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Yiqing Wang [aut]| Title: | A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data |
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| 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>). |
| Authors: | Jinpu Li [aut, cre] ,
Yiqing Wang [aut] |
| Maintainer: | Jinpu Li <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2024-11-06 02:55:42 UTC |
| Source: | https://github.com/castleli/scbsp |
A function to load and filter data from a Seurat object or a data frame.
LoadSpatial(InputData, Dimension = 2)LoadSpatial(InputData, Dimension = 2)
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 |
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 |
This function is designed to identify spatially variable genes through a granularity-based approach.
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")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")
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. |
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.
A data frame with the name of genes and corresponding p-values.
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)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)
A function for filtering low expressed genes
SpFilter(ExpMat_Sp, Threshold = 5)SpFilter(ExpMat_Sp, Threshold = 5)
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 |
A sparse expression matrix in dgCMatrix class
# 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)# 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)