object. — summary.mtree" />

Summary of a "mtree" object.

Reports some summary statistics for a mutation tree.

# S3 method for mtree
summary(x, ...)

Arguments

x

An mtree tree.
...

Extra parameters

Value

None.

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 
#> 

summary(x[[1]])
#>             Length Class     Mode     
#> adj_mat     25     -none-    numeric  
#> tb_adj_mat  10     tbl_graph list     
#> score        1     -none-    numeric  
#> patient      1     -none-    character
#> samples      8     -none-    character
#> drivers     15     tbl_df    list     
#> CCF         14     tbl_df    list     
#> binary       1     -none-    logical  
#> transfer     2     -none-    list     
#> annotation   1     -none-    character
#> tree_type    1     -none-    character
#> evaluation   1     -none-    character
#> binary_data 14     tbl_df    list     
#> Suppes       3     -none-    list