QuantumCat: Data generation

In DeveauP/QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

Description

Creates plausible data as would be observed by genome sequencing

Usage

QuantumCat(
  number_of_clones,
  number_of_mutations,
  ploidy = 2,
  depth = 100,
  number_of_samples = 2,
  Random_clones = FALSE,
  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

Value

list of dataframes containing observations of a tumor (1 dataframe / sample)

Examples

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)

DeveauP/QuantumClone documentation built on Oct. 29, 2021, 8:56 a.m.