CellNOptR-package: R version of CellNOptR, boolean features

In saezlab/CellNOptR: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data

R version of CellNOptR, boolean features

Description

This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data collected upon perturbation of some of the nodes in the network.

Details

Package:	CellNOptR
Type:	Package
Version:	1.25.1
Date:	2018-01-10
License:	GPLv3
LazyLoad:	yes

Author(s)

T. Cokelaer, A. MacNamara, F. Eduati, S. Schrier, C. Terfve

Maintainer: A. Gabor<[e-mail]>, until 2018-01-18: T. Cokelaer <[e-mail]>

References

J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction, Molecular Systems Biology, 5:331, 2009.

Examples

# quick 1 time point optimisation of a Prior Knowledge Network to MIDAS data. 
data(CNOlistToy,package="CellNOptR")
data(ToyModel,package="CellNOptR")

pknmodel = ToyModel
cnolist = CNOlist(CNOlistToy)
model = preprocessing(cnolist, pknmodel)
results = gaBinaryT1(cnolist, model, verbose=FALSE)
plotFit(results)
cutAndPlot(cnolist, model, list(results$bString))

# Same as above and HTML report
CNORwrap(name="Toy", 
    namesData=list(CNOlist="ToyData",model="ToyModel"),
    data=CNOlistToy, model=pknmodel)

saezlab/CellNOptR documentation built on April 16, 2024, 5:21 a.m.