ArticleBioinformatics (Oxford, England)2026
SpatialPEFT: a parameter-efficient fine-tuning framework for spatial transcriptomics foundation models.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
summarySpatialPEFT is a unified parameter-efficient fine-tuning framework that enables the robust adaptation of large spatial transcriptomics foundation models (up to 1.4 billion parameters) on a single 16 GB consumer-grade GPU. By integrating Low-Rank Adaptation (LoRA), gradient checkpointing, and a spatial-aware adapter, it reduces peak VRAM by over 87% while substantially improving downstream spatial annotation accuracy. AVAILABILITY AND IMPLEMENTATION: SpatialPEFT is implemented in Python and released under the MIT license. The source code, documentation, and tutorials are freely available at https://github.com/applerplay/SpatialPEFT, with an archival snapshot deposited at Zenodo (DOI: 10.5281/zenodo.20725321).
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