CVEval {MCbiclust} | R Documentation

## Method for the calculation of a correlation vector

### 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

    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

    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]]))

[Package MCbiclust version 1.18.0 Index]