EGAD (version 1.0.3)

neighbor_voting: Evaluating Gene Function Prediction

Description

The function performs gene function prediction based on 'guilt by association' using cross validation ([1]). Performance and significance are evaluated by calculating the AUROC or AUPRC of each functional group.

Usage

neighbor_voting(genes.labels, network, nFold = 3, output = "AUROC", FLAG_DRAW = FALSE)

Arguments

genes.labels

numeric array

network

numeric array symmetric, gene-by-gene matrix

nFold

numeric value, default is 3

output

string, default is AUROC

FLAG_DRAW

binary flag to draw roc plot

Value

scores numeric matrix

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='')

aurocs <- neighbor_voting(genes.labels, net, output = 'AUROC') 

auprcs <- neighbor_voting(genes.labels, net, output = 'PR')