Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data.


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Documentation for package ‘CellNOptR’ version 1.4.0

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CellNOptR-package R version of CellNOptR, boolean features
CellNOptR R version of CellNOptR, boolean features
checkSignals Check the CNOlist and model matching
CNOlist Class '"CNOlist"'
CNOlist,ANY Class '"CNOlist"'
CNOlist-class Class '"CNOlist"'
CNOlistDREAM Data used for the DREAM3 challenge
CNOlistToy Toy data
CNOlistToy2 Toy data with 2 time points
CNORbool Simple Boolean analysis standalone
CNORwrap CNOR analysis wrapper
compressModel Compress a model
computeScoreT1 Compute the score of a model/data set using a bitString to cut the model.
computeScoreTN Compute the score at TN of a model/data set using a bitString to cut the model.
cSimulator C-implementation of simulatorT1.
cutAndPlot Interface to cutAndPlotResults functions.
cutAndPlotResultsT1 Plot the results of an optimisation at t1
cutAndPlotResultsT2 Plot the results of an optimisation at t2 (deprecated since v1.3.28; use cutAndPlotResultsTN instead)
cutAndPlotResultsTN Plot the results of an optimisation at tN
cutModel Cut a model structure according to a bitstring
cutNONC Cuts the non-observable/non-controllable species from the model
cutSimList Cut a simList structure according to a bitstring
defaultParameters Create a list of default parameters
DreamModel Model used for the DREAM3 challenge
expandGates Expand the gates of a model
findNONC Find the indexes of the non-observable and non controllable species
gaBinaryT1 Genetic algorithm used to optimise a model
gaBinaryT2 Genetic algorithm for time point 2
gaBinaryTN Genetic algorithm for time point N
getFit Compute the score of a model
graph2sif Convert graph to SIF
indexFinder Finds the indices, in the model fields, of the species that are measured/inhibited/stimulated
makeCNOlist Make a CNOlist structure
mapBack Map an optimised model back onto the PKN model.
model2sif Convert a model object in sif format
normaliseCNOlist Normalisation for boolean modelling.
plot-method plot a '"CNOlist"' object - methods
plot.CNOlist plot a '"CNOlist"' object - methods
plotCNOlist Plot the data in a CNOlist
plotCNOlistLarge Plot the data in a CNOlist, for lists with many conditions.
plotCNOlistLargePDF Plots a CNOlist into a pdf file, for lists with many conditions.
plotCNOlistPDF Plots a CNOlist into a pdf file.
plotFit Plot the evolution of an optimisation
plotModel Plot a model
plotOptimResults Plot the data and simulated values
plotOptimResultsPan Plots the data and simulated values from any CellNOptR formalism
plotOptimResultsPDF Plot the data and simulated values in a pdf file
prep4Sim Prepare a model for simulation
prep4sim Prepare a model for simulation
preprocessing Performs the pre-processing steps
readMIDAS Reads in a CSV MIDAS file
readMidas Reads in a CSV MIDAS file
readSIF Read a SIF file and create a model object
readSif Read a SIF file and create a model object
residualError Compute the residual error for a dataset
show-method Class '"CNOlist"'
sif2graph Convert sif to graph
simulateT1 deprecated since version 1.3.28. Cut and simulation of a boolean model at t1
simulateTN Cut and simulation of a boolean model at t1
simulatorT0 Simulation of a boolean model
simulatorT1 Simulation of a boolean model
simulatorT2 Deprecated since 1.3.28. Use simulateTN function instead. Simulation of a boolean model for time 2
simulatorTN Simulation of a boolean model at any time points dependent on a previous one.
ToyModel Toy model
ToyModel2 Toy model
writeDot Write a model, and attached features, to a dot file
writeMIDAS Write a CNOlist structure into a MIDAS file
writeNetwork Write a previous knowledge network model to a sif file (with attribute files), as well as a dot file
writeReport Write a report of a CellNOptR analysis
writeScaffold Writes the scaffold network to a sif file (with attributes) and to a dot file
writeSIF Convert a model into a SIF format and save the result in a file.
writeSif Convert a model into a SIF format and save the result in a file.