SpatialPEFT: A Parameter-Efficient Fine-Tuning Framework for Spatial Transcriptomics Foundation Models

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ID: 320371
2026
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Abstract
SUMMARY: SpatialPEFT 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). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7167829520 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xin Zou, Xiujuan Lei
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag503
URL
https://doi.org/10.1093/bioinformatics/btag503
Keywords Keywords not found

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