Description Usage Arguments Value Examples
This constructor creates a list of objects of class ''mtree'', 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 mtree
, plus the parameters of the sampler. See
mtree
for an explanation of the parameters.
1 2 | mtrees(binary_clusters, drivers, samples, patient, sspace.cutoff = 10000,
n.sampling = 5000, store.max = 100, evaluation = ">=")
|
binary_clusters |
Clusters of binary annotations 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, |
store.max |
When a number of trees are generated, scored and ranked, a maximum
of |
evaluation |
How Suppes conditions should be evaluated ('>=' or '>'). |
M |
The adjacency matrix defined to connect all the nodes of this tree. |
score |
A scalar score that can be associated to this tree. |
annotation |
Any string annotation that one wants to add to this 'ctree'. This will be used by some of the plotting functions that display 'ctree' objects. |
An list of objects of class "mtree"
that represent the trees that
can be fit to the data of this patient.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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
)
print(x[[1]])
plot(x[[1]])
|
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