frequencyHM: frequencyHM

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

Description

Compute the frequency of the histone marks after tuning of parameters (tuningParametersComb)

Usage

  frequencyHM(tuningTABfilter)

Arguments

tuningTABfilter

A data.frame from tuningParametersComb

Details

Some detailled description

Value

A vector containing the frequencies of the histone marks

Author(s)

Guidantonio Malagoli Tagliazucchi [e-mail]

See Also

cisREfind, mclapply

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,]
   
   frequencyHM<-frequencyHM(tuningTABfilter)

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