptron with the energy score and needing no control cells at inference. On 530 repeated Tahoe-100M plate pairs its Pearson Δ was 0.430 versus 0.432 for experimental repeats and its energy distance 0.158 versus 0.154, neither significantly different; top-100 DEG overlap was 0.289 versus 0.299, 3.4% short.

Single-cell perturbation atlases cover only a fraction of possible experimental combinations, and existing generative models map measured control cells to perturbed cells without a reference for how good a prediction can be. GENESIS instead generates single-cell expression distributions directly from covariates such as cell line or donor, drug or cytokine, dose and batch, training a lightweight multilayer perceptron with the energy score and needing no control cells at inference. On 530 repeated Tahoe-100M plate pairs its Pearson Δ was 0.430 versus 0.432 for experimental repeats and its energy distance 0.158 versus 0.154, neither significantly different; top-100 DEG overlap was 0.289 versus 0.299, 3.4% short.