Package {BBNI}

BBNI: Bayesian Inference of Boolean Genetic Networks

Description

Implements a fully Bayesian Markov chain Monte Carlo (MCMC) approach for inferring the topology and Boolean logic transition functions of gene regulatory networks from noisy, binary time-series expression data. Network structure and Boolean rules are sampled jointly from their posterior distribution, providing principled uncertainty quantification rather than a single point estimate. Method described in Han et al. (2014) doi:10.1371/journal.pone.0115806.

Author(s)

Maintainer: Anson Li liyuanrui618@gmail.com

Authors:

• Anson Li liyuanrui618@gmail.com

• Shengtong Han shengtong.han@marquette.edu

See Also

Useful links:

• https://anson-li8.github.io/BBNI/

• https://github.com/anson-li8/BBNI

• Report bugs at https://github.com/anson-li8/BBNI/issues

Generate Initial Network Topology

Description

Randomly generates a valid directed acyclic graph (DAG) topology T and assigns a corresponding Boolean transition function F to each node. The algorithm samples parent set configurations, keeping the constraint that the maximum in-degree for any node is 2, and further ensures the resulting structure does not contain directed cyclic loops.

Usage

GenerateNetwork(num.node)

Arguments

Value

A square transition function matrix combining the initial DAG topology with randomly assigned Boolean logic functions. Elements with a value of 0 indicate no directed edge, while positive integers indicate the presence of an edge and specify the defining Boolean function type (1-14).

Examples

# Generate a true network topology and Boolean rules for 5 nodes
set.seed(123)
true_network <- GenerateNetwork(num.node = 5)
print(true_network)

Simulate Time-Series Observation Dataset

Description

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, num.node, SampleSize, para, error)

Arguments

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)

Execute Metropolis-within-Gibbs MCMC Sampler for Boolean Networks

Description

Executes a Metropolis-within-Gibbs Markov chain Monte Carlo (MCMC) algorithm to sample from the joint posterior distribution of Directed Acyclic Graph (DAG) topologies (T) and Boolean logic transition functions (F). The algorithm iterates through individual network nodes and proposes parent set mutations (edge additions, removals, or swaps) paired with transition function reassignments to one of 14 candidate Boolean rules. Proposed states transitions are strictly verified to follow the DAG constraint and evaluated with a Metropolis-Hastings acceptance threshold using log-posterior values.

Usage

run_bbni(
  GeneData,
  num.node,
  SampleSize,
  prior_para,
  num_update,
  penalty,
  prop.ratio,
  verbose = FALSE
)

Arguments

Value

A list containing the full trajectory of the MCMC chain. Specifically, networks (a list of sampled transition function matrices) and log_posterior (a numeric vector of log-posterior scores for each iteration). These represent samples drawn from the marginal posterior distribution P(T,F|G) used for Bayesian model averaging.

Examples

  # 1. Define network parameters
  set.seed(235)
  num_nodes <- 10
  sample_size <- 50

  # 2. Generate true network and simulate data
  true_network <- GenerateNetwork(num.node = num_nodes)

  # Set up Beta priors for root-node probabilities and the noise rate
  prior_para <- matrix(3, nrow = num_nodes + 1, ncol = 2)
  prior_para[num_nodes + 1, 1] <- 2
  prior_para[num_nodes + 1, 2] <- 100

  # Simulate parameters
  para <- numeric(num_nodes + 1)
  for (i in 1:(num_nodes + 1)) {
    para[i] <- stats::rbeta(1, prior_para[i, 1], prior_para[i, 2])
  }
  para[num_nodes + 1] <- 0.1 # Fixed noise rate for simulation

  error_matrix <- matrix(stats::rbinom(num_nodes * sample_size, 1, para[num_nodes + 1]),
    nrow = num_nodes, ncol = sample_size
  )

  dummy_data <- GenerateSample(
    trans_matrix = true_network,
    num.node = num_nodes,
    SampleSize = sample_size,
    para = para,
    error = error_matrix
  )

  # 3. Run the MCMC sampler (silently)
  mcmc_results <- run_bbni(
    GeneData = dummy_data,
    num.node = num_nodes,
    SampleSize = sample_size,
    prior_para = prior_para,
    num_update = 100, # Scaled down for example speed
    penalty = 0.1,
    prop.ratio = 0.1
  )

  # 4. Inspect results
  tail(mcmc_results$log_posterior)