Cancer Evolution Simulator

This simulator is developed for conducting our simulation study about cancer evolution, the details of which are described in our manuscript and website.

1. requirement

• C++11 compiler
• Python 2.7
• R 3.6 and the following libraries: gplots, vegan and RColorBrewer

2. installation

git clone https://github.com/atusiniida/canevosim.git
cd canevosim/src
(Modify Makefile for calling an adequate C++11 compiler, if necessary.)
make
cd ../
python python/canevosim.py -h

3. usage

usage: python bep.py [option]
option: # the value in the brackets indicates the default value.
　-r baseReplicationRate(0.000100) # corresponding to g₀ in our mauscript
　-d baseDeathRate(0.000000) # corresponding to d₀
　-c driverMutationContainerSize(5) # corresponding to N_d
　-g driverGenomeSize(10000) # number of mutable positions generating driver mutations
　-m driverMutationRate(0.010000) # corresponding to m_d
　-C passengerMutationContainerSize(1000) # corresponding to N_n
　-G passengerGenomeSize(1000000) # number of mutable positions generating neutral mutations
　-M passengerMutationRate(0.100000) # corresponding to m_n
　-D deathRateDecrease(1.000000) # corresponding to e
　-T maxTime(1000000) # corresponding to T
　-P maxPopulationSize(100000) # corresponding to P
　-p initialPopulationSize(1) # number of cells at the start of simulation
　-s tumorSampleSize(1000) # number of cells sampled for generating the output
　-F mutationFrequencyCutoff(0.000000) # clones whose frequency are less than this values are discarded for saving memory space.
　-k populationSizeChangeCutoff (0.000000, if positive, print time course data) # for specifying time points for obtaining evolutionary snapshots
　-K carryingCapacity(inf) # corresponding to m_e
　-S symmetricReplicationProbablity (1.000000) # corresponding to s
　-e differentiatedCellDeathRate (0.010000) # corresponding to d₀^d
　-i infinitizingCarryingCapacityProbability (0.000000) # corresponding to p_c
　-o outfilePrefix(out) # prefix of the result files

3. output

You obtain the following 5 result files:

• [outfilePrefix].dr # each line contains driver mutations in each cell.
• [outfilePrefix].ps # each line contains neutral mutations in each cell.
• [outfilePrefix].prm # containing parameter settings
• [outfilePrefix].stat # containing summary statistics
• [outfilePrefix].pdf # visualizing the mutation profile matrix

When symmetricReplicationProbablity < 1, you additionally obtain the following file:

• [outfilePrefix].df # each line contains differentiation status of each cell.

When infinitizingCarryingCapacityProbability > 0, you additionally obtain the following file:

• [outfilePrefix].ic # each line contains explosive driver mutations infinitizing carrying capacity in each cell.

When populationSizeChangeCutoff > 0, you obtain more files generated as evolutionary snapshots at every time point when the fold increase of the population size exceeds the populationSizeChangeCutoff value.