Dimension-agnostic and granularity-based spatially variable gene identification using BSP

• Published in: Nature communications Vol. 14; no. 1; p. 7367
• Main Authors: Wang, Juexin, Li, Jinpu, Kramer, Skyler T., Su, Li, Chang, Yuzhou, Xu, Chunhui, Eadon, Michael T., Kiryluk, Krzysztof, Ma, Qin, Xu, Dong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14-11-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 38/39; 38/47; 631/114/2164; 631/114/2397; 631/114/2415; 631/1647/48; 631/1647/514/1949; Arthritis, Rheumatoid; Gene expression; Gene Expression Profiling; Genes; Genetic diversity; Humanities and Social Sciences; Humans; Kidney; multidisciplinary; Phenotype; Phenotypes; Rheumatoid arthritis; Science; Science (multidisciplinary); Spatial data; Technology; Transcriptome; Transcriptomics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-43256-5

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 non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This 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.
Identifying spatially variable genes (SVGs) is essential for linking molecular cell functions with tissue phenotypes. Here, authors introduce a non-parametric model that detects SVGs from two or three-dimensional spatial transcriptomics data by comparing gene expression patterns at granularities.