WGCNANet.default: Function to create an object of class WGCNANet

In stringgaussnet: PPI and Gaussian Network Construction from Transcriptomic Analysis Results Integrating a Multilevel Factor

Description

This function is used by getWGCNANet() to convert results from the WGCNA inference.

Usage

## Default S3 method:
WGCNANet(x, SoftThreshold, AThreshold, Correlations, PValues, DEGeneExpr,
	GenesAnnotations = NULL, ...)

Arguments

x	Computed adjacency matrix by getWGCNANet()
SoftThreshold	Soft threshold parameter (alpha) used for adjacency computation by sigmoid function
AThreshold	Threshold on adjacency score for edges inference
Correlations	Correlations (spearman's rho) matrix between all pairs of genes
PValues	Spearman's p-value computed between all pairs of genes
DEGeneExpr	DE genes analysis results contained in an object of class DEGeneExpr. Those will be used as primary node attributes.
GenesAnnotations	Gene annotations got by biomaRt if it was requested by getSIMoNeNet(). Those will be used as secondary node attributes.
...	Additional parameters. Not used here.

Value

A list with at least two data frames: - Edge attributes, with spearman's rhos and p-values. - Node attributes given by DE genes analysis results. A third data frame giving gene annotations can be added if it is not null when calling the function.

See Also

WGCNANet, getWGCNANet, print.WGCNANet, summary.WGCNANet, export.WGCNANet, pickWGCNAParam, compareGaussNetworks

Examples

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

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

# pickWGCNAParam(GaussianSpAData)
# GlobalWGCNANet<-getWGCNANet(GaussianSpAData)
# print(GlobalWGCNANet,5)
# summary(GlobalWGCNANet)
# plot(GlobalWGCNANet)
# export(GlobalWGCNANet,"GlobalWGCNANet",T)

# compareGaussNetworks(GlobalSIMoNeNet,GlobalWGCNANet,c("SIMoNe","WGCNA"))

stringgaussnet documentation built on May 29, 2017, 10:50 a.m.