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bioRxiv · 10.64898/2026.03.01.708392

scUnify: A Unified Framework for Zero-shot Inference of Single-Cell Foundation Models

KIM, D.·Jeong, K.·KIM, K.
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Abstract

Single-cell foundation models (scFMs) differ in software requirements and performance across downstream tasks and adaptation strategies, complicating comparison and reuse. We present scUnify, a framework that preserves each backbone's required processing while separating model-specific trainers, downstream tasks, and adaptation strategies as reusable components. Across five scFMs, scUnify reproduced original inference and training workflows, extended model-native tasks with multiple parameter-efficient fine-tuning methods, and demonstrated extensibility by connecting a newly implemented custom trainable task to multiple backbones and adaptation strategies. Together, these capabilities enable researchers to systematically compare these combinations and extend custom tasks across heterogeneous scFMs within a common workflow.

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KIM, D., Jeong, K., KIM, K.. 2026-03-03. scUnify: A Unified Framework for Zero-shot Inference of Single-Cell Foundation Models. https://doi.org/10.64898/2026.03.01.708392

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