Optimization of clone positions and proportion of mutations in each clone followed by filtering on most likely possibility for each mutation and a re-optimization. Then gives out the possibility with maximal likelihood Relies on foreach
parallelEM(Schrod, nclust, epsilon, contamination, prior_center = NULL,
prior_weight = NULL, Initializations = 1, optim = "default",
keep.all.models = FALSE)A list of dataframes (one for each sample), generated by the Patient_schrodinger_cellularities() function.
Number of clones to look for (mandatory if prior_center or prior_weight are null)
Stopping condition for the algorithm: what is the minimal tolerated difference of position or weighted between two steps
Numeric vector with the fraction of normal cells contaminating the sample
Clone coordinates (from another analysis) to be used
Prior on the fraction of mutation in each clone
Maximal number of independant initial condition tests to be tried
use L-BFS-G optimization from R ("default"), or from optimx ("optimx"), or Differential Evolution ("DEoptim")
Should the function output the best model (default; FALSE), or all models tested (if set to true)