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Last data update: 2014.03.03

R: Function to compute the CPs posterior distribution for the...

CP.postDist

R Documentation

Function to compute the CPs posterior distribution for the ARTIVA network model from the the ouput samples of function ARTIVAsubnet.

Description

Using the ouput RJ-MCMC samples of functions ARTIVAsubnet, this function estimates posterior distributions for the number of CPs and their position.

Usage

CP.postDist(CPsamples, burn_in=NULL, segMinLength=2)

Arguments

`CPsamples`	A matrix with the different iterations (in row) performed with the `ARTIVAsubnet` function and in column the identified positions for CPs.
`burn_in`	Number of initial iterations that are discarded for the estimation of the model distribution (posterior distribution). The `ARTIVAsubnet` function is a RJ-MCMC algorithm which, at each iteration, randomly samples a new configuration of the time-varying regulatory network from probability distributions based on constructing a Markov chain that has the network model distribution as its equilibrium distribution (The equilibrium distribution is obtained when the Markov Chain converges, which requires a large number of iterations). Typically, initial iterations are notconfident because the Markov Chain has not stabilized. The burn-in samples allow to not consider these initial iterations in the final analysis (optional, default: `burn_in=NULL`, if `burn_in=NULL` then the first 25% of the iterations is left for `burn_in`).
`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 `targetData` and `parentData` (optional, default: `segMinLength=2`).

Value

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).

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.

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=10000, 
			    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/CP.postDist.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CP.postDist
> ### Title: Function to compute the CPs posterior distribution for the
> ###   ARTIVA network model from the the ouput samples of function
> ###   ARTIVAsubnet.
> ### Aliases: CP.postDist
> ### Keywords: 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=10000, 
> ##D 			    segMinLength=3)
> ##D 
> ##D # plot the CP posterior distribution
> ##D plotCP.postDist(outCPpostDist, targetName=paste("Target", targetGene), 
> ##D 		  estimatedCPpos=outCPpostDist$estimatedCPpos)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>