QuantumClone (version 1.0.0.6)

QuantumCat: Data generation

Description

Creates plausible data as would be oserved by genome sequencing

Usage

QuantumCat(number_of_clones, number_of_mutations, ploidy = 2, depth = 100,
  number_of_samples = 2, Random_clones = F, contamination = NULL,
  Subclonal.CNA.fraction = NULL)

Arguments

number_of_clones

The wanted number of observable clones (meaning bearing at least 1 mutation)

number_of_mutations

The total observed number of mutations (across all clones)

ploidy

The general ploidy of the tumor. Default is 2. If "disomic" : only AB regions will be generated.

depth

The depth of sequencing (does not account for contamination). Default is 100x

number_of_samples

The number of samples on which the data should be simulated. Default is 2.

Random_clones

Should the number of clones be generated randomly (sample(1:10))

contamination

A numeric vector indicating the fraction of normal cells in each sample.

Subclonal.CNA.fraction

Cell fraction of the subclone that has subclonal CNA

Examples

# NOT RUN {
print("Generate small set of mutations from 2 differents clones...")
print("...in 1 sample, contaminated at 10% by normal cells")
 
QuantumCat(number_of_clones=2,number_of_mutations=50,number_of_samples=1,contamination=0.1)
# }