PredActScore: Computes the DART activation score of the model signature in...

In DART: Denoising Algorithm based on Relevance network Topology

Description

This function computes the DART activation score of the model signature for the samples of a data matrix. This data matrix can be the same data matrix in which DART was applied or it could be a totally independent data set.

Usage

PredActScore(prNet.o, data.m)

Arguments

prNet.o	Output list object from PruneNet.
data.m	A data matrix (numeric) with rows labeling genes/probes and columns labeling samples in which the signature activity scores are desired.

Value

A list with following elements:

adj	Adjacency matrix of the DART network for the genes found in the data set given by data.m.
sign	The corresponding model signature vector for the genes found in the data set.
score	A vector of the signature/pathway activity scores for each sample in the data set.

Author(s)

Andrew E Teschendorff, Yan Jiao

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.

DART documentation built on Nov. 8, 2020, 5:06 p.m.