Gong Yuqiao, Yuan Xin, Jiao Qiong, Yu Zhangsheng
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
Nat Commun. 2025 Apr 28;16(1):3977. doi: 10.1038/s41467-025-59139-w.
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.
我们提出了HERGAST，这是一种用于超大规模和超高分辨率空间转录组学数据中空间结构识别和信号放大的系统。为了处理超大规模的空间转录组学（ST）数据，我们考虑了分而治之策略，并设计了一种专门用于空间转录组学数据分析的分治迭代框架，其他计算方法也可以采用该框架来扩展到超大规模ST数据分析。为了解决数据分割可能产生的过度平滑问题，我们构建了一个异构图网络，以纳入局部和全局空间关系。在模拟中，HERGAST在所有设置下均始终优于其他方法，平均调整兰德指数（ARI）提高了10%以上。在真实世界数据集中，HERGAST的高精度空间聚类识别出结直肠癌中混杂的SPP1+巨噬细胞，而增强的基因表达信号揭示了乳腺癌中关键基因独特的空间表达模式。
