prune.graph: Prunes spurious edges in a phenotypic hierarchy
In cbg-ethz/pcNEM: Probabilistic combinatorial nested effects model

Description Usage Arguments Details Value Author(s) Examples

A heuristic to prune spurious edges in a pehnotypic hierarchy

1	prune.graph(g,cutIN=NULL,cutOUT=NULL,quant=.95,verbose=TRUE)

`g`	an adjacency matrix or a 'graphNEL' object
`cutIN`	minimum number of missing in-edges required to cut all in-edges. Default
`cutOUT`	minimum number of missing out-edges required to cut all out-edges
`quant`	if 'cutIN' or 'cutOUT' are not assigned, a quantile 'quant' of the distribution of missing in- or out-edges for all nodes is used
`verbose`	Default: TRUE

prune.graph provides a heuristic approach to prune surious edges. prune.graph compares the input graph to its transitive closure, and counts for each node how many incoming and outgoing edges are missing. If the number is bigger than a user-defined cutoff, all incoming (outgoing) edges are removed.

`graph`	the pruned phenotypic hierarchy (a 'graphNEL' object)
`removed`	number of removed edges
`missing.in`	number of missing in-edges for each node
`missing.out`	number of missing out-edges for each node

Florian Markowetz

    # a transitively closed core with two spurious edges
    g <- matrix(0,5,5)
    g[1,2] <- 1
    g[2,c(3,4)] <- 1
    g[3,4] <- 1
    g[4,5] <- 1
    dimnames(g) <- list(LETTERS[1:5],LETTERS[1:5])
    g <- as(g,"graphNEL")
    
    # prune graph
    gP <- prune.graph(g)
    
    # plot
    if(require(Rgraphviz)){
      par(mfrow=c(1,2))    
      plot(g,main="two spurious edges")
      plot(gP$graph,main="pruned")
    }