Last data update: 2014.03.03

R: Smoothing and plotting methylation data
mCsmoothingR Documentation

Smoothing and plotting methylation data

Description

Smoothing and plotting methylation data, even chromosome wide.

Usage

## S4 method for signature 'methylPipe,BSdata'
mCsmoothing(Object, refgr, Scorefun='sum', Nbins=20,
Context="CG", plot=TRUE)

Arguments

Object

An object of class BSdata

refgr

GRanges; Genomic Ranges to plot the data

Scorefun

character; either sum or mean for smoothing

Nbins

numeric; the number of interval each range is divided

Context

character; either all or a combination of CG, CHG, and CHH

plot

logical; whether the smoothed profile has to be plotted

Details

The sum or the mean methylation level is determined on each window of size Binsize and smoothed with the smooth.spline function.

Value

A list with three components: pos (the left most point of each window), score (either the sum or the mean methylation levels), smoothed (the smoothed methylation levels).

Author(s)

Mattia Pelizzola

Examples

require(BSgenome.Hsapiens.UCSC.hg18)
uncov_GR <- GRanges(Rle('chr20'), IRanges(c(14350,69251,84185), c(18349,73250,88184)))
H1data <- system.file('extdata', 'H1_chr20_CG_10k_tabix_out.txt.gz', package='methylPipe')
H1.db <- BSdata(file=H1data, uncov=uncov_GR, org=Hsapiens)
gr <- GRanges("chr20",IRanges(1,5e5))
sres <- mCsmoothing(H1.db, gr, Scorefun='sum', Nbins=50, Context="CG", plot=TRUE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> library(methylPipe)
Loading required package: GenomicRanges
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

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

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

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/methylPipe/mCsmoothing.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mCsmoothing
> ### Title: Smoothing and plotting methylation data
> ### Aliases: mCsmoothing mCsmoothing,methylPipe,BSdata
> ###   mCsmoothing,methylPipe,BSdata-method mCsmoothing-methods
> ###   mCsmoothing,BSdata-method
> 
> ### ** Examples
> 
> require(BSgenome.Hsapiens.UCSC.hg18)
Loading required package: BSgenome.Hsapiens.UCSC.hg18
Loading required package: BSgenome
Loading required package: rtracklayer
> uncov_GR <- GRanges(Rle('chr20'), IRanges(c(14350,69251,84185), c(18349,73250,88184)))
> H1data <- system.file('extdata', 'H1_chr20_CG_10k_tabix_out.txt.gz', package='methylPipe')
> H1.db <- BSdata(file=H1data, uncov=uncov_GR, org=Hsapiens)
> gr <- GRanges("chr20",IRanges(1,5e5))
> sres <- mCsmoothing(H1.db, gr, Scorefun='sum', Nbins=50, Context="CG", plot=TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>