RT Journal
A1 Liu, Tianyu
A1 Huang, Tinglin
A1 Jin, Wengong
A1 Chu, Tinyi
A1 Ying, Rex
A1 Zhao, Hongyu
T1 spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
JF Genome Research 
JO Genome Research 
YR 2026 
FD February 03 
DO 10.1101/gr.281001.125 
UL http://genome.cshlp.org/content/early/2026/03/20/gr.281001.125.abstract 
AB The analysis of spatial transcriptomic data is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that spRefine yields more robust cell- and spot-level representations after denoising and imputation, substantially improving data integration. In addition, spRefine serves as a strong framework for model pretraining and the discovery of novel biological signals, as highlighted by multiple downstream applications across data sets of varying scales. Notably, spRefine enhances the accuracy of spatial aging clock estimations and uncovers new aging-related relationships associated with key biological processes, such as neuronal function loss, which offers new insights for analyzing aging effect with spatial transcriptomics.