scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-30 DOI:10.1093/bioinformatics/btaf554

Jinpu Li, Mauminah Raina, Yiqing Wang, Chunhui Xu, Li Su, Qi Guo, Ricardo Melo Ferreira, Michael T Eadon, Qin Ma, Juexin Wang, Dong Xu

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引用次数: 0

Abstract

Motivation: Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three-dimensional space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities.

Results: We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms.

Availability: scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Supplementary information: The supplementary data and code are openly available from Zenodo at https://zenodo.org/records/14768450.

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scBSP：从高分辨率空间组学数据中快速准确地识别空间变量特征的工具。

动机：新兴的空间组学技术使我们能够从多组学的角度对生物系统在二维和三维空间中的原生组织位置进行全面的探索。然而，在空间组学技术中，有限的测序深度、不断增加的空间分辨率和不断增加的空间点在识别具有不同空间分布的具有生物学意义的分子方面提出了重大的计算挑战。结果：我们介绍了scBSP，一个开源的、通用的、用户友好的软件包，用于识别大规模空间组学数据中的空间变量特征。scBSP展示了显著提高的计算效率，在几秒钟内处理高分辨率空间组学数据，并通过在不同测序平台上一致地识别具有高重复性的空间变量特征，展示了强大的跨平台性能。可用性：scBSP可从R CRAN （https://cran.r-project.org/web/packages/scBSP/index.html）和PyPI （https://pypi.org/project/scbsp/.Supplementary）下载：信息：补充数据和代码可从Zenodo （https://zenodo.org/records/14768450）公开获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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