run_GBA: Performing 'Guilt by Association' Analysis

In EGAD: Extending guilt by association by degree

Description

The function runs and evaluates gene function prediction based on the 'guilt by association'-principle using neighbor voting (neighbor_voting) [1]. As a measure of performance and significance of results, AUCs of all evaluated functional groups are calculated.

Usage

run_GBA(network, labels, min = 20, max = 1000, nfold = 3)

Arguments

network	numeric array symmetric, gene-by-gene matrix
labels	numeric array
min	numeric value to limit gene function size
max	numeric value to limit gene function size
nfold	numeric value, default is 3

Value

list roc.sub, genes, auroc

Examples

genes.labels <- matrix( sample( c(0,1), 1000, replace=TRUE), nrow=100)
rownames(genes.labels) = paste('gene', 1:100, sep='')
colnames(genes.labels) = paste('function', 1:10, sep='')
net <- cor( matrix( rnorm(10000), ncol=100), method='spearman')
rownames(net) <- paste('gene', 1:100, sep='')
colnames(net) <- paste('gene', 1:100, sep='')

gba <- run_GBA(net, genes.labels, min=10) 

EGAD documentation built on Nov. 8, 2020, 8:31 p.m.