6. Clone trees from clusters

library(mobster)
library(tidyr)
library(dplyr)

Clone trees from mobster fits can be computing using the internal interface with ctree.

You need to have drivers annotated your object if you want to use ctree, and every driver_label has to be unique, as it will be used as the variantID column to identify the driver event.

We show the analysis with a synthetic dataset.

# Example data where we annotate 3 events as drivers
example_data = Clusters(mobster::fit_example$best)
example_data = example_data %>% dplyr::select(-cluster, -Tail, -C1, -C2)
  
# Drivers annotation
drivers_rows = c(2239, 3246, 3800)

example_data$is_driver = FALSE
example_data$driver_label = NA

example_data$is_driver[drivers_rows] = TRUE
example_data$driver_label[drivers_rows] = c("DR1", "DR2", "DR3")

# Fit and print the data
fit = mobster_fit(example_data, auto_setup = 'FAST')
#>  [ MOBSTER fit ] 
#> 
#> ✔ Loaded input data, n = 5000.
#> ❯ n = 5000. Mixture with k = 1,2 Beta(s). Pareto tail: TRUE and FALSE. Output
#> clusters with π > 0.02 and n > 10.
#> ! mobster automatic setup FAST for the analysis.
#> ❯ Scoring (without parallel) 2 x 2 x 2 = 8 models by reICL.
#> ℹ MOBSTER fits completed in 8.6s.
#> ── [ MOBSTER ] My MOBSTER model n = 5000 with k = 1 Beta(s) and a tail ─────────
#> ● Clusters: π = 54% [C1] and 46% [Tail], with π > 0.
#> ● Tail [n = 2227, 46%] with alpha = 1.1.
#> ● Beta C1 [n = 2773, 54%] with mean = 0.48.
#> ℹ Score(s): NLL = -5332.99; ICL = -10291.86 (-10614.88), H = 323.01 (0). Fit
#> converged by MM in 11 steps.
#> ℹ The fit object model contains also drivers annotated.
#> # A tibble: 3 × 4
#>      VAF is_driver driver_label cluster
#>    <dbl> <lgl>     <chr>        <chr>  
#> 1 0.448  TRUE      DR1          C1     
#> 2 0.159  TRUE      DR2          Tail   
#> 3 0.0629 TRUE      DR3          Tail

best_fit = fit$best
print(best_fit)
#> ── [ MOBSTER ] My MOBSTER model n = 5000 with k = 1 Beta(s) and a tail ─────────
#> ● Clusters: π = 54% [C1] and 46% [Tail], with π > 0.
#> ● Tail [n = 2227, 46%] with alpha = 1.1.
#> ● Beta C1 [n = 2773, 54%] with mean = 0.48.
#> ℹ Score(s): NLL = -5332.99; ICL = -10291.86 (-10614.88), H = 323.01 (0). Fit
#> converged by MM in 11 steps.
#> ℹ The fit object model contains also drivers annotated.
#> # A tibble: 3 × 4
#>      VAF is_driver driver_label cluster
#>    <dbl> <lgl>     <chr>        <chr>  
#> 1 0.448  TRUE      DR1          C1     
#> 2 0.159  TRUE      DR2          Tail   
#> 3 0.0629 TRUE      DR3          Tail

Tree computation

Tree computation removes any mutation that is assigned to a Tail cluster because the clone tree represents the clones.

# Get the trees, select top-rank
trees = get_clone_trees(best_fit)
#>  [ ctree ~ clone trees generator for My_MOBSTER_model ] 
#> 
#> # A tibble: 1 × 5
#>   cluster    R1 nMuts is.clonal is.driver
#>   <chr>   <dbl> <dbl> <lgl>     <lgl>    
#> 1 C1      0.478  2773 TRUE      TRUE
#> ! Model with 1 node, trivial trees returned
#> ✔ 1  trees with non-zero score, storing 1
#> 
#> This tree has 1 node, creating a monoclonal model disregarding the input matrix.

The top-rank tree is in position 1 of trees; ctree implements S3 object methods to print an plot a tree.

top_rank = trees[[1]]

# Print with S3 methods from ctree
ctree:::print.ctree(top_rank)
#>  [ ctree - ctree rank 1/1 for My_MOBSTER_model ] 
#> 
#> # A tibble: 1 × 5
#>   cluster    R1 nMuts is.clonal is.driver
#>   <chr>   <dbl> <dbl> <lgl>     <lgl>    
#> 1 C1      0.478  2773 TRUE      TRUE     
#> 
#> Tree shape (drivers annotated)  
#> 
#>   \-GL
#>    \-C1 [R1] :: DR1
#> 
#> Information transfer  
#> 
#>    GL ---> DR1 
#> 
#> Tree score 1 
#>

We can plot the top tree, aggregating different ctree plots.

# 1) Clone tree
# 2) Input ctree data (here adjusted VAF)
# 3) Clone size barplot
ggpubr::ggarrange(
  ctree::plot.ctree(top_rank),
  ctree::plot_CCF_clusters(top_rank),
  ctree::plot_clone_size(top_rank),
  nrow = 1,
  ncol = 3
)

Giulio Caravagna

21 November, 2025

Tree computation