simulate(minclonesize, maxclonesize, independentclones::Bool; <keyword arguments>)

Return simulation with frequency of subclones >minclones & <maxclonesize and specify whether subclones are independent or not (ie nested or not). Only applicable to >1 subclone.

simulate(minclonesize, maxclonesize, mindiff; <keyword arguments>)

Return simulation with frequency of subclones >minclones & <maxclonesize and subclone frequency are at least mindiff and have at least minmutations mutations.

simulate(minclonesize, maxclonesize, mindiff; <keyword arguments>)

Return simulation with frequency of subclones >minclones & <maxclonesize and subclone frequency are at least mindiff apart.

simulate(minclonesize, maxclonesize; <keyword arguments>)

Return simulation with frequency of subclones >minclones & <maxclonesize.

simulate(; <keyword arguments>)

Simulate a stochastic model of tumour growth with a single subclone introduced at a random time and with a random fitness advantage. Output return synthetic sequencing data. ...

Arguments

• read_depth = 200.0: Mean read depth of the target data set
• detectionlimit = 5/read_depth: Ability to detect low frequency variants. Assumes 5 reads are needed to call a variant.
• μ = 10.0: Mutation rate per division per genome (this will timesed by ploidy for the mutation rate per cell). At each division a Poisson random variable with mean μ is sampled.
• clonalmutations = 100.0: Number of clonal mutations present in the first cell.
• nclones = 1: Number of subclones introduced
• Nmax = 10^4: Maximum population size.
• ρ = 0.0: Overdispersion parameter for beta-binomial model of sequencing data. ρ = 0.0 means model is binomial sampling
• timefunction = exptime: Function for KMC algorithm timestep. exptime returns an exponentially distributed random variable, if you would rather return the mean of the distribution define a function that returns 1. ie returnone() = 1.
• ploidy = 2: ploidy of the genome
• d = 0.0: Death rate of the thost population in the tumour
• b = log(2): Birth rate of the population. Set to log(2) so that tumour doubles with each unit increase in t in the absence of cell death.
• fixedmu = false: If set to false number of mutations per division is fixed and not sampled from a poisson distribution.

...

simulatestemcells(; <keyword arguments>)

Simulate a stochastic model of tumour growth with a stem cell architecture. Output return synthetic sequencing data. ...

Arguments

• α = 0.1: Symmetric division rate
• maxdivisions = 5: Maximum number of divisions of differentiated cells.
• read_depth = 200.0: Mean read depth of the target data set
• detectionlimit = 5/read_depth: Ability to detect low frequency variants. Assumes 5 reads are needed to call a variant.
• μ = 10.0: Mutation rate per division. At each division a Poisson random variable with mean μ is sampled.
• clonalmutations = 100.0: Number of clonal mutations present in the first cell.
• Nmax = 10^4: Maximum population size.
• ρ = 0.0: Overdispersion parameter for beta-binomial model of sequencing data. ρ = 0.0 means model is binomial sampling
• timefunction = exptime: Function for KMC algorithm timestep. exptime returns an exponentially distributed random variable, if you would rather return the mean of the distribution define a function that returns 1. ie returnone() = 1.
• ploidy = 2: ploidy of the genome
• d = 0.0: Death rate of the thost population in the tumour
• b = log(2): Birth rate of the population. Set to log(2) so that tumour doubles with each unit increase in t in the absence of cell death.
• fixedmu = false: If set to false number of mutations per division is fixed and not sampled from a poisson distribution.

...

show(sresult::Simulation)

Print out summary of simulation.