plotROC: Plot the ROC curve of the best model

In guidmt/SVM2CRM: SVM2CRM: support vector machine for cis-regulatory elements detections

Description

Plot the ROC curve of the best model using cross-fold validation. Internal function of tuningParametersCombROC and predictionGW.

Usage

  plotROC(datatrain,k,y,different.weight,type,cost,output)

Arguments

datatrain	training set (data.frame)
y	a vector with the list of labels
k	number of iteration for k-fold cross validation default values is 5.
output	name of output file.
different.weight	specify if the classes are unbalanced.TRUE
type	type of kernel to use
cost	parameter cost of SVM

Details

Some detailled description

Value

A pdf with the k-cross-correlation plots for: 1) k-cross-validation, 2) ROC horizontal 3) ROC vertical

Author(s)

Guidantonio Malagoli Tagliazucchi [e-mail]

See Also

cisREfindbed, tuningParametersCombROC, perftuningParametersCombROC, perfomanceSVM

Examples

	library("GenomicRanges")
        library("SVM2CRMdata")

        setwd(system.file("data",package="SVM2CRMdata"))
        load("CD4_matrixInputSVMbin100window1000.rda")
        completeTABLE<-CD4_matrixInputSVMbin100window1000

        new.strings<-gsub(x=colnames(completeTABLE[,c(6:ncol(completeTABLE))]),pattern="CD4.",replacement="")
        new.strings<-gsub(new.strings,pattern=".norm.w100.bed",replacement="")
        colnames(completeTABLE)[c(6:ncol(completeTABLE))]<-new.strings

        #list_file<-grep(dir(),pattern=".sort.txt",value=TRUE)

        #train_positive<-getSignal(list_file,chr="chr1",reference="p300.distal.fromTSS.txt",win.size=500,bin.size=100,label1="enhancers")
        #train_negative<-getSignal(list_file,chr="chr1",reference="random.region.hg18.nop300.txt",win.size=500,bin.size=100,label1="not_enhancers")
        setwd(system.file("data",package="SVM2CRMdata"))
        load("train_positive.rda")
        load("train_negative.rda")

        training_set<-rbind(train_positive,train_negative)
        #the colnames of the training set should be the same of data_enhancer_svm
        colnames(training_set)[c(5:ncol(training_set))]<-gsub(x=gsub(x=colnames(training_set[,c(5:ncol(training_set))]),pattern="sort.txt.",replacement=""),pattern="CD4.",replacement="")


        setwd(system.file("extdata", package = "SVM2CRMdata"))
        data_level2 <- read.table(file = "GSM393946.distal.p300fromTSS.txt",sep = "\t", stringsAsFactors = FALSE)
        data_level2<-data_level2[data_level2[,1]=="chr1",]

        DB <- data_level2[, c(1:3)]
        colnames(DB)<-c("chromosome","start","end")

        label <- "p300"

        table.final.overlap<-findFeatureOverlap(query=completeTABLE,subject=DB,select="all")

        data_enhancer_svm<-createSVMinput(inputpos=table.final.overlap,inputfull=completeTABLE,label1="enhancers",label2="not_enhancers")
        colnames(data_enhancer_svm)[c(5:ncol(data_enhancer_svm))]<-gsub(gsub(x=colnames(data_enhancer_svm[,c(5:ncol(data_enhancer_svm))]),pattern="CD4.",replacement=""),pattern=".norm.w100.bed",replacement="")

        listcolnames<-c("H2AK5ac","H2AK9ac","H3K23ac","H3K27ac","H3K27me3","H3K4me1","H3K4me3")

        dftotann<-smoothInputFS(train_positive[,c(6:ncol(train_positive))],listcolnames,k=20)


        results<-featSelectionWithKmeans(dftotann,5)

        resultsFS<-results[[7]]

	resultsFSfilter<-resultsFS[which(resultsFS[,2]>median(resultsFS[,2])),]

        resultsFSfilterICRR<-resultsFSfilter[which(resultsFSfilter[,3]<0.50),]

        listHM<-resultsFSfilterICRR[,1]
        listHM<-gsub(gsub(listHM,pattern="_.",replacement=""),pattern="CD4.",replacement="")

        selectFeature<-grep(x=colnames(training_set[,c(6:ncol(training_set))]),pattern=paste(listHM,collapse="|"),value=TRUE)

        colSelect<-c("chromosome","start","end","label",selectFeature)
        training_set<-training_set[,colSelect]

        vecS <- c(2:length(listHM))
        typeSVM <- c(0, 6, 7)[1]
        costV <- c(0.001, 0.01, 0.1, 1, 10, 100, 1000)[6]
        wlabel <- c("not_enhancer", "enhancer")
        infofile<-data.frame(a=c(paste(listHM,"signal",sep=".")))
        infofile[,1]<-gsub(gsub(x=infofile[,1],pattern="CD4.",replacement=""),pattern=".sort.bed",replacement="")

        tuningTAB <- tuningParametersCombROC(training_set = training_set, typeSVM = typeSVM, costV = costV,different.weight="TRUE", vecS = vecS[1],pcClass=100,ncClass=400,infofile)

        tuningTABfilter<-tuningTAB[tuningTAB$fscore<0.95,]
        #row_max_fscore<-which.max(tuningTABfilter[tuningTABfilter$nHM >2,"fscore"])
        row_max_fscore<-which.max(tuningTABfilter[,"fscore"])
        listHM_prediction<-gsub(tuningTABfilter[row_max_fscore,4],pattern="//",replacement="|")

        columnPR<-grep(colnames(training_set),pattern=paste(listHM_prediction,collapse="|"),value=TRUE)

        predictionGW(training_set=training_set,data_enhancer_svm=data_enhancer_svm, listHM=columnPR,pcClass.string="enhancers",nClass.string="not_enhancers",pcClass=100,ncClas=400,cost=100,type=0,"prediction_enhancers_CD4_results_cost=100_type=0")

guidmt/SVM2CRM documentation built on Dec. 20, 2021, 1:48 p.m.