getSignal: Model the signals of each histone marks around genomic...

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

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

  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 [e-mail]

See Also

cisREfindbed

Examples

    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="")

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