R/compute_clone_trees.R
compute_clone_trees.RdCreate clone tree phylogenies from the package
ctree, fitting the input data given to REVOLVER.
A set of patient ids can be given as input, by default all are
used. A parameter controls if you want to overwrite the trees
of the patient, where they already computed. Please refer to the
parameters of the ctrees constructor from package ctree
for the input parameters that you can use to tune the construction.
compute_clone_trees(
x,
patients = Stats_cohort(x) %>% pull(patientID),
overwrite = FALSE,
...
)A REVOLVER cohort with clone tree phylogenies for the
requested patients.
Other Cohort creation:
CCF_parser(),
compute_mutation_trees(),
input_custom_trees(),
revolver_check_cohort(),
revolver_cohort()
# Data released in the 'evoverse.datasets'
data('TRACERx_NEJM_2017_REVOLVER', package = 'evoverse.datasets')
print(TRACERx_NEJM_2017_REVOLVER)
#> [ REVOLVER - Repeated Evolution in Cancer ]
#>
#> Dataset : TRACERx NEJM 2017
#> Cohort : 99 patients, 450 variants and 79 driver events.
#>
#> Trees per patient : YES
#> Fit via TL : YES
#> REVOLVER clustering : YES
#> Jackknife statistics : YES
#>
#> For summary statistics see `?Stats_*(x)` with * = {cohort, drivers, trees, fits, clusters, ...}
#>
#> ┌───────────────────────────────────────────────────────────────────────────────────────────┐
#> │ │
#> │ WARNING - Some patients have only one clone with drivers; they will just be expanded. │
#> │ │
#> └───────────────────────────────────────────────────────────────────────────────────────────┘
#> # A tibble: 54 × 7
#> patientID numBiopsies numMutations numDriverMutations numClonesWithDriver
#> <chr> <int> <int> <int> <int>
#> 1 CRUK0007 2 3 3 1
#> 2 CRUK0010 2 3 3 1
#> 3 CRUK0012 2 1 1 1
#> 4 CRUK0018 4 4 4 1
#> 5 CRUK0019 2 1 1 1
#> 6 CRUK0021 2 4 4 1
#> 7 CRUK0025 3 3 3 1
#> 8 CRUK0026 2 4 4 1
#> 9 CRUK0028 2 2 2 1
#> 10 CRUK0029 6 4 4 1
#> # ℹ 44 more rows
#> # ℹ 2 more variables: numTruncalMutations <int>, numSubclonalMutations <int>
# We use the standard parameters with overwrite = TRUE
# otherwise the computation skips the patient
TRACERx_NEJM_2017_REVOLVER = compute_clone_trees(
TRACERx_NEJM_2017_REVOLVER,
patients = "CRUK0002",
overwrite = TRUE)
#>
#> ── Constructing Clone Trees [ctree - https://caravagn.github.io/ctree/] ────────
#>
#> ── PatientID: CRUK0002 ─────────────────────────────────────────────────────────
#>
#> [ ctree ~ clone trees generator for CRUK0002 ]
#>
#> # A tibble: 4 × 7
#> cluster nMuts is.driver is.clonal R1 R2 R3
#> <chr> <int> <lgl> <lgl> <dbl> <dbl> <dbl>
#> 1 1 3 TRUE FALSE 0 0.92 0
#> 2 2 2 TRUE TRUE 0.99 0.98 0.99
#> 3 5 1 TRUE FALSE 0.78 0 0
#> 4 6 1 TRUE FALSE 0.96 0.03 0.98
#>
#> ✔ Trees per region 1, 2, 1
#> ℹ Total 2 tree structures - search is exahustive
#>
#> ── Ranking trees
#> ✔ 2 trees with non-zero score, storing 2
Phylo(TRACERx_NEJM_2017_REVOLVER, 'CRUK0002', rank = 1)
#> [ ctree - ctree rank 1/2 for CRUK0002 ]
#>
#> # A tibble: 4 × 7
#> cluster nMuts is.driver is.clonal R1 R2 R3
#> <chr> <int> <lgl> <lgl> <dbl> <dbl> <dbl>
#> 1 1 3 TRUE FALSE 0 0.92 0
#> 2 2 2 TRUE TRUE 0.99 0.98 0.99
#> 3 5 1 TRUE FALSE 0.78 0 0
#> 4 6 1 TRUE FALSE 0.96 0.03 0.98
#>
#> Tree shape (drivers annotated)
#>
#> \-GL
#> \-2 :: MET, TERT
#> |-6 :: EP300
#> | \-5 :: NF1
#> \-1 :: RB1, IKZF1, KRAS
#>
#> 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 0.75
#>