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

Value

NULL

Author(s)

S. Lebre and G. Lelandais.

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, CP.postDist, segmentModel.postDist, ARTIVAsubnetAnalysis

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 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)

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)

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> 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/plotCP.postDist.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotCP.postDist
> ### Title: Function to plot the estimated posterior distribution for the
> ###   changepoints (CPs) number and position
> ### Aliases: plotCP.postDist
> ### 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 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: 
> ##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= 20000,
> ##D   savePictures=FALSE)
> ##D 
> ##D # compute the PC posterior distribution with other parameters
> ##D outCPpostDist = CP.postDist(ARTIVAtest$Samples$CP, burn_in=500, 
> ##D 			    segMinLength=3)
> ##D 
> ##D # plot the CP posterior distribution
> ##D plotCP.postDist(outCPpostDist, targetName=paste("Target", targetGene), 
> ##D 		  estimatedCPpos=outCPpostDist$estimatedCPpos)
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>