Beyond Cell Realism: Recovering Disease Signals with Bulk-to-Single-Cell Generation

Bulk RNA-seq provides transcriptomic data for large patient cohorts, but averaging across cell populations can obscure disease-associated expression changes within specific cell types. Single-cell profiling can resolve these changes, yet its cost limits cohort-scale measurements, and many existing bulk cohorts lack matched single-cell data. Generating cell-level expression profiles from bulk could extend such analyses to these cohorts. The central question is whether the generated profiles preserve the differences between patient groups that motivate disease analysis. We evaluate this through the recovery of differentially expressed genes (DEGs): genes showing significant expression differences between patient groups within a specified cell type. For example, our breast cancer evaluation compares cancer epithelial expression between triple-negative and estrogen-receptor-positive tumors. Because each cohort poses a different disease question, the relevant patient groups, cell types, and gene-level evidence must be established for each evaluation. We therefore propose an agentic benchmarking workflow that translates a cohort-specific disease question into a traceable DEG-recovery evaluation. It audits patient pairings and disease contrasts, retrieves supporting evidence from publications and supplements, and assembles a reference of disease-relevant DEGs detected in measured cell-type profiles but missed by matched bulk analysis. This design allows the evaluation to be tailored to different cohorts and biological questions while retaining a common patient-level scoring procedure. Furthermore, to reflect the practical use of existing bulk cohorts, models learn from external reference cells and receive only measured bulk from new patients, without target-cell training or calibration. Under this protocol, the tested generative baselines score zero on important DEG recovery in breast cancer and T-cell leukemia. We address this recovery gap with a patient-conditioned generator that combines bulk-derived context with a source-fitted expression shift. Across three paired cohorts comprising 104 participants, our method recovers 25 of 91 important cohort–gene references, compared with nine for a direct adaptation of scSemiProfiler. It recovers 10 of 26 references in breast cancer and six of 11 in T-cell leukemia, while tying that comparator at nine of 54 in sepsis. Together, the benchmark and generator connect bulk-to-single-cell generation to the disease signals needed for patient-cohort analysis.