Description Usage Arguments Details Value Examples
modulesByConnectivity
Determine the strongest modules in a network
using the mean absolute value of signedKME.
1 | modulesByConnectivity(datExpr, net, mean.kme.threshold = seq(0, 1, 0.1))
|
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. |
More here soon.
A vector of modules for each kme threshold specified.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ## 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)
|
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