Nanxi Guo, Juan Vargas, Samantha Reynoso, Douglas Fritz, Revanth Krishna, Chuangqi Wang, Fan Zhang
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Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. 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引用次数: 0
摘要
动机最近的空间转录组学(ST)技术实现了基因表达模式和空间信息的特征描述,推动了我们对病变组织内细胞系的了解。针对 ST 数据提出了几种分析方法,但有效利用空间信息来揭示基因表达的共同变异仍是一项挑战:我们介绍了一种空间转录组多维表征学习方法 STew,它以可扩展的方式联合分析空间信息和基因表达,然后采用数据驱动的统计框架来衡量模型的拟合度。通过使用带有真实人工注释的人类背外侧前额叶皮层和小鼠主嗅球数据进行基准测试,STew 在聚类准确性和已识别空间域的连续性方面都比其他方法表现出色。STew 还具有很强的鲁棒性,能产生对模型参数(包括稀疏性约束)不敏感的一致结果。接下来,我们将 STew 应用于从 10× Visium、Slide-seqV2 和 10× Xenium(包括单细胞和多细胞分辨率 ST 技术)获得的各种 ST 数据,这些数据揭示了具有空间信息的细胞类型群和具有生物学意义的轴。特别是,我们利用银屑病皮肤的 ST 数据确定了促炎性成纤维细胞的空间生态位。此外,STew 与空间位置的数量几乎成线性关系,保证了其适用于具有数千个空间位置的数据集,从而捕捉复杂组织中与疾病相关的壁龛:源代码和R软件工具STew可从github.com/fanzhanglab/STew获取。
Uncover spatially informed variations for single-cell spatial transcriptomics with STew.
Motivation: The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge.
Results: We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues.
Availability and implementation: Source code and the R software tool STew are available from github.com/fanzhanglab/STew.