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.hicor.genes <- as.numeric(HclustGenesHiCor(CCLE.mito,
CCLE.seed,
cuts = 8))
CCLE.cor.mat <- cor(t(CCLE.mito[CCLE.hicor.genes,CCLE.seed]))
gene.set1 <- labels(as.dendrogram(hclust(dist(CCLE.cor.mat)))[[1]])
gene.set2 <- labels(as.dendrogram(hclust(dist(CCLE.cor.mat)))[[2]])
gene.set1.loc <- which(row.names(CCLE.mito) %in% gene.set1)
gene.set2.loc <- which(row.names(CCLE.mito) %in% gene.set2)
ps.vec <- PointScoreCalc(CCLE.mito,gene.set1.loc,gene.set2.loc)
cor(ps.vec[CCLE.samp.sort], CCLE.pc1)
plot(ps.vec[CCLE.samp.sort])
plot(CCLE.pc1)
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