LinearNet {GRENITS} | R Documentation

## 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

### See Also

`mcmc.defaultParams_Linear, analyse.output`.

### Examples

      # 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)

[Package GRENITS version 1.39.0 Index]