stringgaussnet (version 1.1)

getSIMoNeNet: Infer SIMoNe network from expression data

Description

This function infers a SIMoNe network from expression data. This gives a non-supervised gaussian network with partial correlation computations.

Usage

getSIMoNeNet(DEGeneExpr, NEdges = NA, ClusterMethod = "both", AddAnnotations = F,
	MartDataset = "hsapiens_gene_ensembl")

Arguments

DEGeneExpr

Object of class DEGeneExpr

NEdges

Criter selection of SIMoNe model. Can be the number of edges, 'BIC' or 'AIC'. If it is set to NA, the function chooses the number of edges by computing the mean between those with maximal AIC and BIC scores.

ClusterMethod

Can be TRUE, FALSE, or 'both'. If it is set to 'both', the function computes networks with and without clustering constraints, and pick common edges between the both.

AddAnnotations

Boolean variable indicating whether gene annotations must be added through biomaRt

MartDataset

Which mart dataset to use for querying gene annotations through biomaRt. See getMartDatasets() for some help.

Value

• An object of class SIMoNeNet. See SIMoNeNet.default() for more details.

References

Chiquet, J. et al. SIMoNe Statistical Inference for MOdular NEtworks. Bioinforma. Oxf. Engl. 25, 417 (2009).

See Also

SIMoNeNet, SIMoNeNet.default, print.SIMoNeNet, summary.SIMoNeNet, export.SIMoNeNet, FilterEdges.SIMoNeNet, pickSIMoNeParam

Examples

# data(SpADataExpression)
# data(SpADEGenes)
# SpAData<-DEGeneExpr(t(SpADataExpression),SpADEGenes)

# NodesForSIMoNe<-rownames(SpADEGenes)[1:17]
# GaussianSpAData<-DEGeneExpr(t(SpADataExpression[NodesForSIMoNe,]),SpADEGenes[NodesForSIMoNe,])

# pickSIMoNeParam(GaussianSpAData)

# GlobalSIMoNeNet<-getSIMoNeNet(GaussianSpAData)
# GlobalSIMoNeNet<-FilterEdges(GlobalSIMoNeNet,0.4)
# print(GlobalSIMoNeNet,5)
# summary(GlobalSIMoNeNet)
# plot(GlobalSIMoNeNet)

# export(GlobalSIMoNeNet,"GlobalSIMoNeNet",T)