HclustGenesHiCor {MCbiclust} | R Documentation

## Find the most highly correlated genes using hierarchical clustering

### Description

Upon finding an initial bicluster with `FindSeed()` not all the genes in the chosen geneset will be highly correlated to the bicluster. `HclustGenesHiCor()` uses the output of `FindSeed()` and hierarchical clustering to only select the genes that are most highly correlated to the bicluster. This is achieved by cutting the dendogram produced from the clustering into a set number of groups and then only selecting the groups that are most highly correlated to the bicluster

### Usage

    HclustGenesHiCor(gem, seed, cuts)

### Arguments

`gem` | Gene expression matrix with genes as rows and samples as columns
`seed` | Seed of highly correlating samples
`cuts` | Number of groups to cut dendogram into

### Value

Numeric vector of most highly correlated genes

### Examples

    data(CCLE_small)
    data(Mitochondrial_genes)

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

    random.seed <- sample(seq(length = dim(CCLE.mito)[2]),10)
    CCLE.seed <- FindSeed(gem = CCLE.mito,
                          seed.size = 10,
                          iterations = 100,
                          messages = 100)

    CorScoreCalc(CCLE.mito, random.seed)
    CorScoreCalc(CCLE.mito, CCLE.seed)

    CCLE.hicor.genes <- as.numeric(HclustGenesHiCor(CCLE.mito,
                                                    CCLE.seed,
                                                    cuts = 8))

    CorScoreCalc(CCLE.mito[CCLE.hicor.genes,], CCLE.seed)

[Package MCbiclust version 1.18.0 Index]