In vjcitn/GenomicRangesGHA: Representation and manipulation of genomic intervals; demo with github actions binary generation
BiocStyle::markdown()
Introduction
The r Biocpkg("GenomicRangesGHA") package serves as the foundation for
representing genomic locations within the Bioconductor project.
In the Bioconductor package hierarchy, it builds upon the
r Biocpkg("IRanges") (infrastructure) package and provides
support for the r Biocpkg("BSgenome") (infrastructure),
r Biocpkg("Rsamtools") (I/O), r Biocpkg("ShortRead") (I/O & QA),
r Biocpkg("rtracklayer") (I/O), r Biocpkg("GenomicFeatures")
(infrastructure), r Biocpkg("GenomicAlignments") (sequence reads),
r Biocpkg("VariantAnnotation") (called variants), and many other
Bioconductor packages.
This package lays a foundation for genomic analysis by introducing
three classes (GRanges, GPos, and GRangesList),
which are used to represent genomic ranges, genomic positions, and groups
of genomic ranges. This vignette focuses on the GRanges and
GRangesList classes and their associated methods.
The r Biocpkg("GenomicRangesGHA") package is available at
https://bioconductor.org and can be
installed via BiocManager::install:
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("GenomicRangesGHA")
A package only needs to be installed once. Load the package into an
R session with
library(GenomicRangesGHA)
GRanges: Genomic Ranges
The GRanges class represents a collection of genomic ranges
that each have a single start and end location on the genome. It can be
used to store the location of genomic features such as contiguous binding
sites, transcripts, and exons. These objects can be created by using the
GRanges constructor function. For example,
gr <- GRanges(
seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)),
strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)),
score = 1:10,
GC = seq(1, 0, length=10))
gr
creates a GRanges object with 10 genomic ranges.
The output of the GRanges show method separates the
information into a left and right hand region that are separated by
| symbols. The genomic coordinates (seqnames, ranges, and strand)
are located on the left-hand side and the metadata columns (annotation)
are located on the right. For this example, the metadata is
comprised of score and GC information, but almost
anything can be stored in the metadata portion of a GRanges
object.
The components of the genomic coordinates within a GRanges
object can be extracted using the seqnames, ranges,
and strand accessor functions.
seqnames(gr)
ranges(gr)
strand(gr)
The genomic ranges can be extracted without corresponding metadata
with granges
granges(gr)
Annotations for these coordinates can be extracted as a
DataFrame object using the mcols accessor.
mcols(gr)
mcols(gr)$score
Information about the lengths of the various sequences that the ranges
are aligned to can also be stored in the GRanges object. So
if this is data from Homo sapiens, we can set the values as:
seqlengths(gr) <- c(249250621, 243199373, 198022430)
And then retrieves as:
seqlengths(gr)
Methods for accessing the length and names have
also been defined.
names(gr)
length(gr)
Splitting and combining GRanges objects
GRanges objects can be devided into groups using the
split method. This produces a GRangesList object,
a class that will be discussed in detail in the next section.
sp <- split(gr, rep(1:2, each=5))
sp
Separate GRanges instances can be concatenated by using the
c and append methods.
c(sp[[1]], sp[[2]])
Subsetting GRanges objects
GRanges objects act like vectors of ranges, with the expected
vector-like subsetting operations available
gr[2:3]
A second argument to the [ subset operator can be used
to specify which metadata columns to extract from the
GRanges object. For example,
gr[2:3, "GC"]
Elements can also be assigned to the GRanges object. Here is
an example where the second row of a GRanges object is
replaced with the first row of gr.
singles <- split(gr, names(gr))
grMod <- gr
grMod[2] <- singles[[1]]
head(grMod, n=3)
There are methods to repeat, reverse, or select specific portions of
GRanges objects.
rep(singles[[2]], times = 3)
rev(gr)
head(gr,n=2)
tail(gr,n=2)
window(gr, start=2,end=4)
gr[IRanges(start=c(2,7), end=c(3,9))]
Basic interval operations for GRanges objects
Basic interval characteristics of GRanges objects can
be extracted using the start, end, width,
and range methods.
g <- gr[1:3]
g <- append(g, singles[[10]])
start(g)
end(g)
width(g)
range(g)
The GRanges class also has many methods for manipulating the
ranges. The methods can be classified as intra-range methods,
inter-range methods, and between-range methods.
Intra-range methods operate on each element of a
GRanges object independent of the other ranges in the
object. For example, the flank method can be used to recover
regions flanking the set of ranges represented by the GRanges
object. So to get a GRanges object containing the ranges that
include the 10 bases upstream of the ranges:
flank(g, 10)
And to include the downstream bases:
flank(g, 10, start=FALSE)
Other examples of intra-range methods include resize and
shift. The shift method will move the ranges by a
specific number of base pairs, and the resize method will
extend the ranges by a specified width.
shift(g, 5)
resize(g, 30)
The r Biocpkg("GenomicRangesGHA") help page ?"intra-range-methods"
summarizes these methods.
Inter-range methods involve comparisons between ranges in a
single GRanges object. For instance, the reduce
method will align the ranges and merge overlapping ranges to produce a
simplified set.
reduce(g)
Sometimes one is interested in the gaps or the qualities of the gaps
between the ranges represented by your GRanges object. The
gaps method provides this information:
reduced version of your ranges:
gaps(g)
The disjoin method represents a GRanges object as a
collection of non-overlapping ranges:
disjoin(g)
The coverage method quantifies the degree of overlap for all
the ranges in a GRanges object.
coverage(g)
See the r Biocpkg("GenomicRangesGHA") help page
?"inter-range-methods" for additional help.
Between-range methods involve operations between two
GRanges objects; some of these are summarized in the next
section.
Interval set operations for GRanges objects
Between-range methods calculate relationships between different
GRanges objects. Of central importance are
findOverlaps and related operations; these are discussed
below. Additional operations treat GRanges as mathematical
sets of coordinates; union(g, g2) is the union of the
coordinates in g and g2. Here are examples for
calculating the union, the intersect and the
asymmetric difference (using setdiff).
g2 <- head(gr, n=2)
union(g, g2)
intersect(g, g2)
setdiff(g, g2)
Related methods are available when the structure of the
GRanges objects are 'parallel' to one another, i.e., element
1 of object 1 is related to element 1 of object 2, and so on. These
operations all begin with a p, which is short for
parallel. The methods then perform element-wise, e.g., the union of
element 1 of object 1 with element 1 of object 2, etc. A requirement
for these operations is that the number of elements in each
GRanges object is the same, and that both of the objects have
the same seqnames and strand assignments throughout.
g3 <- g[1:2]
ranges(g3[1]) <- IRanges(start=105, end=112)
punion(g2, g3)
#pintersect(g2, g3)
#psetdiff(g2, g3)
For more information on the GRanges class be sure to consult
the manual page.
?GRanges
A relatively comprehensive list of available methods is discovered
with
methods(class="GRanges")
GRangesList: Groups of Genomic Ranges
Some important genomic features, such as spliced transcripts that
are comprised of exons, are inherently compound structures. Such a
feature makes much more sense when expressed as a compound object
such as a GRangesList. Whenever genomic features consist of
multiple ranges that are grouped by a parent feature, they can be
represented as a GRangesList object. Consider the simple
example of the two transcript GRangesList below created
using the GRangesList constructor.
gr1 <- GRanges(
seqnames = "chr2",
ranges = IRanges(103, 106),
strand = "+",
score = 5L, GC = 0.45)
gr2 <- GRanges(
seqnames = c("chr1", "chr1"),
ranges = IRanges(c(107, 113), width = 3),
strand = c("+", "-"),
score = 3:4, GC = c(0.3, 0.5))
grl <- GRangesList("txA" = gr1, "txB" = gr2)
grl
The show method for a GRangesList object displays
it as a named list of GRanges objects, where the names of
this list are considered to be the names of the grouping feature. In
the example above, the groups of individual exon ranges are represented
as separate GRanges objects which are further organized into a
list structure where each element name is a transcript name. Many
other combinations of grouped and labeled GRanges objects are
possible of course, but this example is expected to be a common
arrangement.
Basic GRangesList accessors
Just as with GRanges object, the components of the genomic
coordinates within a GRangesList object can be extracted
using simple accessor methods. Not surprisingly, the
GRangesList objects have many of the same accessors as
GRanges objects. The difference is that many of these
methods return a list since the input is now essentially a list of
GRanges objects. Here are a few examples:
seqnames(grl)
ranges(grl)
strand(grl)
The length and names methods will return the length
or names of the list and the seqlengths method will return the
set of sequence lengths.
length(grl)
names(grl)
seqlengths(grl)
The elementNROWS method returns a list of integers
corresponding to the result of calling NROW on each
individual GRanges object contained by the
GRangesList. This is a faster alternative to calling
lapply on the GRangesList.
elementNROWS(grl)
isEmpty tests if a GRangesList object contains
anything.
isEmpty(grl)
In the context of a GRangesList object, the mcols
method performs a similar operation to what it does on a
GRanges object. However, this metadata now refers to
information at the list level instead of the level of the individual
GRanges objects.
mcols(grl) <- c("Transcript A","Transcript B")
mcols(grl)
Element-level metadata can be retrieved by unlisting the
GRangesList, and extracting the metadata
mcols(unlist(grl))
Combining GRangesList objects
GRangesList objects can be unlisted to combine the separate
GRanges objects that they contain as an expanded
GRanges.
ul <- unlist(grl)
ul
Append lists using append or c.
A support site user
had two GRangesList objects with 'parallel' elements, and
wanted to combined these element-wise into a single
GRangesList. One solution is to use pc() -- parallel
(element-wise) c(). A more general solution is to concatenate
the lists and then re-group by some factor, in this case the names of
the elements.
grl1 <- GRangesList(
gr1 = GRanges("chr2", IRanges(3, 6)),
gr2 = GRanges("chr1", IRanges(c(7,13), width = 3)))
grl2 <- GRangesList(
gr1 = GRanges("chr2", IRanges(9, 12)),
gr2 = GRanges("chr1", IRanges(c(25,38), width = 3)))
pc(grl1, grl2)
grl3 <- c(grl1, grl2)
regroup(grl3, names(grl3))
Basic interval operations for GRangesList objects
For interval operations, many of the same methods exist for
GRangesList objects that exist for GRanges objects.
start(grl)
end(grl)
width(grl)
These operations return a data structure representing, e.g.,
IntegerList, a list where all elements are integers; it can
be convenient to use mathematical and other operations on
List objects that work on each element, e.g.,
sum(width(grl)) # sum of widths of each grl element
Most of the intra-, inter- and between-range methods operate on
GRangesList objects, e.g., to shift all the GRanges
objects in a GRangesList object, or calculate the
coverage. Both of these operations are also carried out across each
GRanges list member.
shift(grl, 20)
coverage(grl)
Subsetting GRangesList objects
A GRangesList object behaves like a list:
[ returns a GRangesList containing a subset of the
original object; [[ or $ returns the
GRanges object at that location in the list.
grl[1]
grl[[1]]
grl["txA"]
grl$txB
In addition, subsetting a GRangesList also accepts a second
parameter to specify which of the metadata columns you wish to select.
grl[1, "score"]
grl["txB", "GC"]
The head, tail, rep, rev, and
window methods all behave as you would expect them to for a
list object. For example, the elements referred to by window
are now list elements instead of GRanges elements.
rep(grl[[1]], times = 3)
rev(grl)
head(grl, n=1)
tail(grl, n=1)
window(grl, start=1, end=1)
grl[IRanges(start=2, end=2)]
Looping over GRangesList objects
For GRangesList objects there is also a family of
apply methods. These include lapply, sapply,
mapply, endoapply, mendoapply, Map,
and Reduce.
The different looping methods defined for GRangesList objects
are useful for returning different kinds of results. The standard
lapply and sapply behave according to convention,
with the lapply method returning a list and sapply
returning a more simplified output.
lapply(grl, length)
sapply(grl, length)
As with IRanges objects, there is also a multivariate version
of sapply, called mapply, defined for
GRangesList objects. And, if you don't want the results
simplified, you can call the Map method, which does the same
things as mapply but without simplifying the output.
grl2 <- shift(grl, 10)
names(grl2) <- c("shiftTxA", "shiftTxB")
mapply(c, grl, grl2)
Map(c, grl, grl2)
Sometimes you will want to get back a modified version of the
GRangesList that you originally passed in.
An endomorphism is a transformation of an object to another instance
of the same class . This is achieved using the endoapply
method, which will return the results as a GRangesList
object.
endoapply(grl, rev)
mendoapply(c, grl, grl2)
The Reduce method will allow the GRanges objects to
be collapsed across the whole of the GRangesList object.
% Again, this seems like a sub-optimal example to me.
Reduce(c, grl)
Explicit element-wise operations (lapply() and friends) on
GRangesList objects with many elements can be slow. It is
therefore beneficial to explore operations that work on List
objects directly (e.g., many of the 'group generic' operators, see
?S4groupGeneric, and the set and parallel set operators (e.g.,
union, punion). A useful and fast strategy is to
unlist the GRangesList to a GRanges object,
operate on the GRanges object, then relist the
result, e.g.,
gr <- unlist(grl)
gr$log_score <- log(gr$score)
grl <- relist(gr, grl)
grl
See also ?extractList.
For more information on the GRangesList class be sure to
consult the manual page and available methods
?GRangesList
methods(class="GRangesList") # _partial_ list
Interval overlaps involving GRanges and GRangesList objects
Interval overlapping is the process of comparing the ranges in two
objects to determine if and when they overlap. As such, it is perhaps
the most common operation performed on GRanges and
GRangesList objects. To this end, the r Biocpkg("GenomicRangesGHA")
package provides a family of interval overlap functions. The most general
of these functions is findOverlaps, which takes a query and a
subject as inputs and returns a Hits object containing
the index pairings for the overlapping elements.
findOverlaps(gr, grl)
As suggested in the sections discussing the nature of the
GRanges and GRangesList classes, the index in the
above Hits object for a GRanges object is a single range
while for a GRangesList object it is the set of ranges that
define a "feature".
Another function in the overlaps family is countOverlaps,
which tabulates the number of overlaps for each element in the query.
countOverlaps(gr, grl)
A third function in this family is subsetByOverlaps,
which extracts the elements in the query that overlap at least one
element in the subject.
subsetByOverlaps(gr,grl)
Finally, you can use the select argument to get the index
of the first overlapping element in the subject for each element
in the query.
findOverlaps(gr, grl, select="first")
findOverlaps(grl, gr, select="first")
Finding the nearest genomic position in GRanges objects
The r Biocpkg("GenomicRangesGHA") package provides multiple functions to
facilitate the indentification of neighboring genomic positions.
For the following examples, we define an arbitrary GRanges object for x and
we define the GRanges object subject as the collection of genes in
r Biocpkg("TxDb.Hsapiens.UCSC.hg19.knownGene") extracted using the genes
method from the r Biocpkg("GenomicFeatures") package.
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
broads <- GenomicFeatures::genes(txdb)
x <- GRanges(
seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)),
strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)),
score = 1:10, GC = seq(1, 0, length=10))
subject <- broads[ seqnames(broads) %in% seqlevels(gr) ]
The nearest method performs conventional nearest neighbor finding. It
finds the nearest neighbor range in subject for each range in x.
Overlaps are included. If subject is not given as an argument, x will also
be treated as the subject.
nearest(x, subject)
nearest(x)
The precede method will return the index of the range in subject that is
preceded by the range in x. Overlaps are excluded.
precede(x, subject)
The follow method will return the index of the range in subject that is
followed by the range in x.
follow(x, subject)
The nearestKNeighbors method performs conventional k-nearest neighbor finding. For
each range in x, it will find the index of the k-nearest neighbors in
subject. The argument k can be specified to identify more than one nearest
neighbor. Overlaps are included. If subject is not given as an argument, x
will also be treated as the subject.
nearestKNeighbors(x, subject)
nearestKNeighbors(x, subject, k=10)
nearestKNeighbors(x)
nearestKNeighbors(x, k=10)
Session Information
All of the output in this vignette was produced under the following
conditions:
sessionInfo()
vjcitn/GenomicRangesGHA documentation built on Jan. 18, 2021, 12:39 a.m.
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BiocStyle::markdown()
Introduction
The r Biocpkg("GenomicRangesGHA") package serves as the foundation for
representing genomic locations within the Bioconductor project.
In the Bioconductor package hierarchy, it builds upon the
r Biocpkg("IRanges") (infrastructure) package and provides
support for the r Biocpkg("BSgenome") (infrastructure),
r Biocpkg("Rsamtools") (I/O), r Biocpkg("ShortRead") (I/O & QA),
r Biocpkg("rtracklayer") (I/O), r Biocpkg("GenomicFeatures")
(infrastructure), r Biocpkg("GenomicAlignments") (sequence reads),
r Biocpkg("VariantAnnotation") (called variants), and many other
Bioconductor packages.
This package lays a foundation for genomic analysis by introducing three classes (GRanges, GPos, and GRangesList), which are used to represent genomic ranges, genomic positions, and groups of genomic ranges. This vignette focuses on the GRanges and GRangesList classes and their associated methods.
The r Biocpkg("GenomicRangesGHA") package is available at
https://bioconductor.org and can be
installed via BiocManager::install:
if (!require("BiocManager")) install.packages("BiocManager") BiocManager::install("GenomicRangesGHA")
A package only needs to be installed once. Load the package into an R session with
library(GenomicRangesGHA)
GRanges: Genomic Ranges
The GRanges class represents a collection of genomic ranges
that each have a single start and end location on the genome. It can be
used to store the location of genomic features such as contiguous binding
sites, transcripts, and exons. These objects can be created by using the
GRanges constructor function. For example,
gr <- GRanges( seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score = 1:10, GC = seq(1, 0, length=10)) gr
creates a GRanges object with 10 genomic ranges.
The output of the GRanges show method separates the
information into a left and right hand region that are separated by
| symbols. The genomic coordinates (seqnames, ranges, and strand)
are located on the left-hand side and the metadata columns (annotation)
are located on the right. For this example, the metadata is
comprised of score and GC information, but almost
anything can be stored in the metadata portion of a GRanges
object.
The components of the genomic coordinates within a GRanges
object can be extracted using the seqnames, ranges,
and strand accessor functions.
seqnames(gr) ranges(gr) strand(gr)
The genomic ranges can be extracted without corresponding metadata
with granges
granges(gr)
Annotations for these coordinates can be extracted as a
DataFrame object using the mcols accessor.
mcols(gr) mcols(gr)$score
Information about the lengths of the various sequences that the ranges are aligned to can also be stored in the GRanges object. So if this is data from Homo sapiens, we can set the values as:
seqlengths(gr) <- c(249250621, 243199373, 198022430)
And then retrieves as:
seqlengths(gr)
Methods for accessing the length and names have
also been defined.
names(gr) length(gr)
Splitting and combining GRanges objects
GRanges objects can be devided into groups using the
split method. This produces a GRangesList object,
a class that will be discussed in detail in the next section.
sp <- split(gr, rep(1:2, each=5)) sp
Separate GRanges instances can be concatenated by using the
c and append methods.
c(sp[[1]], sp[[2]])
Subsetting GRanges objects
GRanges objects act like vectors of ranges, with the expected vector-like subsetting operations available
gr[2:3]
A second argument to the [ subset operator can be used
to specify which metadata columns to extract from the
GRanges object. For example,
gr[2:3, "GC"]
Elements can also be assigned to the GRanges object. Here is
an example where the second row of a GRanges object is
replaced with the first row of gr.
singles <- split(gr, names(gr)) grMod <- gr grMod[2] <- singles[[1]] head(grMod, n=3)
There are methods to repeat, reverse, or select specific portions of GRanges objects.
rep(singles[[2]], times = 3) rev(gr) head(gr,n=2) tail(gr,n=2) window(gr, start=2,end=4) gr[IRanges(start=c(2,7), end=c(3,9))]
Basic interval operations for GRanges objects
Basic interval characteristics of GRanges objects can
be extracted using the start, end, width,
and range methods.
g <- gr[1:3] g <- append(g, singles[[10]]) start(g) end(g) width(g) range(g)
The GRanges class also has many methods for manipulating the ranges. The methods can be classified as intra-range methods, inter-range methods, and between-range methods.
Intra-range methods operate on each element of a
GRanges object independent of the other ranges in the
object. For example, the flank method can be used to recover
regions flanking the set of ranges represented by the GRanges
object. So to get a GRanges object containing the ranges that
include the 10 bases upstream of the ranges:
flank(g, 10)
And to include the downstream bases:
flank(g, 10, start=FALSE)
Other examples of intra-range methods include resize and
shift. The shift method will move the ranges by a
specific number of base pairs, and the resize method will
extend the ranges by a specified width.
shift(g, 5) resize(g, 30)
The r Biocpkg("GenomicRangesGHA") help page ?"intra-range-methods"
summarizes these methods.
Inter-range methods involve comparisons between ranges in a
single GRanges object. For instance, the reduce
method will align the ranges and merge overlapping ranges to produce a
simplified set.
reduce(g)
Sometimes one is interested in the gaps or the qualities of the gaps
between the ranges represented by your GRanges object. The
gaps method provides this information:
reduced version of your ranges:
gaps(g)
The disjoin method represents a GRanges object as a
collection of non-overlapping ranges:
disjoin(g)
The coverage method quantifies the degree of overlap for all
the ranges in a GRanges object.
coverage(g)
See the r Biocpkg("GenomicRangesGHA") help page
?"inter-range-methods" for additional help.
Between-range methods involve operations between two GRanges objects; some of these are summarized in the next section.
Interval set operations for GRanges objects
Between-range methods calculate relationships between different
GRanges objects. Of central importance are
findOverlaps and related operations; these are discussed
below. Additional operations treat GRanges as mathematical
sets of coordinates; union(g, g2) is the union of the
coordinates in g and g2. Here are examples for
calculating the union, the intersect and the
asymmetric difference (using setdiff).
g2 <- head(gr, n=2) union(g, g2) intersect(g, g2) setdiff(g, g2)
Related methods are available when the structure of the
GRanges objects are 'parallel' to one another, i.e., element
1 of object 1 is related to element 1 of object 2, and so on. These
operations all begin with a p, which is short for
parallel. The methods then perform element-wise, e.g., the union of
element 1 of object 1 with element 1 of object 2, etc. A requirement
for these operations is that the number of elements in each
GRanges object is the same, and that both of the objects have
the same seqnames and strand assignments throughout.
g3 <- g[1:2] ranges(g3[1]) <- IRanges(start=105, end=112) punion(g2, g3) #pintersect(g2, g3) #psetdiff(g2, g3)
For more information on the GRanges class be sure to consult
the manual page.
?GRanges
A relatively comprehensive list of available methods is discovered with
methods(class="GRanges")
GRangesList: Groups of Genomic Ranges
Some important genomic features, such as spliced transcripts that
are comprised of exons, are inherently compound structures. Such a
feature makes much more sense when expressed as a compound object
such as a GRangesList. Whenever genomic features consist of
multiple ranges that are grouped by a parent feature, they can be
represented as a GRangesList object. Consider the simple
example of the two transcript GRangesList below created
using the GRangesList constructor.
gr1 <- GRanges( seqnames = "chr2", ranges = IRanges(103, 106), strand = "+", score = 5L, GC = 0.45) gr2 <- GRanges( seqnames = c("chr1", "chr1"), ranges = IRanges(c(107, 113), width = 3), strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5)) grl <- GRangesList("txA" = gr1, "txB" = gr2) grl
The show method for a GRangesList object displays
it as a named list of GRanges objects, where the names of
this list are considered to be the names of the grouping feature. In
the example above, the groups of individual exon ranges are represented
as separate GRanges objects which are further organized into a
list structure where each element name is a transcript name. Many
other combinations of grouped and labeled GRanges objects are
possible of course, but this example is expected to be a common
arrangement.
Basic GRangesList accessors
Just as with GRanges object, the components of the genomic coordinates within a GRangesList object can be extracted using simple accessor methods. Not surprisingly, the GRangesList objects have many of the same accessors as GRanges objects. The difference is that many of these methods return a list since the input is now essentially a list of GRanges objects. Here are a few examples:
seqnames(grl) ranges(grl) strand(grl)
The length and names methods will return the length
or names of the list and the seqlengths method will return the
set of sequence lengths.
length(grl) names(grl) seqlengths(grl)
The elementNROWS method returns a list of integers
corresponding to the result of calling NROW on each
individual GRanges object contained by the
GRangesList. This is a faster alternative to calling
lapply on the GRangesList.
elementNROWS(grl)
isEmpty tests if a GRangesList object contains
anything.
isEmpty(grl)
In the context of a GRangesList object, the mcols
method performs a similar operation to what it does on a
GRanges object. However, this metadata now refers to
information at the list level instead of the level of the individual
GRanges objects.
mcols(grl) <- c("Transcript A","Transcript B") mcols(grl)
Element-level metadata can be retrieved by unlisting the
GRangesList, and extracting the metadata
mcols(unlist(grl))
Combining GRangesList objects
GRangesList objects can be unlisted to combine the separate GRanges objects that they contain as an expanded GRanges.
ul <- unlist(grl) ul
Append lists using append or c.
A support site user
had two GRangesList objects with 'parallel' elements, and
wanted to combined these element-wise into a single
GRangesList. One solution is to use pc() -- parallel
(element-wise) c(). A more general solution is to concatenate
the lists and then re-group by some factor, in this case the names of
the elements.
grl1 <- GRangesList( gr1 = GRanges("chr2", IRanges(3, 6)), gr2 = GRanges("chr1", IRanges(c(7,13), width = 3))) grl2 <- GRangesList( gr1 = GRanges("chr2", IRanges(9, 12)), gr2 = GRanges("chr1", IRanges(c(25,38), width = 3))) pc(grl1, grl2) grl3 <- c(grl1, grl2) regroup(grl3, names(grl3))
Basic interval operations for GRangesList objects
For interval operations, many of the same methods exist for GRangesList objects that exist for GRanges objects.
start(grl) end(grl) width(grl)
These operations return a data structure representing, e.g., IntegerList, a list where all elements are integers; it can be convenient to use mathematical and other operations on List objects that work on each element, e.g.,
sum(width(grl)) # sum of widths of each grl element
Most of the intra-, inter- and between-range methods operate on GRangesList objects, e.g., to shift all the GRanges objects in a GRangesList object, or calculate the coverage. Both of these operations are also carried out across each GRanges list member.
shift(grl, 20) coverage(grl)
Subsetting GRangesList objects
A GRangesList object behaves like a list:
[ returns a GRangesList containing a subset of the
original object; [[ or $ returns the
GRanges object at that location in the list.
grl[1] grl[[1]] grl["txA"] grl$txB
In addition, subsetting a GRangesList also accepts a second parameter to specify which of the metadata columns you wish to select.
grl[1, "score"] grl["txB", "GC"]
The head, tail, rep, rev, and
window methods all behave as you would expect them to for a
list object. For example, the elements referred to by window
are now list elements instead of GRanges elements.
rep(grl[[1]], times = 3) rev(grl) head(grl, n=1) tail(grl, n=1) window(grl, start=1, end=1) grl[IRanges(start=2, end=2)]
Looping over GRangesList objects
For GRangesList objects there is also a family of
apply methods. These include lapply, sapply,
mapply, endoapply, mendoapply, Map,
and Reduce.
The different looping methods defined for GRangesList objects
are useful for returning different kinds of results. The standard
lapply and sapply behave according to convention,
with the lapply method returning a list and sapply
returning a more simplified output.
lapply(grl, length) sapply(grl, length)
As with IRanges objects, there is also a multivariate version
of sapply, called mapply, defined for
GRangesList objects. And, if you don't want the results
simplified, you can call the Map method, which does the same
things as mapply but without simplifying the output.
grl2 <- shift(grl, 10) names(grl2) <- c("shiftTxA", "shiftTxB") mapply(c, grl, grl2) Map(c, grl, grl2)
Sometimes you will want to get back a modified version of the GRangesList that you originally passed in.
An endomorphism is a transformation of an object to another instance
of the same class . This is achieved using the endoapply
method, which will return the results as a GRangesList
object.
endoapply(grl, rev) mendoapply(c, grl, grl2)
The Reduce method will allow the GRanges objects to
be collapsed across the whole of the GRangesList object.
% Again, this seems like a sub-optimal example to me.
Reduce(c, grl)
Explicit element-wise operations (lapply() and friends) on
GRangesList objects with many elements can be slow. It is
therefore beneficial to explore operations that work on List
objects directly (e.g., many of the 'group generic' operators, see
?S4groupGeneric, and the set and parallel set operators (e.g.,
union, punion). A useful and fast strategy is to
unlist the GRangesList to a GRanges object,
operate on the GRanges object, then relist the
result, e.g.,
gr <- unlist(grl) gr$log_score <- log(gr$score) grl <- relist(gr, grl) grl
See also ?extractList.
For more information on the GRangesList class be sure to
consult the manual page and available methods
?GRangesList methods(class="GRangesList") # _partial_ list
Interval overlaps involving GRanges and GRangesList objects
Interval overlapping is the process of comparing the ranges in two
objects to determine if and when they overlap. As such, it is perhaps
the most common operation performed on GRanges and
GRangesList objects. To this end, the r Biocpkg("GenomicRangesGHA")
package provides a family of interval overlap functions. The most general
of these functions is findOverlaps, which takes a query and a
subject as inputs and returns a Hits object containing
the index pairings for the overlapping elements.
findOverlaps(gr, grl)
As suggested in the sections discussing the nature of the GRanges and GRangesList classes, the index in the above Hits object for a GRanges object is a single range while for a GRangesList object it is the set of ranges that define a "feature".
Another function in the overlaps family is countOverlaps,
which tabulates the number of overlaps for each element in the query.
countOverlaps(gr, grl)
A third function in this family is subsetByOverlaps,
which extracts the elements in the query that overlap at least one
element in the subject.
subsetByOverlaps(gr,grl)
Finally, you can use the select argument to get the index
of the first overlapping element in the subject for each element
in the query.
findOverlaps(gr, grl, select="first") findOverlaps(grl, gr, select="first")
Finding the nearest genomic position in GRanges objects
The r Biocpkg("GenomicRangesGHA") package provides multiple functions to
facilitate the indentification of neighboring genomic positions.
For the following examples, we define an arbitrary GRanges object for x and
we define the GRanges object subject as the collection of genes in
r Biocpkg("TxDb.Hsapiens.UCSC.hg19.knownGene") extracted using the genes
method from the r Biocpkg("GenomicFeatures") package.
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene broads <- GenomicFeatures::genes(txdb) x <- GRanges( seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score = 1:10, GC = seq(1, 0, length=10)) subject <- broads[ seqnames(broads) %in% seqlevels(gr) ]
The nearest method performs conventional nearest neighbor finding. It
finds the nearest neighbor range in subject for each range in x.
Overlaps are included. If subject is not given as an argument, x will also
be treated as the subject.
nearest(x, subject) nearest(x)
The precede method will return the index of the range in subject that is
preceded by the range in x. Overlaps are excluded.
precede(x, subject)
The follow method will return the index of the range in subject that is
followed by the range in x.
follow(x, subject)
The nearestKNeighbors method performs conventional k-nearest neighbor finding. For
each range in x, it will find the index of the k-nearest neighbors in
subject. The argument k can be specified to identify more than one nearest
neighbor. Overlaps are included. If subject is not given as an argument, x
will also be treated as the subject.
nearestKNeighbors(x, subject) nearestKNeighbors(x, subject, k=10) nearestKNeighbors(x) nearestKNeighbors(x, k=10)
Session Information
All of the output in this vignette was produced under the following conditions:
sessionInfo()
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