runClue {ClueR} | R Documentation |

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

runClue(Tc, annotation, rep = 5, kRange = 2:10, clustAlg = "cmeans", effectiveSize = c(5, 100), pvalueCutoff = 0.05, alpha = 0.5)

`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. Default is 5. |

`kRange` |
the range of k to be tested for clustering. Default is 2:10 |

`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 regularisation factor for penalizing large number of clusters. |

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.

## Example 1. Running CLUE with a simulated phosphoproteomics data ## simulate a time-series phosphoproteomics data with 4 clusters and ## each cluster with a size of 100 phosphosites simuData <- temporalSimu(seed=1, groupSize=100, sdd=1, numGroups=4) ## create an artificial annotation database. Specifically, Generate 50 ## kinase-substrate groups each comprising 20 substrates assigned to a kinase. ## Among them, create 5 groups each contains phosphosites defined ## to have the same temporal profile. kinaseAnno <- list() groupSize <- 100 for (i in 1:5) { kinaseAnno[[i]] <- paste("p", (groupSize*(i-1)+1):(groupSize*(i-1)+20), sep="_") } for (i in 6:50) { set.seed(i) kinaseAnno[[i]] <- paste("p", sample.int(nrow(simuData), size = 20), sep="_") } names(kinaseAnno) <- paste("KS", 1:50, sep="_") ## run CLUE with a repeat of 3 times and a range from 2 to 8 set.seed(1) cl <- runClue(Tc=simuData, annotation=kinaseAnno, rep=3, kRange=2:8) ## visualize the evaluation outcome boxplot(cl$evlMat, col=rainbow(8), las=2, xlab="# cluster", ylab="Enrichment", main="CLUE") ## generate optimal clustering results using the optimal k determined by CLUE best <- clustOptimal(cl, rep=3, mfrow=c(2, 3)) ## list enriched clusters best$enrichList ## obtain the optimal clustering object best$clustObj ## Example 2. Running CLUE with a phosphoproteomics dataset, discover optimal number of clusters, ## clustering data accordingly, and identify key kinases involved in each cluster. ## load the human ES phosphoprotoemics data (Rigbolt et al. Sci Signal. 4(164):rs3, 2011) data(hES) # load the PhosphoSitePlus annotations (Hornbeck et al. Nucleic Acids Res. 40:D261-70, 2012) # note that one can instead use PhosphoELM database by typing "data(PhosphoELM)". data(PhosphoSite) ## run CLUE with a repeat of 5 times and a range from 2 to 15 set.seed(1) cl <- runClue(Tc=hES, annotation=PhosphoSite.human, rep=5, kRange=2:15) boxplot(cl$evlMat, col=rainbow(15), las=2, xlab="# cluster", ylab="Enrichment", main="CLUE") best <- clustOptimal(cl, rep=3, mfrow=c(4, 4)) best$enrichList ## Example 3. Running CLUE with a gene expression dataset, discover optimal number of clusters, ## clustering data accordingly, and identify key pathway involved in each cluster. ## load mouse adipocyte gene expression data # (Ma et al. Molecular and Cellular Biology. 2014, 34(19):3607-17) data(adipocyte) ## load the KEGG annotations ## note that one can instead use reactome, GOBP, biocarta database data(Pathways) ## select genes that are differentially expressed during adipocyte differentiation adipocyte.selected <- adipocyte[adipocyte[,"DE"] == 1,] ## run CLUE with a repeat of 5 times and a range from 10 to 22 set.seed(3) cl <- runClue(Tc=adipocyte.selected, annotation=Pathways.KEGG, rep=3, kRange=10:20) xl <- "Number of clusters" yl <- "Enrichment score" boxplot(cl$evlMat, col=rainbow(ncol(cl$evlMat)), las=2, xlab=xl, ylab=yl, main="CLUE")

[Package *ClueR* version 1.4 Index]