BASIL: Batch-Aware Single-Cell Perturbation Response Modeling with Conditional Distribution Alignment

Predicting transcriptional responses to unseen genetic perturbations can reduce the experimental search space in functional genomics. However, single-cell perturbation screens are complicated by experimental batch variation and by destructive sequencing, which provides no one-to-one correspondence between control and perturbed cells. We introduce BASIL, a batch-aware framework centered on batch-conditioned dual-resolution learning. For each perturbation–batch condition, same-batch control cells define an explicit pseudo-bulk response target, while the same deterministic state-conditional response operator transforms individual control cells and is regularized against the corresponding cell-unpaired perturbed population. Conditional maximum mean discrepancy (MMD) provides this population-level regularization without assuming cell-level pairs. To support out-of-distribution prediction, BASIL combines GenePT features with multi-relational biological priors to construct perturbation representations that remain available for unseen genes and combinations. Across three genome-scale CRISPRi datasets and the Norman CRISPRa benchmark, BASIL achieves strong performance against representative perturbation-prediction baselines, including challenging unseen-condition and gene-holdout combinatorial settings. Ablations further support the roles of condition-level pseudo-bulk supervision, batch-aware referencing, GenePT-based perturbation features, and conditional population regularization. Overall, BASIL provides a practical direct predictor for robust genetic perturbation response modeling under batch variation and cell-unpaired observations. Code is available at https://anonymous.4open.science/r/BASIL-main-624C.