Yuze Sun

Cellular responses to perturbation depend on several experimental variables at once, including perturbation identity, cell type, donor and dose, so each measured population occupies one coordinate of a multidimensional condition space. Earlier methods conditioned on explicit representations of those variable values and struggled when a value was absent from training. RELAY instead infers a target from measured control–perturbed pairs that share some variables with it, receiving only which variables are shared and never their values; across chemical, genetic and cytokine perturbation data it beats STACK, scGen and TabPFN on unseen variable values and supervised methods on unseen combinations, reaching 0.495 PDCorr on PBMC-3D against 0.225 for the strongest supervised baseline, STATE.

Cellular responses to perturbation depend on several experimental variables at once, including perturbation identity, cell type, donor and dose, so each measured population occupies one coordinate of a multidimensional condition space. Earlier methods conditioned on explicit representations of those variable values and struggled when a value was absent from training. RELAY instead infers a target from measured control–perturbed pairs that share some variables with it, receiving only which variables are shared and never their values; across chemical, genetic and cytokine perturbation data it beats STACK, scGen and TabPFN on unseen variable values and supervised methods on unseen combinations, reaching 0.495 PDCorr on PBMC-3D against 0.225 for the strongest supervised baseline, STATE.