GOEnrichmentAnalysis {MCbiclust} | R Documentation

## Calculate gene set enrichment of correlation vector using Mann-Whitney test

### Description

The Mann-Whitney test is typically used due to the values of the correlation vector, not being normally distributed. `GOEnrichmentAnalysis` provides an interface with the GO database annotation to find the most significant GO terms.

### Usage

    GOEnrichmentAnalysis(gene.names, gene.values, sig.rate)

### Arguments

`gene.names` | Names of the genes in standard gene name format.
`gene.values` | Values associated with the genes, e.g the correlation vector output of `CVEval`.
`sig.rate` | Level of significance required after multiple hypothesis adjustment.

### Value

Data frame of the significant gene sets, with GOID, GO Term, number of genes, number of genes in GO Term, number of genes in GO Term also in gene set, adjusted p-value, average value of correlation vector in gene set and phenotype describing whether average value of correlation vector is above or below the total average.

### Examples

    data(CCLE_small)
    data(Mitochondrial_genes)

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

    set.seed(101)
    CCLE.seed <- FindSeed(gem = CCLE.mito,
                          seed.size = 10,
                          iterations = 100,
                          messages = 100)

    CCLE.cor.vec <- CVEval(gem.part = CCLE.mito,
                           gem.all = CCLE_small,
                           seed = CCLE.seed, splits = 10)

    # Significant GO terms can be calculated as follows:
    # GEA <- GOEnrichmentAnalysis(gene.names = row.names(CCLE_small),
    #                            gene.values = CCLE.cor.vec,
    #                            sig.rate = 0.05)

[Package MCbiclust version 1.16.0 Index]