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