SURF: A Self-Supervised Deep Learning Method for Reference-Free Deconvolution in Spatial Transcriptomics.

ABSTRACT: Spatial transcriptomics has revolutionized tissue biology by enabling spatially resolved gene expression profiling. Nonetheless, current spot-level spatial transcriptomic technologies consolidate signals from multiple cells, complicating cellular-level analysis. Moreover, matched single-cell references required by reference-based deconvolution methods are frequently unavailable. To overcome these limitations, we present SURF, a reference-free deconvolution tool that integrates high-dimensional gene data analysis with self-supervised deep learning to effectively model nonlinear gene interactions and leverage spot relationships. Benchmarking on both synthetic and real datasets shows that SURF consistently outperforms existing reference-free methods and exceeds reference-based approaches when

SUBMITTER: Liang S 

PROVIDER: S-EPMC12631812 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature