ctrees: Construct a 'ctree' clone tree computing its structure.

In caravagnalab/ctree: ctree - Clone trees in cancer evolution

Construct a 'ctree' clone tree computing its structure.

Description

This constructor creates a list of objects of class ''ctree'', after using a sampling strategy to determine possible trees that fit the data. The strategy to sample trees can be controlled, a maximum number of trees can be sampled with a Monte Carlo procedure and the actual process can be exhausted if there are less than a number of available trees to fit the data.

Note that the parameters of this function includes the same parmeters of function ctree, plus the parameters of the sampler. See ctree for an explanation of the parameters.

Usage

ctrees(
  CCF_clusters,
  drivers,
  samples,
  patient,
  sspace.cutoff = 10000,
  n.sampling = 5000,
  store.max = 100
)

Arguments

CCF_clusters	Clusters of Cancer Cell Fractions available in the data of this patient. See the package vignette to see the format in which this should be specified.
drivers	A list of driver events that should be annotated to each one of the input clusters contained in the 'CCF_clusters' parameter. See the package vignette to see the format in which this should be specified.
samples	A vector of samples names (e.g., the biopsies sequenced for this patient).
patient	A string id that represent this patient.
sspace.cutoff	If there are less than this number of tree available, all the structures are examined in an exhaustive fashion. Otherwise, if there are more than this, a Monte Carlo sampler is used.
n.sampling	If a Monte Carlo sampler is used, n.sampling distinct trees are sampled and scored.
store.max	When a number of trees are generated, scored and ranked, a maximum of store.max are returned to the user (these are selected following the ranking).

Value

An list of objects of class "ctree" that represent the trees that can be fit to the data of this patient..

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
   )
   
print(x[[1]])
plot(x[[1]])

caravagnalab/ctree documentation built on May 12, 2022, 4:42 p.m.