scBSP - A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Spatially Resolved Transcriptomics Data

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This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD-tree/balltree method for distance calculation, for the identification of spatially variable genes on large-scale data. A corresponding Python library is available at https://pypi.org/project/scbsp.

Installation

This package can be installed on R CRAN

install.packages("scBSP")

Usage

# Creating coords and expression matrix
Coords <- expand.grid(1:100,1:100, 1:3)
RandFunc <- function(n) floor(10 * stats::rbeta(n, 1, 5))
Raw_Exp <- Matrix::rsparsematrix(nrow = 10^4, ncol = 3*10^4, density = 0.0001, rand.x = RandFunc)

# Excluding low expressed genes
Filtered_ExpMat <- SpFilter(Raw_Exp)
rownames(Filtered_ExpMat) <- paste0("Gene_", 1:nrow(Filtered_ExpMat))

# Computing p-values
P_values <- scBSP(Coords, Filtered_ExpMat)

Reference

Li, Jinpu, Yiqing Wang, Mauminah Azam Raina, Chunhui Xu, Li Su, Qi Guo, Qin Ma, Juexin Wang, and Dong Xu. "scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data." bioRxiv (2024).

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.