Nothing

```
##This function finds subnetworks enriched for genes with significant
##phenotypes.
networkAnalysis <- function(pvalues, graph, fdr=0.001, verbose=TRUE) {
##check arguments
paraCheck("pvalues", pvalues)
paraCheck("interactome", graph)
paraCheck("fdr", fdr)
paraCheck("verbose", verbose)
cat("-Performing network analysis ... \n")
##store the name of the nodes of the graphNEL object for which we
##have p-value information
scoredNodes<-intersect(names(pvalues),nodes(graph))
##check that there are nodes associated with a p-value
if(length(scoredNodes) == 0)
stop("The rownames of your pvalueMatrix do not match to any ",
"name in the interactionMatrix, check that you have the ",
"right type of identifiers.")
if(verbose)
cat(paste("--Your network consists of ", length(nodes(graph)),
" nodes, of which ", length(scoredNodes),
" have an associated p-value", sep=""), "\n")
##Get the pvalue information for the nodes of the graphNEL object
##only, and fit a bum model on these N.B. the fitting of the bum
##model will produce a diagnostic plot on the screen, to check the
##fitting
dataForNw <- pvalues[scoredNodes]
fb <- fitBumModel(dataForNw)
##Score the nodes of the network
##The nodes without pvalues will get a NA value instead of a score
scores <- scoreNodes(graph, fb = fb, fdr = fdr)
##Compute the mean score, and set the score of all non-scored nodes
##(NAs) to this mean
meanscore <- mean(scores, na.rm = TRUE)
scoreswMean <- scores
scoreswMean[which(is.na(scores))] <- meanscore
##Find the optimal subnetwork
if(verbose)
cat("--Computing the optimal subnetwork", "\n")
module <- runFastHeinz(network = graph, scores = scoreswMean)
cat("-Network analysis complete \n")
##Return a graphNEL object consisting of the enriched sub-network
return(module)
}
```

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