EM_clustering: Expectation Maximization

Description Usage Arguments

Description

Maximization of the likelihood given a mixture of binomial distributions

Usage

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EM_clustering(Schrod, contamination, prior_weight = NULL,
  clone_priors = NULL, maxit = 8, nclone_range = 2:5, epsilon = 5 *
  (10^(-3)), ncores = 2, model.selection = "BIC", optim = "default",
  keep.all.models = FALSE)

Arguments

Schrod

List of dataframes, output of the Schrodinger function or the EM algorithm

contamination

The fraction of normal cells in the sample

prior_weight

If known a list of priors (fraction of mutations in a clone) to be used in the clustering

clone_priors

If known a list of priors (cell prevalence) to be used in the clustering

maxit

Maximal number of independant initial condition tests to be tried

nclone_range

Number of clusters to look for

epsilon

Stop value: maximal admitted value of the difference in cluster position and weights between two optimization steps.

ncores

Number of CPUs to be used

model.selection

The function to minimize for the model selection: can be "AIC", "BIC", or numeric. In numeric, the function uses a variant of the BIC by multiplication of the k*ln(n) factor. If >1, it will select models with lower complexity.

optim

use L-BFS-G optimization from R ("default"), or from optimx ("optimx")

keep.all.models

Should the function output the best model (default; FALSE), or all models tested (if set to true)



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