m.step

Matrix giving the probability of each mutation to belong to a specific clone

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

Schrod

Weights from the previous optimization step (used as priors for this step)

previous.weights

Clone coordinates from previous optimization step (used as priors for this step)

previous.centers

Numeric vector with the fraction of normal cells contaminating the sample

contamination

Factor to compute the probability: makes transition between the cellularity of the clone and the frequency observed

adj.factor

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, based on the previously calculated expectation

Maximization

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.

m.step: Maximization step

Description

Usage

Arguments