data(CCLE_small)
data(Mitochondrial_genes)
mito.loc <- (row.names(CCLE_small) %in% Mitochondrial_genes)
CCLE.mito <- CCLE_small[mito.loc,]
set.seed(102)
CCLE.seed <- FindSeed(gem = CCLE.mito,
seed.size = 10,
iterations = 100,
messages = 1000)
CCLE.sort <- SampleSort(gem = CCLE.mito,seed = CCLE.seed,sort.length = 11)
# Full ordering are in Vignette_sort in sysdata.rda
CCLE.samp.sort <- MCbiclust:::Vignette_sort[[1]]
CCLE.pc1 <- PC1VecFun(top.gem = CCLE.mito,
seed.sort = CCLE.samp.sort,
n = 10)
CCLE.cor.vec <- CVEval(gem.part = CCLE.mito,
gem.all = CCLE_small,
seed = CCLE.seed,
splits = 10)
CCLE.bic <- ThresholdBic(cor.vec = CCLE.cor.vec,sort.order = CCLE.samp.sort,
pc1 = as.numeric(CCLE.pc1))
CCLE.pc1 <- PC1Align(gem = CCLE_small, pc1 = CCLE.pc1,
cor.vec = CCLE.cor.vec ,
sort.order = CCLE.samp.sort,
bic =CCLE.bic)
CCLE.fork <- ForkClassifier(CCLE.pc1, samp.num = length(CCLE.bic[[2]]))
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