# Run CLUster Evaluation

### Description

Takes in a time-course matrix and test for enrichment of the clustering using cmeans or kmeans clustering algorithm with a reference annotation.

### Usage

1 2 |

### Arguments

`Tc` |
a numeric matrix to be clustered. The columns correspond to the time-course and the rows correspond to phosphorylation sites. |

`annotation` |
a list with names correspond to kinases and elements correspond to substrates belong to each kinase. |

`rep` |
number of times the clustering is to be applied. This is to account for variability in the clustering algorithm. |

`kRange` |
the range of k to be tested for clustering. |

`clustAlg` |
the clustering algorithm to be used. The default is cmeans clustering. |

`effectiveSize` |
the size of annotation 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. |

`alpha` |
a penalty factor for penalizing large number of clusters. |

### Value

a clue output that contains the input parameters used for evaluation and the evaluation results. Use ls(x) to see details of output. 'x' be the output here.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
# load the human ES phosphoprotoemics data (Rigbolt et al. Sci Signal. 4(164):rs3, 2011)
data(hES)
# load the PhosphoSitePlus annotations (Hornbeck et al. Nucleic Acids Res. 40:D261-70, 2012)
data(PhosphoSite)
# make a subset of hES dataset for demonstrating the example in a short time frame
ids <- c("CK2A1", "ERK1", "ERK2", "CDK7",
"p90RSK", "p70S6K", "PKACA", "CDK1", "DNAPK", "ATM", "CDK2")
hESs <- hES[rownames(hES) %in% unlist(PhosphoSite.human[ids]),]
# run CLUE with a repeat of 3 times and a range from 2 to 13
set.seed(2)
clueObj <- runClue(Tc=hESs, annotation=PhosphoSite.human, rep=2, kRange=13)
# 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=1:12, labels=paste("k=", 2:13, sep=""))
# generate the optimal clustering results
best <- clustOptimal(clueObj, rep=10, mfrow=c(3, 4))
# list enriched clusters
best$enrichList
# obtain the optimal clustering object (not run)
# best$clustObj
``` |