CNORfeeder-package {CNORfeeder} | R Documentation

## R package to integrate literature-constrained and data-driven methods to infer signalling networks from perturbation experiments

### Description

CNORfeeder permits to extend a network derived from literature with links derived strictly from the data via various inference methods using information on physical interactions of proteins to guide and validate the integration of links. The package is designed to be integrated with CellNOptR.

### Details

Package: | CNORfeeder
Type: | Package
Version: | 1.0.0.
Date: | 2012-11-22
License: | GPLv2
LazyLoad: | yes

### Author(s)

F. Eduati Maintainer: F. Eduati <eduati@ebi.ac.uk>

### References

F. Eduati, J. De Las Rivas, B. Di Camillo, G. Toffolo, J. Saez-Rodriguez. Integrating literature-constrained and data-driven inference of signalling networks. Bioinformatics, 28(18):2311-2317, 2012.

### Examples

    library(CNORfeeder)

    # this is an example of the main steps of the integrated CellNOptR - CNORfeeder pipeline

    # load the data already formatted as CNOlist
    data(CNOlistDREAM,package="CellNOptR")
    # load the model (PKN) already in the CNO format
    data(DreamModel,package="CellNOptR")
    # see CellNOptR documentation to import other data/PKNs)

    # A. INFERENCE - CNORfeeder
    # FEED inference: codified in Boolean Tables
    BTable <- makeBTables(CNOlist=CNOlistDREAM, k=2, measErr=c(0.1, 0))

    # B. COMPRESSION - CellNOptR
    # preprocessing step
    model<-preprocessing(data=CNOlistDREAM, model=DreamModel)

    # C. INTEGRATION - CNORfeeder
    # integration with the compressed model
    modelIntegr <- mapBTables2model(BTable=BTable,model=model,allInter=TRUE)
    # see example in ?MapDDN2Model to use other reverse-engineering methods

    # D. WEGHTING - CNORfeeder
    # integrated links are weighted more according to the integratin factor integrFac
    modelIntegrWeight <- weighting(modelIntegr=modelIntegr, PKNmodel=DreamModel,
                                  CNOlist=CNOlistDREAM, integrFac=10)

    # E. TRAINING - CellNOptR
    # initBstring<-rep(1,length(modelIntegr$reacID))
    # training to data using genetic algorithm (run longer to obtain better results)
    DreamT1opt<-gaBinaryT1W(
    CNOlist=CNOlistDREAM,
    	model=modelIntegrWeight,
    	maxGens=2,
    	popSize=5,
    	verbose=FALSE)

[Package CNORfeeder version 1.28.0 Index]