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ARTIVAsubnet.
A graphical representation in a pdf file and estimated values are
provided in text files.
This function is used in function ARTIVAsubnet
when parameter segmentAnalysis=TRUE. This function can be used
separately for re-computing a time-varying network from the output of function
ARTIVAsubnet with new analysis parameters
segMinLength, edgesThreshold, CPpos,
layout, ... see detail below.
ARTIVAsubnetAnalysis(ARTIVAsubnet=NULL, CPpostDist=NULL, CPsamples=NULL,
coefSamples=NULL, TFnumber=NULL, segMinLength=2, edgesThreshold=0.5,
burn_in=NULL, CPpos=NULL,targetData=NULL, parentData=NULL,
targetName=NULL,parentNames=NULL, savePictures=TRUE,saveEstimations=TRUE,
outputPath=NULL,layout="fruchterman.reingold", silent=FALSE,
inARTIVAsubnet=FALSE , onepage= FALSE)ARTIVAsubnet, a list containing Samples,
Counters, CPpostDist, nbSegs,
SegmentPostDist, network, ...
(optional, default: ARTIVAsubnet=NULL, if
ARTIVAsubnet=NULL then parameters CPpostDist,
CPsamples, coefSamples, TFnumber must be not null.
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 ARTIVAsubnet.
(optional, default: CPpostDist=NULL, but
CPpostDist must be given when parameter ARTIVAsubnet=NULL)ARTIVAsubnet function and in column the identified positions for CPs. (optional, default: CPsamples=NULL, but
CPsamples must be given when parameter ARTIVAsubnet=NULL)
ARTIVAsubnet function and in column the
coefficient values corresponding to the identified regulatory interactions.
(optional, default: coefSamples=NULL, but
coefSamples must be given when parameter ARTIVAsubnet=NULL) parentData used in the ARTIVAsubnet function.
(optional, default: TFnumber=NULL, but
TFnumber must be given when parameter ARTIVAsubnet=NULL)targetData and parentData (optional, default: segMinLength=2).segmentAnalysis=TRUE (optional, default: edgesThreshold=0.5).
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).
CPpos=NULL, if CPpos=NULL then CPpos is
evaluated as in CP.postDist function. )
targetData=NULL, if not null then
the target data is plotted).
parentData=NULL, if not null then
the parent data is plotted).
parentNames=NULL).
TRUE all estimated posterior distributions and
networks are plotted in a pdf file either in a new sub folder named
"ARTIVA_Results" created by default in the current folder or in a
folder specified with argument outputPath (see below)
(optional, default: savePictures=TRUE).TRUE all estimated posterior distributions are saved
as text files either in a new sub folder named "ARTIVA_Results" created
by default in the current folder or in a folder specified with argument
outputPath (see below) (optional, default:
saveEstimations=TRUE).outputPath=NULL)."random",
"circle", "sphere",
"fruchterman.reingold", "kamada.kawai", "spring",
"reingold.tilford", "fruchterman.reingold.grid",
see package igraph0 for more details (default:
layout="fruchterman.reingold").TRUE messages are printed along the ARTIVA procedure (optional, default: silent=FALSE).
TRUE, general information already printed in
function ARTIVAsubnet are not printed a second time (optional, default: inARTIVAsubnet=FALSE).
TRUE, all output pictures are plotted on one page
only (optional, default: onepage=FALSE.
CPpostDist$CPnumberPostDist).
nbSegs temporal segments, according to
CPnumber, CPposition and segMinLength (if
parameter dyn=1, first CP is 2 and final CP is
n+1, where n is the number of time points).
segmentModel.postDist. A list of tables:1) SegmentPostDist$CPpos: A table containing the most
significant CP positions that delimit nbSegs temporal segments,
according to CPpostDist$CPnumber, CPpostDist$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).2) SegmentPostDist$edgesPostDist: A table containing the approximate posterior distribution for the incoming edges (regulatory
interaction between parent and target genes) for each temporal segment
delimited by the CP given in SegmentPostDist$CPpos (see previously). Each raw corresponds to a segment, ordered by time.3) SegmentPostDist$edgesCoeff A table containing the estimated coefficients for the incoming edges (regulatory interaction between parent and target
genes) for each temporal segment delimited by the CP given in SegmentPostDist$CPpos (see previously). Each raw corresponds to a segment, ordered by time.
traceNetworks) the network estimated with the ARTIVAsubnet procedure.
ARTIVAsubnet, ARTIVAnet,
traceNetworks, traceGeneProfiles,
CP.postDist, plotCP.postDist.
# 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")
# 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.
# Run the ARTIVAsubnet function
## Not run:
# ARTIVAtest = ARTIVAsubnet(targetData = simulatedProfiles[targetGene,],
# parentData = simulatedProfiles[parentGenes,],
# targetName = targetGene,
# parentNames = parentGenes,
# segMinLength = 2,
# edgesThreshold = 0.6,
# niter= 20000,
# savePictures=FALSE)
#
# # Re-compute a time-varying network from the output of function
# # ARTIVAsubnet with new analysis parameters
# analysis2 = ARTIVAsubnetAnalysis(ARTIVAsubnet=ARTIVAtest,
# segMinLength = 3,
# edgesThreshold = 0.5,
# outputPath="ARTIVAsubnet2",
# savePictures=FALSE)
#
# # Trace the obtained network.
# traceNetworks(analysis2$network, edgesThreshold = 0.3)
# ## End(Not run)
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