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Simulate Boolean Network Observation Dataset

Source: R/simulate.R
GenerateSample.Rd

Simulates a synthetic binary observation dataset (\(G\)) based on a given directed acyclic graph topology with Boolean rules. It starts by running independent Bernoulli trials on root nodes. The remaining non-root nodes are calculated based on their assigned Boolean logic functions and parent states. A pre-generated binary noise matrix is applied via a bitwise XOR operation to occasionally flip the Boolean outputs, injecting natural biological noise expected by the model.

Usage

GenerateSample(
  trans_matrix,
  SampleSize = 50,
  num.node = nrow(trans_matrix),
  para = rep(0.5, nrow(trans_matrix)),
  error = matrix(0, nrow = nrow(trans_matrix), ncol = SampleSize),
  timeseries = TRUE
)

Arguments

trans_matrix

A square matrix combining the network topology \(T\) and integer-coded Boolean logic functions \(F\) assigned to each directed edge.

SampleSize

An integer representing the total number of samples or time points to simulate. Defaults to 50 if not specified.

num.node

An integer representing the total number of network nodes. Defaults to nrow(trans_matrix) if not specified.

para

A numeric vector of baseline success probabilities (\(\theta_i\)) used to generate the expression states of root nodes via independent Bernoulli trials. Defaults to a rep(0.5, nrow(trans_matrix)) if not specified.

error

A pre-generated binary noise matrix applied to occasionally flip Boolean outputs, injecting natural noise. Defaults to a zero matrix of size nrow(trans_matrix) x SampleSize if not specified (no noise).

timeseries

Logical. If TRUE, simulates time-lagged data where child state at time \(t\) is determined by parent stated at \(t-1\). If FALSE, simulates independent samples where child and parent states are resolved simultaneously. Defaults to TRUE.

Value

A simulated binary gene expression matrix \(G\), where rows represent individual genes/nodes and columns represent samples.

Examples

# 1. Generate a 5-node network
set.seed(123)
num_nodes <- 5
sample_size <- 10
true_network <- GenerateNetwork(num.node = num_nodes)

# 2. Set baseline probabilities and simulate zero-noise error matrix
root_probs <- rep(0.5, num_nodes)
error_matrix <- matrix(0, nrow = num_nodes, ncol = sample_size)

# 3. Generate cross-sectional (independent mode) synthetic data
dummy_data <- GenerateSample(
  trans_matrix = true_network,
  SampleSize = sample_size,
  para = root_probs,
  error = error_matrix,
  timeseries = FALSE
)
print(dummy_data)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,]    1    0    0    0    1    0    0    1    0     1
#> [2,]    1    0    0    0    1    0    0    1    0     1
#> [3,]    1    0    0    0    1    0    0    1    0     1
#> [4,]    1    0    0    0    1    0    0    1    0     1
#> [5,]    0    1    1    1    0    1    1    0    1     0