traceNetworks: Function to plot the network estimated with functions...
In ARTIVA: Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme VArying) Model

Description Usage Arguments Value Author(s) References See Also Examples

This function is used for plotting the network estimated with the ARTIVA procedure (ARTIVAnet, ARTIVAsubnet) and ARTIVAsubnetAnalysis for Auto Regressive TIme-VArying network inference.

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traceNetworks(ARTIVAnet, edgesThreshold, parentColor = "blue",
targetColor = "grey", parentgeneNames = TRUE, targetgeneNames = TRUE,
layout = "fruchterman.reingold", onepage=TRUE)

`ARTIVAnet`	Table containing the information to plot a time-varying regulatory network. In particular, this table can be obtained with function `ARTIVAsubnet`, `ARTIVAsubnetAnalysis` (output value `network`) or `ARTIVAnet` (unique output value). Each row of the table describes one edge. The columns, entitled Target, CPini, CPfinal, Parent, PostProb, describe the name of the target gene, the changepoints defining the start and the end of the regulation, the parent name and the estimated posterior probability of the edge.
`edgesThreshold`	Probability threshold for the selection of the edges to be plotted.
`parentColor`	Color for plotting the node representing parent genes (optional, default: `parentColor= "blue"`).
`targetColor`	Color for plotting the node representing target genes (optional, default: `targetColor= "grey"`).
`parentgeneNames`	Boolean, if `TRUE` the name of the parent gene is plotted (optional, default: `geneNames = TRUE`).
`targetgeneNames`	Boolean, if `TRUE` the name of the target gene is plotted (optional, default: `geneNames = TRUE`).
`layout`	Name of the function determining the placement of the vertices for drawing a graph, possible values among others: `"fruchterman.reingold", "geneLines",` `"random", "circle", "sphere", "kamada.kawai","spring",` `"reingold.tilford", "fruchterman.reingold.grid"`, see package `igraph0` for more details (default: `layout="fruchterman.reingold"`).
`onepage`	Boolean, if `TRUE`, all output pictures are plotted on one page only (optional, default: `onepage=TRUE`.

NULL

Original version by S. Lebre and G. Lelandais, contribution of D. Servillo to the final version.

Statistical inference of the time-varying structure of gene-regulation networks S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, BMC Systems Biology, 4:130, 2010.

ARTIVAnet,ARTIVAsubnet, ARTIVAsubnetAnalysis, CP.postDist,

segmentModel.postDist, plotCP.postDist

# Load the ARTIVA R package
library(ARTIVA)

# Load the dataset with simulated gene expression profiles
data(simulatedProfiles)

# Name of the target gene to be analyzed with ARTIVA 
targetGene = 1

# Names of the parent genes (typically transcription factors) 
parentGenes = c("TF1", "TF2", "TF3", "TF4", "TF5")


# Run the ARTIVAsubnet function
# Note that the number of iterations in the RJ-MCMC sampling is reduced 
# to 'niter=20000' in this example, but it should be increased (e.g. up to
# 50000) for a better estimation.

## Not run: 
ARTIVAtest = ARTIVAsubnet(targetData = simulatedProfiles[targetGene,],
  parentData = simulatedProfiles[parentGenes,],
  targetName = targetGene,
  parentNames = parentGenes,
  segMinLength = 2,
  edgesThreshold = 0.6, 
  niter= 2000,
  savePictures=FALSE)

# Re-compute a time-varying network from the output of function 
# ARTIVAsubnet with new analysis parameters
analysis2 = ARTIVAsubnetAnalysis(ARTIVAsubnet=ARTIVAtest,
  segMinLength = 3,
  edgesThreshold = 0.5,
  outputPath="ARTIVAsubnet2",
  savePictures=FALSE)

# Trace the obtained network.
traceNetworks(analysis2$network, edgesThreshold = 0.3)

## End(Not run)

Loading required package: MASS
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

[1] "--- !!! WARNING MESSAGE !!! ----"
[1] "Plots will not be saved (see the parameter savePictures)"
[1] "---------------------------------"
[1] "========================"
[1] "GENERAL INFORMATION"
[1] " *** Name of the analyzed target gene: 1"
[1] " *** Number of different time point measurements: 30"
[1] " *** Number of repeated measurements: 1"
[1] " *** Number of potential parent gene(s): 5"
[1] "========================"
[1] "ARTIVA PARAMETERS"
[1] " **** Time delay considered in the auto-regressive process: 1"
[1] " **** Minimal length to define a temporal segment: 2 time points"
[1] " **** Maximal number of changepoints (CPs): 13"
[1] " **** Number of CPs at the algorithm initialization: 5"
[1] " **** Number of iterations: 2000"
[1] " **** Is PSRF factor calculated? FALSE"
[1] "========================"
[1] "STEP 1: Starting the initialisation procedure"
[1] "STEP 2: Starting the ARTIVA RJ-MCMC procedure"
[1] "Running 2000 iterations:"
10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
[1] "STEP 3: Computing posterior distributions for the changepoints (CPs) number and location"
[1] "Saving Estimations at: /work/tmp/ARTIVAsubnet/Estimations"
[1] "2 different temporal segments were identified (maximizing the CPs posterior distribution)"
[1] "STEP 4: Computing the posterior distribution for the regulatory models in each temporal phase"
[1] "Graphical representation of the ARTIVA results, analyzing the target gene 1 and searching for potential regulatory interaction with 5 parent genes"
[1] "...............Ending ARTIVA analysis................."
[1] "========================"
[1] "ARTIVA subnet Analysis"
[1] " *** Name of the analyzed target gene: 1"
[1] " **** Minimal length to define a temporal segment: 3 time points"
[1] "========================"
[1] "2 different temporal segments were identified (maximizing the CPs posterior distribution)"
[1] "Computing the posterior distribution for the regulatory models in each temporal phase"
[1] "Graphical representation of the ARTIVA results, analyzing the target gene 1 and searching for potential regulatory interaction with 5 parent genes"
[1] "WARNING : The coefficients for edges with posterior probability below 0.5 were not estimated (grey edges) in the network to be plotted."