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A three-mutants sampling example

Source: vignettes/three_sampling.Rmd
three_sampling.Rmd

Disclaimer: RACES/ProCESS internally implements the probability distributions using the C++11 random number distribution classes. The standard does not specify their algorithms, and the class implementations are left free for the compiler. Thus, the simulation output depends on the compiler used to compile RACES, and because of that, the results reported in this article may differ from those obtained by the reader.

We build a tumour with three populations branching.

library(ProCESS)

# set the seed of the random number generator
set.seed(0)

sim <- SpatialSimulation("Three Populations", width = 250, height = 250)

sim$death_activation_level <- 20

sim$add_mutant(name = "A", growth_rates = 0.08, death_rates = 0.01)
sim$place_cell("A", 125, 125)
sim$run_up_to_size("A", 3000)
#>  [████████████████████████████████████████] 100% [00m:00s] Saving snapshot

plot_tissue(sim)

The tissue of a tumour having one mutant/species.

We sample

n_w <- n_h <- 7
ncells <- 0.8 * n_w * n_h

# Sampling ncells with random box sampling of boxes of size n_w x n_h
bbox <- sim$search_sample(c("A" = ncells), n_w, n_h)
sim$sample_cells("S_1_1", bbox$lower_corner, bbox$upper_corner)

bbox <- sim$search_sample(c("A" = ncells), n_w, n_h)
sim$sample_cells("S_1_2", bbox$lower_corner, bbox$upper_corner)

plot_tissue(sim)

The tissue of a tumour having one mutant/species after two samples have been collected.

Second mutant starts.

# New submutant
sim$add_mutant(name = "B", growth_rates = 0.13, death_rates = 0.01)
sim$mutate_progeny(sim$choose_cell_in("A"), "B")
sim$run_up_to_size("B", 1000)
#>  [████████████████████████████████████████] 100% [00m:00s] Saving snapshot

plot_tissue(sim)

The tissue of the tumour after a second mutant arises.

The Muller plot of the tumour after a second mutant arises.

Random sampling of cells from both mutant A and B.

# Sampling with random box sampling
bbox <- sim$search_sample(c("A" = ncells), n_w, n_h)
sim$sample_cells("S_2_1", bbox$lower_corner, bbox$upper_corner)

bbox <- sim$search_sample(c("B" = ncells), n_w, n_h)
sim$sample_cells("S_2_2", bbox$lower_corner, bbox$upper_corner)

plot_tissue(sim)

Two further samples have been collected from the tissue of the biclonal tumour.

Finally, a third mutant branching from A.

# New submutant
sim$add_mutant(name = "C", growth_rates = 0.6, death_rates = 0.01)
sim$mutate_progeny(sim$choose_cell_in("A"), "C")

sim$run_up_to_size("C", 1000)
#>  [████████████████████████████████████████] 100% [00m:00s] Saving snapshot
plot_tissue(sim)

The tissue of the tumour after a third mutant arises.

The Muller plot of the tumour after a third mutant arises.

With sampling of mutants B and C.

# Sampling with random box sampling
bbox <- sim$search_sample(c("B" = ncells), n_w, n_h)
sim$sample_cells("S_3_1", bbox$lower_corner, bbox$upper_corner)

bbox <- sim$search_sample(c("C" = ncells), n_w, n_h)
sim$sample_cells("S_3_2", bbox$lower_corner, bbox$upper_corner)

plot_tissue(sim, num_of_bins = 100)

The tumour just after two further samples have been collected.

The tumour just after two further samples have been collected.

Finally, the tree plot where we annotate samples and MRCAs of each sample.

# Tree data
forest <- sim$get_samples_forest()

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

plot_forest(forest) %>%
  annotate_forest(forest)

The sample forest of the samples.