RELAY: Relational In-Context Learning for Cellular Perturbation Prediction

Cellular responses to perturbations vary across multiple experimental variables, such as perturbation identity, cell type, donor, and dose, defining a multidimensional condition space in which each measured population occupies one coordinate. Existing methods predict unmeasured conditions using explicit representations of these variable values, generalizing unseen values therefore depends on the availability and quality of such representations. We present RELAY, a relational in-context model that predicts unmeasured coordinates from measured responses at related coordinates. For each target, RELAY uses measured control–perturbed pairs from conditions that share one or more experimental variables with the target, such as the same perturbation in another cell type. The model receives only which variables are shared, not their values, and uses these demonstrations to predict the target response. We evaluate RELAY on chemical, genetic, and cytokine perturbation datasets under two settings: unseen combinations, where test values are individually observed during training but not in their target combinations, and unseen variable values, where test values are introduced only through context at inference. RELAY outperforms existing in-context methods on unseen variable values and supervised perturbation-prediction methods on unseen combinations.