Model the signals of each histone marks around genomic features (e.g. enhancers, not_enhancers).

Description

This function simply model the signal of each histone marks around the features used in the input files and considering the bin.size and window size defined during the pre-processing step.

Usage

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  getSignal(bedfilelist,chr,reference,win.size,bin.size,label1="enhancers")

Arguments

bedfilelist

test_set produced for svm model

chr

a vector containin the list of chromsome that you want use during the analysis (e.g."chr1")

reference

file with the reference position of the features. The genomic coordinates of positive and negative examples (e.g. enhancers, not_enhancers)

win.size

windows size used to smooth the signal

bin.size

original bin size used

label1

class of reference (e.g. enhancers or not_enhancers)

Details

Some detailled description

Value

A data.frame with the signals where in the column there are the signals of the histone marks and in the rows the cis-regulatory elements.

Author(s)

Guidantonio Malagoli Tagliazucchi guidantonio.malagolitagliazucchi@unimore.it

See Also

cisREfindbed

Examples

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

    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="")