scCody distinguishes aging-aligned from divergent disease-associated cellular remodeling

Biological aging clocks summarize molecular changes as a single age estimate, but cannot distinguish disease-associated changes that follow healthy aging from those that deviate from it. Here we introduce scCody, a generative framework that learns age-dependent cellular population dynamics from cross-sectional single-cell cohorts using population-level optimal transport and endpoint-consistency regularization. scCody accurately reconstructed cellular populations at unseen ages, recovered age-associated transcriptional changes more effectively than existing generative dynamics models, and enabled donor-age inference without direct age-regression training. Using the learned healthy aging reference, scCody decomposed disease-associated cellular displacement into aging-aligned and orthogonal components. In Alzheimer's disease, selected neuronal populations showed substantial aging-aligned remodeling alongside shared orthogonal changes associated with established pathology signatures. In contrast, COVID-19 exhibited weaker aging alignment and more pronounced orthogonal immune remodeling. Further decomposition revealed patient-level interferon variation associated with symptom duration and increased transcriptional heterogeneity in CD16 monocytes. This framework provides an extension of biological age assessment beyond a single age estimate, distinguishing cellular changes that resemble healthy aging from those that depart from it.