scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data
scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data
ABSTRACT Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.
Ma Qin、Wang Juexin、Xu Dong、Raina Mauminah Azam、Xu Chunhui、Wang Yiqing、Guo Qi、Li Jinpu、Su Li
Department of Biomedical Informatics, College of Medicine, The Ohio State University||Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State UniversityDepartment of BioHealth Informatics, Indiana University IndianapolisInstitute for Data Science and Informatics, University of Missouri||Christopher S. Bond Life Sciences Center, University of Missouri||Department of Electrical Engineering and Computer Science, University of MissouriDepartment of BioHealth Informatics, Indiana University IndianapolisInstitute for Data Science and Informatics, University of Missouri||Christopher S. Bond Life Sciences Center, University of MissouriChristopher S. Bond Life Sciences Center, University of MissouriDepartment of Biomedical Informatics, College of Medicine, The Ohio State University||Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State UniversityInstitute for Data Science and Informatics, University of Missouri||Christopher S. Bond Life Sciences Center, University of MissouriInstitute for Data Science and Informatics, University of Missouri||Christopher S. Bond Life Sciences Center, University of Missouri
生物科学研究方法、生物科学研究技术计算技术、计算机技术
Ma Qin,Wang Juexin,Xu Dong,Raina Mauminah Azam,Xu Chunhui,Wang Yiqing,Guo Qi,Li Jinpu,Su Li.scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data[EB/OL].(2025-03-28)[2025-04-26].https://www.biorxiv.org/content/10.1101/2024.05.06.592851.点此复制
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