Dimension-agnostic and granularity-based spatially variable gene identification.
Dimension-agnostic and granularity-based spatially variable gene identification.
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与维度无关和基于粒度的空间可变基因识别。
DOI：
10.1101/2023.03.21.533713
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Xu,Dong
中科院分区：
文献类型：
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作者：
Wang,Juexin;Li,Jinpu;Kramer,SkylerT;Su,Li;Chang,Yuzhou;Xu,Chunhui;Ma,Qin;Xu,Dong
识别空间可变基因（SVG）在将分子细胞功能与组织表型联系起来方面至关重要。空间分辨转录组学在二维或三维空间中捕获细胞水平的基因表达，并可用于有效地推断SVG。然而，目前的计算方法可能无法实现可靠的结果，往往不能处理三维空间转录组数据。在这里，我们介绍BSP（大小补丁），空间粒度指导和非参数模型，以快速和鲁棒的方式从二维或三维空间转录组学数据中识别SVG。这种新方法已经在模拟中进行了广泛的测试，证明上级的准确性，鲁棒性和高效率。BSP通过各种类型的空间转录组学技术在癌症、神经科学、类风湿性关节炎和肾脏研究中的生物学发现得到进一步验证。
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.