function_NonLinearNet: Dynamic Bayesian Network Inference Using Non-Linear...
In GRENITS: Gene Regulatory Network Inference Using Time Series

Description Usage Arguments Value References See Also Examples

Run Bayesian inference of non-linear interaction network. Non linear interactions are modelled using Penalised Splines. The function generates MCMC chains that can later be analysed.

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

`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_nonLinear". 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_nonLinear)
`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).

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 Gamma_mcmc (the indicator variables of Gibbs variable selection), Lambda_mcmc (the precision of each regression), Mu_mcmc (the intercept of each regression), Rho_mcmc (the network connectivity parameter), Tau_mcmc (the "smoothness parameter"), all_f (posterior mean of all functions), all_f_sqr (posterior mean of the square of all functions) and Full_F_sqr (posterior mean of the square of the sum of all functions, for each regression). For the files all_f and all_f_sqr functions are placed in column-wise order. The file is filled by placing all interactions for each regression one after another.

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

mcmc.defaultParams_nonLinear, analyse.output.

  # Synthetic data
  data(Athaliana_ODE)
  # Reduced data set to 3 genes 20 TP for faster run
  Athaliana_ODE.reduced <- Athaliana_ODE[c(1,3,5),1:20]
  # Folder where raw runs will be kept and later analysed
  output.folder <- paste(tempdir(), "/ExampleNonLinearNet", sep = "")
  # Run network inference and place raw results in output.folder
  NonLinearNet(output.folder , Athaliana_ODE.reduced)
  # Analyse raw results, place analysis plots and files in output.folder
  analyse.output(output.folder, Athaliana_ODE.reduced)