Last data update: 2014.03.03

R: filterChromosomes
filterChromosomesR Documentation

filterChromosomes

Description

Filters the chromosomes in a region set. It can either filter using a predefined chromosome set (e.g. "autosomal chromosomes in Homo sapiens") or using a custom chromosome set (e.g. only chromosomes "chr22" and "chrX")

Usage

filterChromosomes(A, organism="hg", chr.type="canonical", keep.chr=NULL)

Arguments

A

a region set in any of the formats accepted by toGRanges (GenomicRanges, data.frame, etc...)

organism

a character indicating the organism from which to get the predefined chromosome sets. It can be the organism code as used in BSgenome (e.g. hg for human, mm for mouse...) or the full genome assembly identifier, since any digit will be removed to get the organism code.

chr.type

a character indicating the specific chromosome set to be used. Usually "autosomal" or "canonical", althought other values could be available for certain organisms.

keep.chr

is a character vector stating the names of the chromosomes to keep. Any chromosome not in the vector will be filtered out. If keep.chr is supplied, organism and chr.type are ignored.

Value

A GRanges object containing only the regions in the original region set belonging to the selected chromosomes. All regions in non selected chromosomes are removed.

See Also

getGenomeAndMask, listChrTypes getChromosomesByOrganism

Examples


g <- getGenomeAndMask("hg19")$genome
listChrTypes()
g <- filterChromosomes(g, chr.type="autosomal", organism="hg19")
g <- filterChromosomes(g, keep.chr=c("chr1", "chr2", "chr3"))


Results


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> library(regioneR)
Loading required package: memoise
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: BSgenome
Loading required package: Biostrings
Loading required package: XVector
Loading required package: rtracklayer
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/regioneR/filterChromosomes.Rd_%03d_medium.png", width=480, height=480)
> ### Name: filterChromosomes
> ### Title: filterChromosomes
> ### Aliases: filterChromosomes
> 
> ### ** Examples
> 
> 
> g <- getGenomeAndMask("hg19")$genome
> listChrTypes()
Homo sapiens (hg): autosomal, canonical 
Mus musculus (mm): autosomal, canonical 
Bos taurus (bosTau): autosomal, canonical 
Caenorhabditis elegans (ce): autosomal, canonical 
Danio rerio (danRer): canonical 
Macaca mulata (rheMac): autosomal, canonical 
Rattus norvegicus (rn): autosomal, canonical 
Saccharomyces cerevisiae (sacCer): autosomal, canonical 
Drosophila melanogaster (dm): autosomal, canonical 
Pan troglodytes (panTro): autosomal, canonical 
> g <- filterChromosomes(g, chr.type="autosomal", organism="hg19")
> g <- filterChromosomes(g, keep.chr=c("chr1", "chr2", "chr3"))
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>