ThresholdBic {MCbiclust} | R Documentation

## Methods for defining a bicluster

### Description

A bicluster is the fundamental result found using MCbiclust. These three functions are essential for the precise definition of these biclusters.

### Usage

    ThresholdBic(cor.vec, sort.order, pc1, samp.sig = 0)

    PC1Align(gem, pc1, cor.vec, sort.order, bic)

    ForkClassifier(pc1, samp.num)

### Arguments

`cor.vec` | Correlation vector (output of `CVEval()`).
`sort.order` | Order of samples (output of `SampleSort()`).
`pc1` | PC1 values for samples (output of `PC1VecFun`).
`samp.sig` | Value between 0 and 1 determining number of samples in bicluster
`gem` | Gene expression matrix containing genes as rows and samples as columns.
`bic` | bicluster (output of `ThresholdBic()`)
`samp.num` | Number of samples in the bicluster

### Details

`ThresholdBic()` takes as its main inputs the correlation vector (output of `CVEval()`), sample ordering (output of `SampleSort()`), PC1 vector (output of `PC1VecFun`) and returns a list of the genes and samples which belong to the bicluster according to a certain level of significance.

`PC1Align()` is a function used once the bicluster has been found to ensure that the upper fork samples (those with higher PC1 values) correspond to those samples that have genes with positive correlation vector values up-regulated.

`ForkClassifier()` is a function used to classify which samples are in the upper or lower fork.

### Value

Defined bicluster

### 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 1.10.0 Index]