GenerateTissue {scSpatialSIM}

Generate Tissue

Description

This function generates a simulated tissue using a specified number of clusters and spatial parameters for each pattern in the simulation object. The tissue is represented by a grid of points with probabilities of belonging to tissue 1 or tissue 2, based on a Gaussian kernel density estimate calculated for each pattern

Usage

GenerateTissue(
  sim_object,
  k = NA,
  xmin = NA,
  xmax = NA,
  ymin = NA,
  ymax = NA,
  sdmin = 1/2,
  sdmax = 2,
  force = FALSE,
  density_heatmap = FALSE,
  step_size = 1,
  cores = 1,
  overwrite = FALSE,
  use_window = FALSE
)

Arguments

sim_object

A SpatSimObj created with CreateSimulationObject.

k

Number of clusters to generate for each pattern

xmin

Minimum x-coordinate for cluster centers.

xmax

Maximum x-coordinate for cluster centers.

ymin

Minimum y-coordinate for cluster centers.

ymax

Maximum y-coordinate for cluster centers.

sdmin

Minimum standard deviation for cluster kernels.

sdmax

Maximum standard deviation for cluster kernels.

force

Logical, whether to force generation of tissue even if the generated cluster centers would fall outside the simulation window. If FALSE, an error will be thrown if cluster centers are outside the window.

density_heatmap

Logical, whether to calculate a density heatmap for the simulated tissue. If TRUE, a grid of points will be generated covering the entire simulation window, and the probability of each grid point belonging to tissue 1 will be calculated based on the generated tissue probability.

step_size

Grid step size for the density heatmap.

cores

Number of cores to use for parallel processing of density calculations.

overwrite

boolean whether to overwrite if tissue kernels already exist

use_window

boolean whether to use the simulation window to set x and y limits

Details

This function generates a simulated tissue for each pattern in the simulation object by first generating k clusters within the specified x and y ranges and with a standard deviation within the specified range. Then, a Gaussian kernel density estimate is calculated for each pattern using the generated clusters as center points and the specified standard deviation as kernel size. The density estimates represent the probability of each point in the simulation window belonging to tissue 1 or tissue 2. If density_heatmap = TRUE, a density heatmap will be calculated using a grid of points covering the entire simulation window. Finally, for each simulated point, the probability of belonging to tissue 1 is calculated based on the kernel density estimate, and the tissue type is assigned with probability proportional to the probability of belonging to tissue 1.

Value

A modified 'Spatial Simulation Object' with updated tissue grids and assigned tissue types for each simulated pattern.

Examples

# Create a simulation object with a window and point pattern
sim_object <- CreateSimulationObject()

#simulate points
sim_object <- GenerateSpatialPattern(sim_object, lambda = 20)

# Generate tissue with default parameters
sim_object <- GenerateTissue(sim_object)