Separating Response Ranking and Classification for Bulk-to-Single-Cell Drug Response Prediction

Single-cell drug response prediction helps characterize heterogeneous responses to treatment, providing a finer-grained view of drug sensitivity than population-level measurements. Existing methods transfer drug response supervision from bulk data to single-cell data through domain adaptation. However, transferring drug response information requires both preserving the relative sensitivity of cells and determining an appropriate classification boundary without target response labels. We propose scRA-DRP, a framework that separately adapts response ranking and a classification reference. Starting from a shared domain-adversarial initialization, the ranking branch aligns target features with local source regions constructed using drug response labels. The classification branch uses weighted domain adaptation to adapt the classification rule to the target population and provide a reference number of sensitive cells. This count determines how many top-ranked cells are assigned to the sensitive class, preserving the response ordering. Experiments across 18 drug–single-cell dataset pairs show higher mean performance in both response ranking and classification compared with the evaluated baselines. The ablation studies further support the complementary roles of regional alignment and the separately adapted classification reference.