Author
Listed:
- Yuqiao Gong
(Shanghai Jiao Tong University)
- Xin Yuan
(Shanghai Jiao Tong University
Shanghai Jiao Tong University
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
Shanghai Jiao Tong University
Shanghai Jiao Tong University
Shanghai Jiao Tong University School of Medicine)
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.
Suggested Citation
Yuqiao Gong & Xin Yuan & Qiong Jiao & Zhangsheng Yu, 2025.
"Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST,"
Nature Communications, Nature, vol. 16(1), pages 1-14, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59139-w
DOI: 10.1038/s41467-025-59139-w
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