Big-Small Patch (BSP)

Update: scBSP is now available with better efficiency

Big-small patch is a granularity-guided, data-driven, and parameter-free model for identifying spatial variable genes in 2D and 3D high-throughput spatial transcriptomics data.

Update: Please use scBSP as a better implementation of BSP algorithm

Within the same algorithm of BSP, the newly released scBSP implementes sparse matrix operation and KD-tree/balltree method for distance calculation, for the identification of spatially variable genes on large-scale data. Both Python and R implementations are provided.

Installation (R)

This package can be installed on R CRAN

# Install sparseMatrixStats if not already installed

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

if (!requireNamespace("sparseMatrixStats", quietly = TRUE)) {
    BiocManager::install("sparseMatrixStats")
}

# Install scBSP from CRAN
install.packages("scBSP")

Installation (Python)

This package can be installed using pip at https://pypi.org/project/scbsp.

pip install scbsp

Tutorial

A detailed tutorial is available at here

Reference

Jinpu Li, Mauminah Raina, Yiqing Wang, Chunhui Xu, Li Su, Qi Guo, Ricardo Melo Ferreira, Michael T Eadon, Qin Ma, Juexin Wang, Dong Xu, scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data, Bioinformatics, 2025;, btaf554, https://doi.org/10.1093/bioinformatics/btaf554.

Wang, Juexin, Jinpu Li, Skyler T. Kramer, Li Su, Yuzhou Chang, Chunhui Xu, Michael T. Eadon, Krzysztof Kiryluk, Qin Ma, and Dong Xu. "Dimension-agnostic and granularity-based spatially variable gene identification using BSP." Nature Communications 14, no. 1 (2023): 7367.