Getting started with the plyranges package
2024-05-01
Package
plyranges 1.24.0
1 Ranges revisited
In Bioconductor there are two classes, IRanges and GRanges, that are
standard data structures for representing genomics data. Throughout this
document I refer to either of these classes as Ranges if an operation can be
performed on either class, otherwise I explicitly mention if a function is
appropriate for an IRanges or GRanges.
Ranges objects can either represent sets of integers as IRanges (which have
start, end and width attributes) or represent genomic intervals (which have
additional attributes, sequence name, and strand) as GRanges. In addition,
both types of Ranges can store information about their intervals as metadata
columns (for example GC content over a genomic interval).
Ranges objects follow the tidy data principle: each row of a Ranges object
corresponds to an interval, while each column will represent a variable about
that interval, and generally each object will represent a single unit of
observation (like gene annotations).
Consequently, Ranges objects provide a powerful representation for reasoning
about genomic data. In this vignette, you will learn more about Ranges
objects and how via grouping, restriction and summarisation you can perform
common data tasks.
2 Constructing Ranges
To construct an IRanges we require that there are at least two columns that
represent at either a starting coordinate, finishing coordinate or the width of
the interval.
suppressPackageStartupMessages(library(plyranges))
set.seed(100)
df <- data.frame(start=c(2:-1, 13:15),
width=c(0:3, 2:0))
# produces IRanges
rng <- df %>% as_iranges()
rng## IRanges object with 7 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 2 1 0
## [2] 1 1 1
## [3] 0 1 2
## [4] -1 1 3
## [5] 13 14 2
## [6] 14 14 1
## [7] 15 14 0To construct a GRanges we require a column that represents that sequence name
( contig or chromosome id), and an optional column to represent the
strandedness of an interval.
# seqname is required for GRanges, metadata is automatically kept
grng <- df %>%
transform(seqnames = sample(c("chr1", "chr2"), 7, replace = TRUE),
strand = sample(c("+", "-"), 7, replace = TRUE),
gc = runif(7)) %>%
as_granges()
grng## GRanges object with 7 ranges and 1 metadata column:
## seqnames ranges strand | gc
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr2 2-1 - | 0.762551
## [2] chr1 1 - | 0.669022
## [3] chr2 0-1 + | 0.204612
## [4] chr2 -1-1 - | 0.357525
## [5] chr1 13-14 - | 0.359475
## [6] chr1 14 - | 0.690291
## [7] chr2 15-14 - | 0.535811
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths3 Arithmetic on Ranges
Sometimes you want to modify a genomic interval by altering the width of the
interval while leaving the start, end or midpoint of the coordinates unaltered.
This is achieved with the mutate verb along with anchor_* adverbs.
The act of anchoring fixes either the start, end, center coordinates of the
Range object, as shown in the figure and code below and anchors are used in
combination with either mutate or stretch.
By default, the start coordinate will be anchored, so regardless of strand.
For behavior similar to GenomicRanges::resize, use anchor_5p.
rng <- as_iranges(data.frame(start=c(1, 2, 3), end=c(5, 2, 8)))
grng <- as_granges(data.frame(start=c(1, 2, 3), end=c(5, 2, 8),
seqnames = "seq1",
strand = c("+", "*", "-")))
mutate(rng, width = 10)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 10 10
## [2] 2 11 10
## [3] 3 12 10mutate(anchor_start(rng), width = 10)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 10 10
## [2] 2 11 10
## [3] 3 12 10mutate(anchor_end(rng), width = 10)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] -4 5 10
## [2] -7 2 10
## [3] -1 8 10mutate(anchor_center(rng), width = 10)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] -2 7 10
## [2] -3 6 10
## [3] 1 10 10mutate(anchor_3p(grng), width = 10) # leave negative strand fixed## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] seq1 -4-5 +
## [2] seq1 -7-2 *
## [3] seq1 3-12 -
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsmutate(anchor_5p(grng), width = 10) # leave positive strand fixed## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] seq1 1-10 +
## [2] seq1 2-11 *
## [3] seq1 -1-8 -
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsSimilarly, you can modify the width of an interval using the stretch verb.
Without anchoring, this function will extend the interval in either direction
by an integer amount. With anchoring, either the start, end or midpoint are
preserved.
rng2 <- stretch(anchor_center(rng), 10)
rng2## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] -4 10 15
## [2] -3 7 11
## [3] -2 13 16stretch(anchor_end(rng2), 10)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] -14 10 25
## [2] -13 7 21
## [3] -12 13 26stretch(anchor_start(rng2), 10)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] -4 20 25
## [2] -3 17 21
## [3] -2 23 26stretch(anchor_3p(grng), 10)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] seq1 -9-5 +
## [2] seq1 -8-2 *
## [3] seq1 3-18 -
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsstretch(anchor_5p(grng), 10)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] seq1 1-15 +
## [2] seq1 2-12 *
## [3] seq1 -7-8 -
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsRanges can be shifted left or right. If strand information is available we
can also shift upstream or downstream.
shift_left(rng, 100)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] -99 -95 5
## [2] -98 -98 1
## [3] -97 -92 6shift_right(rng, 100)## IRanges object with 3 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 101 105 5
## [2] 102 102 1
## [3] 103 108 6shift_upstream(grng, 100)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] seq1 -99--95 +
## [2] seq1 -98 *
## [3] seq1 103-108 -
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsshift_downstream(grng, 100)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] seq1 101-105 +
## [2] seq1 102 *
## [3] seq1 -97--92 -
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths4 Grouping Ranges
plyranges introduces a new class of Ranges called RangesGrouped, this is
a similar idea to the grouped data.frame\tibble in dplyr.
Grouping can act on either the core components or the metadata columns of a
Ranges object.
It is most effective when combined with other verbs such as mutate(),
summarise(), filter(), reduce_ranges() or disjoin_ranges().
grng <- data.frame(seqnames = sample(c("chr1", "chr2"), 7, replace = TRUE),
strand = sample(c("+", "-"), 7, replace = TRUE),
gc = runif(7),
start = 1:7,
width = 10) %>%
as_granges()
grng_by_strand <- grng %>%
group_by(strand)
grng_by_strand## GRanges object with 7 ranges and 1 metadata column:
## Groups: strand [2]
## seqnames ranges strand | gc
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr2 1-10 - | 0.889454
## [2] chr2 2-11 + | 0.180407
## [3] chr1 3-12 - | 0.629391
## [4] chr2 4-13 + | 0.989564
## [5] chr1 5-14 - | 0.130289
## [6] chr1 6-15 - | 0.330661
## [7] chr2 7-16 - | 0.865121
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths5 Restricting Ranges
The verb filter can be used to restrict rows in the Ranges. Note that
grouping will cause the filter to act within each group of the data.
grng %>% filter(gc < 0.3)## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | gc
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr2 2-11 + | 0.180407
## [2] chr1 5-14 - | 0.130289
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths# filtering by group
grng_by_strand %>% filter(gc == max(gc))## GRanges object with 2 ranges and 1 metadata column:
## Groups: strand [2]
## seqnames ranges strand | gc
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr2 1-10 - | 0.889454
## [2] chr2 4-13 + | 0.989564
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsWe also provide the convenience methods filter_by_overlaps and
filter_by_non_overlaps for restricting by any overlapping Ranges.
ir0 <- data.frame(start = c(5,10, 15,20), width = 5) %>%
as_iranges()
ir1 <- data.frame(start = 2:6, width = 3:7) %>%
as_iranges()
ir0## IRanges object with 4 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 5 9 5
## [2] 10 14 5
## [3] 15 19 5
## [4] 20 24 5ir1## IRanges object with 5 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 2 4 3
## [2] 3 6 4
## [3] 4 8 5
## [4] 5 10 6
## [5] 6 12 7ir0 %>% filter_by_overlaps(ir1)## IRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 5 9 5
## [2] 10 14 5ir0 %>% filter_by_non_overlaps(ir1)## IRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 15 19 5
## [2] 20 24 56 Summarising Ranges
The summarise function will return a DataFrame because the information
required to return a Ranges object is lost. It is often most useful to use
summarise() in combination with the group_by() family of functions.
ir1 <- ir1 %>%
mutate(gc = runif(length(.)))
ir0 %>%
group_by_overlaps(ir1) %>%
summarise(gc = mean(gc))## DataFrame with 2 rows and 2 columns
## query gc
## <integer> <numeric>
## 1 1 0.675555
## 2 2 0.6357957 Joins, or another way at looking at overlaps between Ranges
A join acts on two GRanges objects, a query and a subject.
query <- data.frame(seqnames = "chr1",
strand = c("+", "-"),
start = c(1, 9),
end = c(7, 10),
key.a = letters[1:2]) %>%
as_granges()
subject <- data.frame(seqnames = "chr1",
strand = c("-", "+"),
start = c(2, 6),
end = c(4, 8),
key.b = LETTERS[1:2]) %>%
as_granges()
Figure 1: Query and Subject Ranges
The join operator is relational in the sense that metadata from the query and
subject ranges is retained in the joined range. All join operators in the
plyranges DSL generate a set of hits based on overlap or proximity of ranges
and use those hits to merge the two datasets in different ways. There are four
supported matching algorithms: overlap, nearest, precede, and follow.
We can further restrict the matching by whether the query is completely
within the subject, and adding the directed suffix ensures that matching
ranges have the same direction (strand).