SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention
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 ...
| Authors | Jiahao Li, Jiayi Dong, Peng Ye et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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