Plot a clone size histogram, and test for.

This function creates a ggplot barplot of the clone size values foe each clone in the patient's data. The size of a clone is defined as the number of mutations assigned to it, and is provided in input.

The barplot is annotated to report wether a subclone with a driver is significantly larger than the expected size for a subclone without driver. To carry out this test subclones without drivers are used to estimate the parameters of a univariate Gaussian distribution (mean and standard deviation), the p-value is then computed from the fit distribution through the `pnorm` function.

The confidence level for the test can be passed as parameter.

plot_clone_size(x, alpha_level = 0.05)

Arguments

x	An mtree tree.
alpha_level	Alpha level for the test, default is 0.05.
...	Extra parameters, not used.

Value

A ggplot 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_clone_size(x[[1]])
#> Warning: Removed 1 rows containing missing values (geom_text).