CorScoreCalc {MCbiclust} | R Documentation

## Calculate correlation score

### Description

The standard method to calculate the correlation score used to judge biclusters in MCbiclust

### Usage

    CorScoreCalc(gene.expr.matrix, sample.vec)

### Arguments

`gene.expr.matrix` | Gene expression matrix with genes as rows and samples as columns
`sample.vec` | Vector of samples

### Value

The correlation score

### 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 1.12.0 Index]