Return clone trees from the fit.

This function uses the output fit of MOBSTER to create a call to ctree (https://caravagn.github.io/ctree/), a package to create clone trees for cancer evolution models.

Creation of a clone tree requires annotations that are not usually necessary for just a plain MOBSTER analyses. These annotations report the status of driver and gene for each one of the input datapoints, and should be part of data given in input for MOBSTER (so they should be in x$data).

MOBSTER clusters are only used if the come from a Beta distribtutions; that is the tail is removed. The clonal cluster is estimated from the cluster with the highest parameter value for the Beta peak.

The output is the result of calling the constructor ctree::cetrees on the input clustering results x.

get_clone_trees(x, ...)

Arguments

x	A MOBSTER fit.
...	Extra parameters passed to the constructor ctree::cetrees, which affect the sampling of the trees.

Value

The output of the constructor ctree::cetrees.

Examples

# We take one of the released datasets
x = mobster::LU4_lung_sample$best

# Annotate some random mutation as driver, we need that to build the trees with ctree
x$data$is_driver = FALSE
x$data$is_driver[1:3] = TRUE

x$data$driver_label = ""
x$data$driver_label[1] = "Fake_driver_1"
x$data$driver_label[2] = "Fake_driver_2"
x$data$driver_label[3] = "Fake_driver_3"

# Get the trees
trees = get_clone_trees(x)
#>  [ ctree ~ generate clone trees for MOBSTER_dataset ] 
#> Sampler :  10000 (cutoff),  5000 (sampling),  100 (max store) 
#> # A tibble: 1 x 5
#>   cluster    R1 nMuts is.clonal is.driver
#>   <chr>   <dbl> <dbl> <lgl>     <lgl>    
#> 1 C1      0.263   972 TRUE      TRUE     
#> 
#> Sampler: this model has 1 node, it has trivial models.
#> ✔ Trees with non-zero sscore 1 storing 1 
#> 
#> [easypar] 2020-10-29 11:27:33 - Overriding parallel execution setup [FALSE] with global option : FALSE
#> 
#> This tree has 1 node, creating a monoclonal model disregarding the input matrix.

# Print and plot the first tree (top rank)
library(ctree)
ctree::print.ctree(trees[[1]])
#>  [ ctree - ctree rank 1/1 for MOBSTER_dataset ] 
#> 
#> # A tibble: 1 x 5
#>   cluster    R1 nMuts is.clonal is.driver
#>   <chr>   <dbl> <dbl> <lgl>     <lgl>    
#> 1 C1      0.263   972 TRUE      TRUE     
#> 
#> Tree shape (drivers annotated)  
#> 
#>   \-GL
#>    \-C1 [R1] :: Fake_driver_1, Fake_driver_3
#> 
#> Information transfer  
#> 
#>    GL ---> Fake_driver_1 
#>    GL ---> Fake_driver_3 
#> 
#> Tree score 1 
#> 
ctree::plot.ctree(trees[[1]])
#> Warning: Duplicated aesthetics after name standardisation: na.rm
#> Warning: Removed 1 rows containing missing values (geom_point).
ctree::plot_CCF_clusters(trees[[1]])
ctree::plot_icon(trees[[1]])
ctree::plot_clone_size(trees[[1]])