Multi-region sampling • tumopp

Workflow

Perform simulation and extract population data:

library(ggplot2)
library(tumopp)
set.seed(24601L)
result = tumopp::tumopp("-N20000 -D3 -Chex -Lconst -k10")
population = result$population[[1L]]
graph = result$graph[[1L]]

Sample cells and put neutral mutations on the lineages:

extant = population |> tumopp::filter_extant()
ncell = 200L
regions = tumopp::sample_uniform_regions(extant, nsam = 4L, ncell = ncell)
subgraph = tumopp::subtree(graph, unlist(regions$id))
vaf = tumopp::make_vaf(subgraph, regions$id, mu = 8.0) |> print()
## # A tibble: 29,094 × 4
##      `1`   `2`   `3`   `4`
##    <dbl> <dbl> <dbl> <dbl>
##  1     0     0 0.005     0
##  2     0     0 0.005     0
##  3     0     0 0.005     0
##  4     0     0 0.005     0
##  5     0     0 0.005     0
##  6     0     0 0.005     0
##  7     0     0 0.005     0
##  8     0     0 0.005     0
##  9     0     0 0.005     0
## 10     0     0 0.005     0
## # ℹ 29,084 more rows

Estimate $F_{ST}$ from VAF.

tumopp::dist_vaf(vaf, ncell) |> tumopp::fst()
## [1] 0.0440782

# True FST from cell genealogy
tumopp::dist_genealogy(subgraph, regions$id) |> tumopp::fst()
## [1] 0.04560559

Summarize and visualize VAF:

vaf_tidy = vaf |>
  tumopp::filter_detectable(0.01) |>
  tumopp::sort_vaf() |>
  tumopp::longer_vaf() |>
  print()

ggplot(vaf_tidy) +
  aes(sample, site) +
  geom_tile(aes(fill = frequency)) +
  scale_fill_distiller(palette = "Spectral", limit = c(0, 1), guide = FALSE) +
  coord_cartesian(expand = FALSE)