SilhouetteClustGroups {MCbiclust} | R Documentation

## Slihouette validation of correlation vector clusters

### Description

MCbiclust is a stochastic method and needs to be run multiple times to identify different biclusters. `SilhouetteClustGroups()` examines the correlation vectors calculated from different runs and uses the technique of examining silhouette widths to identify the number of distinct clusters (and hence biclusters) found.

### Usage

    SilhouetteClustGroups(cor.vec.mat, max.clusters, plots = FALSE, seed1 = 100,
      rand.vec = TRUE)

### Arguments

`cor.vec.mat` | Correlation matrix of the correlation vectors (CVs)
`max.clusters` | Maximum number of clusters to divide CVs into
`plots` | True or False for whether to show silhouette plots
`seed1` | Value used to set random seed
`rand.vec` | True or False for whether to add random correlation vector used for comparison

### Value

The distinct clusters of correlation vectors

### Examples

    data(CCLE_small)
    data(Mitochondrial_genes)

    mito.loc <- (row.names(CCLE_small) %in% Mitochondrial_genes)
    CCLE.mito <- CCLE_small[mito.loc,]

    CCLE.seed <- list()
    CCLE.cor.vec <- list()

    for(i in 1:5){
        set.seed(i)
        CCLE.seed[[i]] <- FindSeed(gem = CCLE.mito,
                                   seed.size = 10,
                                   iterations = 100,
                                   messages = 100)}

    for(i in 1:5){
      CCLE.cor.vec[[i]] <-  CVEval(gem.part = CCLE.mito,
                                   gem.all = CCLE_small,
                                   seed = CCLE.seed[[i]],
                                   splits = 10)}

    CCLE.cor.mat <- as.matrix(as.data.frame(CCLE.cor.vec))

    CCLE.clust.groups <- SilhouetteClustGroups(cor.vec.mat = CCLE.cor.mat,
                                               plots = TRUE,
                                               max.clusters = 10)

    av.corvec.fun <- function(x) rowMeans(CCLE.cor.mat[,x])
    CCLE.average.corvec <- lapply(X = CCLE.clust.groups,
                                  FUN = av.corvec.fun)

    multi.sort.prep <- MultiSampleSortPrep(gem = CCLE_small,
                                           av.corvec = CCLE.average.corvec,
                                           top.genes.num = 750,
                                           groups =CCLE.clust.groups,
                                           initial.seeds = CCLE.seed)

    multi.sort <- list()
    for(i in seq_len(length(CCLE.clust.groups))){
        multi.sort[[i]] <- SampleSort(multi.sort.prep[[1]][[i]],
                                      seed = multi.sort.prep[[2]][[i]],
                                      sort.length = 11)
    }

[Package MCbiclust version 1.4.0 Index]