The GenomicTuples R package defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package.
As you will see, the functionality of the GenomicTuples package
is based almost entirely on the wonderful GenomicRanges
package. Therefore, I have tried to keep the user interface as similar
as possible. This vignette is also heavily based on the vignette “An
Introduction to Genomic Ranges Classes”, which is included with the
GenomicRanges package1 The GenomicRanges vignette can be accessed
by typingvignette("GenomicRangesIntroduction", package = "GenomicRanges") at the R console.. While not essential, familiarity with the GenomicRanges will be of benefit in understanding the GenomicTuples package.
A genomic tuple is defined by a sequence name (seqnames), a
strand (strand) and a tuple (tuples). All positions
in a genomic tuple must be on the same strand and sorted in ascending order.
Each tuple has an associated size, which is a positive integer. For example,
chr1:+:{34, 39, 60} is a 3-tuple (size = 3) of the
positions chr1:34, chr1:39 and chr1:60 on the
+ strand.
When referring to genomic tuples of a general (fixed) size, I
will abbreviate these to \(m\)-tuples, where \(m\) = size. I
will refer to the first position as \(pos_{1}\) (pos1), the second
as \(pos_{2}\) (pos2), \(\ldots{}\), and the final position as
\(pos_{m}\) (posm).
The difference between a genomic tuple and a genomic range can be
thought of as the difference between a set and an interval. For example,
the genomic tuple chr10:-:{800, 900} only includes the
positions chr10:-:800 and chr10:-:900 whereas the
genomic range chr10:-:[800, 900] includes the positions
chr10:-:800, chr10:-:801, chr10:-:802,
\(\ldots{}\), chr10:-:900.
In short, whenever the co-ordinates of your genomic data are better defined by a set than by an interval.
The original use case for the GTuples class was to store the genomic co-ordinates of “methylation patterns”. I am currently developing these ideas in a separate R package, MethylationTuples, which makes heavy use of the GTuples class. Other genomic data, such as long reads containing multiple variants, may also be better conceptualised as genomic tuples rather than as genomic ranges and therefore may benefit from the GenomicTuples infrastructure.
The GTuples class represents a collection of genomic tuples,
where each tuple has the same size. These objects can be
created by using the GTuples constructor function. For example, the following
code creates a GTuples object with 10 genomic tuples:
library(GenomicTuples)
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> 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, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> Filter, Find, Map, Position, Reduce, anyDuplicated, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#> pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#> tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:base':
#>
#> expand.grid
#> Loading required package: IRanges
#>
#> Attaching package: 'IRanges'
#> The following object is masked from 'package:grDevices':
#>
#> windows
#> Loading required package: GenomeInfoDbseqinfo <- Seqinfo(paste0("chr", 1:3), c(1000, 2000, 1500), NA, "mock1")
gt3 <- GTuples(seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"),
c(1, 3, 2, 4)),
tuples = matrix(c(1:10, 2:11, 3:12), ncol = 3),
strand = Rle(strand(c("-", "+", "*", "+", "-")),
c(1, 2, 2, 3, 2)),
score = 1:10, GC = seq(1, 0, length = 10), seqinfo = seqinfo)
names(gt3) <- letters[1:10]
gt3
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 1 2 3 - | 1 1.000000
#> b chr2 2 3 4 + | 2 0.888889
#> c chr2 3 4 5 + | 3 0.777778
#> d chr2 4 5 6 * | 4 0.666667
#> e chr1 5 6 7 * | 5 0.555556
#> f chr1 6 7 8 + | 6 0.444444
#> g chr3 7 8 9 + | 7 0.333333
#> h chr3 8 9 10 + | 8 0.222222
#> i chr3 9 10 11 - | 9 0.111111
#> j chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeThe output of the GTuples show method is very similar to that of the show
method for GenomicRanges::GRanges objects. Namely, it separates
the information into a left and right hand region that are separated by
| symbols. The genomic coordinates (seqnames,
tuples, 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 GTuples object.
The main difference between a GTuples object and GenomicRanges::GRanges object is that the former uses tuples while the latter uses ranges in the genomic coordinates.
For even more information on the GTuples class, be sure to consult the documentation:
?GTuplesMost methods defined for GenomicRanges::GRanges are also defined for GTuples. Those that are not yet defined, which are those that make sense for ranges but generally not for tuples, return error messages.
If you require a method that is not defined for GTuples but is defined for GenomicRanges::GRanges, then this can be achieved by first coercing the GTuples object to a GenomicRanges::GRanges object; Warning: coercing a GTuples object to a GenomicRanges::GRanges is generally a destructive operation.
as(gt3, "GRanges")
#> GRanges object with 10 ranges and 2 metadata columns:
#> seqnames ranges strand | score GC
#> <Rle> <IRanges> <Rle> | <integer> <numeric>
#> a chr1 1-3 - | 1 1.000000
#> b chr2 2-4 + | 2 0.888889
#> c chr2 3-5 + | 3 0.777778
#> d chr2 4-6 * | 4 0.666667
#> e chr1 5-7 * | 5 0.555556
#> f chr1 6-8 + | 6 0.444444
#> g chr3 7-9 + | 7 0.333333
#> h chr3 8-10 + | 8 0.222222
#> i chr3 9-11 - | 9 0.111111
#> j chr3 10-12 - | 10 0.000000
#> -------
#> seqinfo: 3 sequences from mock1 genomeThe components of the genomic coordinates within a GTuples
object can be extracted using the seqnames,
tuples, and strand accessor functions.
Warning: The tuples accessor should be used in place of the ranges accessor. While the ranges method is well-defined, namely it accesses \(pos_{1}\) and \(pos_{m}\) of the object, this is not generally what is desired or required.
seqnames(gt3)
#> factor-Rle of length 10 with 4 runs
#> Lengths: 1 3 2 4
#> Values : chr1 chr2 chr1 chr3
#> Levels(3): chr1 chr2 chr3
tuples(gt3)
#> pos1 pos2 pos3
#> [1,] 1 2 3
#> [2,] 2 3 4
#> [3,] 3 4 5
#> [4,] 4 5 6
#> [5,] 5 6 7
#> [6,] 6 7 8
#> [7,] 7 8 9
#> [8,] 8 9 10
#> [9,] 9 10 11
#> [10,] 10 11 12
strand(gt3)
#> factor-Rle of length 10 with 5 runs
#> Lengths: 1 2 2 3 2
#> Values : - + * + -
#> Levels(3): + - *Stored annotations for these coordinates can be extracted as a
DataFrame object using the mcols accessor:
mcols(gt3)
#> DataFrame with 10 rows and 2 columns
#> score GC
#> <integer> <numeric>
#> a 1 1.000000
#> b 2 0.888889
#> c 3 0.777778
#> d 4 0.666667
#> e 5 0.555556
#> f 6 0.444444
#> g 7 0.333333
#> h 8 0.222222
#> i 9 0.111111
#> j 10 0.000000Seqinfo can be extracted using the seqinfo accessor:
seqinfo(gt3)
#> Seqinfo object with 3 sequences from mock1 genome:
#> seqnames seqlengths isCircular genome
#> chr1 1000 NA mock1
#> chr2 2000 NA mock1
#> chr3 1500 NA mock1Methods for accessing the length and names are also defined:
length(gt3)
#> [1] 10
names(gt3)
#> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"GTuples objects can be divided into groups using the
split method. This produces a GTuplesList object, a
class that will be discussed in detail in the next section:
sp <- split(gt3, rep(1:2, each=5))
sp
#> GTuplesList object of length 2:
#> $`1`
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 1 2 3 - | 1 1.000000
#> b chr2 2 3 4 + | 2 0.888889
#> c chr2 3 4 5 + | 3 0.777778
#> d chr2 4 5 6 * | 4 0.666667
#> e chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $`2`
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> f chr1 6 7 8 + | 6 0.444444
#> g chr3 7 8 9 + | 7 0.333333
#> h chr3 8 9 10 + | 8 0.222222
#> i chr3 9 10 11 - | 9 0.111111
#> j chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeIf you then grab the components of this GenomicTuplesList, they can also be
combined by using the c and append methods:
c(sp[[1]], sp[[2]])
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 1 2 3 - | 1 1.000000
#> b chr2 2 3 4 + | 2 0.888889
#> c chr2 3 4 5 + | 3 0.777778
#> d chr2 4 5 6 * | 4 0.666667
#> e chr1 5 6 7 * | 5 0.555556
#> f chr1 6 7 8 + | 6 0.444444
#> g chr3 7 8 9 + | 7 0.333333
#> h chr3 8 9 10 + | 8 0.222222
#> i chr3 9 10 11 - | 9 0.111111
#> j chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeThe expected subsetting operations are also available for GTuples objects:
gt3[2:3]
#> GTuples object with 2 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> b chr2 2 3 4 + | 2 0.888889
#> c chr2 3 4 5 + | 3 0.777778
#> ---
#> seqinfo: 3 sequences from mock1 genomeA second argument to the [ subset operator can be used to
specify which metadata columns to extract from the GTuples
object. For example:
gt3[2:3, "GC"]
#> GTuples object with 2 x 3-tuples and 1 metadata column:
#> seqnames pos1 pos2 pos3 strand | GC
#> b chr2 2 3 4 + | 0.888889
#> c chr2 3 4 5 + | 0.777778
#> ---
#> seqinfo: 3 sequences from mock1 genomeYou can also assign into elements of the GTuples object. Here
is an example where the 2nd row of a GTuples object is replaced
with the 1st row of gt3:
gt3_mod <- gt3
gt3_mod[2] <- gt3[1]
head(gt3_mod, n = 3)
#> GTuples object with 3 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 1 2 3 - | 1 1.000000
#> b chr1 1 2 3 - | 1 1.000000
#> c chr2 3 4 5 + | 3 0.777778
#> ---
#> seqinfo: 3 sequences from mock1 genomeThere are also methods to repeat, reverse, or select specific portions of GTuples objects:
rep(gt3[2], times = 3)
#> GTuples object with 3 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> b chr2 2 3 4 + | 2 0.888889
#> b chr2 2 3 4 + | 2 0.888889
#> b chr2 2 3 4 + | 2 0.888889
#> ---
#> seqinfo: 3 sequences from mock1 genome
rev(gt3)
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> j chr3 10 11 12 - | 10 0.000000
#> i chr3 9 10 11 - | 9 0.111111
#> h chr3 8 9 10 + | 8 0.222222
#> g chr3 7 8 9 + | 7 0.333333
#> f chr1 6 7 8 + | 6 0.444444
#> e chr1 5 6 7 * | 5 0.555556
#> d chr2 4 5 6 * | 4 0.666667
#> c chr2 3 4 5 + | 3 0.777778
#> b chr2 2 3 4 + | 2 0.888889
#> a chr1 1 2 3 - | 1 1.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
head(gt3, n = 2)
#> GTuples object with 2 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 1 2 3 - | 1 1.000000
#> b chr2 2 3 4 + | 2 0.888889
#> ---
#> seqinfo: 3 sequences from mock1 genome
tail(gt3, n = 2)
#> GTuples object with 2 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> i chr3 9 10 11 - | 9 0.111111
#> j chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
window(gt3, start = 2, end = 4)
#> GTuples object with 3 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> b chr2 2 3 4 + | 2 0.888889
#> c chr2 3 4 5 + | 3 0.777778
#> d chr2 4 5 6 * | 4 0.666667
#> ---
#> seqinfo: 3 sequences from mock1 genomeBasic tuple characteristics of GTuples objects can be extracted
using the start, end, and tuples methods.
Warning: While the width method is well-defined, namely as \(pos_{m} - pos_{1} + 1\), this may not be what is required. Instead, please see the IPD method that will be discussed in the next section.
start(gt3)
#> [1] 1 2 3 4 5 6 7 8 9 10
end(gt3)
#> [1] 3 4 5 6 7 8 9 10 11 12
tuples(gt3)
#> pos1 pos2 pos3
#> [1,] 1 2 3
#> [2,] 2 3 4
#> [3,] 3 4 5
#> [4,] 4 5 6
#> [5,] 5 6 7
#> [6,] 6 7 8
#> [7,] 7 8 9
#> [8,] 8 9 10
#> [9,] 9 10 11
#> [10,] 10 11 12Most of the intra-range methods defined for GenomicRanges::GRanges objects are not currently defined via extension for GTuples objects due to the differences between ranges and tuples. Those not currently defined, and which return an error message, are:
narrowflankpromotersresizeOpsI am happy to add these methods if appropriate, so please contact me if you have suggestions for good definitions.
Both the trim and shift methods are well-defined,
although the former is somewhat limited since it will return an error if
the internal positions exceed the seqlengths:
shift(gt3, 500)
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 501 502 503 - | 1 1.000000
#> b chr2 502 503 504 + | 2 0.888889
#> c chr2 503 504 505 + | 3 0.777778
#> d chr2 504 505 506 * | 4 0.666667
#> e chr1 505 506 507 * | 5 0.555556
#> f chr1 506 507 508 + | 6 0.444444
#> g chr3 507 508 509 + | 7 0.333333
#> h chr3 508 509 510 + | 8 0.222222
#> i chr3 509 510 511 - | 9 0.111111
#> j chr3 510 511 512 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome# Raises warning due to tuple being outside of seqlength
x <- shift(gt3[1], 999)
#> Warning in valid.GenomicRanges.seqinfo(x, suggest.trim = TRUE): GRanges object contains 1 out-of-bound range located on sequence chr1.
#> Note that ranges located on a sequence whose length is unknown (NA) or
#> on a circular sequence are not considered out-of-bound (use
#> seqlengths() and isCircular() to get the lengths and circularity flags
#> of the underlying sequences). You can use trim() to trim these ranges.
#> See ?`trim,GenomicRanges-method` for more information.
x
#> GTuples object with 1 x 3-tuple and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> a chr1 1000 1001 1002 - | 1 1
#> ---
#> seqinfo: 3 sequences from mock1 genome
# Returns an error because internal position exceeds sequence length, resulting
# in a malformed tuple when trimmed.
trim(x)
#> Error in validObject(object): invalid class "GTuples" object:
#> positions in each tuple must be sorted in strictly increasing order,
#> i.e. 'pos1' < ... < 'pos3'None of the inter-range methods defined for GenomicRanges::GRanges objects are currently defined via extension for GTuples objects due to the differences between ranges and tuples. Those not currently defined, and which return an error message, are:
rangereducegapsdisjoinisDisjointdisjointBinsI am happy to add these methods if appropriate, so please contact me if you have suggestions for good definitions.
None of the interval set operations defined for GenomicRanges::GRanges objects are currently defined via extension for GTuples objects due to the differences between ranges and tuples. Those not currently defined, and which return an error message, are:
unionintersectsetdiffpunionpintersectpsetdiffI am happy to add these methods if appropriate, so please contact me if you have suggestions for good definitions.
GTuples have a few specifically defined methods that do not
exist for GenomicRanges::GRanges. These are tuples,
size and IPD.
The tuples method we have already seen and is somewhat
analogous to the ranges method for
GenomicRanges::GRanges, although returning an integer matrix
rather than an IRanges::IRanges object:
tuples(gt3)
#> pos1 pos2 pos3
#> [1,] 1 2 3
#> [2,] 2 3 4
#> [3,] 3 4 5
#> [4,] 4 5 6
#> [5,] 5 6 7
#> [6,] 6 7 8
#> [7,] 7 8 9
#> [8,] 8 9 10
#> [9,] 9 10 11
#> [10,] 10 11 12The size method returns the size of the tuples stored in the
object:
size(gt3)
#> [1] 3Every m-tuple with \(m \geq 2\) has an associated vector of intra-pair
distances (\(IPD\)). This is defined as
\(IPD = (pos_{2} - pos_{1}, \ldots, pos_{m} - pos_{m - 1})\). The
IPD method returns this as an integer matrix, where the \(i^{th}\)
row contains the \(IPD\) for the \(i^{th}\) tuple:
IPD(gt3)
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 1
#> [3,] 1 1
#> [4,] 1 1
#> [5,] 1 1
#> [6,] 1 1
#> [7,] 1 1
#> [8,] 1 1
#> [9,] 1 1
#> [10,] 1 1While the GTuples class can be thought of as a matrix-link
object, with the number of columns equal to the size of the
tuples plus two (one for the seqname and one for the
strand), internally, it extends the GenomicRanges::GRanges
class. Specifically, the ranges slot stores an
IRanges::IRanges object containing \(pos_{1}\) and \(pos_{m}\) and,
if size \(> 2\), a matrix is used to store the co-ordinates of the “internal
positions”, \(pos_{2}, \ldots, pos_{m - 1}\) in the internalPos slot. If
size \(\leq 2\) then the internalPos slot is set to NULL. The
size is stored as an integer in the size slot.
While there are arguments for creating stand-alone GTuples and GTuplesList classes, by extending the GenomicRanges::GRanges and GenomicRanges::GRangesList classes we get a lot of very useful functionality “for free” via appropriately defined inheritance.
The GTuplesList class is a container to store a S4Vectors::List of GTuples objects. It extends the GenomicRanges::GRangesList class.
Currently, all GTuples in a GTuplesList must have the
same size2 This may be changed in future versions of GenomicTuples.. I expect that users will mostly use GTuples objects and
have little need to directly use GTuplesList objects.
seqinfo <- Seqinfo(paste0("chr", 1:3), c(1000, 2000, 1500), NA, "mock1")
gt3 <- GTuples(seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"),
c(1, 3, 2, 4)),
tuples = matrix(c(1:10, 2:11, 3:12), ncol = 3),
strand = Rle(strand(c("-", "+", "*", "+", "-")),
c(1, 2, 2, 3, 2)),
score = 1:10, GC = seq(1, 0, length = 10), seqinfo = seqinfo)
gtl3 <- GTuplesList(A = gt3[1:5], B = gt3[6:10])
gtl3
#> GTuplesList object of length 2:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $B
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeFor even more information on the GTuplesList class, be sure to consult the documentation:
?GTuplesListMost methods defined for GenomicRanges::GRangesList are also applicable to GTuplesList. Those that are not yet defined, which are those that make sense for ranges but generally not for tuples, return error messages.
If a method that is not defined for GTuplesList but is defined for GenomicRanges::GRangesList is truly required, then this can be achieved by first coercing the GTuplesList object to a GenomicRanges::GRangesList object, noting that this is generally a destructive operation:
as(gtl3, "GRangesList")
#> GRangesList object of length 2:
#> $A
#> GRanges object with 5 ranges and 2 metadata columns:
#> seqnames ranges strand | score GC
#> <Rle> <IRanges> <Rle> | <integer> <numeric>
#> [1] chr1 1-3 - | 1 1.000000
#> [2] chr2 2-4 + | 2 0.888889
#> [3] chr2 3-5 + | 3 0.777778
#> [4] chr2 4-6 * | 4 0.666667
#> [5] chr1 5-7 * | 5 0.555556
#> -------
#> seqinfo: 3 sequences from mock1 genome
#>
#> $B
#> GRanges object with 5 ranges and 2 metadata columns:
#> seqnames ranges strand | score GC
#> <Rle> <IRanges> <Rle> | <integer> <numeric>
#> [1] chr1 6-8 + | 6 0.444444
#> [2] chr3 7-9 + | 7 0.333333
#> [3] chr3 8-10 + | 8 0.222222
#> [4] chr3 9-11 - | 9 0.111111
#> [5] chr3 10-12 - | 10 0.000000
#> -------
#> seqinfo: 3 sequences from mock1 genomeThese are very similar to those available for GTuples objects, except that they typically return a list since the input is now essentially a list of GTuples objects:
seqnames(gtl3)
#> RleList of length 2
#> $A
#> factor-Rle of length 5 with 3 runs
#> Lengths: 1 3 1
#> Values : chr1 chr2 chr1
#> Levels(3): chr1 chr2 chr3
#>
#> $B
#> factor-Rle of length 5 with 2 runs
#> Lengths: 1 4
#> Values : chr1 chr3
#> Levels(3): chr1 chr2 chr3
# Returns a list of integer matrices
tuples(gtl3)
#> List of length 2
#> names(2): A B
tuples(gtl3)[[1]]
#> pos1 pos2 pos3
#> [1,] 1 2 3
#> [2,] 2 3 4
#> [3,] 3 4 5
#> [4,] 4 5 6
#> [5,] 5 6 7
strand(gtl3)
#> RleList of length 2
#> $A
#> factor-Rle of length 5 with 3 runs
#> Lengths: 1 2 2
#> Values : - + *
#> Levels(3): + - *
#>
#> $B
#> factor-Rle of length 5 with 2 runs
#> Lengths: 3 2
#> Values : + -
#> Levels(3): + - *The length and names methods will return the length and
names of the list, respectively:
length(gtl3)
#> [1] 2
names(gtl3)
#> [1] "A" "B"Seqinfo can be extracted using the seqinfo accessor:
seqinfo(gtl3)
#> Seqinfo object with 3 sequences from mock1 genome:
#> seqnames seqlengths isCircular genome
#> chr1 1000 NA mock1
#> chr2 2000 NA mock1
#> chr3 1500 NA mock1The elementNROWS method returns a list of integers
corresponding to the result of calling length on each individual
GTuples object contained by the GTuplesList. This is a
faster alternative to calling lapply on the GTuplesList:
elementNROWS(gtl3)
#> A B
#> 5 5You can also use isEmpty to test if a GTuplesList object
contains anything:
isEmpty(gtl3)
#> [1] FALSE
isEmpty(GTuplesList())
#> [1] TRUEFinally, in the context of a GTuplesList object, the
mcols method performs a similar operation to what it does on a
GTuples object. However, this metadata now refers to
information at the list level instead of the level of the individual
GTuples objects:
mcols(gtl3) <- c("Feature A", "Feature B")
mcols(gtl3)
#> DataFrame with 2 rows and 1 column
#> value
#> <character>
#> A Feature A
#> B Feature BGTuplesList objects can be unlisted to combine the separate GTuples objects that they contain as an expanded GTuples:
ul <- unlist(gtl3)
ul
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> A chr1 1 2 3 - | 1 1.000000
#> A chr2 2 3 4 + | 2 0.888889
#> A chr2 3 4 5 + | 3 0.777778
#> A chr2 4 5 6 * | 4 0.666667
#> A chr1 5 6 7 * | 5 0.555556
#> B chr1 6 7 8 + | 6 0.444444
#> B chr3 7 8 9 + | 7 0.333333
#> B chr3 8 9 10 + | 8 0.222222
#> B chr3 9 10 11 - | 9 0.111111
#> B chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeYou can also combine GTuplesList objects together using append or c.
Subsetting of GTuplesList objects is identical to subsetting of GenomicRanges::GRangesList objects:
gtl3[1]
#> GTuplesList object of length 1:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
gtl3[[1]]
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
gtl3["A"]
#> GTuplesList object of length 1:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
gtl3$B
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeWhen subsetting a GTuplesList, you can also pass in a second parameter (as with a GTuples object) to again specify which of the metadata columns you wish to select:
gtl3[1, "score"]
#> GTuplesList object of length 1:
#> $A
#> GTuples object with 5 x 3-tuples and 1 metadata column:
#> seqnames pos1 pos2 pos3 strand | score
#> [1] chr1 1 2 3 - | 1
#> [2] chr2 2 3 4 + | 2
#> [3] chr2 3 4 5 + | 3
#> [4] chr2 4 5 6 * | 4
#> [5] chr1 5 6 7 * | 5
#> ---
#> seqinfo: 3 sequences from mock1 genome
gtl3["B", "GC"]
#> GTuplesList object of length 1:
#> $B
#> GTuples object with 5 x 3-tuples and 1 metadata column:
#> seqnames pos1 pos2 pos3 strand | GC
#> [1] chr1 6 7 8 + | 0.444444
#> [2] chr3 7 8 9 + | 0.333333
#> [3] chr3 8 9 10 + | 0.222222
#> [4] chr3 9 10 11 - | 0.111111
#> [5] chr3 10 11 12 - | 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeThe 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 GTuples elements:
rep(gtl3[[1]], times = 3)
#> GTuples object with 15 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ... ... ... ... ... ... . ... ...
#> [11] chr1 1 2 3 - | 1 1.000000
#> [12] chr2 2 3 4 + | 2 0.888889
#> [13] chr2 3 4 5 + | 3 0.777778
#> [14] chr2 4 5 6 * | 4 0.666667
#> [15] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
rev(gtl3)
#> GTuplesList object of length 2:
#> $B
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
head(gtl3, n = 1)
#> GTuplesList object of length 1:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
tail(gtl3, n = 1)
#> GTuplesList object of length 1:
#> $B
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
window(gtl3, start = 1, end = 1)
#> GTuplesList object of length 1:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genomeBasic tuple characteristics of GTuplesList objects can be
extracted using the start, end, and tuples
methods. These are very similar to those available for GTuples
objects, except that they typically return a list since the input is now
essentially a list of GTuples objects.
WARNING: While the width method is well-defined, namely it returns an IntegerList of \(pos_{m} - pos_{1} + 1\), this is not generally what is desired or required. Instead, please see the IPD method that is discussed later.
start(gtl3)
#> IntegerList of length 2
#> [["A"]] 1 2 3 4 5
#> [["B"]] 6 7 8 9 10
end(gtl3)
#> IntegerList of length 2
#> [["A"]] 3 4 5 6 7
#> [["B"]] 8 9 10 11 12
tuples(gtl3)
#> List of length 2
#> names(2): A BMost of the intra-range methods defined for GenomicRanges::GRangesList objects are not currently defined via extension for GTuples objects due to the differences between ranges and tuples. Those not currently defined, and which return an error message, are:
flankpromotersresizerestrictI am happy to add these methods if appropriate, so please contact me if you have suggestions for good definitions.
The shift method is well-defined:
shift(gtl3, 500)
#> GTuplesList object of length 2:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 501 502 503 - | 1 1.000000
#> [2] chr2 502 503 504 + | 2 0.888889
#> [3] chr2 503 504 505 + | 3 0.777778
#> [4] chr2 504 505 506 * | 4 0.666667
#> [5] chr1 505 506 507 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $B
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 506 507 508 + | 6 0.444444
#> [2] chr3 507 508 509 + | 7 0.333333
#> [3] chr3 508 509 510 + | 8 0.222222
#> [4] chr3 509 510 511 - | 9 0.111111
#> [5] chr3 510 511 512 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
shift(gtl3, IntegerList(A = 300L, B = 500L))
#> GTuplesList object of length 2:
#> $A
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 301 302 303 - | 1 1.000000
#> [2] chr2 302 303 304 + | 2 0.888889
#> [3] chr2 303 304 305 + | 3 0.777778
#> [4] chr2 304 305 306 * | 4 0.666667
#> [5] chr1 305 306 307 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $B
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 506 507 508 + | 6 0.444444
#> [2] chr3 507 508 509 + | 7 0.333333
#> [3] chr3 508 509 510 + | 8 0.222222
#> [4] chr3 509 510 511 - | 9 0.111111
#> [5] chr3 510 511 512 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeNone of the inter-range methods defined for GenomicRanges::GRangesList objects are currently defined via extension for GTuplesList objects due to the differences between ranges and tuples. Those not currently defined, and which return an error message, are:
rangereducedisjoinisDisjointI am happy to add these methods if appropriate, so please contact me if you have suggestions for good definitions.
None of the interval set operations defined for GenomicRanges::GRangesList objects are currently defined via extension for GTuplesList objects due to the differences between ranges and tuples. Those not currently defined, and which return an error message, are:
punionpintersectpsetdiffI am happy to add these methods if appropriate, so please contact me if you have suggestions for good definitions.
Like for GenomicRanges::GRangesList objects, for GTuplesList objects there
is a family of apply methods. These include lapply, sapply, mapply,
endoapply, mendoapply, Map, and Reduce. The different looping methods
defined for GTuplesList 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(gtl3, length)
#> $A
#> [1] 5
#>
#> $B
#> [1] 5
sapply(gtl3, length)
#> A B
#> 5 5As with GenomicRanges::GRangesList objects, there is also a multivariate
version of sapply, called mapply, defined for GTuplesList 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:
gtl3_shift <- shift(gtl3, 10)
names(gtl3) <- c("shiftA", "shiftB")
mapply(c, gtl3, gtl3_shift)
#> $shiftA
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> [6] chr1 11 12 13 - | 1 1.000000
#> [7] chr2 12 13 14 + | 2 0.888889
#> [8] chr2 13 14 15 + | 3 0.777778
#> [9] chr2 14 15 16 * | 4 0.666667
#> [10] chr1 15 16 17 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $shiftB
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> [6] chr1 16 17 18 + | 6 0.444444
#> [7] chr3 17 18 19 + | 7 0.333333
#> [8] chr3 18 19 20 + | 8 0.222222
#> [9] chr3 19 20 21 - | 9 0.111111
#> [10] chr3 20 21 22 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
Map(c, gtl3, gtl3_shift)
#> $shiftA
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> [6] chr1 11 12 13 - | 1 1.000000
#> [7] chr2 12 13 14 + | 2 0.888889
#> [8] chr2 13 14 15 + | 3 0.777778
#> [9] chr2 14 15 16 * | 4 0.666667
#> [10] chr1 15 16 17 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $shiftB
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> [6] chr1 16 17 18 + | 6 0.444444
#> [7] chr3 17 18 19 + | 7 0.333333
#> [8] chr3 18 19 20 + | 8 0.222222
#> [9] chr3 19 20 21 - | 9 0.111111
#> [10] chr3 20 21 22 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeThe endoapply method will return the results as a GTuplesList object rather
than as a list:
endoapply(gtl3, rev)
#> GTuplesList object of length 2:
#> $shiftA
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 5 6 7 * | 5 0.555556
#> [2] chr2 4 5 6 * | 4 0.666667
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 2 3 4 + | 2 0.888889
#> [5] chr1 1 2 3 - | 1 1.000000
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $shiftB
#> GTuples object with 5 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr3 10 11 12 - | 10 0.000000
#> [2] chr3 9 10 11 - | 9 0.111111
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 7 8 9 + | 7 0.333333
#> [5] chr1 6 7 8 + | 6 0.444444
#> ---
#> seqinfo: 3 sequences from mock1 genomeThere is also a multivariate version of the endoapply method in the form of
the mendoapply method:
mendoapply(c, gtl3, gtl3_shift)
#> GTuplesList object of length 2:
#> $shiftA
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> [6] chr1 11 12 13 - | 1 1.000000
#> [7] chr2 12 13 14 + | 2 0.888889
#> [8] chr2 13 14 15 + | 3 0.777778
#> [9] chr2 14 15 16 * | 4 0.666667
#> [10] chr1 15 16 17 * | 5 0.555556
#> ---
#> seqinfo: 3 sequences from mock1 genome
#>
#> $shiftB
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 6 7 8 + | 6 0.444444
#> [2] chr3 7 8 9 + | 7 0.333333
#> [3] chr3 8 9 10 + | 8 0.222222
#> [4] chr3 9 10 11 - | 9 0.111111
#> [5] chr3 10 11 12 - | 10 0.000000
#> [6] chr1 16 17 18 + | 6 0.444444
#> [7] chr3 17 18 19 + | 7 0.333333
#> [8] chr3 18 19 20 + | 8 0.222222
#> [9] chr3 19 20 21 - | 9 0.111111
#> [10] chr3 20 21 22 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeFinally, the Reduce method will allow the GTuples objects to be collapsed
across the whole of the GTuplesList object:
Reduce(c, gtl3)
#> GTuples object with 10 x 3-tuples and 2 metadata columns:
#> seqnames pos1 pos2 pos3 strand | score GC
#> [1] chr1 1 2 3 - | 1 1.000000
#> [2] chr2 2 3 4 + | 2 0.888889
#> [3] chr2 3 4 5 + | 3 0.777778
#> [4] chr2 4 5 6 * | 4 0.666667
#> [5] chr1 5 6 7 * | 5 0.555556
#> [6] chr1 6 7 8 + | 6 0.444444
#> [7] chr3 7 8 9 + | 7 0.333333
#> [8] chr3 8 9 10 + | 8 0.222222
#> [9] chr3 9 10 11 - | 9 0.111111
#> [10] chr3 10 11 12 - | 10 0.000000
#> ---
#> seqinfo: 3 sequences from mock1 genomeLike GTuples, GTuplesList have a few specifically defined methods that do
not exist for GenomicRanges::GRangesList. These are tuples, size and
IPD. These are identical to the methods for GTuples, except that they
typically return a list since the input is now essentially a List of GTuples
objects.
tuples(gtl3)
#> List of length 2
#> names(2): shiftA shiftB
tuples(gtl3)[[1]]
#> pos1 pos2 pos3
#> [1,] 1 2 3
#> [2,] 2 3 4
#> [3,] 3 4 5
#> [4,] 4 5 6
#> [5,] 5 6 7
size(gtl3)
#> [1] 3
IPD(gtl3)
#> List of length 2
#> names(2): shiftA shiftB
IPD(gtl3)[[1]]
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 1
#> [3,] 1 1
#> [4,] 1 1
#> [5,] 1 1The GTuplesList class extends the GenomicRanges::GRangesList class.
findOverlaps-based methodsThe definition of what constitutes an “overlap” between genomic tuples, or
between genomic tuples and genomic ranges, lies at the heart of all
findOverlaps-based methods3 The findOverlaps-based methods are findOverlaps,
countOverlaps, overlapsAny and subsetByOverlaps. for GTuples and GTuplesList
objects.
I have chosen a definition that matches my intuition of what constitutes an “overlap” between genomic tuples or between genomic tuples and genomic ranges. However, I am open to suggestions on amending or extending this behaviour in future versions of GenomicTuples.
I consider two genomic tuples to be equal (type = "equal") if they have
identical sequence names (seqnames), strands (strand) and tuples
(tuples). For 1-tuples and 2-tuples, this means we can simply
defer to the findOverlaps-based methods for
GenomicRanges::GRanges and GenomicRanges::GRangesList
objects via inheritance. However, we cannot do the same for m-tuples
with \(m > 2\) since this would ignore the “internal positions”.
Therefore, I have implemented a special case of the
findOverlaps method for when size \(> 2\) and
type = "equal", which ensures that the “internal positions” are also checked
for equality.
In all other cases genomic tuples are treated as genomic ranges. This
means that when type = "any", type = "start", type = "end" or
type = "within" then the genomic tuples are treated as if they were genomic
ranges. Specifically, GTuples (resp. GTuplesList) are treated as though
they were GenomicRanges::GRanges (resp. GenomicRanges::GRangesList) with
pos1 = start and posm = end.
Genomic tuples are always treated as genomic ranges when searching for overlaps between genomic tuples and genomic ranges.
It is easiest to understand the above definitions by studying a few examples.
Firstly, for 1-tuples where the GTuples methods use the GenomicRanges::GRanges methods:
# Construct example 1-tuples
gt1 <- GTuples(seqnames = c('chr1', 'chr1', 'chr1', 'chr2'),
tuples = matrix(c(10L, 10L, 10L, 10L), ncol = 1),
strand = c('+', '-', '*', '+'))
# GRanges version of gt1
gr1 <- as(gt1, "GRanges")
findOverlaps(gt1, gt1, type = 'any')
#> Hits object with 8 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> -------
#> queryLength: 4 / subjectLength: 4
# GTuples and GRanges methods identical
identical(findOverlaps(gt1, gt1, type = 'any'),
findOverlaps(gr1, gr1, type = 'any'))
#> [1] TRUE
findOverlaps(gt1, gt1, type = 'start')
#> Hits object with 8 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> -------
#> queryLength: 4 / subjectLength: 4
# GTuples and GRanges methods identical
identical(findOverlaps(gt1, gt1, type = 'start'),
findOverlaps(gr1, gr1, type = 'start'))
#> [1] TRUE
findOverlaps(gt1, gt1, type = 'end')
#> Hits object with 8 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> -------
#> queryLength: 4 / subjectLength: 4
# GTuples and GRanges methods identical
identical(findOverlaps(gt1, gt1, type = 'end'),
findOverlaps(gr1, gr1, type = 'end'))
#> [1] TRUE
findOverlaps(gt1, gt1, type = 'within')
#> Hits object with 8 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> -------
#> queryLength: 4 / subjectLength: 4
# GTuples and GRanges methods identical
identical(findOverlaps(gt1, gt1, type = 'within'),
findOverlaps(gr1, gr1, type = 'within'))
#> [1] TRUE
findOverlaps(gt1, gt1, type = 'equal')
#> Hits object with 8 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> -------
#> queryLength: 4 / subjectLength: 4
# GTuples and GRanges methods identical
identical(findOverlaps(gt1, gt1, type = 'equal'),
findOverlaps(gr1, gr1, type = 'equal'))
#> [1] TRUE
# Can pass other arguments, such as select and ignore.strand
findOverlaps(gt1, gt1, type = 'equal', ignore.strand = TRUE, select = 'last')
#> [1] 3 3 3 4Next, for 2-tuples where the GTuples methods use the GenomicRanges::GRanges methods:
# Construct example 2-tuples
gt2 <- GTuples(seqnames = c('chr1', 'chr1', 'chr1', 'chr1', 'chr2'),
tuples = matrix(c(10L, 10L, 10L, 10L, 10L, 20L, 20L, 20L, 25L,
20L), ncol = 2),
strand = c('+', '-', '*', '+', '+'))
# GRanges version of gt2
gr2 <- as(gt2, "GRanges")
findOverlaps(gt2, gt2, type = 'any')
#> Hits object with 13 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 4
#> [2] 1 1
#> [3] 1 3
#> [4] 2 2
#> [5] 2 3
#> ... ... ...
#> [9] 3 3
#> [10] 4 4
#> [11] 4 1
#> [12] 4 3
#> [13] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt2, gt2, type = 'any'),
findOverlaps(gr2, gr2, type = 'any'))
#> [1] TRUE
findOverlaps(gt2, gt2, type = 'start')
#> Hits object with 13 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 4
#> [2] 1 1
#> [3] 1 3
#> [4] 2 2
#> [5] 2 3
#> ... ... ...
#> [9] 3 3
#> [10] 4 4
#> [11] 4 1
#> [12] 4 3
#> [13] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt2, gt2, type = 'start'),
findOverlaps(gr2, gr2, type = 'start'))
#> [1] TRUE
findOverlaps(gt2, gt2, type = 'end')
#> Hits object with 9 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> [9] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt2, gt2, type = 'end'),
findOverlaps(gr2, gr2, type = 'end'))
#> [1] TRUE
findOverlaps(gt2, gt2, type = 'within')
#> Hits object with 11 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 4
#> [2] 1 1
#> [3] 1 3
#> [4] 2 2
#> [5] 2 3
#> [6] 3 4
#> [7] 3 1
#> [8] 3 2
#> [9] 3 3
#> [10] 4 4
#> [11] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt2, gt2, type = 'within'),
findOverlaps(gr2, gr2, type = 'within'))
#> [1] TRUE
findOverlaps(gt2, gt2, type = 'equal')
#> Hits object with 9 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 3
#> [3] 2 2
#> [4] 2 3
#> [5] 3 1
#> [6] 3 2
#> [7] 3 3
#> [8] 4 4
#> [9] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt2, gt2, type = 'equal'),
findOverlaps(gr2, gr2, type = 'equal'))
#> [1] TRUE
# Can pass other arguments, such as select and ignore.strand
findOverlaps(gt2, gt2, type = 'equal', ignore.strand = TRUE, select = 'last')
#> [1] 3 3 3 4 5Finally, for m-tuples with \(m > 2\) where GTuples methods use the
GenomicRanges::GRanges methods unless type = "equal":
# Construct example 3-tuples
gt3 <- GTuples(seqnames = c('chr1', 'chr1', 'chr1', 'chr1', 'chr2'),
tuples = matrix(c(10L, 10L, 10L, 10L, 10L, 20L, 20L, 20L, 25L,
20L, 30L, 30L, 35L, 30L, 30L), ncol = 3),
strand = c('+', '-', '*', '+', '+'))
# GRanges version of gt3
gr3 <- as(gt3, "GRanges")
findOverlaps(gt3, gt3, type = 'any')
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> Hits object with 13 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 3
#> [2] 1 1
#> [3] 1 4
#> [4] 2 3
#> [5] 2 2
#> ... ... ...
#> [9] 3 4
#> [10] 4 3
#> [11] 4 1
#> [12] 4 4
#> [13] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt3, gt3, type = 'any'),
findOverlaps(gr3, gr3, type = 'any')) # TRUE
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> [1] TRUE
findOverlaps(gt3, gt3, type = 'start')
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> Hits object with 13 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 3
#> [2] 1 1
#> [3] 1 4
#> [4] 2 3
#> [5] 2 2
#> ... ... ...
#> [9] 3 4
#> [10] 4 3
#> [11] 4 1
#> [12] 4 4
#> [13] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt3, gt3, type = 'start'),
findOverlaps(gr3, gr3, type = 'start')) # TRUE
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> [1] TRUE
findOverlaps(gt3, gt3, type = 'end')
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> Hits object with 7 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 1 4
#> [3] 2 2
#> [4] 3 3
#> [5] 4 1
#> [6] 4 4
#> [7] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt3, gt3, type = 'end'),
findOverlaps(gr3, gr3, type = 'end')) # TRUE
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> [1] TRUE
findOverlaps(gt3, gt3, type = 'within')
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> Hits object with 10 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 3
#> [2] 1 1
#> [3] 1 4
#> [4] 2 3
#> [5] 2 2
#> [6] 3 3
#> [7] 4 3
#> [8] 4 1
#> [9] 4 4
#> [10] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods identical
identical(findOverlaps(gt3, gt3, type = 'within'),
findOverlaps(gr3, gr3, type = 'within')) # TRUE
#> Warning in .local(query, subject, maxgap, minoverlap, type, select, ...): 'type'
#> is not 'equal' so coercing 'query' and 'subject' to 'GRanges' objects (see docs
#> for details)
#> [1] TRUE
findOverlaps(gt3, gt3, type = 'equal')
#> Hits object with 5 hits and 0 metadata columns:
#> queryHits subjectHits
#> <integer> <integer>
#> [1] 1 1
#> [2] 2 2
#> [3] 3 3
#> [4] 4 4
#> [5] 5 5
#> -------
#> queryLength: 5 / subjectLength: 5
# GTuples and GRanges methods **not** identical because GRanges method ignores
# "internal positions".
identical(findOverlaps(gt3, gt3, type = 'equal'),
findOverlaps(gr3, gr3, type = 'equal')) # FALSE
#> [1] FALSE
# Can pass other arguments, such as select and ignore.strand
findOverlaps(gt3, gt3, type = 'equal', ignore.strand = TRUE, select = 'last')
#> [1] 2 2 3 4 5I have chosen a definition that matches my intuition of what constitutes a
comparison between genomic tuples. However, I am open to suggestions
on amending or extending this behaviour in future versions of
GenomicTuples.
The comparison of two genomic tuples, x and y, is done by
first comparing the seqnames(x) to seqnames(y), then
strand(x) to strand(y) and finally tuples(x) to tuples(y).
Ordering of seqnames and strand is as implemented GenomicRanges::GRanges.
Ordering of tuples is element-wise, i.e. \(pos_{1}, \ldots, pos_{m}\) are
compared in turn. For example, chr1:+:10, 20, 30 is considered less than
chr1:+:10, 20, 40. This defines what I will refer to as the “natural order”
of genomic tuples.
The above is implemented in the pcompare method for
GTuples, which performs “generalized range-wise comparison” of two
GTuples objects, x and y. That is, pcompare(x, y) returns an integer
vector where the \(i^{th}\) element is a code describing how the \(i^{th}\) element
in x is qualitatively positioned relatively to the \(i^{th}\) element in y.
A code that is < 0, = 0, or > 0, corresponds to x[i] < y[i],
x[i] == y[i], or x[i] > y[i], respectively.
The 6 traditional binary comparison operators (==, !=, <=, >=, <, and
>), other comparison operators (match, order, sort, and rank) and
duplicate-based methods (duplicated and unique) all use this “natural
order”.
It is easiest to understand the above definitions by studying a few examples, here using 3-tuples:
# Construct example 3-tuples
gt3 <- GTuples(seqnames = c('chr1', 'chr1', 'chr1', 'chr1', 'chr2', 'chr1',
'chr1'),
tuples = matrix(c(10L, 10L, 10L, 10L, 10L, 5L, 10L, 20L, 20L,
20L, 25L, 20L, 20L, 20L, 30L, 30L, 35L, 30L,
30L, 30L, 35L),
ncol = 3),
strand = c('+', '-', '*', '+', '+', '+', '+'))
gt3
#> GTuples object with 7 x 3-tuples and 0 metadata columns:
#> seqnames pos1 pos2 pos3 strand
#> [1] chr1 10 20 30 +
#> [2] chr1 10 20 30 -
#> [3] chr1 10 20 35 *
#> [4] chr1 10 25 30 +
#> [5] chr2 10 20 30 +
#> [6] chr1 5 20 30 +
#> [7] chr1 10 20 35 +
#> ---
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
# pcompare each tuple to itself
pcompare(gt3, gt3)
#> [1] 0 0 0 0 0 0 0
gt3 < gt3
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
gt3 > gt3
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
gt3 == gt3
#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE
# pcompare the third tuple to all tuples
pcompare(gt3[3], gt3)
#> [1] 2 1 0 2 -1 2 2
gt3[3] < gt3
#> [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
gt3[3] > gt3
#> [1] TRUE TRUE FALSE TRUE FALSE TRUE TRUE
gt3[3] == gt3
#> [1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## Some comparisons where tuples differ only in one coordinate
# Ordering of seqnames
# 'chr1' < 'chr2' for tuples with otherwise identical coordinates
gt3[1] < gt3[5] # TRUE
#> [1] TRUE
# Ordering of strands
# '+' < '-' < '*' for tuples with otherwise identical coordiantes
gt3[1] < gt3[2] # TRUE
#> [1] TRUE
gt3[1] < gt3[2] # TRUE
#> [1] TRUE
gt3[1] < unstrand(gt3[2]) # TRUE
#> [1] TRUE
gt3[2] < unstrand(gt3[2]) # TRUE
#> [1] TRUE
# Ordering of tuples
# Tuples checked sequentially from pos1, ..., posm for tuples with otherwise
# identical coordinates
gt3[6] < gt3[1] # TRUE due to pos1
#> [1] TRUE
gt3[2] < gt3[4] # TRUE due to pos2
#> [1] FALSE
gt3[1] < gt3[7] # TRUE due to pos3
#> [1] TRUE
# Sorting of tuples
# Sorted first by seqnames, then by strand, then by tuples
sort(gt3)
#> GTuples object with 7 x 3-tuples and 0 metadata columns:
#> seqnames pos1 pos2 pos3 strand
#> [1] chr1 5 20 30 +
#> [2] chr1 10 20 30 +
#> [3] chr1 10 20 35 +
#> [4] chr1 10 25 30 +
#> [5] chr1 10 20 30 -
#> [6] chr1 10 20 35 *
#> [7] chr2 10 20 30 +
#> ---
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
# Duplicate tuples
# Duplicate tuples must have identical seqnames, strand and positions (tuples)
duplicated(c(gt3, gt3[1:3]))
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
unique(c(gt3, gt3[1:3]))
#> GTuples object with 7 x 3-tuples and 0 metadata columns:
#> seqnames pos1 pos2 pos3 strand
#> [1] chr1 10 20 30 +
#> [2] chr1 10 20 30 -
#> [3] chr1 10 20 35 *
#> [4] chr1 10 25 30 +
#> [5] chr2 10 20 30 +
#> [6] chr1 5 20 30 +
#> [7] chr1 10 20 35 +
#> ---
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsI am very grateful to all the Bioconductor developers but particularly wish to thank the developers of GenomicRanges (Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).), which GenomicTuples uses heavily and is based upon. A special thanks to Hervé Pagès for his assistance and fixes when making upstream changes to GenomicRanges.
Here is the output of sessionInfo on the system on which
this document was compiled:
sessionInfo()
#> R version 4.0.3 (2020-10-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server 2012 R2 x64 (build 9600)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] GenomicTuples_1.24.0 GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
#> [4] IRanges_2.24.0 S4Vectors_0.28.0 BiocGenerics_0.36.0
#> [7] BiocStyle_2.18.0
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.5 knitr_1.30 XVector_0.30.0
#> [4] magrittr_1.5 zlibbioc_1.36.0 rlang_0.4.8
#> [7] stringr_1.4.0 tools_4.0.3 data.table_1.13.2
#> [10] xfun_0.18 htmltools_0.5.0 yaml_2.2.1
#> [13] digest_0.6.27 bookdown_0.21 GenomeInfoDbData_1.2.4
#> [16] BiocManager_1.30.10 bitops_1.0-6 RCurl_1.98-1.2
#> [19] evaluate_0.14 rmarkdown_2.5 stringi_1.5.3
#> [22] compiler_4.0.3