FindSeed {MCbiclust} | R Documentation

## Find highly correlated seed of samples for gene expression matrix

### Description

`FindSeed()` is the key function in MCbiclust. It takes a gene expression matrix and by a stochastic method greedily searches for a seed of samples that maximizes the correlation score of the chosen gene set.

### Usage

    FindSeed(gem, seed.size, iterations, initial.seed = NULL, messages = 100)

### Arguments

`gem` | Gene expression matrix with genes as rows and samples as columns
`seed.size` | Size of sample seed
`iterations` | Number of iterations
`initial.seed` | Initial seed used, if NULL randomly chosen
`messages` | frequency of progress messages

### Details

Additional options allow for the search to start at a chosen seed, for instance if a improvement to a known seed is desired. The result of `FindSeed()` is dependent on the number of iterations, with above 1000 usually providing a good seed, and above 10000 an optimum seed.

### Value

Highly correlated seed

### Examples

    data(CCLE_small)
    data(Mitochondrial_genes)

    mito.loc <- which(row.names(CCLE_small) %in% Mitochondrial_genes)
    CCLE.mito <- CCLE_small[mito.loc,]

    random.seed <- sample(seq(length = dim(CCLE.mito)[2]),10)
    CCLE.seed <- FindSeed(gem = CCLE.mito,
                          seed.size = 10,
                          iterations = 100,
                          messages = 100)

    CorScoreCalc(CCLE.mito, random.seed)
    CorScoreCalc(CCLE.mito, CCLE.seed)

    CCLE.hicor.genes <- as.numeric(HclustGenesHiCor(CCLE.mito,
                                                    CCLE.seed,
                                                    cuts = 8))

    CorScoreCalc(CCLE.mito[CCLE.hicor.genes,], CCLE.seed)

[Package MCbiclust version 1.6.1 Index]