plotCP.postDist: Function to plot the estimated posterior distribution for the changepoints (CPs) number and position

Description

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.

Usage

plotCP.postDist(CPpostDist, targetName = NULL, onepage = TRUE,
color1 = "green", color2 = "black", estimatedCPpos=NULL)

Arguments

CPpostDist

A list of 2 tables : 1)CPpostDist$CPnumberPostDist: A table containing the distribution for the number of CPs approximated with ARTIVAsubnet. 2)CPpostDist$CPpositionPostDist: A table containing the distribution for the position of the CPs approximated with function ARTIVAsubnet or CP.postDist

targetName

Name of the target gene (optional, default: targetName=NULL).

onepage

Boolean, if TRUE the two estimated posterior distributions are plotted in one window next to each other (optional, default: mfrow=TRUE).

color1

Color for plotting the estimated posterior distribution for the changepoints (CPs) number (default: color1="green").

color2

Color for plotting the estimated posterior distribution for the changepoints (CPs) position (default: color2="black").

estimatedCPpos

CP positions to be highlighted as most significant, e.g. CP positions estimated with function CP.postDist (optional, default: estimatedCPpos=NULL, if estimatedCPpos=NULL then the number of highlighted CPs is the maximum of CPpostDist$CPnumberPostDist and the positions are the top best of CPpostDist$CPpositionPostDist).

Value

NULL

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.

Examples

Run this code

# 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 ARTIVAsubnet
# 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= 20000,
#   savePictures=FALSE)
# 
# # compute the PC posterior distribution with other parameters
# outCPpostDist = CP.postDist(ARTIVAtest$Samples$CP, burn_in=500, 
# 			    segMinLength=3)
# 
# # plot the CP posterior distribution
# plotCP.postDist(outCPpostDist, targetName=paste("Target", targetGene), 
# 		  estimatedCPpos=outCPpostDist$estimatedCPpos)
# 
# ## End(Not run)

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