SAINT: Sequence-Aware Integration for Spatial Transcriptomics Multi-View Clustering

Xinwang Liu (National University of Defense Technology) · KE LIANG · Meng Liu (Shandong Jianzhu University) · Suyuan Liu (National University of Defense Technology) · Miaomiao Li (Changsha University) · Zeyu Zhu (National University of Defense Technology) · Lingyuan Meng (National University of Defense Technology) · Renxiang Guan (National University of Defense Technology) · Wanwei Liu (National University of Defense Technology)

attention-based poolingclustering clustering performancecomplementary valuefunctional regionsgene-level embeddingslate fusion modulenucleotide transformernucleotide-derived featuressaintsequence-augmented datasetssequence-level biological priorsspatial representation learningspatial transcriptomicsspatial-expression representationstransferability

Spatial transcriptomics (ST) technologies provide gene expression measurements with spatial resolution, enabling the dissection of tissue structure and function. A fundamental challenge in ST analysis is clustering spatial spots into coherent functional regions. While existing models effectively integrate expression and spatial signals, they largely overlook sequence-level biological priors encoded in the DNA sequences of expressed genes. To bridge this gap, we propose SAINT (Sequence-Aware Integration for Nucleotide-informed Transcriptomics), a unified framework that augments spatial representation learning with nucleotide-derived features. We construct sequence-augmented datasets across 14 tissue sections from three widely used ST benchmarks (DLPFC, HBC, and MBA), retrieving reference DNA sequences for each expressed gene and encoding them using a pretrained Nucleotide Transformer. For each spot, gene-level embeddings are aggregated via expression-weighted and attention-based pooling, then fused with spatial-expression representations through a late fusion module. Extensive experiments demonstrate that SAINT consistently improves clustering performance across multiple datasets. Experiments validate the superiority, effectiveness, sensitivity, and transferability of our framework, confirming the complementary value of incorporating sequence-level priors into spatial transcriptomics clustering.