ClueR (version 1.0)

clustOptimal: Generate optimal clustering

Description

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

Usage

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

Arguments

clueObj

the output from runClue.

rep

number of times the clustering is to be applied to find the best clustering result.

user.maxK

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

visualize

a boolean parameter indicating whether to visualize the clustering results.

...

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

Value

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

Examples

# 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) {
 set.seed(i)
 kinaseAnno[[i]] <- paste("p", sample.int(nrow(simuData), 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
set.seed(1)
clueObj <- runClue(Tc=simuData, annotation=kinaseAnno, rep=5, kRange=7)

# visualize the evaluation outcome
Ms <- apply(clueObj$evlMat, 2, mean, na.rm=TRUE)
Ss <- apply(clueObj$evlMat, 2, sd, na.rm=TRUE)
library(Hmisc)
errbar(1:length(Ms), Ms, Ms+Ss, Ms-Ss, cex=1.2, type="b", xaxt="n", xlab="k", ylab="E")
axis(1, at=1:6, labels=paste("k=", 2:7, sep=""))

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

# list enriched clusters
best$enrichList

# obtain the optimal clustering object (not run)
# best$clustObj