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

• URL: http://arxiv.org/abs/2604.16776v1
• Date: Sat, 18 Apr 2026 01:45:16 GMT
• Title: SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention
• Abstract summary: SAVE is a unified generative framework based on conditional Transformers for single-cell modeling.<n>We evaluate SAVE on a range of benchmarks including conditional generation, batch effect correction, and perturbation prediction.<n>Save offers a scalable and generalizable solution for modeling complex single-cell data.
• Score: 10.404186274295222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling single-cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high-level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framework based on conditional Transformers for multi-condition single-cell modeling. SAVE leverages a coarse-grained representation by grouping semantically related genes into blocks, capturing higher-order dependencies among gene modules. A Flow Matching mechanism and condition-masking strategy further enhance flexible simulation and enable 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 generalization, 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 interpretation. Our code is publicly available at https://github.com/fdu-wangfeilab/sc-save

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