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Plot a tissue

Source: R/plot_tissue.R
plot_tissue.Rd

Plots cells distribution over a tissue highlighting species by color. To facilitate the plot and avoid excessive number of cells, for instance, when a simulation deals with millions of cells, the plot draws a hexagonal heatmap of 2D bins.

Usage

plot_tissue(
  simulation,
  num_of_bins = 100,
  before_sample = NULL,
  at_sample = NULL,
  plot_next_sample_regions = FALSE,
  plot_sample_region = TRUE,
  color_map = NULL,
  list_all_species = FALSE
)

Arguments

simulation

A simulation object.

num_of_bins

The number of bins (default: 100).

before_sample

A sample name. When provided, this function represents the tissue as appeared just before the specified sampling. The parameters before_sample and at_sample are mutually exclusive (optional).

at_sample

A sample name. When provided, this function represents the tissue as appeared when the specified sampling occurred. The parameters before_sample and at_sample are mutually exclusive (optional).

plot_next_sample_regions

A Boolean value. When before_sample is set and plot_next_sample_regions is set to be TRUE, this function plots the regions of the samples collected at the same simulated time of the specified sample. When, instead, at_sample is set and plot_next_sample_regions is set to be TRUE, the function plots the regions of the samples collected at the same simulated time of the specified sample, but not before the specified sample (default: FALSE).

plot_sample_region

A Boolean value. When either at_sample or before_sample are set and plot_sample_region is set to be TRUE, the function also plots the region of the specified sample (default: TRUE).

color_map

A named vector representing the simulation species color map (optional).

list_all_species

A Boolean flag to show all species in the legend (default: FALSE).

Value

An editable ggplot plot.

Examples

set.seed(0)
sim <- SpatialSimulation()
sim$add_mutant(name = "A",
               epigenetic_rates = c("+-" = 0.01, "-+" = 0.01),
               growth_rates = c("+" = 0.2, "-" = 0.08),
               death_rates = c("+" = 0.1, "-" = 0.01))
sim$place_cell("A+", 500, 500)
sim$run_up_to_size("A-", 60000)
#> 
 [███████████████-------------------------] 35% [00m:00s] Cells: 27566                                            

 [███████████████████████████-------------] 67% [00m:01s] Cells: 48805                                            

 [██████████████████████████████████████--] 94% [00m:02s] Cells: 66397                                            

 [████████████████████████████████████████] 100% [00m:02s] Saving snapshot                                        


# collect 3 samples: "Sample_A", "Sample_B", and "Sample_C"
sim$sample_cells("Sample_A", c(425, 425), c(475, 475))
sim$sample_cells("Sample_B", c(525, 525), c(575, 575))
sim$sample_cells("Sample_C", c(425, 525), c(475, 575))

# let the simulation evolve until the species "A-" account
# for 80000 cells
sim$run_up_to_size("A-", 80000)
#> 
 [████████████████████████████████--------] 79% [00m:00s] Cells: 75138                                            

 [███████████████████████████████████████-] 95% [00m:01s] Cells: 88471                                            

 [████████████████████████████████████████] 100% [00m:01s] Saving snapshot                                        


# plot the tissue in the current status
plot_tissue(sim)


# plot the tissue as it was when "Sample_B" was collected
plot_tissue(sim, at_sample="Sample_B")


# plot the tissue as it was when "Sample_B" was collected and
# highlight the regions of the samples collected at the same
# simulated time, but not before it, i.e., "Sample_B" and
# "Sample_C"
plot_tissue(sim, at_sample="Sample_B",
            plot_next_sample_regions = TRUE)


# plot the tissue as it was just before sampling "Sample_B"
plot_tissue(sim, before_sample="Sample_B")


# plot the tissue as it was just before sampling "Sample_B"
# and highlight the regions of the samples collected at the
# same simulated time, i.e., "Sample_A", "Sample_B", and
# "Sample_C"
plot_tissue(sim, before_sample="Sample_B",
            plot_next_sample_regions = TRUE)


# define a custom color map
color_map <- c("#B2DF8A", "#E31A1C")
names(color_map) <- c("A+", "A-")

plot_tissue(sim, color_map=color_map)