CVEval: Method for the calculation of a correlation vector

Description Usage Arguments Details Value Examples

View source: R/CVEval.R

Description

Upon identifying a bicluster seed with FindSeed, one of the next steps is to identify which genes not in your chosen gene set are also highly correlated to the bicluster found. This is done by CVEval, and the output is known as the correlation vector.

Usage

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CVEval(gem.part, gem.all, seed, splits)

Arguments

gem.part

Part of gene expression matrix only containing gene set of interest with genes as rows and samples as columns

gem.all

All of gene expression matrix

seed

Seed of highly correlating samples

splits

Number of cuts from hierarchical clustering

Details

CVeval uses hierarchical clustering to select the genes most representative of the bicluster and then uses the average expression of these genes across the sample seed and calculates the correlation of every gene measured across the sample seed to this average expression value.

The correlation vector is the output of this calculation.

Value

Correlation vector

Examples

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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]]))

compmedlab/MCbiclust documentation built on March 9, 2020, 12:14 a.m.