Plot the information transfer of a tree (drivers ordering).

The information transfer of the tree is the set of orderings associated to the internal annotated driver events.

This function plots these orderings following a topological sort of the node of the corresponding clone tree.

plot_information_transfer(x,
  node_palette = colorRampPalette(RColorBrewer::brewer.pal(n = 9,
  "Set1")), tree_layout = "tree", ...)

Arguments

x	A mtree tree.
node_palette	A function that can return, for an input number, a number of colours.
tree_layout	Layout of this model, as of ggraph.
...	Other parameters, not used in this case.

Value

A ggplot object for the plot.

Examples

data(mtree_input)

x = mtrees(
mtree_input$binary_clusters,
mtree_input$drivers,
mtree_input$samples,
mtree_input$patient,
mtree_input$sspace.cutoff,
mtree_input$n.sampling,
mtree_input$store.max
)
#>  [ mtree ~ generate mutation trees for PD9193 ] 
#> Sampler :  10000 (cutoff),  5000 (sampling),  100 (max store) 
#> Suppes' conditions :  >= 
#> # A tibble: 4 x 14
#>   Misc  patientID cluster is.driver is.clonal PD9193d PD9193e PD9193f PD9193g
#>   <chr> <chr>     <chr>   <lgl>     <lgl>       <dbl>   <dbl>   <dbl>   <dbl>
#> 1 cuto… PD9193    2       FALSE     FALSE           1       1       1       0
#> 2 cuto… PD9193    3       TRUE      FALSE           1       0       0       0
#> 3 cuto… PD9193    1       TRUE      TRUE            1       1       1       1
#> 4 cuto… PD9193    4       TRUE      FALSE           1       0       1       1
#> # … with 5 more variables: PD9193h <dbl>, PD9193i <dbl>, PD9193j <dbl>,
#> #   PD9193k <dbl>, nMuts <dbl>
#> 
#> ✔ Structures 3 - search is exahustive 
#> ✔ Trees with non-zero sscore 3 storing 3 
#> 

plot_information_transfer(x[[1]])

# Change layout -- use igraph's "kk" layout
plot_information_transfer(x[[1]], tree_layout = 'kk')