FindSeed: Find highly correlated seed of samples for gene expression...

Description Usage Arguments Details Value Examples

Description

FindSeed() is the key function in MCbiclust. It takes a gene expression matrix and by a stochastic method greedily searches for a seed of samples that maximizes the correlation score of the chosen gene set.

Usage

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FindSeed(gem, seed.size, iterations, initial.seed = NULL, messages = 100)

Arguments

gem

Gene expression matrix with genes as rows and samples as columns

seed.size

Size of sample seed

iterations

Number of iterations

initial.seed

Initial seed used, if NULL randomly chosen

messages

frequency of progress messages

Details

Additional options allow for the search to start at a chosen seed, for instance if a improvement to a known seed is desired. The result of FindSeed() is dependent on the number of iterations, with above 1000 usually providing a good seed, and above 10000 an optimum seed.

Value

Highly correlated seed

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)

compmedlab/MCbiclust documentation built on March 9, 2020, 12:14 a.m.