smoothInputFS {SVM2CRM} | R Documentation |
Give the matrix obtained using getSignal this functions smooth the signals of each histone marks using a particular window (if bin=100).To size of smooth is bin*k (e.g. a parameter k equal to 2 means thatthe signal is smooth every 200bp).
smoothInputFS(input_ann,k,listcolnames)
input_ann |
the data.frame with the training set |
k |
the size of smooth in bp |
listcolnames |
the names of column in which perform the smoothing. A vector with the list of histone marks. |
The smoothing is perfomed using the median
A data.frame with the smoothed signals of histone marks
Guidantonio Malagoli Tagliazucchi guidantonio.malagolitagliazucchi@unimore.it
cisREfindbed, featSelectionWithKmeans, tuningParametersCombROC
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) 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)