writeNetwork: Write a previous knowledge network model to a sif file (with attribute files), as well as a dot file

Description

This function writes the original previous knowledge network (the model that you loaded in the beginning of your analysis) in a sif file, with a nodes attribute file that specifies if each node was stimulated/inhibited/signal/compressed/non-controllable-non-observable and an edge attribute file that specifies if the edge was absent in the optimal model (0) present in the optimal model at t1 (1) or present in the optimal model at t2 (2).

This function also writes a Graphviz dot file that contains the same information (see writeDot for more information about the dot file conventions).

Usage

writeNetwork(modelOriginal, modelComprExpanded, optimResT1, optimResT2, CNOlist,
tag = NULL,verbose=FALSE)

Arguments

modelOriginal

The PKN model

modelComprExpanded

The scaffold model (i.e. compressed and expanded)

optimResT1

The results of the optimisation process at t1

optimResT2

The results of the optimisation process at t2 (set this to NA if you have performed a one time point optimisation).

CNOlist

The CNOlist on which the optimisation is based

tag

NULL or string; tells whether you want to prefix filenames with a tag (replaces the default behaviour).

verbose

If verbose=TRUE, the function prints a message every time an edge in the scaffold network couldn't be mapped back to the PKN

Value

This function does not have any output, it just writes a sif file, an edge attribute file, and a node attribute file

Details

The weights of the edges are computed as the mean across models within the relative tolerance limits, as output in the results from the optimisation $stringsTol. Strings that are in $stringsTol are the ones that are within the relative tolerance limits around the best solution in the population across all generations of the optimisation.

!If there is no time 2, then the argument optimResT2 should be = NA

This function maps back the edges weights from the optimised (expanded and compressed) model to the original model. The mapping back only works if the path has length 2 at most (i.e. you have node1-comp1-comp2-node2, where comp refer to nodes that have been compressed).

Examples

Run this code

tmpdir<-tempdir()
setwd(tmpdir)

#load data

data(CNOlistToy,package="CellNOptR")
data(ToyModel,package="CellNOptR")

#pre-process model

indicesToy<-indexFinder(CNOlistToy,ToyModel,verbose=TRUE)
ToyNCNOindices<-findNONC(ToyModel,indicesToy,verbose=TRUE)
ToyNCNOcut<-cutNONC(ToyModel,ToyNCNOindices)
indicesToyNCNOcut<-indexFinder(CNOlistToy,ToyNCNOcut)
ToyNCNOcutComp<-compressModel(ToyNCNOcut,indicesToyNCNOcut)
indicesToyNCNOcutComp<-indexFinder(CNOlistToy,ToyNCNOcutComp)
ToyNCNOcutCompExp<-expandGates(ToyNCNOcutComp)

#optimise

ToyFields4Sim<-prep4sim(ToyNCNOcutCompExp)
initBstring<-rep(1,length(ToyNCNOcutCompExp$reacID))
ToyT1opt<-gaBinaryT1(
	CNOlist=CNOlistToy,
	model=ToyNCNOcutCompExp,
	initBstring=initBstring,
	verbose=TRUE,
	maxGens=2,
	popSize=5)

#write network

writeNetwork(
	modelOriginal=ToyModel,
	modelComprExpanded=ToyNCNOcutCompExp,
	optimResT1=ToyT1opt,
	optimResT2=NA,
	CNOlist=CNOlistToy)

Run the code above in your browser using DataLab

Data Engineering and BI courses are free this week!