## ----setup, echo=TRUE------------------------------------------------------ library(ASSIGN) dir.create("tempdir") tempdir <- "tempdir" ## ----datasets-and-labels, eval=FALSE--------------------------------------- # data(trainingData1) # data(testData1) # data(geneList1) # trainingLabel1 <- list(control = list(bcat=1:10, e2f3=1:10, # myc=1:10, ras=1:10, src=1:10), # bcat = 11:19, e2f3 = 20:28, myc= 29:38, # ras = 39:48, src = 49:55) # testLabel1 <- rep(c("Adeno", "Squamous"), c(53,58)) ## ----all-in-one-assign-wrapper-example1, eval=FALSE, results='hide'-------- # dir.create(file.path(tempdir,"wrapper_example1")) # assign.wrapper(trainingData=trainingData1, testData=testData1, # trainingLabel=trainingLabel1, testLabel=testLabel1, # geneList=NULL, n_sigGene=rep(200,5), adaptive_B=TRUE, # adaptive_S=FALSE, mixture_beta=TRUE, # outputDir=file.path(tempdir,"wrapper_example1"), # iter=2000, burn_in=1000) ## ----all-in-one-assign-wrapper-example2, eval=FALSE, results='hide'-------- # dir.create(file.path(tempdir,"wrapper_example2")) # assign.wrapper(trainingData=trainingData1, testData=testData1, # trainingLabel=trainingLabel1, testLabel=NULL, # geneList=geneList1, n_sigGene=NULL, adaptive_B=TRUE, # adaptive_S=FALSE, mixture_beta=TRUE, # outputDir=file.path(tempdir,"wrapper_example2"), # iter=2000, burn_in=1000) ## ----all-in-one-assign-wrapper-example3, eval=FALSE, results='hide'-------- # dir.create(file.path(tempdir,"wrapper_example3")) # assign.wrapper(trainingData=NULL, testData=testData1, # trainingLabel=NULL, testLabel=NULL, # geneList=geneList1, n_sigGene=NULL, adaptive_B=TRUE, # adaptive_S=TRUE, mixture_beta=TRUE, # outputDir=file.path(tempdir,"wrapper_example3"), # iter=2000, burn_in=1000) ## ----assign-preprocess-function1, eval=FALSE, results='hide'--------------- # # training dataset is available; # # the gene list of pathway signature is NOT available # processed.data <- assign.preprocess(trainingData=trainingData1, # testData=testData1, # trainingLabel=trainingLabel1, # geneList=NULL, n_sigGene=rep(200,5)) ## ----assign-preprocess-function2, eval=FALSE, results='hide'--------------- # # training dataset is available; # # the gene list of pathway signature is available # processed.data <- assign.preprocess(trainingData=trainingData1, # testData=testData1, # trainingLabel=trainingLabel1, # geneList=geneList1) ## ----assign-preprocess-function3, eval=FALSE, results='hide'--------------- # # training dataset is NOT available; # # the gene list of pathway signature is available # processed.data <- assign.preprocess(trainingData=NULL, # testData=testData1, # trainingLabel=NULL, # geneList=geneList1) ## ----assign-mcmc-function, eval=FALSE, results='hide'---------------------- # mcmc.chain <- assign.mcmc(Y=processed.data$testData_sub, # Bg = processed.data$B_vector, # X=processed.data$S_matrix, # Delta_prior_p = processed.data$Pi_matrix, # iter = 2000, adaptive_B=TRUE, # adaptive_S=FALSE, mixture_beta=TRUE) ## ----assign-convergence-function, eval=FALSE, results='hide'--------------- # trace.plot <- assign.convergence(test=mcmc.chain, burn_in=0, iter=2000, # parameter="B", whichGene=1, # whichSample=NA, whichPath=NA) ## ----assign-summary-function, eval=FALSE, results='hide'------------------- # mcmc.pos.mean <- assign.summary(test=mcmc.chain, burn_in=1000, # iter=2000, adaptive_B=TRUE, # adaptive_S=FALSE, mixture_beta=TRUE) ## ----assign-cv-output-function, eval=FALSE, results='hide'----------------- # # For cross-validation, Y in the assign.mcmc function # # should be specified as processed.data$trainingData_sub. # assign.cv.output(processed.data=processed.data, # mcmc.pos.mean.trainingData=mcmc.pos.mean, # trainingData=trainingData1, # trainingLabel=trainingLabel1, adaptive_B=FALSE, # adaptive_S=FALSE, mixture_beta=TRUE, # outputDir=tempdir) ## ----assign-output-function, eval=FALSE, results='hide'-------------------- # assign.output(processed.data=processed.data, # mcmc.pos.mean.testData=mcmc.pos.mean, # trainingData=trainingData1, testData=testData1, # trainingLabel=trainingLabel1, # testLabel=testLabel1, geneList=NULL, # adaptive_B=TRUE, adaptive_S=FALSE, # mixture_beta=TRUE, outputDir=tempdir) ## ----anchor-exclude-example, eval=FALSE, results='hide'-------------------- # dir.create(file.path(tempdir, "anchor_exclude_example")) # # anchorList = list(bcat="224321_at", # e2f3="202589_at", # myc="221891_x_at", # ras="201820_at", # src="224567_x_at") # excludeList = list(bcat="1555340_x_at", # e2f3="1555340_x_at", # myc="1555340_x_at", # ras="204748_at", # src="1555339_at") # # assign.wrapper(trainingData=trainingData1, testData=testData1, # trainingLabel=trainingLabel1, testLabel=NULL, # geneList=geneList1, n_sigGene=NULL, adaptive_B=TRUE, # adaptive_S=TRUE, mixture_beta=TRUE, # outputDir=file.path(tempdir, "anchor_exclude_example"), # anchorGenes=anchorList, excludeGenes=excludeList, # iter=2000, burn_in=1000) ## ----gfrn-optimization-dl, eval=FALSE, results='hide'---------------------- # dir.create(file.path(tempdir, "optimization_example")) # setwd(file.path(tempdir, "optimization_example")) # # testData <- read.table("https://drive.google.com/uc?authuser=0&id=1mJICN4z_aCeh4JuPzNfm8GR_lkJOhWFr&export=download", # sep='\t', row.names=1, header=1) # # corData1 <- read.table("https://drive.google.com/uc?authuser=0&id=1MDWVP2jBsAAcMNcNFKE74vYl-orpo7WH&export=download", # sep='\t', row.names=1, header=1) ## ----gfrn-optimization-cor, eval=FALSE, results='hide'--------------------- # #this is a list of pathways and columns in the correlation data that will # #be used for correlation # corList <- list(akt=c("Akt","PDK1","PDK1p241")) ## ----gfrn-optimization-optimize, eval=FALSE, results='hide'---------------- # #run the batch correction procedure between the test and training data # combat.data <- ComBat.step2(testData, pcaPlots = TRUE) # # #run the default optimization procedure # optimization_results <- optimizeGFRN(combat.data, corData, # corList, run="akt") ## ----sessionInfo, echo=FALSE----------------------------------------------- sessionInfo()