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

Juexin Wang (), Jinpu Li, Skyler T. Kramer, Li Su, Yuzhou Chang, Chunhui Xu, Michael T. Eadon, Krzysztof Kiryluk, Qin Ma () and Dong Xu ()
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Juexin Wang: Computing, and Engineering, Indiana University Indianapolis
Jinpu Li: University of Missouri
Skyler T. Kramer: University of Missouri
Li Su: University of Missouri
Yuzhou Chang: The Ohio State University
Chunhui Xu: University of Missouri
Michael T. Eadon: Indiana University
Krzysztof Kiryluk: Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center
Qin Ma: The Ohio State University
Dong Xu: University of Missouri

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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 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.

Date: 2023
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DOI: 10.1038/s41467-023-43256-5

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