DART (version 1.20.0)

PruneNet: Prunes relevance network to allow only edges that are consistent with the predictions of the model signature

Description

Prunes relevance network to allow only edges that are consistent with the predictions of the model signature, and extracts the maximally connected component. This is the denoising step in DART.

Usage

PruneNet(evalNet.o)

Arguments

evalNet.o

Output list object from EvalConsNet

Value

A list with following entries:

pradj

The adjacency matrix of the pruned i.e consistent network.

sign

The model signature vector of genes in pruned network.

score

The fraction of edges surviving the pruning/denoising.

netconst

Same output as for EvalConsNet.

pradjMC

The adjacency matrix of the maximally connected component of pruned network.

signMC

The model signature vector of the genes in the maximally connected component.

References

Jiao Y, Lawler K, Patel GS, Purushotham A, Jones AF, Grigoriadis A, Ng T, Teschendorff AE. (2011) Denoising algorithm based on relevance network topology improves molecular pathway activity inference. BMC Bioinformatics 12:403.

Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, et al. (2010) Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer 10:604.

Examples

data(dataDART)
rn.o <- BuildRN(dataDART$data, dataDART$sign, fdr=0.05)
evalNet.o <- EvalConsNet(rn.o)
prNet.o <- PruneNet(evalNet.o)
pred.o <- PredActScore(prNet.o,dataDART$data)
## See ?DoDART and vignette('DART') for further examples.