Summary of a "ctree" object.
summary.ctree.RdReports some summary statistics for a "rev_phylo" object,
which is a bit more than just using print.
Usage
# S3 method for ctree
summary(object, ...)Examples
data(ctree_input)
x = ctrees(
ctree_input$CCF_clusters,
ctree_input$drivers,
ctree_input$samples,
ctree_input$patient,
ctree_input$sspace.cutoff,
ctree_input$n.sampling,
ctree_input$store.max
)
#> [ ctree ~ clone trees generator for CUK12345 ]
#>
#> # A tibble: 7 × 7
#> cluster nMuts is.driver is.clonal R1 R2 R3
#> <chr> <int> <lgl> <lgl> <dbl> <dbl> <dbl>
#> 1 1 72 TRUE FALSE 0 0.92 0
#> 2 2 69 TRUE TRUE 0.99 0.98 0.99
#> 3 3 48 FALSE FALSE 0 0 0.49
#> 4 4 29 FALSE FALSE 0.01 0.01 0.93
#> 5 5 24 TRUE FALSE 0.78 0 0
#> 6 6 23 TRUE FALSE 0.98 0.03 0.98
#> 7 7 15 FALSE FALSE 0 0.41 0
#>
#> ✔ Trees per region 1, 3, 1
#> ℹ Total 3 tree structures - search is exahustive
#>
#> ── Ranking trees
#> ✔ 3 trees with non-zero score, storing 3
summary(x[[1]])
#> [ ctree - ctree rank 1/3 for CUK12345 ]
#>
#> # A tibble: 7 × 7
#> cluster nMuts is.driver is.clonal R1 R2 R3
#> <chr> <int> <lgl> <lgl> <dbl> <dbl> <dbl>
#> 1 1 72 TRUE FALSE 0 0.92 0
#> 2 2 69 TRUE TRUE 0.99 0.98 0.99
#> 3 3 48 FALSE FALSE 0 0 0.49
#> 4 4 29 FALSE FALSE 0.01 0.01 0.93
#> 5 5 24 TRUE FALSE 0.78 0 0
#> 6 6 23 TRUE FALSE 0.98 0.03 0.98
#> 7 7 15 FALSE FALSE 0 0.41 0
#>
#> Tree shape (drivers annotated)
#>
#> \-GL
#> \-2 :: MET, TERT
#> |-1 :: RB1, IKZF1, KRAS
#> | \-7
#> \-6 :: EP300
#> |-4
#> | \-3
#> \-5 :: NF1
#>
#> Information transfer
#>
#> MET ---> RB1
#> MET ---> IKZF1
#> MET ---> KRAS
#> TERT ---> RB1
#> TERT ---> IKZF1
#> TERT ---> KRAS
#> GL ---> MET
#> GL ---> TERT
#> EP300 ---> NF1
#> MET ---> EP300
#> TERT ---> EP300
#>
#> Tree score 0.6
#>
#> CCF clusters:
#>
#> # A tibble: 7 × 7
#> cluster nMuts is.driver is.clonal R1 R2 R3
#> <chr> <int> <lgl> <lgl> <dbl> <dbl> <dbl>
#> 1 1 72 TRUE FALSE 0 0.92 0
#> 2 2 69 TRUE TRUE 0.99 0.98 0.99
#> 3 3 48 FALSE FALSE 0 0 0.49
#> 4 4 29 FALSE FALSE 0.01 0.01 0.93
#> 5 5 24 TRUE FALSE 0.78 0 0
#> 6 6 23 TRUE FALSE 0.98 0.03 0.98
#> 7 7 15 FALSE FALSE 0 0.41 0
#>
#> Drivers:
#>
#> # A tibble: 7 × 8
#> patientID variantID is.driver is.clonal cluster R1 R2 R3
#> <chr> <chr> <lgl> <lgl> <chr> <dbl> <dbl> <dbl>
#> 1 CRUK0002 RB1 TRUE FALSE 1 0 0.92 0
#> 2 CRUK0002 IKZF1 TRUE FALSE 1 0 0.92 0
#> 3 CRUK0002 KRAS TRUE FALSE 1 0 0.93 0
#> 4 CRUK0002 MET TRUE TRUE 2 0.99 0.98 0.99
#> 5 CRUK0002 TERT TRUE TRUE 2 0.99 0.98 0.99
#> 6 CRUK0002 NF1 TRUE FALSE 5 0.78 0 0
#> 7 CRUK0002 EP300 TRUE FALSE 6 0.96 0.03 0.98
#>
#> Pigeonhole principle: 12 0
#>
#> R1 R2 R3
#> 1 TRUE TRUE TRUE
#> 2 TRUE TRUE TRUE
#> 4 TRUE TRUE TRUE
#> 6 TRUE TRUE TRUE
#>
#> Goodness-of-fit: 1
#>
#>