scAMod: Generalizing Perturbation Prediction through Adaptive Gene Modules

In silico perturbation prediction offers a scalable view of how cells respond to interventions. Sample variation, destructive sequencing, and dropout make this prediction hard to generalize to the response distributions of unseen contexts. Existing distributional perturbation models emphasize population-level matching, yet jointly capturing distributional shifts and coordinated gene responses within individual cells remains challenging. We propose scAMod, a distributional perturbation prediction model built on adaptive gene modules. We use prior gene interactions and balanced optimal transport reassignment to construct semantically anchored modules, whose gene composition adapts to cellular expression context while retaining shared module semantics. In our method, the Adaptive Module Tokenizer first converts cell-specific transcriptomes into module tokens, which are compressed through a module-centered bottleneck and reconstructed by a gene query decoder. We then use perturbation-conditioned flow matching to generate perturbed cell distributions. Finally, to model condition-specific responses, we introduce Perturbation Module Readout (PMR), which uses the perturbation condition to guide the readout of generated module states into gene-level predictions. On the PBMC donor leave-out benchmark, scAMod reduces DEG energy distance by 24.2% and improves DEG pseudobulk R² by 8.7% over the strongest evaluated baseline. Together with component ablations, these results support the effectiveness of module-centered modeling for generalizable distributional perturbation prediction. Code is available at https://anonymous.4open.science/r/scAMod-A775.