ClueR (version 1.4.2)

clustOptimal: Generate optimal clustering

Description

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

Usage

clustOptimal(
  clueObj,
  rep = 5,
  user.maxK = NULL,
  effectiveSize = NULL,
  pvalueCutoff = 0.05,
  visualize = TRUE,
  universe = NULL,
  mfrow = c(1, 1)
)

Value

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

Arguments

clueObj

the output from runClue.

rep

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

effectiveSize

the size of kinase-substrate groups to be considered for calculating enrichment. Groups that are too small or too large will be removed from calculating overall enrichment of the clustering.

pvalueCutoff

a pvalue cutoff for determining which kinase-substrate groups to be included in calculating overall enrichment of the clustering.

visualize

a boolean parameter indicating whether to visualize the clustering results.

universe

the universe of genes/proteins/phosphosites etc. that the enrichment is calculated against. The default are the row names of the dataset.

mfrow

control the subplots in graphic window.

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=2:7)

# 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
best$enrichList

# obtain the optimal clustering object
# \donttest{
  best$clustObj
# }