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.
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"), ... )
| resamples | Number of jackknife samples. |
|---|---|
| options.fit | List of parameters for fitting models. See |
| options.clustering | List of parameters for clustering with the germline node GL. See |
| cohort | A cohort object where fit and clusters have been computed. |
| removal | A number in |
| cores.ratio | Ratio of cores for parallel execution |
A cohort where a new jackknife field contains result from this analysis
if (FALSE) { TODO }