## First simulate some data and the resulting network dendrogram
set.seed(100)
MEturquoise = sample(1:100,50)
MEblue = sample(1:100,50)
MEbrown = sample(1:100,50)
MEyellow = sample(1:100,50)
MEgreen = c(MEyellow[1:30], sample(1:100,20))
MEred = c(MEbrown [1:20], sample(1:100,30))
#MEblack = c(MEblue [1:25], sample(1:100,25))
ME = data.frame(MEturquoise, MEblue, MEbrown, MEyellow, MEgreen, MEred)#, MEblack)
dat1 = simulateDatExpr(ME, 400, c(0.15,0.13,0.12,0.10,0.09,0.09,0.1), signed=TRUE)
TOM1 = TOMsimilarityFromExpr(dat1$datExpr, networkType="signed")
tree1 = fastcluster::hclust(as.dist(1-TOM1),method="average")
## Here is an example using different mergePercentages,
# setting an inclusive threshPercent (91)
colorh1 <- colorPlot <- labels2colors(dat1$allLabels)
merges = c(65,40,20,5)
for (m in merges)
colorPlot = cbind(colorPlot,
moduleMergeUsingKME(dat1$datExpr,colorh1,
threshPercent=91, mergePercent=m)$moduleColors)
plotDendroAndColors(tree1, colorPlot, c("ORIG",merges), dendroLabels=FALSE)
## Here is an example using a lower reassignScale (so that more genes get reassigned)
colorh1 <- colorPlot <- labels2colors(dat1$allLabels)
merges = c(65,40,20,5)
for (m in merges) colorPlot = cbind(colorPlot,
moduleMergeUsingKME(dat1$datExpr,colorh1,threshPercent=91,
reassignScale=0.7, mergePercent=m)$moduleColors)
plotDendroAndColors(tree1, colorPlot, c("ORIG",merges), dendroLabels=FALSE)
## Here is an example using a less-inclusive threshPercent (75),
# little if anything is merged.
colorh1 <- colorPlot <- labels2colors(dat1$allLabels)
merges = c(65,40,20,5)
for (m in merges) colorPlot = cbind(colorPlot,
moduleMergeUsingKME(dat1$datExpr,colorh1,
threshPercent=75, mergePercent=m)$moduleColors)
plotDendroAndColors(tree1, colorPlot, c("ORIG",merges), dendroLabels=FALSE)
# (Note that with real data, the default threshPercent=50 usually results
# in some modules being merged)
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