HclustGenesHiCor: Find the most highly correlated genes using hierarchical...

Description Usage Arguments Value Examples

View source: R/HclustGenesHiCor.R

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

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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

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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.