clustOptimal: Generate optimal clustering

Description Usage Arguments Value Examples

View source: R/clustOptimal.R

Description

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

Usage

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

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.

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

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# simulate a time-series data with 4 distinctive profile groups and each group with
# a size of 50 phosphorylation sites. (not run)
# 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. (not run)
# 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 (not run)
# set.seed(1)
# clueObj <- runClue(Tc=simuData, annotation=kinaseAnno, rep=5, kRange=7)

# visualize the evaluation outcome (not run)
# 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 (not run)
# best <- clustOptimal(clueObj, rep=3, mfrow=c(2, 3))


ClueR documentation built on May 19, 2017, 1:34 p.m.
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