run_GBA {EGAD} | R Documentation |
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.
run_GBA(network, labels, min = 20, max = 1000, nfold = 3)
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 |
list roc.sub, genes, auroc
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)