Liu Tianyu, Huang Tinglin, Jin Wengong, Chu Tinyi, Ying Rex, Zhao Hongyu
Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, 06511, CT, USA.
Department of Biostatistics, Yale University, New Haven, 06511, CT, USA.
bioRxiv. 2025 Jul 7:2025.04.22.649977. doi: 10.1101/2025.04.22.649977.
The analysis of spatial transcriptomics 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 , 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 pre-training and the discovery of novel biological signals, as highlighted by multiple downstream applications across datasets of varying scales. Notably, spRefine enhances the accuracy of spatial ageing clock estimations and uncovers new aging-related relationships associated with key biological processes, such as neuronal function loss, which offers new insights for analyzing ageing effect with spatial transcriptomics.
空间转录组学的分析受到高噪声水平和基因测量缺失的阻碍，与传统单细胞数据相比，空间数据成本更高，这进一步加剧了这些挑战。为了克服这一挑战，我们引入了spRefine，这是一个深度学习框架，它利用基因组语言模型对空间转录组数据进行联合去噪和插补。我们的结果表明，spRefine在去噪和插补后产生了更稳健的细胞和斑点水平表征，显著改善了数据整合。此外，spRefine作为模型预训练和发现新生物信号的强大框架，不同规模数据集的多个下游应用突出了这一点。值得注意的是，spRefine提高了空间衰老时钟估计的准确性，并揭示了与关键生物过程（如神经元功能丧失）相关的新的衰老相关关系，这为利用空间转录组学分析衰老效应提供了新的见解。
