FullEM

A list of dataframes (one for each sample), generated by the Patient_schrodinger_cellularities() function.

Schrod

Number of clones to look for (mandatory if prior_center or prior_weight are null)

nclust

Clone coordinates (from another analysis) to be used

prior_center

Prior on the fraction of mutation in each clone

prior_weight

Numeric vector with the fraction of normal cells contaminating the sample

contamination

Stopping condition for the algorithm: what is the minimal tolerated difference of position 
or weighted between two steps

epsilon

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

optim

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.

Using HTS data, clusters mutations in order to recreate putative
clones from the data provided. It requires genotype at the location of the
variant as well as the depth of coverage and number of reads supporting the
mutation. Additional information may be provided, such as the contamination
in the tumor sample. This package also provides a function QuantumCat() which
simulates data obtained from tumor sequencing.

FullEM: Expectation Maximization algorithm

Description

Usage

Arguments