modulesByConnectivity: Find modules that have high connectivity

In jtlovell/limmaDE2: A pipeline for differential expression analyses in LIMMA

Description

modulesByConnectivity Determine the strongest modules in a network using the mean absolute value of signedKME.

Usage

modulesByConnectivity(datExpr, net, mean.kme.threshold = seq(0, 1, 0.1))

Arguments

datExpr	The expression dataset, transposed so that genes are columns and individuals are rows.
net	WGCNA generated network. Usually from WGCNA::blockwiseModules
mean.kme.threshold	Numeric, the thresholds to test. Can be of length >1.

Details

More here soon.

Value

A vector of modules for each kme threshold specified.

Examples

## Not run: 
library(WGCNA)
library(igraph)
data(kidney) #' from simseq
counts<-kidney$counts
counts<-counts[sample(1:nrow(counts),1000),]
info<-with(kidney,
           data.frame(id = paste(replic, treatment, sep = "_"),
                      rep=replic,
                      Treatment=ifelse(treatment == "Tumor","tumor","cntr"),
                      stringsAsFactors=F))
colnames(counts)<-info$id
stats <- pipeLIMMA(counts = counts,
                   info = info,
                   block = NULL,
                   formula = "~ Treatment")

datExpr.1=t(stats$voom$E)
pow=6
net.1 = blockwiseModules(datExpr.1, power = pow,
                         maxBlockSize = 10000, deepSplit = 2,
                         minModuleSize = 10,
                         saveTOMs = FALSE,
                         verbose = F)
modulesByConnectivity(net = net.1, datExpr = datExpr.1)

## End(Not run)

jtlovell/limmaDE2 documentation built on May 20, 2019, 3:14 a.m.