GRanges object containing the UMR/LMR segmentation. Return value of the
segmentUMRsLMRs function (see example).
PMDs
GRanges object of PMDs. Set to either the return value of the
function segmentPMDs (see example) or to NA if there are no PMDs.
meth.cutoff
Cut-off on methylation for calling hypomethylated regions.
numRegions
The number of randomly chosen regions to be plotted. The default (1)
can only be changed if a pdfFilename is provided (see below).
pdfFilename
Name of the pdf file in which the figure is saved. If no name is
provided (default), the figure is printed to the screen.
minCover
Only CpGs with a coverage of at least minCover reads will be used
for plotting.
nCpG.smoothing
The number of consecutive CpGs that the methylation levels are
averaged over.
Value
No return value. The function creates a figure showing the inferred
segmentation for a randomly chosen region. The figure is either
printed to the screen (default) or saved as a pdf if a filename is
provided. If a filename (pdfFilename) is provided, several regions
(set via the numRegions argument) can be plotted and saved in a
multi-page pdf file. The randomly chosen region that is displayed is
broken up into 3 pairs of panels, where in each pair the same region
is shown twice, once with raw methylation levels (top) and once with
methylation levels smoothed over 3 consecutive CpGs (bottom). In both
cases only CpGs with a coverage of at least minCover reads are
shown. The raw data best illustrates the disordered nature of
methylation levels in PMDs, whereas the smoothed methylation levels
more clearly show UMRs and LMRs. In all figures, UMRs are shown as
blue squares (placed at the middle of the identified segment), LMRs as
red triangles (placed at the middle of the identified segment) and
PMDs as green bars (extending over the entire PMD). The cut-off on
methylation (meth.cutoff) to determine UMRs and LMRs is shown as a
grey dashed line.
Author(s)
Lukas Burger lukas.burger@fmi.ch
Examples
library(MethylSeekR)
# get chromosome lengths
library("BSgenome.Hsapiens.UCSC.hg18")
sLengths=seqlengths(Hsapiens)
# read methylation data
methFname <- system.file("extdata", "Lister2009_imr90_hg18_chr22.tab",
package="MethylSeekR")
meth.gr <- readMethylome(FileName=methFname, seqLengths=sLengths)
FDR.cutoff <- 5
m.sel <- 0.5
n.sel <- 3
#segment UMRs and LMRs, assuming no PMDs
UMRLMRsegments.gr <- segmentUMRsLMRs(m=meth.gr, meth.cutoff=m.sel,
nCpG.cutoff=n.sel, myGenomeSeq=Hsapiens, seqLengths=sLengths)
#plot final segmentation, assuming no PMDs
plotFinalSegmentation(m=meth.gr, segs=UMRLMRsegments.gr, meth.cutoff=m.sel, numRegions=1)
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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MethylSeekR)
Loading required package: rtracklayer
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: mhsmm
Loading required package: mvtnorm
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MethylSeekR/plotFinalSegmentation.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotFinalSegmentation
> ### Title: Plotting final segmentation
> ### Aliases: plotFinalSegmentation
>
> ### ** Examples
>
>
> library(MethylSeekR)
>
> # get chromosome lengths
> library("BSgenome.Hsapiens.UCSC.hg18")
Loading required package: BSgenome
Loading required package: Biostrings
Loading required package: XVector
> sLengths=seqlengths(Hsapiens)
>
> # read methylation data
> methFname <- system.file("extdata", "Lister2009_imr90_hg18_chr22.tab",
+ package="MethylSeekR")
> meth.gr <- readMethylome(FileName=methFname, seqLengths=sLengths)
reading methylome data
Read 200000 records
>
> FDR.cutoff <- 5
> m.sel <- 0.5
> n.sel <- 3
>
> #segment UMRs and LMRs, assuming no PMDs
> UMRLMRsegments.gr <- segmentUMRsLMRs(m=meth.gr, meth.cutoff=m.sel,
+ nCpG.cutoff=n.sel, myGenomeSeq=Hsapiens, seqLengths=sLengths)
identifying UMRs and LMRs
>
> #plot final segmentation, assuming no PMDs
> plotFinalSegmentation(m=meth.gr, segs=UMRLMRsegments.gr, meth.cutoff=m.sel, numRegions=1)
>
>
>
>
>
>
>
> dev.off()
null device
1
>