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scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data
Jinpu Li1,2, Mauminah Raina3, Yiqing Wang2
1Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States.
Bioinformatics (Oxford, England)
|October 1, 2025
Summary
We developed scBSP, a new tool to efficiently identify spatially variable molecules in large spatial omics datasets. This open-source package ensures reproducible results across different sequencing platforms.
Area of Science:
- Spatial omics
- Computational biology
- Bioinformatics
Background:
- Spatial omics technologies enable multi-omics exploration within native tissue contexts.
- Computational challenges arise from limited sequencing depth, high resolution, and numerous spatial spots.
- Identifying spatially variable molecules across modalities is complex.
Purpose of the Study:
- Introduce scBSP, an open-source package for spatial omics data analysis.
- Address computational challenges in identifying spatially variable features.
- Provide a user-friendly and versatile tool for researchers.
Main Methods:
- scBSP is an open-source software package.
- Designed for identifying spatially variable features in large-scale spatial omics data.
- Available on R CRAN and PyPI.
Main Results:
- scBSP offers enhanced computational efficiency, processing high-resolution data rapidly.
- Demonstrates robust cross-platform performance.
- Consistently identifies spatially variable features with high reproducibility.
Conclusions:
- scBSP is a versatile and user-friendly tool for spatial omics research.
- Facilitates efficient identification of biologically meaningful molecules.
- Supports reproducible analysis across diverse sequencing platforms.
