run_GBA {EGAD} | R Documentation

## Performing 'Guilt by Association' Analysis

### 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)

[Package EGAD version 1.12.0 Index]