SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers

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ID: 319469
2026
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Abstract
Abstract Motivation Spatial transcriptomics (ST) enables the precise mapping of gene expression within tissue architecture, however its application is often limited by low spatial resolution and sparse sampling. While existing deep learning methods leverage histology images, spatial coordinates, or low-resolution expression data to predict high-density profiles, these methods are limited in either capturing the intrinsic constraints between histological context and spatial topology or ignoring the complex local neighborhood relationships between spots. Result To address these limitations, we propose SpaBiT, a multimodal framework designed to enhance ST resolution via a bidirectional attention mechanism. At its core, SpaBiT employs a bidirectional cross-attention module to facilitate precise information exchange between image features and neighborhood-aware representations learned via a graph attention network. This design explicitly models the synergistic constraints between local morphology and spatial graph topology, yielding high-fidelity, high-density gene expression maps. SpaBiT exhibits competitive performance in reconstructing complex spatial gene expression, outperforming the benchmark models utilized in this study across various quantitative metrics, providing a robust tool for deciphering complex tissue microenvironments. Availability The source code and datasets are available at https://github.com/wenwenmin/SpaBiT. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7167082925 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors X W Liu, Ao Li, Wenwen Min
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag443
URL
https://doi.org/10.1093/bioinformatics/btag443
Keywords Keywords not found

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