Version: 1.1.0
Date: 2025-09-01
Title: A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data
Description: Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
Imports: Matrix, sparseMatrixStats, fitdistrplus, RANN, spam
Suggests: knitr, rmarkdown
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-09-01 18:25:17 UTC; castl
Author: Jinpu Li ORCID iD [aut, cre], Yiqing Wang [aut]
Maintainer: Jinpu Li <castle.lee.f@gmail.com>
Repository: CRAN
Date/Publication: 2025-09-01 19:30:02 UTC

Combine p-values across multiple samples from scBSP

Description

Given the results from multiple samples with gene names and p-values, this function merges them by gene and computes a combined p-value for each gene. Fisher's method or Stouffer's method can be used.

Usage

CombinePvalues(list_of_pvalues, method = c("fisher", "stouffer"))

Arguments

list_of_pvalues

A list of data.frames, each with columns: GeneNames, P_values.

method

Combination method. One of "fisher" (default) or "stouffer".

Value

A data.frame with columns:

Examples

df1 <- data.frame(GeneNames = c("A","B","C"),
                  P_values = c(0.01, 0.20, 0.03))
df2 <- data.frame(GeneNames = c("A","C","D"),
                  P_values = c(0.04, 0.10, 0.50))
df3 <- data.frame(GeneNames = c("B","C","E"),
                  P_values = c(0.05, 0.02, 0.80))

CombinePvalues(list(df1, df2, df3), method = "fisher")

Loading data from a Seurat object or a data frame.

Description

A function to load and filter data from a Seurat object or a data frame.

Usage

LoadSpatial(InputData, Dimension = 2)

Arguments

InputData

A Seurat spatial object or a M x (D + N) data matrix representing the D-dimensional coordinates and expressions of N genes on M spots. The coordinates should be placed at the first D columns

Dimension

The dimension of coordinates

Value

A list of two data frame:

Coords

A M x D matrix representing D-dimensional coordinates for M spots

ExpMatrix

A sparse, N x M expression matrix in dgCMatrix class with N genes and M spots

A function for filtering low expressed genes

Description

A function for filtering low expressed genes

Usage

SpFilter(ExpMat_Sp, Threshold = 5)

Arguments

ExpMat_Sp

A sparse, N x M expression matrix in dgCMatrix class with N genes and M spots

Threshold

A threshold set to filter out genes with a total read count below this specified value

Value

A sparse expression matrix in dgCMatrix class

Examples

# create a sparse expression matrix
Raw_ExpMat <- Matrix::rsparsematrix(nrow = 10000, ncol = 2000, 
density = 0.01, rand.x = function(n) rpois(n, 15))
Filtered_ExpMat <- SpFilter(Raw_ExpMat)

A Granularity-Based Approach to identify Spatially Variable Genes

Description

This function is designed to identify spatially variable genes through a granularity-based approach.

Usage

scBSP(Coords, ExpMat_Sp, D_1 = 1.0, D_2 = 3.0, 
Exp_Norm = TRUE, Coords_Norm_Method = c("Sliced", "Overall", "None"), 
K_NN = 100, treetype = "kd")

Arguments

Coords

A M x D matrix representing D-dimensional coordinates for M spots

ExpMat_Sp

A sparse, N x M expression matrix in dgCMatrix class with N genes and M spots

D_1

Size of the small patch

D_2

Size of the big patch

Exp_Norm

A Boolean value indicating whether the expression matrix should be normalized

Coords_Norm_Method

Normalization method for the coordinates matrix, which can be "None", "Sliced", or "Overall".

K_NN

The maximum number of nearest neighbours to compute.

treetype

Character vector specifying the standard 'kd' tree or a 'bd' (box-decomposition, AMNSW98) tree which may perform better for larger point sets.

Details

This function utilizes a MxD matrix (Coords) representing D-dimensional coordinates with M spots and a sparse, NxM expression matrix (ExpMat_Sp) with N genes and M spots.

Value

A data frame with the name of genes and corresponding p-values.

Examples

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)
Filtered_ExpMat <- SpFilter(Raw_Exp)
rownames(Filtered_ExpMat) <- paste0("Gene_", 1:nrow(Filtered_ExpMat))
P_values <- scBSP(Coords, Filtered_ExpMat)