PC1VecFun {MCbiclust} | R Documentation |
The correlations found between the chosen geneset in a subset of samples can be summarised by looking at the first principal component (PC1) using principal coponent analysis (PCA).
PC1VecFun(top.gem, seed.sort, n)
top.gem |
Gene expression matrix containing only highly correlating genes |
seed.sort |
Ordering of samples according to strength of correlation |
n |
Number of samples to use in calculation of PC1 |
PC1VecFun()
takes a gene expression matrix and the sample ordering
and fits a PC1 value to all the samples based on a PCA analysis done on
the first n samples.
PC1 value for each sample
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]]))