Source Information for Few-Shot Prediction of Donor-Dependent Regulatory Responses

Perturbation effects on a transcriptional program vary across donors. Can a few measured responses predict differences between other regulators' effects, and what source data enable this? Such predictions could extend regulatory comparisons within each donor. We model reusable variation through a background-specific transformation of shared perturbation representations. Our response model LHB learns these representations and a nonlinear decoder from source donors, then fits the transformation using target RNA responses. We show that identifiable perturbation relationships can leave nonlinear query responses undetermined, so repeating observed conditions cannot resolve this ambiguity. Across ten induced pluripotent stem cell donors, LHB with ten measured perturbations reduced mean squared error in differences between program effects by 42.0 percent relative to a baseline without target perturbation responses. Cross-line transfer supported partial reuse. Numerical experiments linked source additions to improved prediction after refitting.