Abstract
AbstractIdentifying 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.
Funder
U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases
U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine
U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute
U.S. Department of Health & Human Services | NIH | National Institute on Aging
U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Cited by
2 articles.
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