Description Usage Arguments Value Author(s) References See Also Examples
View source: R/plotCP.postDist.R
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.
1 2 | plotCP.postDist(CPpostDist, targetName = NULL, onepage = TRUE,
color1 = "green", color2 = "black", estimatedCPpos=NULL)
|
CPpostDist |
A list of 2 tables :
1) |
targetName |
Name of the target gene (optional, default: |
onepage |
Boolean, if TRUE the two estimated posterior distributions are
plotted in one window next to each other (optional, default: |
color1 |
Color for plotting the estimated posterior distribution for the
changepoints (CPs) number (default: |
color2 |
Color for plotting the estimated posterior distribution for the
changepoints (CPs) position (default: |
estimatedCPpos |
CP positions to be highlighted as most
significant, e.g. CP positions estimated with function
|
NULL
S. Lebre and G. Lelandais.
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
,
CP.postDist
, segmentModel.postDist
,
ARTIVAsubnetAnalysis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # 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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