revolver_jackknife: Compute clusters stability via the jackknife.

In caravagn/revolver: REVOLVER - Repeated Evolution in Cancer

Compute clusters stability via the jackknife.

Description

For a set of clusters computed via revolver_cluster, you can compute their stability via a jackknife. routine. This funcion runs a kind of bootstrap routine where subset of patients - a desired number - is removed from the cohort and before re-computing the clusters. In this way, the co-clustering probability of each patient is computed, which leads to a mean clustering stability for each one of the original set of clusters and a frequency for the inference of a particular evolutionary trajectory.

A number of functions are available to plot the results from this jackknife analysis. Note that in general if you require a large number of runs (i.e., resamples), this computation can take some time. This implementation leverages on the easypar package to run in parallel all the re-runs, therefore we suggest to run it on a multi-core machine to appreciate a speed up in the computations.

Usage

revolver_jackknife(
  x,
  resamples = 100,
  leave.out = 0.1,
  options.fit = list(initial.solution = NA, max.iterations = 10, n = 10),
  options.clustering = list(min.group.size = 3, hc.method = "ward", split.method =
    "cutreeHybrid"),
  ...
)

Arguments

resamples	Number of jackknife samples.
options.fit	List of parameters for fitting models. See revolver_fit.
options.clustering	List of parameters for clustering with the germline node GL. See revolver_cluster.
cohort	A cohort object where fit and clusters have been computed.
removal	A number in [0,1] for the percentage of samples to leave out in each jackknife iteration.
cores.ratio	Ratio of cores for parallel execution

Value

A cohort where a new jackknife field contains result from this analysis

Examples

## Not run: 
 TODO

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caravagn/revolver documentation built on May 21, 2022, 5:48 p.m.