crossvalidateBoolean: k-fold crossvalidation for Boolean model.

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

k-fold crossvalidation for Boolean model.

Description

Cross-validation analysis for the boolean case.

Usage

crossvalidateBoolean(CNOlist,model,nfolds=10,foldid=NULL, 
                                type=c('datapoint','experiment','observable'),timeIndex = 2,parallel=FALSE,...)

Arguments

CNOlist	a CNOlist on which the score is based (based on valueSignals[[2]], i.e. data at time 1).
model	a model structure, as created by readSIF, normally pre-processed but that is not a requirement of this function.
nfolds	number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets.
foldid	an optional vector of values between '1' and 'nfold' identifying what fold each observation is in. If supplied, 'nfold' can be missing.
type	define the way to do the crossvalidation. The default is type="datapoint"', which assigns the data randomly into folds. The option 'type="experiment"' uses whole experiments for crossvalidation (all data corresponding to a cue combination). The 'type=observable' uses the subset of nodes across all experiments for crossvalidation.
timeIndex	the index of the time point to optimize. Must be greater or equal to 2 (1 corresponds to time=0). Must be less than the number of time points. Default is 2.
parallel	verbose parameter, indicating wheter to parallelize the cross-validation procedure or not (default set to FALSE).
...	further arguments are passed to gaBinaryT1

Details

Does a k-fold cross-validation for Boolean CellNOpt models. In k-iterations a fraction of the data is eliminated from the CNOlist. The model is trained on the remaining data and then the model predicts the held-out data. Then the prediction accuracy is reported for each iteration.

Value

This function returns a list with elements:

cvScores	cross-validation scores
fitScores	fitting scores
bStrings	the optimal bit-string list for each run
crossvalidate.call	echo of the function which was called
foldid	the fold id's

Author(s)

A. Gabor, E. Gjerga

Examples

data("ToyModel", package="CellNOptR")
data("CNOlistToy", package="CellNOptR")
pknmodel = ToyModel
cnodata = CNOlist(CNOlistToy)

# original and preprocessed network 
plotModel(pknmodel,cnodata)
model = preprocessing(data = cnodata,
					model = pknmodel,
					compression = TRUE,
					expansion = TRUE)
plotModel(model,cnodata)

# original CNOlist contains many timepoints, we use only a subset
plot(cnodata)
selectedTime = c(0,10)
cnodata_prep = cutCNOlist(cnodata,
                          model = model,
                          cutTimeIndices = which(!getTimepoints(cnodata) %in% selectedTime))

plot(cnodata_prep)

# optimise and show results
opt = gaBinaryT1(CNOlist = cnodata_prep,model = model,verbose = FALSE)

# 10-fold crossvalidation using T1 data
# We use only T1 data for crossvalidation, because data in the T0 matrix is not independent.
# All rows of data in T0 describes the basal condition.

# Crossvalidation produce some text in the command window:  
## Not run: 
library(doParallel)
registerDoParallel(cores=3)
R=crossvalidateBoolean(CNOlist = cnodata_prep,
						model = model,
						type = "datapoint",
						nfolds = 10,
						parallel = TRUE)


## End(Not run)

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