genomicvars {GenomicRanges} | R Documentation

## Manipulating genomic variables

### Description

A genomic variable is a variable defined along a genome. Here are 2 ways a genomic variable is generally represented in Bioconductor:

1. as a named RleList object with one list element per chromosome;

2. as a metadata column on a disjoint GRanges object.

This man page documents tools for switching from one form to the other.

### Usage

    bindAsGRanges(...)
    mcolAsRleList(x, varname)
    binnedAverage(bins, numvar, varname, na.rm=FALSE)

### Arguments

`...` | One or more genomic variables in the form of named RleList objects.
`x` | A disjoint GRanges object with metadata columns on it. A GRanges object is said to be disjoint if it contains ranges that do not overlap with each other. This can be tested with `isDisjoint`. See `?`isDisjoint,GenomicRanges-method`` for more information about the `isDisjoint` method for GRanges objects.
`varname` | The name of the genomic variable. For `mcolAsRleList` this must be the name of the metadata column on `x` to be turned into an RleList object. For `binnedAverage` this will be the name of the metadata column that contains the binned average in the returned object.
`bins` | A GRanges object representing the genomic bins. Typically obtained by calling `tileGenome` with `cut.last.tile.in.chrom=TRUE`.
`numvar` | A named RleList object representing a numerical variable defined along the genome covered by `bins` (which is the genome described by `seqinfo(bins)`).
`na.rm` | A logical value indicating whether `NA` values should be stripped before the average is computed.

### Details

`bindAsGRanges` allows to switch the representation of one or more genomic variables from the named RleList form to the metadata column on a disjoint GRanges object form by binding the supplied named RleList objects together and putting them on the same GRanges object. This transformation is lossless.

`mcolAsRleList` performs the opposite transformation and is also lossless (however the circularity flags and genome information in `seqinfo(x)` won't propagate). It works for any metadata column on `x` that can be put in Rle form i.e. that is an atomic vector or a factor.

`binnedAverage` computes the binned average of a numerical variable defined along a genome.

### Value

For `bindAsGRanges`: a GRanges object with 1 metadata column per supplied genomic variable.

For `mcolAsRleList`: a named RleList object with 1 list element per seqlevel in `x`.

For `binnedAverage`: input GRanges object `bins` with an additional metadata column named `varname` containing the binned average.

### Author(s)

H. Pagès

### See Also

- RleList objects in the IRanges package.

- coverage,GenomicRanges-method for computing the coverage of a GRanges object.

- The `tileGenome` function for putting tiles on a genome.

- GRanges objects and isDisjoint,GenomicRanges-method for the `isDisjoint` method for GenomicRanges objects.

### Examples

    ## ---------------------------------------------------------------------
    ## A. TWO WAYS TO REPRESENT A GENOMIC VARIABLE
    ## -----------------------------------------------------------------

    ## 1) As a named RleList object
    ## ----------------------------
    ## Let's create a genomic variable in the "named RleList" form:
    library(BSgenome.Scerevisiae.UCSC.sacCer2)
    set.seed(55)
    my_var <- RleList(
        lapply(seqlengths(Scerevisiae),
            function(seqlen) {
                tmp <- sample(50L, seqlen, replace=TRUE)
                Rle(cumsum(tmp - rev(tmp)))
            }
        ),
        compress=FALSE)
    my_var

    ## 2) As a metadata column on a disjoint GRanges object
    ## ----------------------------------------------------
    gr1 <- bindAsGRanges(my_var=my_var)
    gr1

    gr2 <- GRanges(c("chrI:1-150",
                     "chrI:211-285",
                     "chrI:291-377",
                     "chrV:51-60"),
                   score=c(0.4, 8, -10, 2.2),
                   id=letters[1:4],
                   seqinfo=seqinfo(Scerevisiae))
    gr2

    ## Going back to the "named RleList" form:
    mcolAsRleList(gr1, "my_var")
    score <- mcolAsRleList(gr2, "score")
    score
    id <- mcolAsRleList(gr2, "id")
    id
    bindAsGRanges(score=score, id=id)

    ## Bind 'my_var', 'score', and 'id' together:
    gr3 <- bindAsGRanges(my_var=my_var, score=score, id=id)

    ## Sanity checks:
    stopifnot(identical(my_var, mcolAsRleList(gr3, "my_var")))
    stopifnot(identical(score, mcolAsRleList(gr3, "score")))
    stopifnot(identical(id, mcolAsRleList(gr3, "id")))
    gr2b <- bindAsGRanges(score=score, id=id)
    seqinfo(gr2b) <- seqinfo(gr2)
    stopifnot(identical(gr2, gr2b))

    ## ---------------------------------------------------------------------
    ## B. BIND TOGETHER THE COVERAGES OF SEVERAL BAM FILES
    ## ---------------------------------------------------------------------

    library(pasillaBamSubset)
    library(GenomicAlignments)
    untreated1_cvg <- coverage(BamFile(untreated1_chr4()))
    untreated3_cvg <- coverage(BamFile(untreated3_chr4()))
    all_cvg <- bindAsGRanges(untreated1=untreated1_cvg,
                             untreated3=untreated3_cvg)

    ## Keep regions with coverage:
    all_cvg[with(mcols(all_cvg), untreated1 + untreated3 >= 1)]

    ## Plot the coverage profiles with the Gviz package:
    library(Gviz)
    plotNumvars <- function(numvars, region, name="numvars", ...)
    {
        stopifnot(is(numvars, "GRanges"))
        stopifnot(is(region, "GRanges"), length(region) == 1L)
        gtrack <- GenomeAxisTrack()
        dtrack <- DataTrack(numvars,
                            chromosome=as.character(seqnames(region)),
                            name=name,
                            groups=colnames(mcols(numvars)), type="l", ...)
        plotTracks(list(gtrack, dtrack), from=start(region), to=end(region))
    }
    plotNumvars(all_cvg, GRanges("chr4:1-25000"),
                "coverage", col=c("red", "blue"))
    plotNumvars(all_cvg, GRanges("chr4:1.03e6-1.08e6"),
                "coverage", col=c("red", "blue"))

    ## Sanity checks:
    stopifnot(identical(untreated1_cvg, mcolAsRleList(all_cvg, "untreated1")))
    stopifnot(identical(untreated3_cvg, mcolAsRleList(all_cvg, "untreated3")))

    ## ---------------------------------------------------------------------
    ## C. COMPUTE THE BINNED AVERAGE OF A NUMERICAL VARIABLE DEFINED ALONG A
    ##    GENOME
    ## ---------------------------------------------------------------------

    ## In some applications (e.g. visualization), there is the need to compute
    ## the average of a genomic variable for a set of predefined fixed-width
    ## regions (sometimes called "bins").
    ## Let's use tileGenome() to create such a set of bins:
    bins1 <- tileGenome(seqinfo(Scerevisiae), tilewidth=100,
                        cut.last.tile.in.chrom=TRUE)

    ## Compute the binned average for 'my_var' and 'score':
    bins1 <- binnedAverage(bins1, my_var, "binned_var")
    bins1
    bins1 <- binnedAverage(bins1, score, "binned_score")
    bins1

    ## Binned average in "named RleList" form:
    binned_var1 <- mcolAsRleList(bins1, "binned_var")
    binned_var1
    stopifnot(all.equal(mean(my_var), mean(binned_var1)))  # sanity check

    mcolAsRleList(bins1, "binned_score")

    ## With bigger bins:
    bins2 <- tileGenome(seqinfo(Scerevisiae), tilewidth=50000,
                        cut.last.tile.in.chrom=TRUE)
    bins2 <- binnedAverage(bins2, my_var, "binned_var")
    bins2 <- binnedAverage(bins2, score, "binned_score")
    bins2

    binned_var2 <- mcolAsRleList(bins2, "binned_var")
    binned_var2
    stopifnot(all.equal(mean(my_var), mean(binned_var2)))  # sanity check

    mcolAsRleList(bins2, "binned_score")

    ## Not surprisingly, the "binned" variables are much more compact in
    ## memory than the original variables (they contain much less runs):
    object.size(my_var)
    object.size(binned_var1)
    object.size(binned_var2)

    ## ---------------------------------------------------------------------
    ## D. SANITY CHECKS
    ## ---------------------------------------------------------------------

    bins3 <- tileGenome(c(chr1=10, chr2=8), tilewidth=5,
                        cut.last.tile.in.chrom=TRUE)
    my_var3 <- RleList(chr1=Rle(c(1:3, NA, 5:7)), chr2=Rle(c(-3, NA, -3, NaN)))
    bins3 <- binnedAverage(bins3, my_var3, "binned_var3", na.rm=TRUE)
    binned_var3 <- mcols(bins3)$binned_var3
    stopifnot(
      identical(mean(my_var3$chr1[1:5], na.rm=TRUE),
                binned_var3[1]),
      identical(mean(c(my_var3$chr1, 0, 0, 0)[6:10], na.rm=TRUE),
                binned_var3[2]),
      #identical(mean(c(my_var3$chr2, 0), na.rm=TRUE),
      #          binned_var3[3]),
      identical(0, binned_var3[4])
    )

[Package GenomicRanges version 1.42.0 Index]