CorScoreCalc: Calculate correlation score

In MCbiclust: Massive correlating biclusters for gene expression data and associated methods

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)

Example output

Iteration	Correlation Score
100 		 0.36178
[1] 0.2871502
[1] 0.3617806
[1] 0.3862503

MCbiclust documentation built on Nov. 8, 2020, 11:09 p.m.