AGENTIC-SYSTEMSApril 21, 20261 min read8 views

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

Researchers have introduced SAVE, a generative framework utilizing Gene Block Attention to accurately model single-cell gene expression across diverse conditions. This technology promises significant advancements in virtual cell synthesis and predicting biological responses in unseen scenarios.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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 this https URL

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Source: arXiv cs.AI Recent

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