QuantumCat: Data generation
In QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

Creates plausible data as would be oserved by genome sequencing

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

`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

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)