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Last data update: 2014.03.03

R: Classify two Binding Profiles

pairwiseRegions

R Documentation

Classify two Binding Profiles

Description

Classify two Binding Profiles into unique and common binding regions and write results to bed files.

Usage

pairwiseRegions(xSet, sgrset = c(1, 2), bound.cutoff, diff.cutoff, probes, probe.max.spacing, writeBedFile=TRUE)

Arguments

`xSet`	object of class ExpressionSet
`sgrset`	vector of lenght 2; specifying which data sets to compare from the ExpressionSet
`bound.cutoff`	numeric; threshold above probes are considered “bound”
`diff.cutoff`	numeric; difference threshold to determine if object 1 and object 2 are unqiquely bound
`probes`	integer; minimum number of probes in a valid region
`probe.max.spacing`	integer; maximum number of base pairs in a gap before splitting a region into 2 regions
`writeBedFile`	logical; should bed file be written

Details

Probe signal values above the bound.cutoff in both data are classified as common bound. Probes which are above the bound.cutoff and in one data and higher than the diff.cutoff to the other data are called unique. Then these probes are then filtered into regions using the probes and probe.max.spacing details. The score for the unique regions is calculated as mean (probes in region set 1 minus set 2), or vise versa. The score for the common region is the mean (probes in region (set 1 plus set 2)/2). Optional bed file formated result files are written using the choosen options in the file names.

Value

data.frame with the following columns:

`name`	name(s) of data set to which region belongs
`class.group`	class group; 1, 2 or 3 for common regions between both sets
`chr`	chromsome
`start`	start position of region
`end`	end position of region
`scrore`	score of region
`nProbes`	number of probes in region

Author(s)

Bettina Fischer, Robert Stojnic

Examples

  dataPath <- system.file("data",package="SimBindProfiles")
  load(file.path(dataPath,"SGR.RData"))
  pairAB <- pairwiseRegions(SGR, sgrset=c(1,2), bound.cutoff=1.86, diff.cutoff=1.4, 
            probes=10, probe.max.spacing=200)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> library(SimBindProfiles)
Loading required package: Ringo
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: RColorBrewer
Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:BiocGenerics':

    plotMA

Loading required package: Matrix
Loading required package: grid
Loading required package: lattice
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/SimBindProfiles/pairwiseRegions.Rd_%03d_medium.png", width=480, height=480)
> ### Name: pairwiseRegions
> ### Title: Classify two Binding Profiles
> ### Aliases: pairwiseRegions
> 
> ### ** Examples
> 
>   dataPath <- system.file("data",package="SimBindProfiles")
>   load(file.path(dataPath,"SGR.RData"))
>   pairAB <- pairwiseRegions(SGR, sgrset=c(1,2), bound.cutoff=1.86, diff.cutoff=1.4, 
+             probes=10, probe.max.spacing=200)

Pairwise comparison of SoxNDam vs SoxN-DDam 

Filter data into regions...
Writing SoxNDam.vs.SoxN-DDam.unique_b1.86d1.4v10g200.bed ,regions = 83 ...
Writing SoxN-DDam.vs.SoxNDam.unique_b1.86d1.4v10g200.bed ,regions = 61 ...
Writing SoxNDam.SoxN-DDam.common_b1.86d1.4v10g200.bed ,regions = 64 ...
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>