### R code from vignette source 'CNORfeeder-vignette.Rnw' ################################################### ### code chunk number 1: installBio (eval = FALSE) ################################################### ## source("http://bioconductor.org/biocLite.R") ## biocLite(c("RBGL","graph","minet","CellNOptR","igraph","catnet")) ################################################### ### code chunk number 2: installPackage (eval = FALSE) ################################################### ## source("http://bioconductor.org/biocLite.R") ## biocLite("CNORfeeder") ################################################### ### code chunk number 3: installPackage2 (eval = FALSE) ################################################### ## install.packages("path_to_CNORfeeder/CNORfeeder_1.0.0.tar.gz", ## repos=NULL, type="source") ################################################### ### code chunk number 4: loadLib (eval = FALSE) ################################################### ## library(CNORfeeder) ################################################### ### code chunk number 5: getData (eval = FALSE) ################################################### ## # load the data already formatted as CNOlist ## data(CNOlistDREAM,package="CellNOptR") ## # load the model (PKN) already in the CNO format ## data(DreamModel,package="CellNOptR") ################################################### ### code chunk number 6: getData (eval = FALSE) ################################################### ## BTable <- makeBTables(CNOlist=CNOlistDREAM, k=2, measErr=c(0.1, 0)) ################################################### ### code chunk number 7: linkRank (eval = FALSE) ################################################### ## Lrank <- linksRanking(CNOlist=CNOlistDREAM, measErr=c(0.1, 0), savefile=FALSE) ################################################### ### code chunk number 8: getData (eval = FALSE) ################################################### ## model<-preprocessing(data=CNOlistDREAM, model=DreamModel) ################################################### ### code chunk number 9: integration (eval = FALSE) ################################################### ## modelIntegr <- mapBTables2model(BTable=BTable,model=model,allInter=TRUE) ################################################### ### code chunk number 10: integLinks (eval = FALSE) ################################################### ## modelIntegr$reacID[modelIntegr$indexIntegr] ################################################### ### code chunk number 11: plotData (eval = FALSE) ################################################### ## plotModel(model=modelIntegr, CNOlist=CNOlistDREAM, indexIntegr=modelIntegr$indexIntegr) ################################################### ### code chunk number 12: weight (eval = FALSE) ################################################### ## modelIntegrWeight <- weighting(modelIntegr=modelIntegr, PKNmodel=DreamModel, ## CNOlist=CNOlistDREAM, integrFac=10) ################################################### ### code chunk number 13: weightPPI (eval = FALSE) ################################################### ## data(PPINigraph,package="CNORfeeder") ## data(UniprotIDdream,package="CNORfeeder") ## modelIntegrWeight <- weighting(modelIntegr=modelIntegr, PKNmodel=DreamModel, ## CNOlist=CNOlistDREAM, integrFac=10, ## UniprotID=UniprotIDdream, PPI=PPINigraph) ################################################### ### code chunk number 14: train (eval = FALSE) ################################################### ## # training to data using genetic algorithm (run longer to obtain better results) ## DreamT1opt<-gaBinaryT1W(CNOlist=CNOlistDREAM, model=modelIntegrWeight, ## maxGens=2, popSize=5, verbose=FALSE) ################################################### ### code chunk number 15: results (eval = FALSE) ################################################### ## # model ## plotModel(model=modelIntegrWeight, CNOlist=CNOlistDREAM, bString=DreamT1opt$bString) ## # data ## cutAndPlotResultsT1(model=modelIntegrWeight, CNOlist=CNOlistDREAM, ## bString=DreamT1opt$bString) ################################################### ### code chunk number 16: results (eval = FALSE) ################################################### ## # loading the necessary packages ## library(CellNOptR) ## library(MEIGOR) ## library(CNORode) ## library(doParallel) ## library(readr) ## library(infotheo) ## library(igraph) ## library(OmnipathR) ## library(CNORfeeder) ## ## # loading the model ## data(ToyModel_Gene, package="CNORfeeder") ## # loading the data ## data(CNOlistToy_Gene, package="CNORfeeder") ## # plotting the model and the data ## plotModel(model = model, CNOlist = cnolist) ## plotCNOlist(CNOlist = cnolist) ################################################### ### code chunk number 17: results (eval = FALSE) ################################################### ## ## Loading database ## data(database, package="CNORfeeder") ## ## Alternatively, users can download the database from Omnipath and retain only ## ## activatory/inhibitory interactions as shown below. ## ## Please note that OmniPath is in continuous update and new links are ## ## continuously added ## ## # interactions <- import_Omnipath_Interactions(filter_databases= ## # c("SignaLink3", ## # "PhosphoSite", ## # "Signor")) ## # interactions = interactions[which(interactions$is_stimulation+interactions$is_inhibition==1), ] ## # database = matrix(data = , nrow = nrow(interactions), ncol = 3) ## # database[, 1] = interactions$source_genesymbol ## # database[which(interactions$is_stimulation==1), 2] = "1" ## # database[which(interactions$is_inhibition==1), 2] = "-1" ## # database[, 3] = interactions$target_genesymbol ################################################### ### code chunk number 18: results (eval = FALSE) ################################################### ## # set initial parameters (here parameters k and tau are optimised and n fixed to 3) ## ode_parameters=createLBodeContPars(model, LB_n = 1, LB_k = 0, ## LB_tau = 0, UB_n = 3, UB_k = 1, ## UB_tau = 1, default_n = 3, ## default_k = 0.5, default_tau = 0.01, ## opt_n = FALSE, opt_k = TRUE, ## opt_tau = TRUE, random = TRUE) ## ## Parameter Optimization ## # essm ## paramsSSm=defaultParametersSSm() ## paramsSSm$local_solver = "DHC" ## paramsSSm$maxtime = 60; ## paramsSSm$maxeval = Inf; ## paramsSSm$atol=1e-6; ## paramsSSm$reltol=1e-6; ## paramsSSm$nan_fac=1000; ## paramsSSm$dim_refset=30; ## paramsSSm$n_diverse=1000; ## paramsSSm$maxStepSize=Inf; ## paramsSSm$maxNumSteps=10000; ## paramsSSm$transfer_function = 4; ## paramsSSm$lambda_tau=0.1 ## paramsSSm$lambda_k=0.01 ## paramsSSm$bootstrap=F ## paramsSSm$SSpenalty_fac=0 ## paramsSSm$SScontrolPenalty_fac=0 ## ## Training of the initial model ## opt_pars=parEstimationLBode(cnolist, model, method="essm", ## ode_parameters=ode_parameters, paramsSSm=paramsSSm) ## simData = plotLBodeFitness(cnolist = cnolist, model = model, ## ode_parameters = opt_pars, transfer_function = 4) ################################################### ### code chunk number 19: results (eval = FALSE) ################################################### ## # Identifying the mis-fits (measurements with mse worse than 0.05) ## indices = identifyMisfitIndices(cnolist = cnolist, model = model, ## simData = simData, mseThresh = 0.05) ################################################### ### code chunk number 20: results (eval = FALSE) ################################################### ## # Identifying the mis-fits (measurements with mse worse than 0.05) ## indices = identifyMisfitIndices(cnolist = cnolist, model = model, simData = NULL) ################################################### ### code chunk number 21: results (eval = FALSE) ################################################### ## # interactions from the database and from the FEED algorithm which we want to integrate ## feederObject = buildFeederObjectDynamic(model = model, cnolist = cnolist, ## indices = indices, database = database, ## DDN = TRUE, pathLength = 2) # max path-length=2 ## # for database search ## ## integratedModel = integrateLinks(feederObject = feederObject, cnolist = cnolist, ## database = database) ## ## plotModel(model = integratedModel$model, CNOlist = cnolist, ## indexIntegr = integratedModel$integLinksIdx) ################################################### ### code chunk number 22: results (eval = FALSE) ################################################### ## # interactions from the database and from the FEED algorithm which we want to integrate ## ode_parameters=createLBodeContPars(integratedModel$model, LB_n = 1, ## LB_k = 0, LB_tau = 0, UB_n = 3, ## UB_k = 1, UB_tau = 1, default_n = 3, ## default_k = 0.5, default_tau = 0.01, ## opt_n = FALSE, opt_k = TRUE, ## opt_tau = TRUE, random = TRUE) ## ## res1 = runDynamicFeeder(cnolist = cnolist, integratedModel = integratedModel, ## ode_parameters = ode_parameters, paramsSSm = paramsSSm, ## penFactor_k = 2, penFactorPIN_k = 0.1, penFactor_tau = 1) ## ## plotLBodeFitness(cnolist = res1$CNOList, model = res1$`Integrated-Model`$model, ## ode_parameters = res1$Parameters, transfer_function = 4) ################################################### ### code chunk number 23: results (eval = FALSE) ################################################### ## # interactions from the database and from the FEED algorithm which we want to integrate ## ode_parameters=createLBodeContPars(integratedModel$model, LB_n = 1, ## LB_k = 0, LB_tau = 0, UB_n = 3, ## UB_k = 1, UB_tau = 1, default_n = 3, ## default_k = 0.5, default_tau = 0.01, ## opt_n = FALSE, opt_k = TRUE, ## opt_tau = TRUE, random = TRUE) ## ## res2 = runDynamicFeeder(cnolist = cnolist, integratedModel = integratedModel, ## ode_parameters = ode_parameters, paramsSSm = paramsSSm, ## penFactor_k = 10000, penFactorPIN_k = 10000, ## penFactor_tau = 10000) ## ## plotLBodeFitness(cnolist = res2$CNOList, model = res2$`Integrated-Model`$model, ## ode_parameters = res2$Parameters, transfer_function = 4)