Description Usage Arguments Value Examples
View source: R/HclustGenesHiCor.R
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
1 | HclustGenesHiCor(gem, seed, cuts)
|
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 |
Numeric vector of most highly correlated genes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | 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)
|
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