FindSeed {MCbiclust} | R Documentation |
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.
FindSeed(gem, seed.size, iterations, initial.seed = NULL, messages = 100)
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
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)