R/revolver_check_cohort.R
revolver_check_cohort.RdPerform some basic diagnostic of a cohort object. It will inform of patients without drivers and other information that can be used to reshape the data before fitting a model.
revolver_check_cohort(x, stopOnError = FALSE)
| x | A |
|---|---|
| stopOnError | Whether or not it should raise a stop on error. |
Nothing, all relevant information are print to screen
and the stop is raised only if stopOnError = TRUE.
Other Cohort creation:
CCF_parser(),
compute_clone_trees(),
compute_mutation_trees(),
input_custom_trees(),
revolver_cohort()
# Data released in the 'evoverse.datasets' data('TRACERx_NEJM_2017_REVOLVER', package = 'evoverse.datasets') revolver_check_cohort(TRACERx_NEJM_2017_REVOLVER)#> ┌───────────────────────────────────────────────────────────────────────────────────────────┐ #> │ │ #> │ WARNING - Some patients have only one clone with drivers; they will just be expanded. │ #> │ │ #> └───────────────────────────────────────────────────────────────────────────────────────────┘ #> # A tibble: 54 x 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 #> # … with 44 more rows, and 2 more variables: numTruncalMutations <int>, #> # numSubclonalMutations <int>#> [ 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 x 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 #> # … with 44 more rows, and 2 more variables: numTruncalMutations <int>, #> # numSubclonalMutations <int>