clustOptimal {ClueR}R Documentation

Generate optimal clustering


Takes a clue output and generate the optimal clustering of the time-course data.


clustOptimal(clueObj, rep = 5, user.maxK = NULL, visualize = TRUE, ...)



the output from runClue.


number of times (default is 5) the clustering is to be repeated to find the best clustering result.


user defined optimal k value for generating optimal clustering. If not provided, the optimal k that is identified by clue will be used.


a boolean parameter indicating whether to visualize the clustering results.


pass additional parameter for controlling the plot if visualize is TRUE.


return a list containing optimal clustering object and enriched kinases or gene sets.


# simulate a time-series data with 4 distinctive profile groups and each group with
# a size of 50 phosphorylation sites.
simuData <- temporalSimu(seed=1, groupSize=50, sdd=1, numGroups=4)

# create an artificial annotation database. Generate 20 kinase-substrate groups each
# comprising 10 substrates assigned to a kinase.
# among them, create 4 groups each contains phosphorylation sites defined to have the
# same temporal profile.
kinaseAnno <- list()
groupSize <- 50
for (i in 1:4) {
 kinaseAnno[[i]] <- paste("p", (groupSize*(i-1)+1):(groupSize*(i-1)+10), sep="_")

for (i in 5:20) {
 kinaseAnno[[i]] <- paste("p",, size = 10), sep="_")
names(kinaseAnno) <- paste("KS", 1:20, sep="_")

# run CLUE with a repeat of 2 times and a range from 2 to 7
clueObj <- runClue(Tc=simuData, annotation=kinaseAnno, rep=5, kRange=7)

# visualize the evaluation outcome
xl <- "Number of clusters"
yl <- "Enrichment score"
boxplot(clueObj$evlMat, col=rainbow(ncol(clueObj$evlMat)), las=2, xlab=xl, ylab=yl, main="CLUE")
abline(v=(clueObj$maxK-1), col=rgb(1,0,0,.3))

# generate optimal clustering results using the optimal k determined by CLUE
best <- clustOptimal(clueObj, rep=3, mfrow=c(2, 3))

# list enriched clusters

# obtain the optimal clustering object


[Package ClueR version 1.4 Index]