GOEnrichmentAnalysis: Calculate gene set enrichment of correlation vector using...

In compmedlab/MCbiclust: Massive correlating biclusters for gene expression data and associated methods

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)

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