simulateNetwork: Simulate Boolean Network

In levimcclenny/BoolFilter: Optimal Estimation of Partially Observed Boolean Dynamical Systems

Description

Simulates a Boolean network and generates noise of user-defined model preference and paramters.

Usage

simulateNetwork(net, n.data, p, obsModel)

Arguments

net	A boolean Network object (specified in BoolNet vernacular) to be simulated
n.data	Length of time-series to simulate
p	Process noise to build into the simulation. Should be 0<p<0.5.
obsModel	Parameters for the chosen observation model.

Details

The simulateNetwork function can simulate many observation models to create observational data. An overvoew of how to use the various types of observation models present in BoolFilter is given below:

• Bernoulli observation model requires only one parameter, aside from declaring the type, e.g.
  
  obsModel = list(type = 'Bernoulli', q = 0.05)
  

• Gaussian observation model requires a vector of the observation parameters, which include the mean and standard deviation of Boolean variables in inactivated and activated states. This will be defined as a vector, e.g.
  
  mu0 = 1
sigma0 = 2
mu1 = 5
sigma1 = 2
obsModel = list(type = 'Gaussian', model = c(mu0, sigma0, mu1, sigma1))
  

• Poisson observation model requires a list of parameters. This list will have 3 entries in addition to the type definition, for a total of 4 entries:

  • Sequencing depth s

  • Baseline espression in inactivated state, referred to as mu

  • The differential expression, referred to as delta, which must be input as a vector of the same length as the number of genes in the network.

  In this way, the user can define the exact observation parameter for each individual gene. For a 4-gene network, a potential obsModel parameter for a Poisson distribution could be defined as:
  
  obsModel = list(type = 'Poisson', s = 10.875, mu = 0.01, delta = c(2, 2, 2, 2))
  

• Negative-Binomial observation models also require a list of parameters. This list will have 4 entries in addition to the type definition, for a total of 5 entries:

  • Sequencing depth s

  • Baseline espression in inactivated state, referred to as mu

  • Differential expression, referred to as delta, which must be input as a vector of the same length as the number of genes in the network.

  • Inverse Dispersion, referred to as phi, which must also be input as a vector of the same length as the number of genes in the network.

  For a 4-gene network, a potential obsModel parameter for a Negative-Binomial observation model could be defined as:
  
  delta = c(2, 2, 2, 2)
phi = c(3, 3, 3, 3)
obsModel = list(type = 'NB', s = 10.875, mu = 0.01, delta, phi)
  

Value

X	Original Boolean state trajectory, without observation noise
Y	Observation trajectory

Examples

data(p53net_DNAdsb1)

#generate data from poisson observation model
dataPoisson <- simulateNetwork(p53net_DNAdsb1, n.data = 100, p = 0.02, 
                          obsModel = list(type = 'Poisson',
                                          s = 10.875, 
                                          mu = 0.01, 
                                          delta = c(2,2,2,2)))

#generate data from Bernoulli observation model
dataBernoulli <- simulateNetwork(p53net_DNAdsb1, n.data = 100, p = 0.02, 
                          obsModel = list(type = 'Bernoulli',
                                          q = 0.05))

levimcclenny/BoolFilter documentation built on May 21, 2019, 5:11 a.m.