runFastHeinz: Calculate heuristically maximum scoring subnetwork

Description Usage Arguments Value Author(s) See Also Examples

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

1
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
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)


Search within the BioNet package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.