SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention

Abstract

Modeling single-cell gene expression across diverse biological and technical conditions is essential for understanding cellular states and simulating unobserved scenarios. We present SAVE, a unified generative framework for multi-condition single-cell modeling. SAVE combines a variational autoencode with conditional Transformer, enhanced by gene block attention and a novel conditional mask modeling strategy. This design enables effective modeling of biological structure under multi-condition effects and supports generalization to unseen condition combinations. We evaluate SAVE on a range of benchmarks, including conditional generation, batch effect correction, and perturbation prediction. SAVE consistently outperforms state-of-the-art methods in generation fidelity and extrapolative gener-alization, especially in low-resource or combinatorially held-out settings. Overall, SAVE offers a scalable and generalizable solution for modeling complex single-cell data, with broad utility in virtual cell synthesis and biological discovery.