## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(eval = FALSE) knitr::opts_chunk$set(tidy.opts=list(width.cutoff=65),tidy=TRUE) ## ----------------------------------------------------------------------------- # install.packages("igraph") # install.packages("seriation") # install.packages("WGCNA") # install.packages("snow") # install.packages("doSNOW") # install.packages("foreach") # source("http://bioconductor.org/biocLite.R") # biocLite("biomaRt") # biocLite("GO.db") # install.packages("R2HTML") # install.packages("survival") ## ----eval=FALSE--------------------------------------------------------------- # library("NetSAM") # inputNetworkDir <- system.file("extdata","exampleNetwork.net",package="NetSAM") # outputFileName <- paste(getwd(),"/NetSAM",sep="") # result <- NetSAM(inputNetwork=inputNetworkDir, outputFileName=outputFileName, outputFormat="nsm", # edgeType="unweighted", map_to_genesymbol=FALSE, organism="hsapiens", idType="auto", minModule=0.003, # stepIte=FALSE, maxStep=4, moduleSigMethod="cutoff", modularityThr=0.2, ZRanNum=10, # PerRanNum=100, ranSig=0.05, edgeThr=(-1), nodeThr=(-1), nThreads=3) ## ----------------------------------------------------------------------------- # library("NetSAM") # inputNetwork <- system.file("extdata","exampleNetwork.net",package="NetSAM") # outputFileName <- paste(getwd(),"/NetSAM",sep="") # NetAnalyzer(inputNetwork,outputFileName,"unweighted") ## ----------------------------------------------------------------------------- # library("NetSAM") # inputMatDir <- system.file("extdata","exampleExpressionData_nonsymbol.cct",package="NetSAM") # inputMat <- read.table(inputMatDir,header=TRUE,sep="\t",stringsAsFactors=FALSE,check.names=FALSE) # mergedData <- mergeDuplicate(id=inputMat[,1],data=inputMat[,2:ncol(inputMat)],collapse_mode="maxSD") ## ----------------------------------------------------------------------------- # library("NetSAM") # print("transform ids from a gene list to gene symbols...") # geneListDir <- system.file("extdata","exampleGeneList.txt",package="NetSAM") # geneList <- read.table(geneListDir,header=FALSE,sep="\t",stringsAsFactors=FALSE) # geneList <- as.vector(as.matrix(geneList)) # geneList_symbol <- mapToSymbol(inputData=geneList, organism="hsapiens", inputType="genelist",idType="affy_hg_u133_plus_2") # # print("transform ids in the input network to gene symbols...") # inputNetwork <- system.file("extdata","exampleNetwork_nonsymbol.net",package="NetSAM") # network_symbol <- mapToSymbol(inputData=inputNetwork,organism="hsapiens",inputType="network",idType="entrezgene",edgeType="unweighted") # # print("transform ids in the input matrix to gene symbols...") # inputMatDir <- system.file("extdata","exampleExpressionData_nonsymbol.cct",package="NetSAM") # matrix_symbol <- mapToSymbol(inputData=inputMatDir,organism="hsapiens",inputType="matrix",idType="affy_hg_u133_plus_2",collapse_mode="maxSD") # # print("transform ids in the sbt file to gene symbols...") # inputSBTDir <- system.file("extdata","exampleSBT.sbt",package="NetSAM") # sbt_symbol <- mapToSymbol(inputData= inputSBTDir,organism="hsapiens",inputType="sbt",idType="affy_hg_u133_plus_2") # # print("transform ids in the sct file to gene symbols...") # inputSCTDir <- system.file("extdata","exampleSCT.sct",package="NetSAM") # sct_symbol <- mapToSymbol(inputData= inputSCTDir,organism="hsapiens",inputType="sct",idType="affy_hg_u133_plus_2",collapse_mode="min") ## ----------------------------------------------------------------------------- # library("NetSAM") # inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM") # matNetwork <- MatNet(inputMat=inputMatDir, collapse_mode="maxSD", naPer=0.7, meanPer=0.8, varPer=0.8, # corrType="spearman", matNetMethod="rank", valueThr=0.6, rankBest=0.003, networkType="signed", # netFDRMethod="BH", netFDRThr=0.05, idNumThr=(-1), nThreads=3) ## ----------------------------------------------------------------------------- # library("NetSAM") # inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM") # data <- read.table(inputMatDir, header=TRUE, row.names=1, stringsAsFactors=FALSE) # net <- consensusNet(data=data, organism="hsapiens",bootstrapNum=10, naPer=0.5, meanPer=0.8,varPer=0.8,method="rank_unsig",value=3/1000,pth=1e-6, nMatNet=2, nThreads=4) ## ----------------------------------------------------------------------------- # library("NetSAM") # inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM") # sampleAnnDir <- system.file("extdata","sampleAnnotation.tsi",package="NetSAM") # formatedData <- testFileFormat(inputMat=inputMatDir,sampleAnn=sampleAnnDir,collapse_mode="maxSD") ## ----------------------------------------------------------------------------- # library("NetSAM") # inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM") # sampleAnnDir <- system.file("extdata","sampleAnnotation.tsi",package="NetSAM") # data(NetSAMOutput_Example) # outputHtmlFile <- paste(getwd(),"/featureAsso_HTML",sep="") # featureAsso <- featureAssociation(inputMat=inputMatDir, sampleAnn=sampleAnnDir, NetSAMOutput=netsam_output, outputHtmlFile=outputHtmlFile, CONMethod="spearman", CATMethod="kruskal", BINMethod="ranktest", fdrmethod="BH",pth=0.05,collapse_mode="maxSD") ## ----------------------------------------------------------------------------- # library("NetSAM") # data(NetSAMOutput_Example) # outputHtmlFile <- paste(getwd(),"/GOAsso_HTML",sep="") # GOAsso <- GOAssociation(NetSAMOutput=netsam_output, outputHtmlFile=outputHtmlFile, organism="hsapiens", fdrmethod="BH", fdrth=0.05, topNum=5) ## ----------------------------------------------------------------------------- # library("NetSAM") # inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM") # sampleAnnDir <- system.file("extdata","sampleAnnotation.tsi",package="NetSAM") # outputFileName <- paste(getwd(),"/MatSAM",sep="") # matModule <- MatSAM(inputMat=inputMatDir, sampleAnn=sampleAnnDir, # outputFileName = outputFileName, outputFormat="msm", # organism="hsapiens", map_to_symbol=FALSE, idType="auto", collapse_mode="maxSD", naPer=0.7, meanPer=0.8, varPer=0.8, # corrType="spearman", matNetMethod="rank", # valueThr=0.6, rankBest=0.003, networkType="signed", netFDRMethod="BH", # netFDRThr=0.05, minModule=0.003, stepIte=FALSE, # maxStep=4, moduleSigMethod="cutoff", modularityThr=0.2, ZRanNum=10, PerRanNum=100, ranSig=0.05, idNumThr=(-1), nThreads=3)