PruneNet: Prunes relevance network to allow only edges that are...
In DART: Denoising Algorithm based on Relevance network Topology

Description Usage Arguments Value Author(s) References Examples

View source: R/PruneNet.R

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.

1	PruneNet(evalNet.o)

evalNet.o

Output list object from EvalConsNet

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.

Andrew E Teschendorff, Yan Jiao

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.

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.