,
Boyi Guo [aut] ,
Stephanie C. Hicks [aut] | Title: | Simulate Spatial Transcriptomics Data with the Mean-variance Relationship |
|---|---|
| Description: | This packages simulates spatial transcriptomics data with the mean- variance relationship using a Gaussian Process model per gene. |
| Authors: | Kinnary Shah [aut, cre] ,
Boyi Guo [aut] ,
Stephanie C. Hicks [aut] |
| Maintainer: | Kinnary Shah <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.0 |
| Built: | 2024-10-31 05:28:04 UTC |
| Source: | https://github.com/bioc/spatialSimGP |
Simulate a SpatialExperiment object with spatially varying genes
spatial_simulate( n_genes, proportion, coords, range_sigma.sq, range_beta, length_scale, length_scale_option = "fixed" )spatial_simulate( n_genes, proportion, coords, range_sigma.sq, range_beta, length_scale, length_scale_option = "fixed" )
n_genes |
an integer specifying the number of genes to simulate. |
proportion |
a numeric value specifying the proportion of genes that will have no spatially varying patterns. |
coords |
a matrix of coordinates. |
range_sigma.sq |
a numeric vector of length 2 specifying the range of the spatial variance parameter. |
range_beta |
a numeric vector of length 2 specifying the range of the mean expression value. |
length_scale |
if length_scale_option is "fixed", a numeric value specifying the length scale parameter. If length_scale_option is "unique", a numeric vector of length n_genes specifying the length scale parameter for each gene. |
length_scale_option |
a character string specifying the length scale option. Options are "fixed" for a single length scale for all genes or "unique" for a unique length scale for each gene. |
This function simulates a SpatialExperiment object with spatially varying genes. The function takes in the number of genes to simulate, the proportion of genes that will have no spatially varying patterns, a matrix of coordinates, the range of the spatial variance parameter, the range of the mean expression value, the length scale parameter, and the length scale option.
A SpatialExperiment object with the simulated data.
library(STexampleData) set.seed(1) n_genes <- 1 proportion <- 0.5 range_sigma.sq <- c(0.2, 3) range_beta <- c(0.5, 9) length_scale <- 60 spe_demo <- Visium_mouseCoronal() colData(spe_demo)$subset <- ifelse( colData(spe_demo)$array_row > 20 & colData(spe_demo)$array_row < 65 & colData(spe_demo)$array_col > 30 & colData(spe_demo)$array_col < 65, TRUE, FALSE ) spe_demo <- spe_demo[, colData(spe_demo)$subset] coords <- spatialCoords(spe_demo) spe <- spatial_simulate(n_genes, proportion, coords, range_sigma.sq, range_beta, length_scale, length_scale_option = "fixed")library(STexampleData) set.seed(1) n_genes <- 1 proportion <- 0.5 range_sigma.sq <- c(0.2, 3) range_beta <- c(0.5, 9) length_scale <- 60 spe_demo <- Visium_mouseCoronal() colData(spe_demo)$subset <- ifelse( colData(spe_demo)$array_row > 20 & colData(spe_demo)$array_row < 65 & colData(spe_demo)$array_col > 30 & colData(spe_demo)$array_col < 65, TRUE, FALSE ) spe_demo <- spe_demo[, colData(spe_demo)$subset] coords <- spatialCoords(spe_demo) spe <- spatial_simulate(n_genes, proportion, coords, range_sigma.sq, range_beta, length_scale, length_scale_option = "fixed")