This package generates Reversible Jump MCMC (RJ-MCMC) sampling for approximating the posterior distribution of a time varying regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems Biology, 2010).
● Data Source:
CranContrib
● Keywords: DBN, graphical model, inference, network inference, time series
● Alias: ARTIVA, ARTIVA-package
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This function generates Reversible Jump MCMC (RJ-MCMC) sampling for approximating the posterior distribution of a time varying regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems Biology, 2010).
● Data Source:
CranContrib
● Keywords: DBN, graphical model, inference, network inference, time series
● Alias: ARTIVAsubnet
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0 images
This function is used for printing a summary of the gene network estimated with the ARTIVA procedure (ARTIVAnet, ARTIVAsubnet) for Auto Regressive TIme-VArying network inference.
This function is used for plotting the estimated changepoint number and position posterior distribution after running the ARTIVA procedure (function ARTIVAsubnet) for Auto Regressive TIme-VArying network inference.
This function estimates a regulatory time-varying network from the output of function ARTIVAsubnet. A graphical representation in a pdf file and estimated values are provided in text files. This function is used in function ARTIVAsubnet when parameter segmentAnalysis=TRUE. This function can be used separately for re-computing a time-varying network from the output of function ARTIVAsubnet with new analysis parameters segMinLength, edgesThreshold, CPpos, layout, ... see detail below.
● Data Source:
CranContrib
● Keywords: DBN, graphical model, inference, network inference, time series
● Alias: ARTIVAsubnetAnalysis
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This function runs function ARTIVAsubnet for all target genes in targetData successively. This function generates Reversible Jump MCMC (RJ-MCMC) sampling for approximating the posterior distribution of a time varying regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model (for a detailed description of the algorithm, see ARTIVAsubnet and see Lebre et al. BMC Systems Biology, 2010). A network representing the interactions between the factor genes and the target genes is estimated and plotted.
● Data Source:
CranContrib
● Keywords: DBN, graphical model, inference, network inference, time series
● Alias: ARTIVAnet
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0 images
Plots an overview of some possible priors used in the ARTIVAsubnet function for the number of changepoints (resp. incoming edges) according to a given number of maximum changepoints maxCP (resp. incoming Edges maxPred) when parameters (alphaCP, betaCP for the CPs or alphaEdges, betaEdges for the edges) in function ARTIVAsubnet are set to default (alpha=1, beta=0.5). In the ARTIVAsubnet procedure, the number of CPs (respectively the number of incoming edges) is sampled from a truncated Poisson with mean lambda, where lambda is drawn from an Inverse Gamma distribution (alpha, beta), see Lebre et al. (2010) for more details.
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