Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST

Yuqiao Gong, Xin Yuan, Qiong Jiao () and Zhangsheng Yu ()
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Yuqiao Gong: Shanghai Jiao Tong University
Xin Yuan: Shanghai Jiao Tong University
Qiong Jiao: Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Zhangsheng Yu: Shanghai Jiao Tong University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large spatial transcriptomics (ST) data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conquer framework especially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential over-smoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulations, HERGAST consistently outperforms other methods across all settings with more than a 10% increase in average adjusted rand index (ARI). In real-world datasets, HERGAST’s high-precision spatial clustering identifies SPP1+ macrophages intermingled within colorectal tumors, while the enhanced gene expression signals reveal unique spatial expression patterns of key genes in breast cancer.

Date: 2025
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DOI: 10.1038/s41467-025-59139-w

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