In heavywatal/tumorr: R interface to tumopp, a tumor growth simulator in C++

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.height = 6,
  fig.width = 6
)
library(ggplot2)
theme_here = theme_bw(base_size = 12) +
  theme(panel.grid.minor = element_blank(),
        axis.ticks = element_blank(),
        strip.background = element_rect(linetype = 0))
theme_set(theme_here)

Run simulation with benchmark information

library(tidyverse)
library(tumopp)

.const = list(D = 3, C = "hex", k = 10, N = 2 ** 17, benchmark = TRUE)
.alt = list(
  L = c("const", "linear"),
  P = c("random", "mindrag"),
  d = c(0, 0.2),
  m = c(0, 2)
)
args_tbl = make_args(alt = .alt, const = .const) |> print()

results = tumopp::tumopp(args_tbl, graph = FALSE) |> print()

unnested = results |>
  dplyr::select(!c(population, seed)) |>
  dplyr::mutate(outdir = fs::path_file(outdir)) |>
  dplyr::select(where(\(x) dplyr::n_distinct(x) > 1L)) |>
  tidyr::unnest(benchmark) |>
  print()

df_benchmark = unnested |>
  dplyr::distinct(outdir, size, .keep_all = TRUE) |>
  dplyr::mutate(
    local = factor(local, levels = .alt[["L"]]),
    path = factor(path, levels = .alt[["P"]]),
    size = size / 1000,      # to K
    memory = maxrss / 1024,  # K to M
    utime = utime / 1000,    # ms to sec
    stime = stime / 1000,    # ms to sec
    maxrss = NULL
  ) |>
  print()

# usethis::use_data(df_benchmark, internal = TRUE, overwrite = TRUE)

Memory

Memory usage increases linearly with the increasing number of cells because tumopp is an individual-based model of tumor cells.
Memory is also used to trace back cell lineages and write them to a text file after growth simulation.
Cell death makes cell lineages longer given the same number of extant cells.
The effect of cell death is more pronounced when the population average of birth rate is decreased with local competition (-Llinear).

ggplot(tumopp:::df_benchmark) +
  aes(size, memory) +
  geom_line(linewidth = 1, color = "#337ab7") +
  facet_grid(vars(local, path), vars(delta0, rho0), label = label_both) +
  labs(x = "Size (K cells)", y = "Memory (MB)")

Time

The larger a population gets, the longer it takes
- (-Pmindrag) to search for nearest empty sites from dividing cells,
- (-Lconst) to push out other cells,
- (-Llinear) to skip event queues of dormant (surrounded) cells.
The combination of -Lconst -Pmindrag is slower than the others because it involves both search and push from interior cells.
The effects of cell death and migration are smaller than those of local competition (-L) and placement of daughter cells(-P).

ggplot(tumopp:::df_benchmark) +
  aes(size, utime + stime) +
  geom_line(linewidth = 1, color = "#337ab7") +
  facet_grid(vars(local, path), vars(delta0, rho0), label = label_both) +
  labs(x = "Size (K cells)", y = "Time (sec)")