getSignal: Model the signals of each histone marks around genomic...
In SVM2CRM: SVM2CRM: support vector machine for cis-regulatory elements detections

Description Usage Arguments Details Value Author(s) See Also Examples

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.

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

`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)

Some detailled description

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.

Guidantonio Malagoli Tagliazucchi guidantonio.malagolitagliazucchi@unimore.it

cisREfindbed

    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="")