runFastHeinz {BioNet} | R Documentation

## Calculate heuristically maximum scoring subnetwork

### Description

The function uses an heuristic approach to calculate the maximum scoring subnetwork. Based on the given network and scores the positive nodes are in the first step aggregated to meta-nodes between which minimum spanning trees are calculated. In regard to this, shortest paths yield the approximated maximum scoring subnetwork. This function can be used if a CPLEX license is not available to calculate the optimal solution.

### Usage

    runFastHeinz(network, scores)

### Arguments

`network` | A graph in igraph or graphNEL format.
`scores` | A named vector, containing the scores for the nodes of the network. All nodes need to be scored in order to run the algorithm.

### Value

A subnetwork in the input network format.

### Author(s)

Daniela Beisser

### See Also

`writeHeinzEdges`, `writeHeinzNodes`, `readHeinzTree`, `readHeinzGraph`, `runHeinz`

### Examples

    library(DLBCL)
    # load p-values
    data(dataLym)
    # load graph
    data(interactome)
    # get induced subnetwork for all genes contained on the chip
    interactome <- subNetwork(dataLym$label, interactome)
    p.values <- dataLym$t.pval
    names(p.values) <- dataLym$label
    bum <- fitBumModel(p.values, plot=TRUE)
    scores <- scoreNodes(network=interactome, fb=bum, fdr=0.0001)
    module <- runFastHeinz(network=interactome, scores=scores)
    ## Not run: plotModule(module)

[Package BioNet version 1.50.0 Index]