plyinteractions
29 October 2024
Package
plyinteractions 1.4.0
1 Introduction
The plyinteractions package
introduces tidy methods for the GInteractions class defined in the
InteractionSet package (Lun, Perry, and Ing-Simmons, 2016).
1.1 GInteractions objects
GInteractions are objects describing interactions between two parallel
sets of genomic ranges.
library(GenomicRanges)
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library(InteractionSet)
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anchor1 <- GRanges("chr1:10-20:+")
anchor2 <- GRanges("chr1:50-60:-")
gi <- GInteractions(anchor1, anchor2)
gi
#> GInteractions object with 1 interaction and 0 metadata columns:
#> seqnames1 ranges1 seqnames2 ranges2
#> <Rle> <IRanges> <Rle> <IRanges>
#> [1] chr1 10-20 --- chr1 50-60
#> -------
#> regions: 2 ranges and 0 metadata columns
#> seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe InteractionSet package provides basic methods to interact with this class, but does not support tidy grammar principles.
1.2 Tidy grammar principles
The grammar of tidy genomic data transformation defined in
plyranges
and available for GInteractions currently supports:
dplyr verbs (for
GInteractionsandGroupedGInteractions):- Group genomic interactions with
group_by(); - Summarize grouped genomic interactions with
summarize(); - Tally/count grouped genomic interactions with
tallyandcount(); - Modify genomic interactions with
mutate(); - Subset genomic interactions with
filter()using<data-masking>and logical expressions; - Pick out any columns from the associated metadata with
select()using<tidy-select>arguments; - Subset using indices with
slice(); - Order genomic interactions with
arrange()using categorical/numerical variables.
- Group genomic interactions with
plyranges verbs (for
PinnedGInteractionsandAnchoredPinnedGInteractions):- Stretch specific anchors of genomic interactions to a given width with
stretch(); anchor_*()functions to control how stretching is performed;- Shift specific anchors of genomic interactions with
shift(); - Obtain flanking
GRangesfrom specific anchors of genomic interactions withflank().
- Stretch specific anchors of genomic interactions to a given width with
2 Importing genomic interactions in R
plyinteractions provides a consistent interface for importing
genomic interactions from pairs and bedpe files into GInteractions in R,
following grammar of tidy data manipulation defined in the
tidyverse ecosystem.
2.1 From bed-like text files
Tidy genomic data maniuplation implies that we first parse genomic files stored on disk as tabular data frames.
## This uses an example `bedpe` file provided in the `rtracklayer` package
bedpe_file <- system.file("tests", "test.bedpe", package = "rtracklayer")
bedpe_df <- read.delim(bedpe_file, header = FALSE, sep = '\t')
bedpe_df
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> 1 chr7 118965072 118965122 chr7 118970079 118970129 TUPAC_0001:3:1:0:1452#0 37 + -
#> 2 chr11 46765606 46765656 chr10 46769934 46769984 TUPAC_0001:3:1:0:1472#0 37 + -
#> 3 chr20 54704674 54704724 chr20 54708987 54709037 TUPAC_0001:3:1:1:1833#0 37 + -Genomic interactions in tabular format are not easy to manipulate.
We can easily parse a data.frame into a GInteractions object using
the as_ginteractions() function.
library(plyinteractions)
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gi <- bedpe_df |>
as_ginteractions(
seqnames1 = V1, start1 = V2, end1 = V3, strand1 = V9,
seqnames2 = V4, start2 = V5, end2 = V6, strand2 = V10,
starts.in.df.are.0based = TRUE
)
#> Warning in .merge_two_Seqinfo_objects(x, y): Each of the 2 combined objects has sequence levels not in the other:
#> - in 'x': chr11
#> - in 'y': chr10
#> Make sure to always combine/compare objects based on the same reference
#> genome (use suppressWarnings() to suppress this warning).
gi
#> GInteractions object with 3 interactions and 2 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | V7 V8
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer>
#> [1] chr7 118965073-118965122 + --- chr7 118970080-118970129 - | TUPAC_0001:3:1:0:145.. 37
#> [2] chr11 46765607-46765656 + --- chr10 46769935-46769984 - | TUPAC_0001:3:1:0:147.. 37
#> [3] chr20 54704675-54704724 + --- chr20 54708988-54709037 - | TUPAC_0001:3:1:1:183.. 37
#> -------
#> regions: 6 ranges and 0 metadata columns
#> seqinfo: 4 sequences from an unspecified genome; no seqlengthsThe columns containing information for core fields of the future GInteractions
object (e.g. seqnames1, strand2, …) can be specified using the
key = value (supported by quasiquotation).
2.2 From pairs files
The pairs file format has been formally defined by the 4DN consortium.
Its specifications are available here.
It can be imported in R as a data.frame using read.delim() or any other
tabular data import functions (including fread() or vroom() for larger files),
and readily coerced into GInteractions with as_ginteractions().
## This uses an example `pairs` file provided in this package
pairs_file <- system.file('extdata', 'pairs.gz', package = 'plyinteractions')
pairs_df <- read.delim(pairs_file, sep = "\t", header = FALSE, comment.char = "#")
head(pairs_df)
#> V1 V2 V3 V4 V5 V6 V7 V8 V9
#> 1 NS500150:527:HHGYNBGXF:3:21611:19085:3986 II 105 II 48548 + - 1358 1681
#> 2 NS500150:527:HHGYNBGXF:4:13604:19734:2406 II 113 II 45003 - + 1358 1658
#> 3 NS500150:527:HHGYNBGXF:2:11108:25178:11036 II 119 II 687251 - + 1358 5550
#> 4 NS500150:527:HHGYNBGXF:1:22301:8468:1586 II 160 II 26124 + - 1358 1510
#> 5 NS500150:527:HHGYNBGXF:4:23606:24037:2076 II 169 II 39052 + + 1358 1613
#> 6 NS500150:527:HHGYNBGXF:1:12110:9220:19806 II 177 II 10285 + - 1358 1416
pairs <- as_ginteractions(pairs_df,
seqnames1 = V2, start1 = V3, strand1 = V6,
seqnames2 = V4, start2 = V5, strand2 = V7,
width1 = 1, width2 = 1,
keep.extra.columns = FALSE
)
pairs
#> GInteractions object with 50000 interactions and 0 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle>
#> [1] II 105 + --- II 48548 -
#> [2] II 113 - --- II 45003 +
#> [3] II 119 - --- II 687251 +
#> [4] II 160 + --- II 26124 -
#> [5] II 169 + --- II 39052 +
#> ... ... ... ... ... ... ... ...
#> [49996] II 86996 + --- II 487591 +
#> [49997] II 86997 + --- II 96353 -
#> [49998] II 86997 + --- II 114748 -
#> [49999] II 86998 + --- II 88955 +
#> [50000] II 86999 + --- II 87513 +
#> -------
#> regions: 62911 ranges and 0 metadata columns
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths2.3 Reverting from GInteractions to tabular data frames
The reverse operation to coerce GInteractions back to a tabular form is
also possible using the as_tibble() function from the tibble package:
tibble::as_tibble(gi)
#> # A tibble: 3 × 12
#> seqnames1 start1 end1 width1 strand1 seqnames2 start2 end2 width2 strand2 V7 V8
#> <fct> <int> <int> <int> <fct> <fct> <int> <int> <int> <fct> <chr> <int>
#> 1 chr7 118965073 118965122 50 + chr7 118970080 118970129 50 - TUPAC_0001:3:1:0:1452#0 37
#> 2 chr11 46765607 46765656 50 + chr10 46769935 46769984 50 - TUPAC_0001:3:1:0:1472#0 37
#> 3 chr20 54704675 54704724 50 + chr20 54708988 54709037 50 - TUPAC_0001:3:1:1:1833#0 373 Getter functions
3.1 anchors{12}
A GInteractions object consists of two sets of anchors:
anchors1 and anchors2. Each set can be accessed with the corresponding
function (anchors1() or anchors2()):
gi <- read.table(text = "
chr1 1 10 chr1 1 15 + + cis
chr1 6 15 chr1 1 20 + + cis
chr1 6 20 chr1 6 30 - - cis
chr1 11 30 chr2 11 30 - - trans",
col.names = c(
"seqnames1", "start1", "end1",
"seqnames2", "start2", "end2", "strand1", "strand2",
"type")
) |>
as_ginteractions()
## `anchors` returns the two sets of anchors (i.e. "left" and "right"
## loci) for each genomic interaction
anchors(gi)
#> $first
#> GRanges object with 4 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 1-10 +
#> [2] chr1 6-15 +
#> [3] chr1 6-20 -
#> [4] chr1 11-30 -
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
#>
#> $second
#> GRanges object with 4 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 1-15 +
#> [2] chr1 1-20 +
#> [3] chr1 6-30 -
#> [4] chr2 11-30 -
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## `anchors1` and `anchors2` specifically return the "left" OR "right"
## loci) for each genomic interaction
anchors1(gi)
#> GRanges object with 4 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 1-10 +
#> [2] chr1 6-15 +
#> [3] chr1 6-20 -
#> [4] chr1 11-30 -
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
anchors2(gi)
#> GRanges object with 4 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 1-15 +
#> [2] chr1 1-20 +
#> [3] chr1 6-30 -
#> [4] chr2 11-30 -
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsImportant note: the term anchors, when used for GInteractions,
refers to the “left-hand” or “right-hand” GRanges when looking at genomic
interactions. This is different from the anchor term used in plyranges.
This is due to the fact that “anchor” is used in the chromatin interaction
field to refer to the ends of a potential chromatin loop.
3.2 Core GInteractions fields
seqnames(), start()/end(), width() and strand() return informative core fields
of a GRanges object. Appending 1 or 2 to these functions allow
the end-user to fetch the corresponding fields from GInteractions objects.
## Similarly to `GRanges` accessors, `seqnames`, `start`, `end`, `strand` and
## `width` are all available for each set of `anchors` of a `GInteractions`.
seqnames1(gi)
#> factor-Rle of length 4 with 1 run
#> Lengths: 4
#> Values : chr1
#> Levels(2): chr1 chr2
start1(gi)
#> [1] 1 6 6 11
end2(gi)
#> [1] 15 20 30 30
strand2(gi)
#> factor-Rle of length 4 with 2 runs
#> Lengths: 2 2
#> Values : + -
#> Levels(3): + - *
width2(gi)
#> [1] 15 20 25 203.3 Metadata columns
GInteractions contain associated metadata stored as a DataFrame which
can be recovered using the standard mcols() function:
mcols(gi)
#> DataFrame with 4 rows and 1 column
#> type
#> <character>
#> 1 cis
#> 2 cis
#> 3 cis
#> 4 transIndividual metadata columns can also be accessed using the $ notation.
Auto-completion is enabled for this method.
gi$type
#> [1] "cis" "cis" "cis" "trans"4 Pinned (and anchored) GInteractions
The anchoring approach developed in the plyranges package allows handy control
over the way a GRanges object is extended when using the stretch()
function. To enable such workflow for GInteractions, two classes were defined:
PinnedGInteractions and AnchoredPinnedGInteractions.
4.1 PinnedGInteractions
Pinning a GInteractions object is used to specify which set of anchors
(i.e. anchors1 or anchors2) should be affected by plyranges functions.
## `pin_by` is used to pin a `GInteractions` on "first" (i.e. "left") or
## "second" (i.e. "right") anchors.
gi |> pin_by("first")
#> PinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
pgi <- gi |> pin_by("second")
pin(pgi)
#> [1] 2
pinned_anchors(pgi)
#> GRanges object with 4 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 1-15 +
#> [2] chr1 1-20 +
#> [3] chr1 6-30 -
#> [4] chr2 11-30 -
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsA pinned GInteractions object can be reverted back to a unpinned
GInteractions object.
unpin(pgi)
#> GInteractions object with 4 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths4.2 AnchoredPinnedGInteractions
Some plyranges operations can work on “anchored" GRanges. To
enable these operations either on anchors1 or anchors2 from
a GInteractions object, the”pinned"
anchors{12} of the GInteractions object can be further “anchored”.
gi |> pin_by("first") |> anchor_5p()
#> AnchoredPinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1 | Anchored by: 5p
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths5 plyranges operations on GInteractions
plyranges arithmetic functions are available for
(Anchored)PinnedGInteractions objects.
Important note 1: GInteractions must be pinned to a specific anchor set
(anchors1 or anchors2) for plyranges functions to work. Please use
pin_by() function to pin GInteractions.
Important note 2: the stretch() function will behave on
PinnedGInteractions and AnchoredPinnedGInteractions objects similarly
to GRanges or AnchoredGRanges objects.
5.1 On PinnedGInteractions objects
plyinteractions extends the use of verbs developed in plyranges
to manipulate GRanges objects, to ensure they work on GInteractions.
The GInteractions must be “pinned” (using pin_by()) in order
to specify which set of anchors should be affected by plyranges functions.
gi
#> GInteractions object with 4 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This pins the "first" (i.e. "left") anchors and strecthes them by 10bp
gi |> pin_by("first") |> stretch(10)
#> PinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 -4-15 + --- chr1 1-15 + | cis
#> [2] chr1 1-20 + --- chr1 1-20 + | cis
#> [3] chr1 1-25 - --- chr1 6-30 - | cis
#> [4] chr1 6-35 - --- chr2 11-30 - | trans
#> -------
#> regions: 7 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This pins the "first" (i.e. "left") anchors and shift them
## by 20bp to the right
gi |> pin_by("first") |> shift_right(20)
#> PinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 21-30 + --- chr1 1-15 + | cis
#> [2] chr1 26-35 + --- chr1 1-20 + | cis
#> [3] chr1 26-40 - --- chr1 6-30 - | cis
#> [4] chr1 31-50 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This pins the "first" (i.e. "left") anchors and extracts 20bp
## flanking these "first" anchors
gi |> pin_by("first") |> flank_right(20)
#> PinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 11-30 + --- chr1 1-15 + | cis
#> [2] chr1 16-35 + --- chr1 1-20 + | cis
#> [3] chr1 21-40 - --- chr1 6-30 - | cis
#> [4] chr1 31-50 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths5.2 On AnchoredPinnedGInteractions objects
When a pinned GInteractions is further anchored, stretching with plyranges
relies on the anchoring:
## This pins the "first" (i.e. "left") anchors and strecthes them by 10bp,
## with the "first" anchors being anchored at their **start**.
gi |> pin_by("first") |> anchor_start() |> stretch(10)
#> PinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-20 + --- chr1 1-15 + | cis
#> [2] chr1 6-25 + --- chr1 1-20 + | cis
#> [3] chr1 6-30 - --- chr1 6-30 - | cis
#> [4] chr1 11-40 - --- chr2 11-30 - | trans
#> -------
#> regions: 6 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This pins the "first" (i.e. "left") anchors and strecthes them by 10bp,
## with the "first" anchors being anchored at their **center**.
gi |> pin_by("first") |> anchor_center() |> stretch(10)
#> PinnedGInteractions object with 4 interactions and 1 metadata column:
#> Pinned on: anchors1
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 -4-15 + --- chr1 1-15 + | cis
#> [2] chr1 1-20 + --- chr1 1-20 + | cis
#> [3] chr1 1-25 - --- chr1 6-30 - | cis
#> [4] chr1 6-35 - --- chr2 11-30 - | trans
#> -------
#> regions: 7 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths6 dplyr operations on GInteractions
plyinteractions provides a set of verbs for developing
analysis pipelines based on GInteractions objects that represent genomic
interactions. The verbs extend dplyr functionalities to operate on a
GInteractions object as if it were a tabular data object.
6.1 Mutating columns
mutate() supports accessing other existing columns:
## This creates a new metadata column named `cis`
gi |> mutate(cis = seqnames1 == seqnames2)
#> GInteractions object with 4 interactions and 2 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type cis
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <Rle>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis TRUE
#> [2] chr1 6-15 + --- chr1 1-20 + | cis TRUE
#> [3] chr1 6-20 - --- chr1 6-30 - | cis TRUE
#> [4] chr1 11-30 - --- chr2 11-30 - | trans FALSE
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This creates a new metadata column named `both_chr`
gi |> mutate(both_chr = paste(seqnames1, seqnames2, sep = "_"))
#> GInteractions object with 4 interactions and 2 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type both_chr
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <Rle>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis chr1_chr1
#> [2] chr1 6-15 + --- chr1 1-20 + | cis chr1_chr1
#> [3] chr1 6-20 - --- chr1 6-30 - | cis chr1_chr1
#> [4] chr1 11-30 - --- chr2 11-30 - | trans chr1_chr2
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This modifies `start1`, i.e. the `start` coordinates of the "first"
## (i.e. "left") anchors of the `GInteractions` object.
gi |> mutate(start1 = 1)
#> GInteractions object with 4 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 1-15 + --- chr1 1-20 + | cis
#> [3] chr1 1-20 - --- chr1 6-30 - | cis
#> [4] chr1 1-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 7 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths6.2 Grouping columns
group_by() supports accessing both core and metadata columns:
## This groups the `GInteractions` object using the `seqnames2` variable
## (i.e. the `seqnames` of the "second" anchors of the `GInteractions`).
gi |> group_by(seqnames2)
#> GroupedGInteractions object with 4 interactions and 1 metadata column:
#> Groups: seqnames2 [2]
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This groups the `GInteractions` object by a new variable named `cis`
gi |> group_by(cis = seqnames1 == seqnames2)
#> GroupedGInteractions object with 4 interactions and 2 metadata columns:
#> Groups: cis [2]
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type cis
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <Rle>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis TRUE
#> [2] chr1 6-15 + --- chr1 1-20 + | cis TRUE
#> [3] chr1 6-20 - --- chr1 6-30 - | cis TRUE
#> [4] chr1 11-30 - --- chr2 11-30 - | trans FALSE
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This groups the `GInteractions` object by two variables, `seqnames2`
## and the new variable `cis`
gi |> group_by(seqnames2, cis = seqnames1 == seqnames2)
#> GroupedGInteractions object with 4 interactions and 2 metadata columns:
#> Groups: seqnames2, cis [2]
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type cis
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <Rle>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis TRUE
#> [2] chr1 6-15 + --- chr1 1-20 + | cis TRUE
#> [3] chr1 6-20 - --- chr1 6-30 - | cis TRUE
#> [4] chr1 11-30 - --- chr2 11-30 - | trans FALSE
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths6.3 Summarizing columns
Summarizing grouped GInteractions with summarize() can be extremely powerful.
## This counts the number of occurences of each combination of the variables
## `strand1` and `strand2`
pairs |> count(strand1, strand2)
#> DataFrame with 4 rows and 3 columns
#> strand1 strand2 n
#> <Rle> <Rle> <integer>
#> 1 + + 14046
#> 2 + - 10823
#> 3 - + 10288
#> 4 - - 14843
## This counts the number of pairs located on the same strand
## or different strands
gi |> group_by(same_strand = strand1 == strand2) |> tally()
#> DataFrame with 1 row and 2 columns
#> same_strand n
#> <Rle> <integer>
#> 1 TRUE 4
## This counts the number of pairs located on the same strand
## or different strands
pairs |> group_by(same_strand = strand1 == strand2) |>
summarize(
neg_strand = sum(strand1 == "-"),
pos_strand = sum(strand1 == "+")
)
#> DataFrame with 2 rows and 3 columns
#> same_strand neg_strand pos_strand
#> <Rle> <integer> <integer>
#> 1 FALSE 10288 10823
#> 2 TRUE 14843 140466.4 Filtering columns
filter() supports logical expressions:
gi |> filter(seqnames1 == 'chr11')
#> GInteractions object with 0 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
gi |> filter(start1 >= 1e8)
#> GInteractions object with 0 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
gi |> filter(seqnames1 == seqnames2)
#> GInteractions object with 3 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths6.5 Selecting columns
select() supports <tidy-select> arguments:
## This only keeps the "type" column from the metadata columns,
## using <tidy-select> methodology
gi |> select(type)
#> GInteractions object with 4 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
gi |> select(contains("typ"))
#> GInteractions object with 4 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
gi |> select(starts_with("ty"))
#> GInteractions object with 4 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 6-20 - --- chr1 6-30 - | cis
#> [4] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsNote that core fields (e.g. seqnames1, strand2, …) cannot be retrieved
using this approach, only metadata columns are parsed.
Selecting a subset of core fields from a GInteractions would lead to
loss of required information (the other non-selected core fields).
## This does not restrict to `seqnames1` and `seqnames2` columns.
gi |> select(starts_with('seq'))
#> GInteractions object with 4 interactions and 0 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle>
#> [1] chr1 1-10 + --- chr1 1-15 +
#> [2] chr1 6-15 + --- chr1 1-20 +
#> [3] chr1 6-20 - --- chr1 6-30 -
#> [4] chr1 11-30 - --- chr2 11-30 -
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsForcing selection of core fields is still possible when using the .drop_ranges
argument of select(). This results in the coercion of the selected columns
into a DataFrame.
## This selects `seqnames1` and `seqnames2` columns but converts the output
## into a `DataFrame`.
gi |> select(starts_with('seq'), .drop_ranges = TRUE)
#> DataFrame with 4 rows and 2 columns
#> seqnames1 seqnames2
#> <Rle> <Rle>
#> 1 chr1 chr1
#> 2 chr1 chr1
#> 3 chr1 chr1
#> 4 chr1 chr26.6 Slicing rows
## This only retains specific pair indices
gi |> slice(1, 2)
#> GInteractions object with 2 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
gi |> slice(-3)
#> GInteractions object with 3 interactions and 1 metadata column:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis
#> [2] chr1 6-15 + --- chr1 1-20 + | cis
#> [3] chr1 11-30 - --- chr2 11-30 - | trans
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths7 Overlapping operations on GInteractions
Several operlapping functions defined in plyranges are available for
GInteractions:
find_overlaps();count_overlaps();filter_by_overlaps()andfilter_by_non_overlaps();join_overlap_left().
All these functions can take a GInteractions query and a GRanges subject
to perform overlapping operations, and maxgap and minoverlap arguments
are available to refine the query.
These functions are unstranded by default. find_overlaps(),
count_overlaps() and join_overlap_left() functions have *_directed()
counterparts for when strandness is required.
7.1 Overlapping GInteractions
overlapping methods defined for GInteractions have also been adapted to
work in a “tidy” manner.
gr <- GRanges(c("chr1:25-30:-", "chr2:16-20:+"))
gi$id <- seq_len(length(gi))
gr$id <- seq_len(length(gr))
## This returns the `GInteractions` entries overlapping with a `GRanges`
## (with either of both anchors)
find_overlaps(gi, gr)
#> GInteractions object with 3 interactions and 3 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id.x id.y
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer> <integer>
#> [1] chr1 6-20 - --- chr1 6-30 - | cis 3 1
#> [2] chr1 11-30 - --- chr2 11-30 - | trans 4 1
#> [3] chr1 11-30 - --- chr2 11-30 - | trans 4 2
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This overlap requires the same strandness between
## the `GInteractions` anchors and the `GRanges` object
find_overlaps_directed(gi, gr)
#> GInteractions object with 2 interactions and 3 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id.x id.y
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer> <integer>
#> [1] chr1 6-20 - --- chr1 6-30 - | cis 3 1
#> [2] chr1 11-30 - --- chr2 11-30 - | trans 4 1
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This counts how many times each entry in a `GInteractions` object
## overlaps with a `GRanges` object (with either of both anchors)
count_overlaps(gi, gr)
#> [1] 0 0 1 2
count_overlaps_directed(gi, gr)
#> [1] 0 0 1 1
## This filters a `GInteractions` object to only retain the entries
## overlapping (or not) with a `GRanges` (with either of both anchors)
filter_by_overlaps(gi, gr)
#> GInteractions object with 2 interactions and 2 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer>
#> [1] chr1 6-20 - --- chr1 6-30 - | cis 3
#> [2] chr1 11-30 - --- chr2 11-30 - | trans 4
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
filter_by_non_overlaps(gi, gr)
#> GInteractions object with 2 interactions and 2 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis 1
#> [2] chr1 6-15 + --- chr1 1-20 + | cis 2
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This performs a left join between `GInteractions` entries and
## a `GRanges` of interest (with/without considering strandness)
join_overlap_left(gi, gr)
#> GInteractions object with 5 interactions and 3 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id.x id.y
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer> <integer>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis 1 <NA>
#> [2] chr1 6-15 + --- chr1 1-20 + | cis 2 <NA>
#> [3] chr1 6-20 - --- chr1 6-30 - | cis 3 1
#> [4] chr1 11-30 - --- chr2 11-30 - | trans 4 1
#> [5] chr1 11-30 - --- chr2 11-30 - | trans 4 2
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
join_overlap_left_directed(gi, gr)
#> GInteractions object with 4 interactions and 3 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id.x id.y
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer> <integer>
#> [1] chr1 1-10 + --- chr1 1-15 + | cis 1 <NA>
#> [2] chr1 6-15 + --- chr1 1-20 + | cis 2 <NA>
#> [3] chr1 6-20 - --- chr1 6-30 - | cis 3 1
#> [4] chr1 11-30 - --- chr2 11-30 - | trans 4 1
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths7.2 Overlapping pinned GInteractions
PinnedGInteractions can also be used in overlapping functions. In this case,
only the pinned anchors are used when computing overlaps.
## This returns the `GInteractions` entries for which
## the "first" anchor overlaps with a `GRanges`
gi |> pin_by("first") |> find_overlaps(gr)
#> GInteractions object with 1 interaction and 3 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id.x id.y
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer> <integer>
#> [1] chr1 11-30 - --- chr2 11-30 - | trans 4 1
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths
## This returns the `GInteractions` entries for which
## the "second" anchor overlaps with a `GRanges`
gi |> pin_by("second") |> find_overlaps(gr)
#> GInteractions object with 2 interactions and 3 metadata columns:
#> seqnames1 ranges1 strand1 seqnames2 ranges2 strand2 | type id.x id.y
#> <Rle> <IRanges> <Rle> <Rle> <IRanges> <Rle> | <character> <integer> <integer>
#> [1] chr1 6-20 - --- chr1 6-30 - | cis 3 1
#> [2] chr1 11-30 - --- chr2 11-30 - | trans 4 2
#> -------
#> regions: 8 ranges and 0 metadata columns
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths8 Citing plyinteractions
We hope that plyinteractions will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("plyinteractions")
#> To cite package 'plyinteractions' in publications use:
#>
#> Serizay J (2024). _plyinteractions: Extending tidy verbs to genomic interactions_. doi:10.18129/B9.bioc.plyinteractions <https://doi.org/10.18129/B9.bioc.plyinteractions>, R package version 1.4.0, <https://bioconductor.org/packages/plyinteractions>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {plyinteractions: Extending tidy verbs to genomic interactions},
#> author = {Jacques Serizay},
#> year = {2024},
#> note = {R package version 1.4.0},
#> url = {https://bioconductor.org/packages/plyinteractions},
#> doi = {10.18129/B9.bioc.plyinteractions},
#> }9 Acknowledgments
The plyinteractions package
introduces tidy methods for the GInteractions class defined in the
InteractionSet package (Lun, Perry, and Ing-Simmons, 2016).
The plyinteractions package follows tidy principles defined for tabular data and genomic ranges:
- dplyr (Wickham, François, Henry, Müller, and Vaughan, 2023)
- rlang (Henry and Wickham, 2024)
- plyranges (Lee, Stuart, Cook, Dianne, Lawrence, and Michael, 2019)
The plyinteractions package (Serizay, 2024) was written using the following resources:
Supporting documentation was generated using the following resources:
- BiocStyle (Oleś, 2024)
- knitr (Xie, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024)
10 Reproducibility
R session information:
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#> date 2024-10-29
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────11 Bibliography
[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.28. 2024. URL: https://github.com/rstudio/rmarkdown.
[2] L. Henry and H. Wickham. rlang: Functions for Base Types and Core R and ‘Tidyverse’ Features. R package version 1.1.4. 2024. URL: https://CRAN.R-project.org/package=rlang.
[3] Lee, Stuart, Cook, et al. “plyranges: a grammar of genomic data transformation”. In: Genome Biol. 20.1 (2019), p. 4. URL: http://dx.doi.org/10.1186/s13059-018-1597-8.
[4] A. T. L. Lun, M. Perry, and E. Ing-Simmons. “Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments”. In: F1000Res. 5 (2016), p. 950.
[5] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.
[6] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.34.0. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.
[7] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/.
[8] J. Serizay. plyinteractions: Extending tidy verbs to genomic interactions. R package version 1.4.0. 2024. DOI: 10.18129/B9.bioc.plyinteractions. URL: https://bioconductor.org/packages/plyinteractions.
[9] H. Wickham, R. François, L. Henry, et al. dplyr: A Grammar of Data Manipulation. R package version 1.1.4. 2023. URL: https://CRAN.R-project.org/package=dplyr.
[10] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.48. 2024. URL: https://yihui.org/knitr/.