背景:空间转录组学技术的进展能够同时分析来自同一组织的细胞的基因表达和空间位置。迫切需要用于整合转录组学数据和空间上下文信息的计算工具和方法,以全面探索潜在的结构模式。在这份手稿中,我们提出HyperGCN用于从同一组织分析的基因表达和空间信息的综合分析。HyperGCN支持数据可视化和聚类,并促进下游分析,包括域分割,用于特定结构域结构和GO富集分析的标记基因的表征。
结果:在来自不同组织(包括人类背外侧前额叶皮层,人类阳性乳腺肿瘤,老鼠的大脑,小鼠嗅球组织和Zabrafish黑色素瘤)和技术(包括10倍铯,osmfish,seqFISH+,具有不同空间分辨率的10XXenium和Stereo-seq)。结果表明,HyperGCN实现了优越的聚类性能,并在识别生物学上有意义的空间表达模式的同时产生了良好的域分割效果。这项研究提供了一个灵活的框架来分析具有高几何复杂度的空间转录组学数据。
结论:HyperGCN是一种基于超图诱导图卷积网络的无监督方法,假设存在具有高几何复杂性的不相交组织,并通过超图对细胞的语义关系进行建模,这更好地解决了空间转录组学数据中细胞和噪声水平的高阶相互作用。
BACKGROUND: Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis.
RESULTS: Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity.
CONCLUSIONS: HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.