Description Usage Arguments Details Value Examples
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.
1 |
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 |
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.
Highly correlated seed
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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