# Disentangled Representation Learning

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.

The work introduces DSReg (Dependency-Sparsity Regularization), which, on top of the linear-identifiability premise provided by LeJEPA, proves that under Structural Diversity—distinct dependency footprints of latents on observations—each world latent can be recovered up to signed permutation with no reconstruction, no decoder, and no labels; the method applies post hoc to any linearly identified representation, reuses trained checkpoints, and across synthetic regimes, world-model probes, pixel-trained encoders, and external renderers preserves dense prediction while improving individual-latent recovery and downstream sparse control, editing, and few-shot readout.

The work introduces DSReg (Dependency-Sparsity Regularization), which, on top of the linear-identifiability premise provided by LeJEPA, proves that under Structural Diversity—distinct dependency footprints of latents on observations—each world latent can be recovered up to signed permutation with no reconstruction, no decoder, and no labels; the method applies post hoc to any linearly identified representation, reuses trained checkpoints, and across synthetic regimes, world-model probes, pixel-trained encoders, and external renderers preserves dense prediction while improving individual-latent recovery and downstream sparse control, editing, and few-shot readout.