CNORbool: Simple Boolean analysis standalone

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

Description

This function performs the optimisation of a PKN model to a CNOlist data set. It optimises each time point found in the data and returns the processed model as well as a list of optimised bitstring corresponding to each time points that has been optimised.

This function does not create any plots or reports unlike CNORwrap.

Usage

CNORbool(CNOlist, model, paramsList=defaultParameters(),
    compression=TRUE, expansion=TRUE, cutNONC=TRUE, verbose=FALSE,
    timeIndices = NULL)

Arguments

CNOlist	a CNOlist structure, as created by makeCNOlist or a MIDAS filename
model	a model structure, as created by readSIF or a SIF filename.
paramsList	Parameters of the genetic algorithm. If not provided, it is populated with the defaultParameters function.
compression	compress the model (default TRUE)
expansion	expand the gates (default TRUE)
cutNONC	cut the NONC nodes off the model by (default TRUE)
verbose	FALSE
timeIndices	by default, optimise T1 and T2 assuming there are the 2 first time points. However, with this argument you can change that behaviour to arbitrary time points.

Value

This function returns 2 components. The first one is the processed model used in the optimisation. The second is a list of optimised bitstrings found for each time points available in the MIDAS data set.

Author(s)

T.Cokelaer, S.Schrier

Examples

data(CNOlistToy,package="CellNOptR")
data(ToyModel,package="CellNOptR")
res = CNORbool(CNOlist=CNOlistToy, model=ToyModel)

CellNOptR documentation built on Nov. 8, 2020, 6:58 p.m.