createBed {SVM2CRM} | R Documentation |
With this function the user can create a bed files with the regions of predicted cis-regulatory elements. Internal function.
createBed(test_set,label1,pred,outputfile)
test_set |
test_set produced for svm model |
label1 |
class1 |
pred |
p$prediction object |
outputfile |
name of bed file |
Some detailled description
A bed files with the genomic coordinates of the cis-regulatory elements predicted using SVM2CRM.
Guidantonio Malagoli Tagliazucchi guidantonio.malagolitagliazucchi@unimore.it
cisREfindbed
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")