Bayesian Inference of Boolean Genetic Networks

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) .

BBNI

BBNI (Bayesian Boolean Network Inference) is a Bayesian approach to Boolean gene regulatory network inference from noisy gene expression data. The method is discussed in more detail in Han et al. (2014). Unlike methods that return a single best-fit network topology, such as REVEAL and BFE, found in BoolNet, BBNI uses Markov chain Monte Carlo (MCMC) to sample from a joint posterior distribution of network topologies and Boolean transition functions. BBNI deliberately takes biological noise into account and allows for summarization (such as Bayesian model averaging, or BMA) that stabilizes results through posterior edge probabilities rather than a single point estimate.

Installation

Install the stable version from CRAN:

install.packages("BBNI")

The development version can be installed from GitHub:

# install.packages("devtools")
devtools::install_github("anson-li8/BBNI")

Performance

BBNI’s MCMC sampler was significantly optimized after the initial CRAN release. Controlled benchmarks show roughly a 10-fold speedup for 20-node networks; exact runtimes depend on hardware and system load.

Example

The following example provides a minimal check that the package loads successfully and can generate simulated data:

library(BBNI)

set.seed(1)
true_network <- GenerateNetwork(num.node = 5)
dummy_data <- GenerateSample(
  trans_matrix = true_network,
  num.node = 5,
  SampleSize = 20,
  para = rep(0.5, 5),
  error = matrix(0, nrow = 5, ncol = 20)
)
dummy_data
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    0    0    0    0    0    1    1    1    0     0     1     1     1     0
#> [2,]    0    0    0    0    0    0    0    1    0     0     0     0     1     0
#> [3,]    0    1    1    1    1    1    0    0    0     1     1     0     0     0
#> [4,]    0    0    0    0    0    1    1    0    0     0     1     1     0     0
#> [5,]    1    1    1    1    0    0    1    1    1     0     0     1     1     0
#>      [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,]     1     1     1     1     0     0
#> [2,]     0     0     0     1     0     0
#> [3,]     1     0     0     0     0     1
#> [4,]     1     1     1     0     0     0
#> [5,]     0     0     1     1     1     1

For a complete demonstration running the MCMC sampler and evaluating final convergence and overall network recovery, see the Introduction to BBNI vignette.

Citation

To cite package 'BBNI' in publications use:

  Han S, Wong RKW, Lee TCM, Shen L, Li S-YR, Fan X (2014). A Full
  Bayesian Approach for Boolean Genetic Network Inference. PLoS ONE
  9(12): e115806. doi:10.1371/journal.pone.0115806

License

BSD-3-Clause