LinearNet: Dynamic Bayesian Network Inference Using Linear Interactions

Description

Run Bayesian inference of linear interaction network. The function generates MCMC chains that can later be analysed.

Usage

LinearNet( resultsFolder,        timeSeries,   ParamVec = NULL,  chains = 2, user.seeds = NULL, Regulators = NULL, 
	    fixMe = NULL)

Arguments

resultsFolder

Name of output folder. The folder will be created and the output of the run will be placed there.

timeSeries

Data matrix containing gene expression time series. Where genes will be placed in rows and time points in columns. Gene names may be included as row names.

ParamVec

A parameter vector created using "mcmc.defaultParams_Linear". If none is given, default parameters will be used. The vector contains parameters associated to the priors as well as MCMC run length. (See mcmc.defaultParams_Linear)

chains

Number of MCMC chains to run.

user.seeds

An optional vector with seeds to use for MCMC chains.

Regulators

An optional vector with the indices of which genes are regulators. If provided, all non-regulator genes will not be allowed to regulate.

fixMe

An optional matrix of size genes x genes, where columns represent regulators and rows regulated genes. The matrix informs the model of network connections known to be present/absent. For each position use either 0 (no regulation, fix off), 1 (known regulatory interaction, fix on) or NaN (no information, do not fix).

Value

For each chain run, a folder (chain1, chain2, ...) will be created and the output of the MCMC run will be placed there. The files will be B_mcmc (the coeffcients of the linear regression), Gamma_mcmc (the indicator variables of Gibbs variable selection), Lambda_mcmc (the precision of each regression), Mu_mcmc (the intercept of each regression) and Rho_mcmc (the network connectivity parameter).

References

Morrissey, E.R., Juarez, M.A., Denby, K.J. and Burroughs, N.J. 2010. On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 2010; doi: 10.1093/bioinformatics/btq421

Morrissey, E.R., Juarez, M.A., Denby, K.J. and Burroughs, N.J. 2011 Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression Biostatistics 2011; doi: 10.1093/biostatistics/kxr009

Examples

Run this code

  # Load A. thaliana circadian clock ODE generated data
  data(Athaliana_ODE)
  # Folder where raw runs will be kept and analysed
  output.folder <- paste(tempdir(), "/Example_LinearNet",sep="")
  # Run network inference, place raw results in output.folder
  LinearNet(output.folder, Athaliana_ODE)
  # Analyse raw results, place analysis plots and files in output.folder
  analyse.output(output.folder)