Function to

Description

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.

Usage

geneNetworkSummary(ARTIVAnet, edgesThreshold)

Arguments

Value

NULL

Author(s)

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

References

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.

See Also

ARTIVAnet,ARTIVAsubnet, ARTIVAsubnetAnalysis, CP.postDist,

segmentModel.postDist, plotCP.postDist

Examples

# 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)

# Print a summary of the obtained network 
geneNetworkSummary(ARTIVAtest$network, edgesThreshold = 0.3)

# List of target genes to be analyzed independantly with ARTIVA
targetGenes = c("TF3", 45, 50)
ARTIVAtest2 = ARTIVAnet(targetData = simulatedProfiles[targetGenes,],
  parentData = simulatedProfiles[parentGenes,],
  targetName = targetGenes,
  parentNames = parentGenes,
  segMinLength = 2,
  edgesThreshold = 0.6, 
  niter= 2000,
  savePictures=FALSE)

# Print a summary of the obtained network 
geneNetworkSummary(ARTIVAtest2, edgesThreshold = 0.3)

# 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)

# Print a summary of the network obtained with the 2nd analysis.
geneNetworkSummary(analysis2$network, edgesThreshold = 0.3)

## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ARTIVA)
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

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ARTIVA/geneNetworkSummary.Rd_%03d_medium.png", width=480, height=480)
> ### Name: geneNetworkSummary
> ### Title: Function to
> ### Aliases: geneNetworkSummary
> ### Keywords: graphics util
> 
> ### ** Examples
> 
> # 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: 
> ##D ARTIVAtest = ARTIVAsubnet(targetData = simulatedProfiles[targetGene,],
> ##D   parentData = simulatedProfiles[parentGenes,],
> ##D   targetName = targetGene,
> ##D   parentNames = parentGenes,
> ##D   segMinLength = 2,
> ##D   edgesThreshold = 0.6, 
> ##D   niter= 2000,
> ##D   savePictures=FALSE)
> ##D 
> ##D # Print a summary of the obtained network 
> ##D geneNetworkSummary(ARTIVAtest$network, edgesThreshold = 0.3)
> ##D 
> ##D # List of target genes to be analyzed independantly with ARTIVA
> ##D targetGenes = c("TF3", 45, 50)
> ##D ARTIVAtest2 = ARTIVAnet(targetData = simulatedProfiles[targetGenes,],
> ##D   parentData = simulatedProfiles[parentGenes,],
> ##D   targetName = targetGenes,
> ##D   parentNames = parentGenes,
> ##D   segMinLength = 2,
> ##D   edgesThreshold = 0.6, 
> ##D   niter= 2000,
> ##D   savePictures=FALSE)
> ##D 
> ##D # Print a summary of the obtained network 
> ##D geneNetworkSummary(ARTIVAtest2, edgesThreshold = 0.3)
> ##D 
> ##D # Re-compute a time-varying network from the output of function 
> ##D # ARTIVAsubnet with new analysis parameters
> ##D analysis2 = ARTIVAsubnetAnalysis(ARTIVAsubnet=ARTIVAtest,
> ##D   segMinLength = 3,
> ##D   edgesThreshold = 0.5,
> ##D   outputPath="ARTIVAsubnet2",
> ##D   savePictures=FALSE)
> ##D 
> ##D # Print a summary of the network obtained with the 2nd analysis.
> ##D geneNetworkSummary(analysis2$network, edgesThreshold = 0.3)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>