CorScoreCalc: Calculate correlation score

Description Usage Arguments Value Examples

View source: R/CorScoreCalc.r

Description

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

Usage

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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

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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.