## ----style-knitr,eval=TRUE,echo=FALSE,results="asis"-------------------------- ## ----------------------------------------------------------------------------- library(CausalR) ## ----eval=FALSE--------------------------------------------------------------- # 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,writeResultFiles = TRUE, writeNetworkFiles = "none",quiet=TRUE) ## ----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("Node1", +1,ccg,experimentalData,delta=2) ## --------------------------------------------------------------------------------------------------------------------- scanResults <- runSCANR(ccg, experimentalData, numberOfDeltaToScan=2, topNumGenes=4,correctPredictionsThreshold=1, writeResultFiles = FALSE, writeNetworkFiles = "none",quiet=FALSE) WriteAllExplainedNodesToSifFile(scanResults, ccg, experimentalData, delta=2, correctlyExplainedOnly = TRUE, quiet = TRUE) ## ----eval=FALSE------------------------------------------------------------------------------------------------------- # runSCANR(ccg, experimentalData, numberOfDeltaToScan=2, # topNumGenes=4,correctPredictionsThreshold=1,quiet=TRUE, # writeResultFiles = TRUE, writeNetworkFiles = "correct") ## ----eval=FALSE------------------------------------------------------------------------------------------------------- # CreateCCG(filename, nodeInclusionFile = 'NodesList.txt', # excludeNodesInFile = TRUE) ## ----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------------------------------------------------------------------------------------------------------- # # Indirect Individual Hypothesis Network Generation (after running SCAN) # WriteExplainedNodesToSifFile("Node1", +1,ccg,experimentalData,delta=2) ## ----eval=FALSE------------------------------------------------------------------------------------------------------- # # Indirect Network Generation for All Hypotheses (after running SCAN) # scanResults <- runSCANR(ccg, experimentalData, numberOfDeltaToScan=2, # topNumGenes=4,correctPredictionsThreshold=1, # writeResultFiles = FALSE, writeNetworkFiles = "none",quiet=FALSE) # WriteAllExplainedNodesToSifFile(scanResults, ccg, experimentalData, # delta=2, correctlyExplainedOnly = TRUE, quiet = TRUE) ## ----eval=FALSE------------------------------------------------------------------------------------------------------- # # Direct Network Generation for All Hypotheses (whilst running SCAN) # runSCANR(ccg, experimentalData, numberOfDeltaToScan=2, # topNumGenes=4,correctPredictionsThreshold=1,quiet=TRUE, # writeResultFiles = TRUE, writeNetworkFiles = "correct") ## ----eval=FALSE------------------------------------------------------------------------------------------------------- # RankTheHypotheses(ccg,experimentalData,delta,doParallel=TRUE) ## ----eval=FALSE------------------------------------------------------------------------------------------------------- # RankTheHypotheses(ccg,experimentalData,delta, # doParallel=TRUE, numCores=3) ## --------------------------------------------------------------------------------------------------------------------- library(compiler) enableJIT=3