Simulate Time-Series Observation Dataset

Simulates a synthetic time-series observation dataset (\(G\)). It starts by running independent Bernoulli trials on root nodes. The remaining non-root nodes are calculated from time \(t-1\) to time \(t\) using their assigned Boolean logic functions. 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)
)

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 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).

Value

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

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 the synthetic time-series data
dummy_data <- GenerateSample(
  trans_matrix = true_network,
  num.node = num_nodes,
  SampleSize = sample_size,
  para = root_probs,
  error = error_matrix
)
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,]    0    0    1    0    0    0    1    0    0     1
#> [3,]    0    1    0    0    0    1    0    0    1     0
#> [4,]    0    1    0    0    0    1    0    0    1     0
#> [5,]    0    1    1    1    1    1    1    1    1     1