function_ReplicatesNet_student: Dynamic Bayesian Network Inference Using Linear Interactions...
In GRENITS: Gene Regulatory Network Inference Using Time Series

Description Usage Arguments Details Value References See Also Examples

Run Bayesian inference of linear interaction network on data with replicates. The replicates are assumed to follow a Student distribution. The function generates MCMC chains that can later be analysed.

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ReplicatesNet_student(     resultsFolder,   timeSeries,           numReps, 
		         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. Each times series must be placed one after another, so that the first n columns correspond to time series replicate one, the next n columns to time series replicate two, etc. Gene names may be included as row names.
`numReps`	Number of replicate time series included in timeSeries matrix.
`ParamVec`	A parameter vector created using "mcmc.defaultParams_student". 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_student)
`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).

The order in which the replicates are placed do not affect the output. In other words swapping timepoint 2 replicate 1 and timepoint 2 replicate 2 makes no difference. For the cases where a measurement is not available, an NaN may be used.

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), Rho_mcmc (the network connectivity parameter), DataMean_standarised (a times series with the mean of the replicates), Lambda_exp_mcmc (the precision of the replicate noise), Y_mean (the mean posterior value of the inferred "true mRNA"), DegFreedom_mcmc (degrees of freedom of the student distributed replicate noise) and acceptanceRatio (acceptance ratio of the Metropolis-Hastings degrees of freedom update).

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

mcmc.defaultParams_student, analyse.output.

  # Synthetic data
  data(Athaliana_ODE_4NoiseReps)
  # Folder where raw runs will be kept and later analysed
  output.folder <- paste(tempdir(), "/Example_ReplicatesNet_Student",sep="")
  # Run network inference and place raw results in output.folder
  ReplicatesNet_student(output.folder, Athaliana_ODE_4NoiseReps, numReps = 4)
  # Analyse raw results, place analysis plots and files in output.folder
  analyse.output(output.folder)