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

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yuqiao Gong, Xin Yuan, Qiong Jiao, Zhangsheng Yu
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引用次数: 0

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.

Abstract Image

利用HERGAST在超大ST切片中揭示精细空间结构和扩增基因表达信号
我们提出了HERGAST,一个用于超大规模和超高分辨率空间转录组学数据的空间结构识别和信号放大系统。为了处理超大规模的空间转录组数据,我们考虑了分而治之的策略,并设计了一个专门用于空间转录组数据分析的分-迭代-征服框架,该框架也可以被其他计算方法所采用,以扩展到超大规模的空间转录组数据分析。为了解决由数据分裂引起的潜在的过度平滑问题,我们构建了一个异构图网络来结合局部和全局空间关系。在模拟中,HERGAST在所有设置中始终优于其他方法,平均调整后的rand指数(ARI)增加了10%以上。在真实数据集中,HERGAST的高精度空间聚类识别出结直肠癌肿瘤中混杂的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的独特空间表达模式。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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