基于维度无关性和粒度的空间可变基因识别

Dimension-agnostic and granularity-based spatially variable gene identification.

作者信息

Wang Juexin, Li Jinpu, Kramer Skyler T, Su Li, Chang Yuzhou, Xu Chunhui, Ma Qin, Xu Dong

机构信息

Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA.

Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.

出版信息

bioRxiv. 2023 Mar 24:2023.03.21.533713. doi: 10.1101/2023.03.21.533713.

DOI:10.1101/2023.03.21.533713

PMID:36993544

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10055351/

Abstract

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.

摘要

识别空间可变基因（SVG）对于将分子细胞功能与组织表型联系起来至关重要。空间分辨转录组学在二维或三维空间中捕获具有相应空间坐标的细胞水平基因表达，并可用于有效推断SVG。然而，当前的计算方法可能无法获得可靠的结果，并且通常无法处理三维空间转录组数据。在这里，我们介绍了BSP（大小补丁），这是一种空间粒度引导的非参数模型，用于以快速且稳健的方式从二维或三维空间转录组数据中识别SVG。这种新方法已在模拟中进行了广泛测试，证明了其卓越的准确性、稳健性和高效率。BSP通过在癌症、神经科学、类风湿性关节炎和肾脏研究中使用各种类型的空间转录组学技术所取得的确凿生物学发现得到了进一步验证。