splineNetRecon reconstructs gene association networks from time-course data. Based on given object of class
ExpressionSet, longitudinal data object is created. Subsequantly the function estimates edges using partial correlation method with shrinkage approach applying
network.test.edges functions. As a result an object or list of object of class
igraph is created.
a character string containing a type of
a vector of character string containing names/IDs used for network reconstruction
number or vector of numbers between 0 and 1 defining cutoff for significant posterior probability; default value is 0.8
method used to estimate the partial correlation matrix; available options are
eSetObject must be provided as an object of class
ExpressionSet which contains
Treatment and if applicable
Replicates variables (columns) included in the phenotypic data of the
pData(eSetObject)). Two types of
Treatment defining two groups to compare have to be definied.
Gene association network reconstruction is conducted for a selected type of
Treatment. This allows to find regulatory association between genes under a certain condition (treatment). First, a
longitudinal data object of the gene expression data with possible repicates is created. This object is used to estimate partial correlation with selected shrinkage method (
"static") with the
ggm.estimate.pcor function (for details see
ggm.estimate.pcor function help). Finally, the
network.test.edges function estimates the probabilities for all possible edges and lists them in descending order (for details see
cutoff.ggm can be a single number or a vector of numbers. If more than one value for
cutoff.ggm is definied than function returns a
list of objects of class
igraph for each definied
cutoff.ggm value. Otherwise a single object of class
igraph with one selected probability is returned.
An object or list of objects of class
TRUE, .Rdata file with all possible edges is created.
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## load "eSetObject" containing simulated time-course data data(TCsimData) ## define function parameters treatmentType = "T2" probesForNR = "all" cutoff.ggm = 0.8 method = "dynamic" ## reconstruct gene association network from time-course data igr <- splineNetRecon(eSetObject = TCsimData, treatmentType, probesForNR, cutoff.ggm, method) plot(igr, vertex.label = NA, vertex.size = 3)
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