Description Usage Arguments Value Author(s) References See Also Examples
Using the ouput RJMCMC samples of functions ARTIVAsubnet
, this function estimates posterior distributions for the number of CPs and their position.
1  CP.postDist(CPsamples, burn_in=NULL, segMinLength=2)

CPsamples 
A matrix with the different iterations (in row)
performed with the 
burn_in 
Number of initial iterations that are discarded for the
estimation of the model distribution (posterior
distribution). The 
segMinLength 
Minimal length (number of time points) to define a
temporal segment. Must be  strictly  greater than 1 if there is
no repeated measurements for each time point in arguments

A list of 4 elements:
1) CPnumber
: a table containing the approximate posterior distribution for the number of CPs.
2) CPposition
: a table containing the approximate posterior
distribution for the CPs position.
3) estimatedCPnumber
: number of CP position with the greatest
posterior probability according to the approximate posterior
distribution for the number of CPs CPnumber
.
4) estimatedCPpos
: a table containing the
estimatedCPnumber
most significant CP positions according to
CPnumber
, CPposition
and segMinLength
(if parameter dyn=1
, first CP is 2
and final CP is n+1
, where n
is the number of time points).
S. Lebre and G. Lelandais.
Statistical inference of the timevarying structure of generegulation networks S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, BMC Systems Biology, 4:130, 2010.
ARTIVAsubnet
, ARTIVAnet
,
plotCP.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  # 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 RJMCMC 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=10000,
segMinLength=3)
# plot the CP posterior distribution
plotCP.postDist(outCPpostDist, targetName=paste("Target", targetGene),
estimatedCPpos=outCPpostDist$estimatedCPpos)
## End(Not run)

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