## ----style-knitr,eval=TRUE,echo=FALSE,results="asis"--------------------- ## ------------------------------------------------------------------------ library(CausalR) ## ------------------------------------------------------------------------ library(igraph) ## ------------------------------------------------------------------------ cg <- CreateCG(system.file( "extdata", "testNetwork1.sif", package="CausalR")) ## ------------------------------------------------------------------------ PlotGraphWithNodeNames(cg) # producing the following graph. ## ------------------------------------------------------------------------ ccg <- CreateCCG(system.file( "extdata", "testNetwork1.sif", package="CausalR")) ## ------------------------------------------------------------------------ PlotGraphWithNodeNames(ccg) # producing the following graph. ## ------------------------------------------------------------------------ experimentalData <- ReadExperimentalData(system.file( "extdata", "testData1.txt", package="CausalR"),ccg) ## ------------------------------------------------------------------------ options(width=120) RankTheHypotheses(ccg, experimentalData, delta=2) ## ------------------------------------------------------------------------ options(width=120) testlist<-c('Node0','Node2','Node3') RankTheHypotheses(ccg, experimentalData, delta=2, listOfNodes=testlist) ## ------------------------------------------------------------------------ options(width=120) RankTheHypotheses(ccg, experimentalData, 2, listOfNodes='Node0') ## ----results='hide'------------------------------------------------------ GetShortestPathsFromCCG(ccg, 'Node0', 'Node3') ## ------------------------------------------------------------------------ predictions <- MakePredictionsFromCCG('Node0',+1,ccg,2) predictions ## ------------------------------------------------------------------------ ScoreHypothesis(predictions, experimentalData) ## ------------------------------------------------------------------------ GetNodeName(ccg,CompareHypothesis(predictions, experimentalData)) ## ------------------------------------------------------------------------ options(width=120) Rankfor4<-RankTheHypotheses(ccg, experimentalData, 2, correctPredictionsThreshold=4) Rankfor4 # For example output only subset(Rankfor4,Correct>=4) ## ------------------------------------------------------------------------ runSCANR(ccg, experimentalData, NumberOfDeltaToScan=2, topNumGenes=4, correctPredictionsThreshold=1) ## ----eval=FALSE---------------------------------------------------------- ## AllData<-read.table(file="testData1.txt", sep = "\t") ## DifferentialData<-AllData[AllData[,2]!=0,] ## write.table(DifferentialData, file="DifferentialData.txt", ## sep="\t", row.names=FALSE, col.names=FALSE, quote=FALSE) ## ## runSCANR(ccg, ReadExperimentalData("DifferentialData.txt", ccg), ## NumberOfDeltaToScan=2,topNumGenes=100, ## correctPredictionsThreshold=2) ## ----results='hide'------------------------------------------------------ testlist<-c('Node0','Node3','Node2') RankTheHypotheses(ccg, experimentalData,2,listOfNodes=testlist) ## ----eval=FALSE---------------------------------------------------------- ## WriteExplainedNodesToSifFile("IL1A", +1, ccg, experimentalData, ## delta, file="testOutput") ## ----eval=FALSE---------------------------------------------------------- ## # Set-up ## library(CausalR) ## library(igraph) ## ## # Load network, create CG and plot ## cg <- CreateCG('testNetwork1.sif') ## ## PlotGraphWithNodeNames(cg) ## ----results='hide'------------------------------------------------------ # Load network, create CCG and plot ccg <- CreateCCG(system.file( "extdata", "testNetwork1.sif", package="CausalR")) ## ----eval=FALSE---------------------------------------------------------- ## PlotGraphWithNodeNames(ccg) ## ----results='hide'------------------------------------------------------ # Load experimental data experimentalData <- ReadExperimentalData(system.file( "extdata", "testData1.txt", package="CausalR"),ccg) ## ----results='hide'------------------------------------------------------ # Make predictions for all hypotheses, with pathlength set to 2. RankTheHypotheses(ccg, experimentalData, 2) ## ----eval=FALSE---------------------------------------------------------- ## # Make predictions for all hypotheses, running in parallel ## # NOTE: this requires further set-up as detailed in Appendix B. ## RankTheHypotheses(ccg,experimentalData,delta,doParallel=TRUE) ## ----results='hide'------------------------------------------------------ # Make predictions for a single node (results for + and - # hypotheses for the node will be generated), RankTheHypotheses(ccg, experimentalData,2,listOfNodes='Node0') ## ----results='hide'------------------------------------------------------ # Make predictions for an arbitrary list of nodes (gives results # for up- and down-regulated hypotheses for each named node), testlist <- c('Node0','Node3','Node2') RankTheHypotheses(ccg, experimentalData,2,listOfNodes=testlist) ## ----results='hide'------------------------------------------------------ # An example of making predictions for a particular signed hypo- # -thesis at delta=2, for up-regulated node0, i.e.node0+. # (shown to help understanding of hidden functionality) predictions<-MakePredictionsFromCCG('Node0',+1,ccg,2) GetNodeName(ccg,CompareHypothesis(predictions,experimentalData)) ## ----results='hide'------------------------------------------------------ # Scoring the hypothesis predictions ScoreHypothesis(predictions,experimentalData) ## ----eval=FALSE---------------------------------------------------------- ## # Compute statistics required for Calculating Significance ## # p-value ## Score<-ScoreHypothesis(predictions,experimentalData) ## CalculateSignificance(Score, predictions, experimentalData) ## PreexperimentalDataStats <- ## GetNumberOfPositiveAndNegativeEntries(experimentalData) ## ## #this gives integer values for n_+ and n_- for the ## #experimental data,as shown in Table 2. ## ## PreexperimentalDataStats ## ## # add required value for n_0, number of non-differential ## # experimental results, ## experimentalDataStats<-c(PreexperimentalDataStats,1) ## # then use, ## AnalysePredictionsList(predictions,8) ## # ...to output integer values q_+, q_- and q_0 for ## # significance calculations (see Table 2) ## # then store this in the workspace for later use, ## predictionListStats<-AnalysePredictionsList(predictions,8) ## ----eval=FALSE---------------------------------------------------------- ## # Compute Significance p-value using default cubic algorithm ## CalculateSignificance(Score,predictionListStats, ## experimentalDataStats, useCubicAlgorithm=TRUE) ## # or simply, ## CalculateSignificance(Score,predictionListStats, ## experimentalDataStats) ## # as use cubic algorithm is the default setting. ## ----eval=FALSE---------------------------------------------------------- ## # Compute Significance p-value using default quartic algorithm ## CalculateSignificance(Score,predictionListStats, ## experimentalDataStats,useCubicAlgorithm=FALSE) ## ----eval=FALSE---------------------------------------------------------- ## # Compute enrichment p-value ## CalculateEnrichmentPvalue(predictions, experimentalData) ## ----eval=FALSE---------------------------------------------------------- ## # Running SCAN whilst excluding scoring of hypotheses for non- ## # -differential nodes ## AllData<-read.table(file="testData1.txt", sep="\t") ## DifferentialData<-AllData[AllData[,2]!=0,] ## write.table(DifferentialData, file="DifferentialData.txt", ## sep="\t",row.names=FALSE, col.names=FALSE, quote=FALSE ) ## ## runSCANR(ccg, ReadExperimentalData("DifferentialData.txt", ccg), ## NumberOfDeltaToScan=3, topNumGenes=100, ## correctPredictionsThreshold=3) ## ----eval=FALSE---------------------------------------------------------- ## # Generate a .sif file, testOutput.sif, to visualise a network ## # of nodes explained by a specific hypothesis in Cytoscape ## WriteExplainedNodesToSifFile("IL1A", +1, ccg, experimentalData, ## delta, file="testOutput") ## ----eval=FALSE---------------------------------------------------------- ## RankTheHypotheses(ccg,experimentalData,delta,doParallel=TRUE) ## ----eval=FALSE---------------------------------------------------------- ## RankTheHypotheses(ccg,experimentalData,delta, ## doParallel=TRUE, numCores=3) ## ------------------------------------------------------------------------ library(compiler) enableJIT=3