`CancerSeqSim.simulate`

— Method`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.

`CancerSeqSim.simulate`

— Method`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.

`CancerSeqSim.simulate`

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

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

`CancerSeqSim.simulate`

— Method`simulate(minclonesize, maxclonesize; <keyword arguments>)`

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

`CancerSeqSim.simulate`

— Method`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.

...

`CancerSeqSim.simulatestemcells`

— Method`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.

...

`Base.show`

— Method`show(sresult::Simulation)`

Print out summary of simulation.