Identifying Partially Methylated Domains (PMDs)

Description

This function is a wrapper function to identify partially methylated domains (PMDs) in Bis-seq data.

Usage

## S4 method for signature 'methylPipe,BSdata'
findPMDs(Object, Nproc=1, Chrs=NULL)

Arguments

Details

This functions is a wrapper function of segmentPMDs method of package MethylSeekR. This function trains a Hidden Markov Model (HMM) to detect partially methylated domains (PMDs) in Bis-seq data.

Value

A GRangesList object containing segments that partition the genome into PMDs and regions outside of PMDs. The object contains two metadata columns indicating the type of region (PMD/notPMD) and the number of covered (by at least 5 reads) CpGs (nCG) in the region.

Author(s)

Kamal Kishore

See Also

findDMR

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)
PMDs <- findPMDs(H1.db, Nproc=1, Chrs="chr20")

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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> 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/findPMDs.Rd_%03d_medium.png", width=480, height=480)
> ### Name: findPMDs
> ### Title: Identifying Partially Methylated Domains (PMDs)
> ### Aliases: findPMDs findPMDs,methylPipe,BSdata
> ###   findPMDs,methylPipe,BSdata-method findPMDs-methods
> ###   findPMDs,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)
> PMDs <- findPMDs(H1.db, Nproc=1, Chrs="chr20")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>