The genealogy of sampled cells can be visualized with the code like this:
library(ggplot2)
library(tumopp)
result = tumopp::tumopp("-N4096 -D3 -Chex -k1e6 -Lconst")
population = result$population[[1L]]
graph = result$graph[[1L]]
extant = population |> tumopp::filter_extant()
regions = tumopp::sample_uniform_regions(extant, nsam = 8L, ncell = 4L)
subgraph = tumopp::subtree(graph, purrr::list_c(regions$id))
tbl_genealogy = subgraph |> augment_genealogy()
p = gggenealogy(tbl_genealogy) +
theme_classic() + theme(
axis.title.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.y = element_blank(), axis.line.y = element_blank()
)
# ggsave("sample-tree.png", p, width = 4, height = 4)The left panel of Fig. 5. can be drawn like this:
.alt = list(k = c(1, 8, 2 ** 30))
.const = list(D = 3L, C = "hex", L = "const", N = 4096L)
args_table = tumopp::make_args(alt = .alt, const = .const, each = 1L)
results = tumopp::tumopp(args_table)
df = results |>
dplyr::arrange(shape) |>
dplyr::mutate(k = as.list(ifelse(shape < 1e6, shape, expression(infinity)))) |>
dplyr::mutate(genealogy = parallel::mclapply(graph, augment_genealogy))
.max_d = max(purrr::map_dbl(df[["genealogy"]], \(x) {max(x$d)}))
plts = df |> purrr::pmap(function(k, shape, genealogy, ...) {
gggenealogy(genealogy, alpha = 0.3, size = 0.3) +
geom_point(
data = function(.) dplyr::filter(., .data$node_type == "tip"),
size = 0.8, color = "#2B83BA", alpha = 0.2
) +
scale_x_continuous(expand = expansion(c(0, 0.2))) +
coord_cartesian(xlim = c(0, .max_d)) +
labs(title = bquote(italic(k) == .(k))) +
theme_void()
})
# fig5a = cowplot::plot_grid(plotlist = rev(plts), ncol = 1L)
# ggsave("fig5a.png", fig5a, width = 6, height = 6)