,
Michael Lawrence [aut, ctb],
Dianne Cook [aut, ctb],
Spencer Nystrom [ctb] ,
Pierre-Paul Axisa [ctb],
Michael Love [ctb, cre]| Title: | A fluent interface for manipulating GenomicRanges |
|---|---|
| Description: | A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. |
| Authors: | Stuart Lee [aut] ,
Michael Lawrence [aut, ctb],
Dianne Cook [aut, ctb],
Spencer Nystrom [ctb] ,
Pierre-Paul Axisa [ctb],
Michael Love [ctb, cre] |
| Maintainer: | Michael Love <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.25.0 |
| Built: | 2024-07-17 11:35:21 UTC |
| Source: | https://github.com/bioc/plyranges |
plyranges is a dplyr like API to the Ranges/GenomicRanges infrastructure in Bioconductor.
plryanges provides a consistent interface for importing and wrangling genomics data from a variety of sources. The package defines a grammar of genomic data manipulation through a set of verbs. These verbs can be used to construct human readable analysis pipelines based on Ranges objects.
Modify genomic regions with the set_width() and stretch() functions.
Modify genomic regions while fixing the start/end/center coordinates
with the anchors() family of functions.
Sort genomic ranges with arrange().
Modify, subset, and aggregate genomic data with the mutate(),
filter(), and summarise()functions.
Any of the above operations can be performed on partitions of the
data with group_by().
Find nearest neighbour genomic regions with the join_nearest() family
of functions.
Find overlaps between ranges with the join_overlap_inner() family of functions.
Merge all overlapping and adjacent genomic regions with reduce_ranges().
Merge the end points of all genomic regions with disjoin_ranges().
Import and write common genomic data formats with the read_/write_ family
of functions.
For more details on the features of plryanges, read the vignette:
browseVignettes(package = "plyranges")
Maintainer: Stuart Lee [email protected] (ORCID)
Authors:
Michael Lawrence [contributor]
Dianne Cook [contributor]
Other contributors:
Spencer Nystrom (ORCID) [contributor]
Useful links:
Report bugs at https://github.com/sa-lee/plyranges
Row-wise set operations on Ranges objects
x %union% y x %intersect% y x %setdiff% y between(x, y) span(x, y)x %union% y x %intersect% y x %setdiff% y between(x, y) span(x, y)
x, y
|
Ranges objects |
Each of these functions acts on the rows between pairs of
Ranges object.
The function %union%().
will return the entire range between two ranges objects assuming there
are no gaps, if you would like to force gaps use span() instead.
The function %intersect%() will create a new ranges object
with a hit column indicating whether or not the two ranges intersect.
The function %setdiff%()will return the ranges for each
row in x that are not in the corresponding row of y.
The function between() will return the gaps between
two ranges.
A Ranges object
[IRanges::punion()][IRanges::pintersect()][IRanges::pgap()][IRanges::psetdiff()]
x <- as_iranges(data.frame(start = 1:10, width = 5)) # stretch x by 3 on the right y <- stretch(anchor_start(x), 3) # take the rowwise union x %union% y # take the rowwise intersection x %intersect% y # asymetric difference y %setdiff% x x %setdiff% y # if there are gaps between the rows of each range use span y <- as_iranges(data.frame(start = c(20:15, 2:5), width = c(10:15,1:4))) # fill in the gaps and take the rowwise union span(x,y) # find the gaps between(x,y)x <- as_iranges(data.frame(start = 1:10, width = 5)) # stretch x by 3 on the right y <- stretch(anchor_start(x), 3) # take the rowwise union x %union% y # take the rowwise intersection x %intersect% y # asymetric difference y %setdiff% x x %setdiff% y # if there are gaps between the rows of each range use span y <- as_iranges(data.frame(start = c(20:15, 2:5), width = c(10:15,1:4))) # fill in the gaps and take the rowwise union span(x,y) # find the gaps between(x,y)
Appends distance to nearest subject range to query ranges similar to setting
distance in join_nearest_. Distance is set to NA for features with no
nearest feature by the selected nearest metric.
add_nearest_distance(x, y = x, name = "distance") add_nearest_distance_left(x, y = x, name = "distance") add_nearest_distance_right(x, y = x, name = "distance") add_nearest_distance_upstream(x, y = x, name = "distance") add_nearest_distance_downstream(x, y = x, name = "distance")add_nearest_distance(x, y = x, name = "distance") add_nearest_distance_left(x, y = x, name = "distance") add_nearest_distance_right(x, y = x, name = "distance") add_nearest_distance_upstream(x, y = x, name = "distance") add_nearest_distance_downstream(x, y = x, name = "distance")
x |
The query ranges |
y |
the subject ranges within which the nearest ranges are found. If missing, query ranges are used as the subject. |
name |
column name to create containing distance values |
By default add_nearest_distance will find arbitrary nearest
neighbours in either direction and ignore any strand information.
The add_nearest_distance_left and add_nearest_distance_right methods
will find arbitrary nearest neighbour ranges on x that are left/right of
those on y and ignore any strand information.
The add_nearest_distance_upstream method will find arbitrary nearest
neighbour ranges on x that are upstream of those on y. This takes into
account strandedness of the ranges.
On the positive strand nearest upstream will be on the
left and on the negative strand nearest upstream will be on the right.
The add_nearest_distance_downstream method will find arbitrary nearest
neighbour ranges on x that are upstream of those on y. This takes into
account strandedness of the ranges. On the positive strand nearest downstream
will be on the right and on the negative strand nearest upstream will be on
the left.
ranges in x with additional column containing the distance to the
nearest range in y.
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = c(2:6, 24), width = 3:8, label = letters[1:6]) %>% as_iranges() add_nearest_distance(query, subject) add_nearest_distance_left(query, subject) add_nearest_distance_left(query)query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = c(2:6, 24), width = 3:8, label = letters[1:6]) %>% as_iranges() add_nearest_distance(query, subject) add_nearest_distance_left(query, subject) add_nearest_distance_left(query)
The GRangesAnchored class and the IRangesAnchored
class allow components of a GRanges or IRanges (start, end, center)
to be held fixed.
anchor(x) unanchor(x) anchor_start(x) anchor_end(x) anchor_center(x) anchor_centre(x) anchor_3p(x) anchor_5p(x)anchor(x) unanchor(x) anchor_start(x) anchor_end(x) anchor_center(x) anchor_centre(x) anchor_3p(x) anchor_5p(x)
x |
a Ranges object |
Anchoring will fix a Ranges start, end, or center positions,
so these positions will remain the same when performing arithimetic.
For GRanges objects, the function
(anchor_3p()) will fix the start for the negative strand,
while anchor_5p() will fix the end for the
positive strand. Anchoring modifies how arithmetic is performed, for example
modifying the width of a range with set_width() or stretching a
range with stretch(). To remove anchoring use unanchor().
a RangesAnchored object which has the same appearance as a regular Ranges object but with an additional slot displaying an anchor.
Depending on how you want to fix the components of a Ranges, there are
five ways to construct a RangesAnchored class. Here x is either
an IRanges or GRanges object.
anchor_start(x)Fix the start coordinates
anchor_end(x)Fix the end coordinates
anchor_center(x)Fix the center coordinates
anchor_3p(x)On the negative strand fix the start coordinates,
and for positive or unstranded ranges fix the end coordinates.
anchor_5p(x)On the positive or unstranded ranges fix the start coordinates,
coordinates and for negative stranded ranges fix the end coordinates.
To see what has been anchored use the function anchor.
This will return a character vector containing a valid anchor.
It will be set to one of c("start", "end", "center") for an
IRanges object or one of
c("start", "end", "center", "3p", "5p") for a GRanges object.
df <- data.frame(start = 1:10, width = 5) rng <- as_iranges(df) rng_by_start <- anchor_start(rng) rng_by_start anchor(rng_by_start) mutate(rng_by_start, width = 3L) grng <- as_granges(df, seqnames = "chr1", strand = c(rep("-", 5), rep("+", 5))) rng_by_5p <- anchor_5p(grng) rng_by_5p mutate(rng_by_5p, width = 3L)df <- data.frame(start = 1:10, width = 5) rng <- as_iranges(df) rng_by_start <- anchor_start(rng) rng_by_start anchor(rng_by_start) mutate(rng_by_start, width = 3L) grng <- as_granges(df, seqnames = "chr1", strand = c(rep("-", 5), rep("+", 5))) rng_by_5p <- anchor_5p(grng) rng_by_5p mutate(rng_by_5p, width = 3L)
Sort a Ranges object
## S3 method for class 'Ranges' arrange(.data, ...)## S3 method for class 'Ranges' arrange(.data, ...)
.data |
A Ranges object. |
... |
Comma seperated list of variable names. |
A sorted Ranges object
rng <- as_iranges(data.frame(start = 1:10, width = 10:1)) rng <- mutate(rng, score = runif(10)) arrange(rng, score) # you can also use dplyr::desc to arrange by descending orderrng <- as_iranges(data.frame(start = 1:10, width = 10:1)) rng <- mutate(rng, score = runif(10)) arrange(rng, score) # you can also use dplyr::desc to arrange by descending order
The as_i(g)ranges function looks for column names in .data called start, end, width, seqnames and strand in order to construct an IRanges or GRanges object. By default other columns in .data are placed into the mcols ( metadata columns) slot of the returned object.
as_iranges(.data, ..., keep_mcols = TRUE) as_granges(.data, ..., keep_mcols = TRUE)as_iranges(.data, ..., keep_mcols = TRUE) as_granges(.data, ..., keep_mcols = TRUE)
.data |
a |
... |
optional named arguments specifying which the columns in .data containin the core components a Ranges object. |
keep_mcols |
place the remaining columns into the metadata columns slot (default=TRUE) |
a Ranges object.
IRanges::IRanges(),
GenomicRanges::GRanges()
df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0)) as_iranges(df) df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0), strand = "+") # will return an IRanges object as_iranges(df) df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0), strand = "+", seqnames = "chr1") as_granges(df) # as_g/iranges understand alternate name specification df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0), strand = "+", chr = "chr1") as_granges(df, seqnames = chr) # can also handle DFrame input df <- methods::as(df, "DFrame") df$y <- IRanges::IntegerList(c(1,2,3), NA, 5, 6, 8, 9, 10:12) as_iranges(df) as_granges(df, seqnames = chr)df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0)) as_iranges(df) df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0), strand = "+") # will return an IRanges object as_iranges(df) df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0), strand = "+", seqnames = "chr1") as_granges(df) # as_g/iranges understand alternate name specification df <- data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0), strand = "+", chr = "chr1") as_granges(df, seqnames = chr) # can also handle DFrame input df <- methods::as(df, "DFrame") df$y <- IRanges::IntegerList(c(1,2,3), NA, 5, 6, 8, 9, 10:12) as_iranges(df) as_granges(df, seqnames = chr)
Coerce an Rle or RleList object to Ranges
as_ranges(.data)as_ranges(.data)
.data |
This function is behind compute_coverage().
an IRanges() object if the input is an
Rle() object or a GRanges() object for
an RleList() object.
S4Vectors::Rle(),
IRanges::RleList()
x <- S4Vectors::Rle(10:1, 1:10) as_ranges(x) # must have names set y <- IRanges::RleList(chr1 = x) as_ranges(y)x <- S4Vectors::Rle(10:1, 1:10) as_ranges(x) # must have names set y <- IRanges::RleList(chr1 = x) as_ranges(y)
Combine Ranges by concatentating them together
bind_ranges(..., .id = NULL)bind_ranges(..., .id = NULL)
... |
Ranges objects to combine. Each argument can be a Ranges object, or a list of Ranges objects. |
.id |
Ranges object identifier. When .id is supplied a new column is created that links each row to the original Range object. The contents of the column correspond to the named arguments or the names of the list supplied. |
a concatenated Ranges object
Currently GRangesList or IRangesList objects are not supported.
gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1")) gr2 <- as_granges(data.frame(start = 11:14, width = 1:4, seqnames = "seq2")) bind_ranges(gr, gr2) bind_ranges(a = gr, b = gr2, .id = "origin") bind_ranges(gr, list(gr, gr2), gr2) bind_ranges(list(a = gr, b = gr2), c = gr, .id = "origin")gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1")) gr2 <- as_granges(data.frame(start = 11:14, width = 1:4, seqnames = "seq2")) bind_ranges(gr, gr2) bind_ranges(a = gr, b = gr2, .id = "origin") bind_ranges(gr, list(gr, gr2), gr2) bind_ranges(list(a = gr, b = gr2), c = gr, .id = "origin")
Group a GRanges object by introns or gaps
chop_by_introns(x) chop_by_gaps(x)chop_by_introns(x) chop_by_gaps(x)
x |
a GenomicRanges object with a cigar string column |
Creates a grouped Ranges object from a cigar string
column, for chop_by_introns() will check for the presence of
"N" in the cigar string and create a new column called
intron where TRUE indicates the alignment has a skipped
region from the reference. For chop_by_gaps() will check
for the presence of "N" or "D" in the cigar string and
create a new column called "gaps" where TRUE indicates
the alignment has a deletion from the reference or has an intron.
a GRanges object
if (require(pasillaBamSubset)) { bamfile <- untreated1_chr4() # define a region of interest roi <- data.frame(seqnames = "chr4", start = 5e5, end = 7e5) %>% as_granges() # results in a grouped ranges object rng <- read_bam(bamfile) %>% filter_by_overlaps(roi) %>% chop_by_gaps() # to find ranges that have gaps use filter with `n()` rng %>% filter(n() >= 2) }if (require(pasillaBamSubset)) { bamfile <- untreated1_chr4() # define a region of interest roi <- data.frame(seqnames = "chr4", start = 5e5, end = 7e5) %>% as_granges() # results in a grouped ranges object rng <- read_bam(bamfile) %>% filter_by_overlaps(roi) %>% chop_by_gaps() # to find ranges that have gaps use filter with `n()` rng %>% filter(n() >= 2) }
Compute coverage over a Ranges object
compute_coverage(x, shift, width, weight, ...)compute_coverage(x, shift, width, weight, ...)
x |
a |
shift |
shift how much should each range in x be shifted by? (default = 0L) |
width |
width how long should the returned coverage score be? This must be either a positive integer or NULL (default = NULL) |
weight |
weight how much weight should be assigned to each range? Either an integer or numeric vector or a column in x. (default = 1L) |
... |
other optional parameters to pass to coverage |
An expanded Ranges object with a score column corresponding to the coverage value over that interval. Note that compute_coverage drops metadata associated with the orginal ranges.
IRanges::coverage(),
GenomicRanges::coverage()
rng <- as_iranges(data.frame(start = 1:10, width = 5)) compute_coverage(rng) compute_coverage(rng, shift = 14L) compute_coverage(rng, width = 10L)rng <- as_iranges(data.frame(start = 1:10, width = 5)) compute_coverage(rng) compute_coverage(rng, shift = 14L) compute_coverage(rng, width = 10L)
Count the number of overlaps between two Ranges objects
count_overlaps(x, y, maxgap, minoverlap) ## S3 method for class 'IntegerRanges' count_overlaps(x, y, maxgap = -1L, minoverlap = 0L) ## S3 method for class 'GenomicRanges' count_overlaps(x, y, maxgap = -1L, minoverlap = 0L) count_overlaps_within(x, y, maxgap, minoverlap) ## S3 method for class 'IntegerRanges' count_overlaps_within(x, y, maxgap = 0L, minoverlap = 1L) ## S3 method for class 'GenomicRanges' count_overlaps_within(x, y, maxgap = 0L, minoverlap = 1L) count_overlaps_directed(x, y, maxgap, minoverlap) ## S3 method for class 'GenomicRanges' count_overlaps_directed(x, y, maxgap = -1L, minoverlap = 0L) count_overlaps_within_directed(x, y, maxgap, minoverlap) ## S3 method for class 'GenomicRanges' count_overlaps_within_directed(x, y, maxgap = -1L, minoverlap = 0L)count_overlaps(x, y, maxgap, minoverlap) ## S3 method for class 'IntegerRanges' count_overlaps(x, y, maxgap = -1L, minoverlap = 0L) ## S3 method for class 'GenomicRanges' count_overlaps(x, y, maxgap = -1L, minoverlap = 0L) count_overlaps_within(x, y, maxgap, minoverlap) ## S3 method for class 'IntegerRanges' count_overlaps_within(x, y, maxgap = 0L, minoverlap = 1L) ## S3 method for class 'GenomicRanges' count_overlaps_within(x, y, maxgap = 0L, minoverlap = 1L) count_overlaps_directed(x, y, maxgap, minoverlap) ## S3 method for class 'GenomicRanges' count_overlaps_directed(x, y, maxgap = -1L, minoverlap = 0L) count_overlaps_within_directed(x, y, maxgap, minoverlap) ## S3 method for class 'GenomicRanges' count_overlaps_within_directed(x, y, maxgap = -1L, minoverlap = 0L)
x, y
|
Objects representing ranges |
maxgap, minoverlap
|
The maximimum gap between intervals as an integer greater than or equal to zero. The minimum amount of overlap between intervals as an integer greater than zero, accounting for the maximum gap. |
An integer vector of same length as x.
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() query %>% mutate(n_olap = count_overlaps(., subject), n_olap_within = count_overlaps_within(., subject))query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() query %>% mutate(n_olap = count_overlaps(., subject), n_olap_within = count_overlaps_within(., subject))
Enables deferred reading of files (currently only BAM files) by caching results after a plyranges verb is called.
delegatea GenomicRanges object to be cached
opsA FileOperator object
read_bam()
Disjoin then aggregate a Ranges object
disjoin_ranges(.data, ...) disjoin_ranges_directed(.data, ...)disjoin_ranges(.data, ...) disjoin_ranges_directed(.data, ...)
.data |
a Ranges object to disjoin |
... |
Name-value pairs of summary functions. |
a Ranges object that is now disjoint (no bases overlap).
df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) rng %>% disjoin_ranges() rng %>% disjoin_ranges(gc = mean(gc)) rng %>% disjoin_ranges_directed(gc = mean(gc))df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) rng %>% disjoin_ranges() rng %>% disjoin_ranges(gc = mean(gc)) rng %>% disjoin_ranges_directed(gc = mean(gc))
Expand list-columns in a Ranges object
expand_ranges( data, ..., .drop = FALSE, .id = NULL, .keep_empty = FALSE, .recursive = FALSE )expand_ranges( data, ..., .drop = FALSE, .id = NULL, .keep_empty = FALSE, .recursive = FALSE )
data |
A Ranges object |
... |
list-column names to expand then unlist |
.drop |
Should additional list columns be dropped (default = FALSE)?
By default |
.id |
A character vector of length equal to number of list columns.
If supplied will create new column(s) with name |
.keep_empty |
If a list-like column contains empty elements, should those elements be kept? (default = FALSE) |
.recursive |
If there are multiple list-columns, should the columns be treated as parallel? If FALSE each column will be unnested recursively, otherwise they are treated as parallel, that is each list column has identical lengths. (deafualt = FALSE) |
a GRanges object with expanded list columns
grng <- as_granges(data.frame(seqnames = "chr1", start = 20:23, width = 1000)) grng <- mutate(grng, exon_id = IntegerList(a = 1, b = c(4,5), c = 3, d = c(2,5)) ) expand_ranges(grng) expand_ranges(grng, .id = "name") # empty list elements are not preserved by default grng <- mutate(grng, exon_id = IntegerList(a = NULL, b = c(4,5), c= 3, d = c(2,5)) ) expand_ranges(grng) expand_ranges(grng, .keep_empty = TRUE) expand_ranges(grng, .id = "name", .keep_empty = TRUE)grng <- as_granges(data.frame(seqnames = "chr1", start = 20:23, width = 1000)) grng <- mutate(grng, exon_id = IntegerList(a = 1, b = c(4,5), c = 3, d = c(2,5)) ) expand_ranges(grng) expand_ranges(grng, .id = "name") # empty list elements are not preserved by default grng <- mutate(grng, exon_id = IntegerList(a = NULL, b = c(4,5), c= 3, d = c(2,5)) ) expand_ranges(grng) expand_ranges(grng, .keep_empty = TRUE) expand_ranges(grng, .id = "name", .keep_empty = TRUE)
An abstract class to represent operations performed over a file
This class is used internally by DeferredGenomicRanges objects. Currently, this class is only implemented for bam files (as a BamFileOperator) but will eventually be extended to the other avaialable readers.
Filter by overlapping/non-overlapping ranges
filter_by_overlaps(x, y, maxgap = -1L, minoverlap = 0L) filter_by_non_overlaps(x, y, maxgap, minoverlap) filter_by_overlaps_directed(x, y, maxgap = -1L, minoverlap = 0L) filter_by_non_overlaps_directed(x, y, maxgap, minoverlap)filter_by_overlaps(x, y, maxgap = -1L, minoverlap = 0L) filter_by_non_overlaps(x, y, maxgap, minoverlap) filter_by_overlaps_directed(x, y, maxgap = -1L, minoverlap = 0L) filter_by_non_overlaps_directed(x, y, maxgap, minoverlap)
x, y
|
Objects representing ranges |
maxgap |
The maximimum gap between intervals as a single
integer greater than or equal to -1. If you modify this argument,
|
minoverlap |
The minimum amount of overlap between intervals
as a single integer greater than 0. If you modify this argument,
|
By default, filter_by_overlaps and
filter_by_non_overlaps ignore strandedness for GRanges()
objects. To perform stranded operations use filter_by_overlaps_directed and filter_by_non_overlaps_directed. The argument maxgap is the maximum number of positions
between two ranges for them to be considered overlapping. Here the default
is set to be -1 as that is the the gap between two ranges that
has its start or end strictly inside the other. The argugment
minoverlap refers to the minimum number of positions
overlapping between ranges, to consider there to be overlap.
a Ranges object
IRanges::subsetByOverlaps()
df <- data.frame(seqnames = c("chr1", rep("chr2", 2), rep("chr3", 3), rep("chr4", 4)), start = 1:10, width = 10:1, strand = c("-", "+", "+", "*", "*", "+", "+", "+", "-", "-"), name = letters[1:10]) query <- as_granges(df) df2 <- data.frame(seqnames = c(rep("chr2", 2), rep("chr1", 3), "chr2"), start = c(4,3,7,13,1,4), width = c(6,6,3,3,3,9), strand = c(rep("+", 3), rep("-", 3))) subject <- as_granges(df2) filter_by_overlaps(query, subject) filter_by_overlaps_directed(query, subject) filter_by_non_overlaps(query, subject) filter_by_non_overlaps_directed(query, subject)df <- data.frame(seqnames = c("chr1", rep("chr2", 2), rep("chr3", 3), rep("chr4", 4)), start = 1:10, width = 10:1, strand = c("-", "+", "+", "*", "*", "+", "+", "+", "-", "-"), name = letters[1:10]) query <- as_granges(df) df2 <- data.frame(seqnames = c(rep("chr2", 2), rep("chr1", 3), "chr2"), start = c(4,3,7,13,1,4), width = c(6,6,3,3,3,9), strand = c(rep("+", 3), rep("-", 3))) subject <- as_granges(df2) filter_by_overlaps(query, subject) filter_by_overlaps_directed(query, subject) filter_by_non_overlaps(query, subject) filter_by_non_overlaps_directed(query, subject)
Ranges objectSubset a Ranges object
## S3 method for class 'Ranges' filter(.data, ..., .preserve = FALSE)## S3 method for class 'Ranges' filter(.data, ..., .preserve = FALSE)
.data |
A |
... |
valid logical predictates to subset .data by. These
are determined by variables in |
.preserve |
when FALSE (the default) grouping structure is recalculated, TRUE is currently not implemented. |
For any Ranges objects
filter can act on all core components of the class including start, end,
width (for IRanges) or seqnames and strand (for GRanges) in addition to
metadata columns. If the Ranges object is grouped, filter will act
seperately on each parition of the data.
a Ranges object
set.seed(100) df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) filter(rng, strand == "+") filter(rng, gc > 0.5) # multiple criteria filter(rng, strand == "+" | start > 5) filter(rng, strand == "+" & start > 5) # multiple conditions are the same as and filter(rng, strand == "+", start > 5) # grouping acts on each subset of the data rng %>% group_by(strand) %>% filter(gc > 0.5)set.seed(100) df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) filter(rng, strand == "+") filter(rng, gc > 0.5) # multiple criteria filter(rng, strand == "+" | start > 5) filter(rng, strand == "+" & start > 5) # multiple conditions are the same as and filter(rng, strand == "+", start > 5) # grouping acts on each subset of the data rng %>% group_by(strand) %>% filter(gc > 0.5)
Find overlap between two Ranges
find_overlaps(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'IntegerRanges' find_overlaps(x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y")) ## S3 method for class 'GenomicRanges' find_overlaps(x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y")) find_overlaps_within(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'IntegerRanges' find_overlaps_within( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) ## S3 method for class 'GenomicRanges' find_overlaps_within( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) find_overlaps_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'GenomicRanges' find_overlaps_directed( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) find_overlaps_within_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'GenomicRanges' find_overlaps_within_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) group_by_overlaps(x, y, maxgap, minoverlap) ## S3 method for class 'IntegerRanges' group_by_overlaps(x, y, maxgap = -1L, minoverlap = 0L) ## S3 method for class 'GenomicRanges' group_by_overlaps(x, y, maxgap = -1L, minoverlap = 0L)find_overlaps(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'IntegerRanges' find_overlaps(x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y")) ## S3 method for class 'GenomicRanges' find_overlaps(x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y")) find_overlaps_within(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'IntegerRanges' find_overlaps_within( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) ## S3 method for class 'GenomicRanges' find_overlaps_within( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) find_overlaps_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'GenomicRanges' find_overlaps_directed( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) find_overlaps_within_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) ## S3 method for class 'GenomicRanges' find_overlaps_within_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) group_by_overlaps(x, y, maxgap, minoverlap) ## S3 method for class 'IntegerRanges' group_by_overlaps(x, y, maxgap = -1L, minoverlap = 0L) ## S3 method for class 'GenomicRanges' group_by_overlaps(x, y, maxgap = -1L, minoverlap = 0L)
x, y
|
Objects representing ranges |
maxgap, minoverlap
|
The maximimum gap between intervals as an integer greater than or equal to negative one. The minimum amount of overlap between intervals as an integer greater than zero, accounting for the maximum gap. |
suffix |
A character vector of length two used to identify metadata columns coming from x and y. |
find_overlaps() will search for any overlaps between ranges
x and y and return a Ranges object of length equal to the number of times x
overlaps y. This Ranges object will have additional metadata columns
corresponding to the metadata columns in y. find_overlaps_within() is
the same but will only search for overlaps within y. For GRanges objects strand is
ignored, unless find_overlaps_directed() is used. If the Ranges objects have no
metadata, one could use group_by_overlaps() to be able to
identify the index of the input Range x that overlaps a Range in y.
Alternatively,
pair_overlaps() could be used to place the x ranges next to the range
in y they overlap.
A Ranges object with rows corresponding to the
ranges in x that overlap y. In the case of group_by_overlaps(), returns
a GroupedRanges object, grouped by the number of overlaps
of ranges in x that overlap y (stored in a column called query).
IRanges::findOverlaps(),
GenomicRanges::findOverlaps()
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() find_overlaps(query, subject) find_overlaps(query, subject, minoverlap = 5) find_overlaps_within(query, subject) # same result as minoverlap find_overlaps(query, subject, maxgap = 1) # -- GRanges objects, strand is ignored by default query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() subject <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() # ignores strandedness find_overlaps(query, subject, suffix = c(".query", ".subject")) find_overlaps(query, subject, suffix = c(".query", ".subject"), minoverlap = 2) # adding directed prefix includes strand find_overlaps_directed(query, subject, suffix = c(".query", ".subject"))query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() find_overlaps(query, subject) find_overlaps(query, subject, minoverlap = 5) find_overlaps_within(query, subject) # same result as minoverlap find_overlaps(query, subject, maxgap = 1) # -- GRanges objects, strand is ignored by default query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() subject <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() # ignores strandedness find_overlaps(query, subject, suffix = c(".query", ".subject")) find_overlaps(query, subject, suffix = c(".query", ".subject"), minoverlap = 2) # adding directed prefix includes strand find_overlaps_directed(query, subject, suffix = c(".query", ".subject"))
Find flanking regions to the left or right or upstream or downstream of a Ranges object.
flank_left(x, width = 0L) flank_right(x, width = 0L) flank_upstream(x, width = 0L) flank_downstream(x, width = 0L)flank_left(x, width = 0L) flank_right(x, width = 0L) flank_upstream(x, width = 0L) flank_downstream(x, width = 0L)
x |
a Ranges object. |
width |
the width of the flanking region relative to the ranges in
|
The function
flank_left will create the flanking region to the left of starting
coordinates in x, while flank_right will create the flanking
region to the right of the starting coordinates in x. The function
flank_upstream will flank_left if the strand of rows in x is
not negative and will flank_right if the strand of rows in x is
negative. The function flank_downstream will flank_right if the strand of rows in x is
not negative and will flank_leftt if the strand of rows in x is
negative.
By default flank_left and flank_right will
ignore strandedness of any ranges, while flank_upstream and
flank_downstream will take into account the strand of x.
A Ranges object of same length as x.
IRanges::flank(),
GenomicRanges::flank()
gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1", strand = c("+", "+", "-", "-", "+", "*"))) flank_left(gr, width = 5L) flank_right(gr, width = 5L) flank_upstream(gr, width = 5L) flank_downstream(gr, width = 5L)gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1", strand = c("+", "+", "-", "-", "+", "*"))) flank_left(gr, width = 5L) flank_right(gr, width = 5L) flank_upstream(gr, width = 5L) flank_downstream(gr, width = 5L)
The function group_by takes a Ranges object and defines
groups by one or more variables. Operations are then performed on the Ranges
by their "group". ungroup() removes grouping.
## S3 method for class 'GenomicRanges' group_by(.data, ..., add = FALSE) ## S3 method for class 'GroupedGenomicRanges' ungroup(x, ...) ## S3 method for class 'GroupedGenomicRanges' groups(x) ## S3 method for class 'GroupedIntegerRanges' groups(x)## S3 method for class 'GenomicRanges' group_by(.data, ..., add = FALSE) ## S3 method for class 'GroupedGenomicRanges' ungroup(x, ...) ## S3 method for class 'GroupedGenomicRanges' groups(x) ## S3 method for class 'GroupedIntegerRanges' groups(x)
.data |
a Ranges object. |
... |
Variable names to group by. These can be either metadata columns or the core variables of a Ranges. |
add |
if |
x |
a GroupedRanges object. |
group_by() creates a new object of class GroupedGenomicRanges if
the input is a GRanges object or an object of class GroupedIntegerRanges
if the input is a IRanges object. Both of these classes contain a slot
called groups corresponding to the names of grouping variables. They
also inherit from their parent classes, Ranges and GenomicRanges
respectively. ungroup() removes the grouping and will return
either a GRanges or IRanges object.
The group_by() function will return a GroupedRanges object.
These have the same appearance as a regular Ranges object but with an
additional groups slot.
To return grouping variables on a grouped Ranges use either
groups(x)Returns a list of symbols
group_vars(x)Returns a character vector
set.seed(100) df <- data.frame(start = 1:10, width = 5, gc = runif(10), cat = sample(letters[1:2], 10, replace = TRUE)) rng <- as_iranges(df) rng_by_cat <- rng %>% group_by(cat) # grouping does not change appearance or shape of Ranges rng_by_cat # a list of symbols groups(rng_by_cat) # ungroup removes any grouping ungroup(rng_by_cat) # group_by works best with other verbs grng <- as_granges(df, seqnames = "chr1", strand = sample(c("+", "-"), size = 10, replace = TRUE)) grng_by_strand <- grng %>% group_by(strand) grng_by_strand # grouping with other verbs grng_by_strand %>% summarise(gc = mean(gc)) grng_by_strand %>% filter(gc == min(gc)) grng_by_strand %>% ungroup() %>% summarise(gc = mean(gc))set.seed(100) df <- data.frame(start = 1:10, width = 5, gc = runif(10), cat = sample(letters[1:2], 10, replace = TRUE)) rng <- as_iranges(df) rng_by_cat <- rng %>% group_by(cat) # grouping does not change appearance or shape of Ranges rng_by_cat # a list of symbols groups(rng_by_cat) # ungroup removes any grouping ungroup(rng_by_cat) # group_by works best with other verbs grng <- as_granges(df, seqnames = "chr1", strand = sample(c("+", "-"), size = 10, replace = TRUE)) grng_by_strand <- grng %>% group_by(strand) grng_by_strand # grouping with other verbs grng_by_strand %>% summarise(gc = mean(gc)) grng_by_strand %>% filter(gc == min(gc)) grng_by_strand %>% ungroup() %>% summarise(gc = mean(gc))
Vector-wise Range set-operations
intersect_ranges(x, y) intersect_ranges_directed(x, y) union_ranges(x, y) union_ranges_directed(x, y) setdiff_ranges(x, y) setdiff_ranges_directed(x, y) complement_ranges(x) complement_ranges_directed(x)intersect_ranges(x, y) intersect_ranges_directed(x, y) union_ranges(x, y) union_ranges_directed(x, y) setdiff_ranges(x, y) setdiff_ranges_directed(x, y) complement_ranges(x) complement_ranges_directed(x)
x, y
|
Two Ranges objects to compare. |
These are usual set-operations that act on the sets of the ranges represented in x and y. By default these operations will ignore any strand information. The directed versions of these functions will take into account strand for GRanges objects.
A Ranges object
gr1 <- data.frame(seqnames = "chr1", start = c(2,9), end = c(7,9), strand = c("+", "-")) %>% as_granges() gr2 <- data.frame(seqnames = "chr1", start = 5, width = 5, strand = "-") %>% as_granges() union_ranges(gr1, gr2) union_ranges_directed(gr1, gr2) intersect_ranges(gr1, gr2) intersect_ranges_directed(gr1, gr2) setdiff_ranges(gr1, gr2) setdiff_ranges_directed(gr1, gr2) # taking the complement of a ranges requires annotation information gr1 <- set_genome_info(gr1, seqlengths = 100) complement_ranges(gr1)gr1 <- data.frame(seqnames = "chr1", start = c(2,9), end = c(7,9), strand = c("+", "-")) %>% as_granges() gr2 <- data.frame(seqnames = "chr1", start = 5, width = 5, strand = "-") %>% as_granges() union_ranges(gr1, gr2) union_ranges_directed(gr1, gr2) intersect_ranges(gr1, gr2) intersect_ranges_directed(gr1, gr2) setdiff_ranges(gr1, gr2) setdiff_ranges_directed(gr1, gr2) # taking the complement of a ranges requires annotation information gr1 <- set_genome_info(gr1, seqlengths = 100) complement_ranges(gr1)
Interweave a pair of Ranges objects together
interweave(left, right, .id = NULL)interweave(left, right, .id = NULL)
left, right
|
Ranges objects. |
.id |
When supplied a new column that represents the origin column and is linked to each row of the resulting Ranges object. |
The output of interweave() takes pairs of Ranges
objects and combines them into a single Ranges object. If an .id
argument is supplied, an origin column with name .id is created indicated which side
the resulting Range comes from (eit)
a Ranges object
gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1", strand = c("+", "+", "-", "-", "+", "*"))) interweave(flank_left(gr, width = 5L), flank_right(gr, width = 5L)) interweave(flank_left(gr, width = 5L), flank_right(gr, width = 5L), .id = "origin")gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1", strand = c("+", "+", "-", "-", "+", "*"))) interweave(flank_left(gr, width = 5L), flank_right(gr, width = 5L)) interweave(flank_left(gr, width = 5L), flank_right(gr, width = 5L), .id = "origin")
Find following Ranges
join_follow(x, y, suffix = c(".x", ".y")) join_follow_left(x, y, suffix = c(".x", ".y")) join_follow_upstream(x, y, suffix = c(".x", ".y"))join_follow(x, y, suffix = c(".x", ".y")) join_follow_left(x, y, suffix = c(".x", ".y")) join_follow_upstream(x, y, suffix = c(".x", ".y"))
x, y
|
Ranges objects, which ranges in x follow those in y. |
suffix |
A character vector of length two used to identify metadata columns coming from x and y. |
By default join_follow will find abritrary ranges
in y that are followed by ranges in x and ignore any strand information.
On the other hand join_follow_left will find all ranges in y
that are on the left-hand side of the ranges in x ignoring any strand
information. Finally, join_follow_upstream will find all ranges in x
that are that are upstream of the ranges in y. On the positive strand this
will result in ranges in y that are left of those in x and on the negative
strand it will result in ranges in y that are right of those in x.
A Ranges object corresponding to the ranges in x`` that are followed by the ranges in y, all metadata is copied over from the right-hand side ranges y'.
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() join_follow(query, subject) subject <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() query <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() join_follow(query, subject) join_follow_left(query, subject) join_follow_upstream(query, subject)query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() join_follow(query, subject) subject <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() query <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() join_follow(query, subject) join_follow_left(query, subject) join_follow_upstream(query, subject)
Find nearest neighbours between two Ranges objects
join_nearest(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_left(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_right(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_upstream(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_downstream(x, y, suffix = c(".x", ".y"), distance = FALSE)join_nearest(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_left(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_right(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_upstream(x, y, suffix = c(".x", ".y"), distance = FALSE) join_nearest_downstream(x, y, suffix = c(".x", ".y"), distance = FALSE)
x, y
|
Ranges objects, add the nearest neighbours of ranges in x to those in y. |
suffix |
A character vector of length two used to identify metadata columns |
distance |
logical vector whether to add a column named "distance" containing the distance to the nearest region. If set to a character vector of length 1, will use that as distance column name. |
By default join_nearest will find arbitrary nearest
neighbours in either direction and ignore any strand information.
The join_nearest_left and join_nearest_right methods
will find arbitrary nearest neighbour ranges on x that are left/right of
those on y and ignore any strand information.
The join_nearest_upstream method will find arbitrary nearest
neighbour ranges on x that are upstream of those on y. This takes into
account strandedness of the ranges.
On the positive strand nearest upstream will be on the
left and on the negative strand nearest upstream will be on the right.
The join_nearest_downstream method will find arbitrary nearest
neighbour ranges on x that are upstream of those on y. This takes into
account strandedness of the ranges.On the positive strand nearest downstream
will be on the right and on the negative strand nearest upstream will be on
the left.
A Ranges object corresponding to the nearest ranges, all metadata
is copied over from the right-hand side ranges y.
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = c(2:6, 24), width = 3:8, label = letters[1:6]) %>% as_iranges() join_nearest(query, subject) join_nearest_left(query, subject) join_nearest_right(query, subject) subject <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() query <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() join_nearest_upstream(query, subject) join_nearest_downstream(query, subject)query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = c(2:6, 24), width = 3:8, label = letters[1:6]) %>% as_iranges() join_nearest(query, subject) join_nearest_left(query, subject) join_nearest_right(query, subject) subject <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() query <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() join_nearest_upstream(query, subject) join_nearest_downstream(query, subject)
Join by overlapping Ranges
join_overlap_intersect(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_intersect_within(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_intersect_directed( x, y, maxgap, minoverlap, suffix = c(".x", ".y") ) join_overlap_intersect_within_directed( x, y, maxgap, minoverlap, suffix = c(".x", ".y") ) join_overlap_inner(x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y")) join_overlap_inner_within( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) join_overlap_inner_directed( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) join_overlap_inner_within_directed( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) join_overlap_left(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_left_within(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_left_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_left_within_directed( x, y, maxgap, minoverlap, suffix = c(".x", ".y") )join_overlap_intersect(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_intersect_within(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_intersect_directed( x, y, maxgap, minoverlap, suffix = c(".x", ".y") ) join_overlap_intersect_within_directed( x, y, maxgap, minoverlap, suffix = c(".x", ".y") ) join_overlap_inner(x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y")) join_overlap_inner_within( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) join_overlap_inner_directed( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) join_overlap_inner_within_directed( x, y, maxgap = -1L, minoverlap = 0L, suffix = c(".x", ".y") ) join_overlap_left(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_left_within(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_left_directed(x, y, maxgap, minoverlap, suffix = c(".x", ".y")) join_overlap_left_within_directed( x, y, maxgap, minoverlap, suffix = c(".x", ".y") )
x, y
|
Objects representing ranges |
maxgap, minoverlap
|
The maximimum gap between intervals as an integer greater than or equal to zero. The minimum amount of overlap between intervals as an integer greater than zero, accounting for the maximum gap. |
suffix |
Character to vectors to append to common columns in x and y
(default = |
The function join_overlap_intersect() finds
the genomic intervals that are the overlapping ranges between x and y and
returns a new ranges object with metadata columns from x and y.
The function join_overlap_inner() is equivalent to find_overlaps().
The function join_overlap_left() performs a left outer join between x
and y. It returns all ranges in x that overlap or do not overlap ranges in y
plus metadata columns common to both. If there is no overlapping range
the metadata column will contain a missing value.
The function join_overlap_self() find all overlaps between a ranges
object x and itself.
All of these functions have two suffixes that modify their behavior.
The within suffix, returns only ranges in x that are completely
overlapped within in y. The directed suffix accounts for the strandedness
of the ranges when performing overlaps.
a GRanges object
join_overlap_self(), join_overlap_left(), find_overlaps()
x <- as_iranges(data.frame(start = c(11, 101), end = c(21, 201))) y <- as_iranges(data.frame(start = c(10, 20, 50, 100, 1), end = c(19, 21, 105, 202, 5))) # self join_overlap_self(y) # intersect takes common interval join_overlap_intersect(x,y) # within join_overlap_intersect_within(x,y) # left, and inner join, it's often useful having an id column here y <- y %>% mutate(id = 1:n()) x <- x %>% mutate(id = 1:n()) join_overlap_inner(x,y) join_overlap_left(y,x, suffix = c(".left", ".right"))x <- as_iranges(data.frame(start = c(11, 101), end = c(21, 201))) y <- as_iranges(data.frame(start = c(10, 20, 50, 100, 1), end = c(19, 21, 105, 202, 5))) # self join_overlap_self(y) # intersect takes common interval join_overlap_intersect(x,y) # within join_overlap_intersect_within(x,y) # left, and inner join, it's often useful having an id column here y <- y %>% mutate(id = 1:n()) x <- x %>% mutate(id = 1:n()) join_overlap_inner(x,y) join_overlap_left(y,x, suffix = c(".left", ".right"))
Find overlaps within a Ranges object
join_overlap_self(x, maxgap, minoverlap) join_overlap_self_within(x, maxgap, minoverlap) join_overlap_self_directed(x, maxgap, minoverlap) join_overlap_self_within_directed(x, maxgap, minoverlap)join_overlap_self(x, maxgap, minoverlap) join_overlap_self_within(x, maxgap, minoverlap) join_overlap_self_directed(x, maxgap, minoverlap) join_overlap_self_within_directed(x, maxgap, minoverlap)
x |
A Ranges object |
maxgap, minoverlap
|
The maximimum gap between intervals as an integer greater than or equal to zero. The minimum amount of overlap between intervals as an integer greater than zero, accounting for the maximum gap. |
Self overlaps find any overlaps (or overlaps within or overlaps directed) between a ranges object and itself.
a Ranges object
find_overlaps(), join_overlap_inner()
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() join_overlap_self(query) # -- GRanges objects, strand is ignored by default query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() # ignores strandedness join_overlap_self(query) join_overlap_self_within(query) # adding directed prefix includes strand join_overlap_self_directed(query)query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() join_overlap_self(query) # -- GRanges objects, strand is ignored by default query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() # ignores strandedness join_overlap_self(query) join_overlap_self_within(query) # adding directed prefix includes strand join_overlap_self_directed(query)
Find preceding Ranges
join_precede(x, y, suffix = c(".x", ".y")) join_precede_right(x, y, suffix = c(".x", ".y")) join_precede_downstream(x, y, suffix = c(".x", ".y"))join_precede(x, y, suffix = c(".x", ".y")) join_precede_right(x, y, suffix = c(".x", ".y")) join_precede_downstream(x, y, suffix = c(".x", ".y"))
x, y
|
Ranges objects, which ranges in x precede those in y. |
suffix |
A character vector of length two used to identify metadata columns coming from x and y. |
By default join_precede will return the ranges
in x that come before the ranges in y and ignore any strand information.
The function join_precede_right will find all ranges in y
that are on the right-hand side of the ranges in x ignoring any strand
information. Finally, join_precede_downstream will find all ranges in y
that are that are downstream of the ranges in x. On the positive strand this
will result in ranges in y that are right of those in x and on the negative
strand it will result in ranges in y that are left of those in x.
A Ranges object corresponding to the ranges in y that are
preceded by the ranges in x, all metadata is copied over from the
right-hand side ranges y.
subject <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() query <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() join_precede(query, subject) query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() subject <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() join_precede(query, subject) join_precede_right(query, subject) join_precede_downstream(query, subject)subject <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() query <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() join_precede(query, subject) query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() subject <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() join_precede(query, subject) join_precede_right(query, subject) join_precede_downstream(query, subject)
Modify a Ranges object
## S3 method for class 'Ranges' mutate(.data, ...)## S3 method for class 'Ranges' mutate(.data, ...)
.data |
a |
... |
Pairs of name-value expressions. The name-value pairs can either create new metadata columns or modify existing ones. |
a Ranges object
df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) # mutate adds new columns rng %>% mutate(avg_gc = mean(gc), row_id = 1:n()) # can also compute on newly created columns rng %>% mutate(score = gc * width, score2 = score + 1) # group by partitions the data and computes within each group rng %>% group_by(strand) %>% mutate(avg_gc = mean(gc), row_id = 1:n()) # mutate can be used in conjuction with anchoring to resize ranges rng %>% mutate(width = 10) # by default width modfication fixes by start rng %>% anchor_start() %>% mutate(width = 10) # fix by end or midpoint rng %>% anchor_end() %>% mutate(width = width + 1) rng %>% anchor_center() %>% mutate(width = width + 1) # anchoring by strand rng %>% anchor_3p() %>% mutate(width = width * 2) rng %>% anchor_5p() %>% mutate(width = width * 2)df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) # mutate adds new columns rng %>% mutate(avg_gc = mean(gc), row_id = 1:n()) # can also compute on newly created columns rng %>% mutate(score = gc * width, score2 = score + 1) # group by partitions the data and computes within each group rng %>% group_by(strand) %>% mutate(avg_gc = mean(gc), row_id = 1:n()) # mutate can be used in conjuction with anchoring to resize ranges rng %>% mutate(width = 10) # by default width modfication fixes by start rng %>% anchor_start() %>% mutate(width = 10) # fix by end or midpoint rng %>% anchor_end() %>% mutate(width = width + 1) rng %>% anchor_center() %>% mutate(width = width + 1) # anchoring by strand rng %>% anchor_3p() %>% mutate(width = width * 2) rng %>% anchor_5p() %>% mutate(width = width * 2)
This function should only be used
within summarise(), mutate() and filter().
n()n()
n() will only be evaluated inside a function call, where it
returns an integer.
ir <- as_iranges( data.frame(start = 1:10, width = 5, name = c(rep("a", 5), rep("b", 3), rep("c", 2)) ) ) by_names <- group_by(ir, name) summarise(by_names, n = n()) mutate(by_names, n = n()) filter(by_names, n() >= 3)ir <- as_iranges( data.frame(start = 1:10, width = 5, name = c(rep("a", 5), rep("b", 3), rep("c", 2)) ) ) by_names <- group_by(ir, name) summarise(by_names, n = n()) mutate(by_names, n = n()) filter(by_names, n() >= 3)
This is a wrapper to length(unique(x)) or
lengths(unique(x)) if x is a List object
n_distinct(var)n_distinct(var)
var |
a vector of values |
an integer vector
x <- CharacterList(c("a", "b", "c", "a"), "d") n_distinct(x) n_distinct(unlist(x))x <- CharacterList(c("a", "b", "c", "a"), "d") n_distinct(x) n_distinct(unlist(x))
Create an overscoped environment from a Ranges object
overscope_ranges(x, envir = parent.frame())overscope_ranges(x, envir = parent.frame())
x |
a Ranges object |
envir |
the environment to place the Ranges in (default = |
This is the backend for non-standard evaluation in plyranges.
an environment
rlang::new_data_mask(), rlang::eval_tidy()
Pair together two ranges objects
pair_overlaps(x, y, maxgap, minoverlap, suffix) pair_nearest(x, y, suffix) pair_precede(x, y, suffix) pair_follow(x, y, suffix)pair_overlaps(x, y, maxgap, minoverlap, suffix) pair_nearest(x, y, suffix) pair_precede(x, y, suffix) pair_follow(x, y, suffix)
x, y
|
Ranges objects to pair together. |
maxgap, minoverlap
|
The maximimum gap between intervals as an integer greater than or equal to negative one. The minimum amount of overlap between intervals as an integer greater than zero, accounting for the maximum gap. |
suffix |
A character vector of length two used to identify metadata columns coming from x and y. |
These functions return a DataFrame object, and is one way of representing paired alignments with plyranges.
a DataFrame with two ranges columns and the corresponding metadata columns.
[join_nearest()][join_overlap_inner()][join_precede()][join_follow()]
query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() pair_overlaps(query, subject) pair_overlaps(query, subject, minoverlap = 5) pair_nearest(query, subject) query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() subject <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() # ignores strandedness pair_overlaps(query, subject, suffix = c(".query", ".subject")) pair_follow(query, subject, suffix = c(".query", ".subject")) pair_precede(query, subject, suffix = c(".query", ".subject")) pair_precede(query, subject, suffix = c(".query", ".subject"))query <- data.frame(start = c(5,10, 15,20), width = 5, gc = runif(4)) %>% as_iranges() subject <- data.frame(start = 2:6, width = 3:7, label = letters[1:5]) %>% as_iranges() pair_overlaps(query, subject) pair_overlaps(query, subject, minoverlap = 5) pair_nearest(query, subject) query <- data.frame(seqnames = "chr1", start = c(11,101), end = c(21, 200), name = c("a1", "a2"), strand = c("+", "-"), score = c(1,2)) %>% as_granges() subject <- data.frame(seqnames = "chr1", strand = c("+", "-", "+", "-"), start = c(21,91,101,201), end = c(30,101,110,210), name = paste0("b", 1:4), score = 1:4) %>% as_granges() # ignores strandedness pair_overlaps(query, subject, suffix = c(".query", ".subject")) pair_follow(query, subject, suffix = c(".query", ".subject")) pair_precede(query, subject, suffix = c(".query", ".subject")) pair_precede(query, subject, suffix = c(".query", ".subject"))
To construct annotations by supplying annotation information
use genome_info. To add
annotations to an existing Ranges object use set_genome_info. To retrieve
an annotation as a Ranges object use get_genome_info.
genome_info( genome = NULL, seqnames = NULL, seqlengths = NULL, is_circular = NULL ) set_genome_info( .data, genome = NULL, seqnames = NULL, seqlengths = NULL, is_circular = NULL ) get_genome_info(.data)genome_info( genome = NULL, seqnames = NULL, seqlengths = NULL, is_circular = NULL ) set_genome_info( .data, genome = NULL, seqnames = NULL, seqlengths = NULL, is_circular = NULL ) get_genome_info(.data)
genome |
A character vector of length one indicating the genome build. |
seqnames |
A character vector containing the name of sequences. |
seqlengths |
An optional integer vector containg the lengths of sequences. |
is_circular |
An optional logical vector indicating whether a sequence is ciruclar. |
.data |
A Ranges object to annotate or retrieve an annotation for. |
a GRanges object containing annotations. To retrieve the annotations
as a Ranges object use get_genome_info.
x <- genome_info(genome = "toy", seqnames = letters[1:4], seqlengths = c(100, 300, 15, 600), is_circular = c(NA, FALSE, FALSE, TRUE)) x rng <- as_granges(data.frame(seqnames = "a", start = 30:50, width = 10)) rng rng <- set_genome_info(rng, genome = "toy", seqnames = letters[1:4], seqlengths = c(100, 300, 15, 600), is_circular = c(NA, FALSE, FALSE, TRUE)) get_genome_info(rng) ## Not run: if (interactive()) { # requires internet connection genome_info(genome = "hg38") } ## End(Not run)x <- genome_info(genome = "toy", seqnames = letters[1:4], seqlengths = c(100, 300, 15, 600), is_circular = c(NA, FALSE, FALSE, TRUE)) x rng <- as_granges(data.frame(seqnames = "a", start = 30:50, width = 10)) rng rng <- set_genome_info(rng, genome = "toy", seqnames = letters[1:4], seqlengths = c(100, 300, 15, 600), is_circular = c(NA, FALSE, FALSE, TRUE)) get_genome_info(rng) ## Not run: if (interactive()) { # requires internet connection genome_info(genome = "hg38") } ## End(Not run)
Read a BAM file
read_bam(file, index = file, paired = FALSE)read_bam(file, index = file, paired = FALSE)
file |
A connection or path to a BAM file |
index |
The path to the BAM index file |
paired |
Whether to treat alignments as paired end (TRUE) or single end (FALSE). Default is FALSE. |
Reading a BAM file is deferred until an action
such as using summarise() or mutate() occurs. If paired is set to
TRUE, when alignments are loaded, the GRanges has two additional
columns called read_pair_id and read_pair_group corresponding
to paired reads and is grouped by the read_pair_group.
Certain verbs have different behaviour, after using read_bam().
For select() valid columns are the fields available in the
BAM file. Valid entries are qname (QNAME), flag (FLAG),
rname (RNAME), strand, pos (POS), qwidth (width of query),
mapq (MAPQ), cigar (CIGAR), mrnm (RNEXT), mpos (PNEXT), isize
(TLEN), seq (SEQ), and qual (QUAL). Any two character tags in the BAM file
are also valid.
For filter() the following fields are valid, to select the FALSE option
place ! in front of the field:
is_paired Select either unpaired (FALSE) or paired (TRUE) reads.
is_proper_pair Select either improperly paired (FALSE) or properly
paired (TRUE) reads. This is dependent on the alignment software used.
'is_unmapped_query“ Select unmapped (TRUE) or mapped (FALSE) reads.
has_unmapped_mate Select reads with mapped (FALSE) or unmapped (TRUE) mates.
is_minus_strand Select reads aligned to plus (FALSE) or minus (TRUE) strand.
is_mate_minus_strand Select reads where mate is aligned to plus (FALSE) or
minus (TRUE) strand.
is_first_mate_read Select reads if they are the first mate (TRUE) or
not (FALSE).
is_second_mate_read Select reads if they are the second mate (TRUE) or
not (FALSE).
is_secondary_alignment Select reads if their alignment status is
secondary (TRUE) or not (FALSE). This might be relevant if there are
multimapping reads.
is_not_passing_quality_controls Select reads that either pass
quality controls (FALSE) or that do not (TRUE).
is_duplicate Select reads that are unduplicated (FALSE) or
duplicated (TRUE). This may represent reads that are PCR or
optical duplicates.
A DeferredGenomicRanges object
Rsamtools::BamFile(),GenomicAlignments::readGAlignments()
if (require(pasillaBamSubset)) { bamfile <- untreated1_chr4() # nothing is read until an action has been performed print(read_bam(bamfile)) # define a region of interest roi <- data.frame(seqnames = "chr4", start = 5e5, end = 7e5) %>% as_granges() rng <- read_bam(bamfile) %>% select(mapq) %>% filter_by_overlaps(roi) }if (require(pasillaBamSubset)) { bamfile <- untreated1_chr4() # nothing is read until an action has been performed print(read_bam(bamfile)) # define a region of interest roi <- data.frame(seqnames = "chr4", start = 5e5, end = 7e5) %>% as_granges() rng <- read_bam(bamfile) %>% select(mapq) %>% filter_by_overlaps(roi) }
This is a lightweight wrapper to the import family of functions defined in rtracklayer.
Read common interval based formats as GRanges.
read_bed(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_bed_graph( file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL ) read_narrowpeaks( file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL )read_bed(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_bed_graph( file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL ) read_narrowpeaks( file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL )
file |
A path to a file or a connection. |
col_names |
An optional character vector for including additional
columns in |
genome_info |
An optional character string or a Ranges object that contains information about the genome build. For example the USSC identifier "hg19" will add build information to the returned GRanges. |
overlap_ranges |
An optional Ranges object. Only the intervals in the file that overlap the Ranges will be returned. |
This is a lightweight wrapper to the import family
of functions defined in rtracklayer.
The read_narrowpeaks function parses the ENCODE narrowPeak BED format (see
https://genome.ucsc.edu/FAQ/FAQformat.html#format12 for details.). As
such the parser expects four additional columns called (corresponding to
the narrowPeaks spec):
signalValue
pValue
qValue
peak
A GRanges object
rtracklayer::BEDFile()
test_path <- system.file("tests", package = "rtracklayer") bed_file <- file.path(test_path, "test.bed") gr <- read_bed(bed_file) gr gr <- read_bed(bed_file, genome_info = "hg19") gr olap <- as_granges(data.frame(seqnames = "chr7", start = 1, end = 127473000)) gr <- read_bed(bed_file, overlap_ranges = olap) # bedGraph bg_file <- file.path(test_path, "test.bedGraph") gr <- read_bed_graph(bg_file) gr # narrowpeaks np_file <- system.file("extdata", "demo.narrowPeak.gz", package="rtracklayer") gr <- read_narrowpeaks(np_file, genome_info = "hg19") grtest_path <- system.file("tests", package = "rtracklayer") bed_file <- file.path(test_path, "test.bed") gr <- read_bed(bed_file) gr gr <- read_bed(bed_file, genome_info = "hg19") gr olap <- as_granges(data.frame(seqnames = "chr7", start = 1, end = 127473000)) gr <- read_bed(bed_file, overlap_ranges = olap) # bedGraph bg_file <- file.path(test_path, "test.bedGraph") gr <- read_bed_graph(bg_file) gr # narrowpeaks np_file <- system.file("extdata", "demo.narrowPeak.gz", package="rtracklayer") gr <- read_narrowpeaks(np_file, genome_info = "hg19") gr
Read a BigWig file
read_bigwig(file, genome_info = NULL, overlap_ranges = NULL)read_bigwig(file, genome_info = NULL, overlap_ranges = NULL)
file |
A path to a file or URL. |
genome_info |
An optional character string or a Ranges object that contains information about the genome build. For example the identifier "hg19" will add build information to the returned GRanges. |
overlap_ranges |
An optional Ranges object. Only the intervals in the file that overlap the Ranges will be loaded. |
a GRanges object
rtracklayer::BigWigFile()
if (.Platform$OS.type != "windows") { test_path <- system.file("tests", package = "rtracklayer") bw_file <- file.path(test_path, "test.bw") gr <- read_bigwig(bw_file) gr }if (.Platform$OS.type != "windows") { test_path <- system.file("tests", package = "rtracklayer") bw_file <- file.path(test_path, "test.bw") gr <- read_bigwig(bw_file) gr }
This is a lightweight wrapper to the import family of functions defined in rtracklayer.
read_gff(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_gff1(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_gff2(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_gff3(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL)read_gff(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_gff1(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_gff2(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL) read_gff3(file, col_names = NULL, genome_info = NULL, overlap_ranges = NULL)
file |
A path to a file or a connection. |
col_names |
An optional character vector for parsing specific
columns in |
genome_info |
An optional character string or a Ranges object that contains information about the genome build. For example the UCSC identifier "hg19" will add build information to the returned GRanges. |
overlap_ranges |
An optional Ranges object. Only the intervals in the file that overlap the Ranges will be returned. |
A GRanges object
a GRanges object
rtracklayer::GFFFile()
test_path <- system.file("tests", package = "rtracklayer") # gff3 test_gff3 <- file.path(test_path, "genes.gff3") gr <- read_gff3(test_gff3) gr # alternatively with read_gff gr <- read_gff(test_gff3, genome_info = "hg19") grtest_path <- system.file("tests", package = "rtracklayer") # gff3 test_gff3 <- file.path(test_path, "genes.gff3") gr <- read_gff3(test_gff3) gr # alternatively with read_gff gr <- read_gff(test_gff3, genome_info = "hg19") gr
This is a lightweight wrapper to the import family of functions defined in rtracklayer.
read_wig(file, genome_info = NULL, overlap_ranges = NULL)read_wig(file, genome_info = NULL, overlap_ranges = NULL)
file |
A path to a file or a connection. |
genome_info |
An optional character string or a Ranges object that contains information about the genome build. For example the USSC identifier "hg19" will add build information to the returned GRanges. |
overlap_ranges |
An optional Ranges object. Only the intervals in the file that overlap the Ranges will be returned. |
A GRanges object
A GRanges object
rtracklayer::WIGFile()
test_path <- system.file("tests", package = "rtracklayer") test_wig <- file.path(test_path, "step.wig") gr <- read_wig(test_wig) gr gr <- read_wig(test_wig, genome_info = "hg19")test_path <- system.file("tests", package = "rtracklayer") test_wig <- file.path(test_path, "step.wig") gr <- read_wig(test_wig) gr gr <- read_wig(test_wig, genome_info = "hg19")
Reduce then aggregate a Ranges object
reduce_ranges(.data, min.gapwidth = 1L, ...) reduce_ranges_directed(.data, min.gapwidth = 1L, ...)reduce_ranges(.data, min.gapwidth = 1L, ...) reduce_ranges_directed(.data, min.gapwidth = 1L, ...)
.data |
a Ranges object to reduce |
min.gapwidth |
Ranges separated by a gap of at least min.gapwidth positions are not merged. |
... |
Name-value pairs of summary functions. |
a Ranges object with the
set.seed(10) df <- data.frame(start = sample(1:10), width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) rng %>% reduce_ranges() rng %>% reduce_ranges(gc = mean(gc)) rng %>% reduce_ranges_directed(gc = mean(gc)) rng %>% reduce_ranges_directed(gc = mean(gc), min.gapwidth = 10) x <- data.frame(start = c(11:13, 2, 7:6), width=3, id=sample(letters[1:3], 6, replace = TRUE), score= sample(1:6)) x <- as_iranges(x) x %>% reduce_ranges() x %>% reduce_ranges(score = sum(score)) x %>% group_by(id) %>% reduce_ranges(score = sum(score))set.seed(10) df <- data.frame(start = sample(1:10), width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) rng %>% reduce_ranges() rng %>% reduce_ranges(gc = mean(gc)) rng %>% reduce_ranges_directed(gc = mean(gc)) rng %>% reduce_ranges_directed(gc = mean(gc), min.gapwidth = 10) x <- data.frame(start = c(11:13, 2, 7:6), width=3, id=sample(letters[1:3], 6, replace = TRUE), score= sample(1:6)) x <- as_iranges(x) x %>% reduce_ranges() x %>% reduce_ranges(score = sum(score)) x %>% group_by(id) %>% reduce_ranges(score = sum(score))
Tools for working with named Ranges
remove_names(.data) names_to_column(.data, var = "name") id_to_column(.data, var = "id")remove_names(.data) names_to_column(.data, var = "name") id_to_column(.data, var = "id")
.data |
a Ranges object |
var |
Name of column to use for names |
The function names_to_column() and id_to_column() always places
var as the first column in mcols(.data), shifting all other columns
to the left. The id_to_column() creates a column with sequential row
identifiers starting at 1, it will also remove any existing names.
Returns a Ranges object with empty names
ir <- IRanges::IRanges(start = 1:3, width = 4, names = c("a", "b", "c")) remove_names(ir) ir_noname <- names_to_column(ir) ir_noname ir_with_id <- id_to_column(ir) ir_with_idir <- IRanges::IRanges(start = 1:3, width = 4, names = c("a", "b", "c")) remove_names(ir) ir_noname <- names_to_column(ir) ir_noname ir_with_id <- id_to_column(ir) ir_with_id
Select metadata columns of the Ranges object by name or position
## S3 method for class 'Ranges' select(.data, ..., .drop_ranges = FALSE)## S3 method for class 'Ranges' select(.data, ..., .drop_ranges = FALSE)
.data |
a |
... |
One or more metadata column names. |
.drop_ranges |
If TRUE select will always return a tibble. In this case, you may select columns that form the core part of the Ranges object. |
Note that by default select only acts on the metadata columns (and will therefore return a Ranges object) if a core component of a Ranges is dropped or selected without the other required components (this includes the seqnames, strand, start, end, width names), then select will throw an error unless .drop_ranges is set to TRUE.
a Ranges object or a tibble
df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10), counts = rpois(10, 2)) rng <- as_granges(df) select(rng, -gc) select(rng, gc) select(rng, counts, gc) select(rng, 2:1) select(rng, seqnames, strand, .drop_ranges = TRUE)df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10), counts = rpois(10, 2)) rng <- as_granges(df) select(rng, -gc) select(rng, gc) select(rng, counts, gc) select(rng, 2:1) select(rng, seqnames, strand, .drop_ranges = TRUE)
Functional setters for Ranges objects
set_width(x, width) set_start(x, start = 0L) set_end(x, end = 0L) set_seqnames(x, seqnames) set_strand(x, strand)set_width(x, width) set_start(x, start = 0L) set_end(x, end = 0L) set_seqnames(x, seqnames) set_strand(x, strand)
x |
a Ranges object |
width |
integer amount to modify width by |
start |
integer amount to modify start by |
end |
integer amount to modify end by |
seqnames |
update seqnames column |
strand |
update strand column |
These methods are used internally in mutate() to modify
core columns in Ranges objects.
a Ranges object
Shift all coordinates in a genomic interval left or right, upstream or downstream
shift_left(x, shift = 0L) shift_right(x, shift = 0L) shift_upstream(x, shift = 0L) shift_downstream(x, shift = 0L)shift_left(x, shift = 0L) shift_right(x, shift = 0L) shift_upstream(x, shift = 0L) shift_downstream(x, shift = 0L)
x |
a Ranges object . |
shift |
the amount to move the genomic interval in the Ranges object by. Either a non-negative integer vector of length 1 or an integer vector the same length as x. |
Shifting left or right will ignore any strand information
in the Ranges object, while shifting upstream/downstream will shift coordinates
on the positive strand left/right and the negative strand right/left. By
default, unstranded features are treated as positive. When
using shift_upstream() or shift_downstream() when the shift argument is
indexed by the strandedness of the input ranges.
a Ranges object with start and end coordinates shifted.
IRanges::shift(),
GenomicRanges::shift()
ir <- as_iranges(data.frame(start = 10:15, width = 5)) shift_left(ir, 5L) shift_right(ir, 5L) gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1", strand = c("+", "+", "-", "-", "+", "*"))) shift_upstream(gr, 5L) shift_downstream(gr, 5L)ir <- as_iranges(data.frame(start = 10:15, width = 5)) shift_left(ir, 5L) shift_right(ir, 5L) gr <- as_granges(data.frame(start = 10:15, width = 5, seqnames = "seq1", strand = c("+", "+", "-", "-", "+", "*"))) shift_upstream(gr, 5L) shift_downstream(gr, 5L)
Choose rows by their position
## S3 method for class 'Ranges' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'GroupedGenomicRanges' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'GroupedIntegerRanges' slice(.data, ..., .preserve = FALSE)## S3 method for class 'Ranges' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'GroupedGenomicRanges' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'GroupedIntegerRanges' slice(.data, ..., .preserve = FALSE)
.data |
a |
... |
Integer row values indicating rows to keep. If |
.preserve |
when FALSE (the default) the grouping structure is recomputed, otherwise it is kept as is. Currently ignored. |
a GRanges object
df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) dplyr::slice(rng, 1:2) dplyr::slice(rng, -n()) dplyr::slice(rng, -5:-n()) by_strand <- group_by(rng, strand) # slice with group by finds positions within each group dplyr::slice(by_strand, n()) dplyr::slice(by_strand, which.max(gc)) # if the index is beyond the number of groups slice are ignored dplyr::slice(by_strand, 1:3)df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) dplyr::slice(rng, 1:2) dplyr::slice(rng, -n()) dplyr::slice(rng, -5:-n()) by_strand <- group_by(rng, strand) # slice with group by finds positions within each group dplyr::slice(by_strand, n()) dplyr::slice(by_strand, which.max(gc)) # if the index is beyond the number of groups slice are ignored dplyr::slice(by_strand, 1:3)
By default, stretch(x) will anchor by the center of a Ranges
object. This means that half of the value of extend will be added to
the end of the range and the remaining half subtracted from the start of
the Range. The other anchors will leave the start/end fixed and stretch
the end/start respectively.
stretch(x, extend)stretch(x, extend)
x |
a Ranges object, to fix by either the start, end or center
of an interval use |
extend |
the amount to alter the width of a Ranges object by. Either an integer vector of length 1 or an integer vector the same length as x. |
a Ranges object with modified start or end (or both) coordinates
anchor(), mutate()
rng <- as_iranges(data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0))) rng2 <- stretch(anchor_center(rng), 10) stretch(anchor_start(rng2), 10) stretch(anchor_end(rng2), 10) grng <- as_granges(data.frame(seqnames = "chr1", strand = c("+", "-", "-", "+", "+", "-", "+"), start=c(2:-1, 13:15), width=c(0:3, 2:0))) stretch(anchor_3p(grng), 10) stretch(anchor_5p(grng), 10)rng <- as_iranges(data.frame(start=c(2:-1, 13:15), width=c(0:3, 2:0))) rng2 <- stretch(anchor_center(rng), 10) stretch(anchor_start(rng2), 10) stretch(anchor_end(rng2), 10) grng <- as_granges(data.frame(seqnames = "chr1", strand = c("+", "-", "-", "+", "+", "-", "+"), start=c(2:-1, 13:15), width=c(0:3, 2:0))) stretch(anchor_3p(grng), 10) stretch(anchor_5p(grng), 10)
Reduce multiple values in a Ranges down to a single value
## S3 method for class 'Ranges' summarise(.data, ...)## S3 method for class 'Ranges' summarise(.data, ...)
.data |
a Ranges object |
... |
Name-value pairs of summary functions. The name will be the name of the variable in the result. The value should be an expression that will return a value that has length one or length equal to the number of groups. |
Creates one or more variables as a S4Vectors::DataFrame()
from the input Ranges object. If the ranges object is grouped, there will
be a row for each group. Because grouping may remove whether a Ranges object
is valid, a DataFrame is always returned.
A S4Vectors::DataFrame()
df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) rng %>% summarise(gc = mean(gc)) rng %>% group_by(strand) %>% summarise(gc = mean(gc))df <- data.frame(start = 1:10, width = 5, seqnames = "seq1", strand = sample(c("+", "-", "*"), 10, replace = TRUE), gc = runif(10)) rng <- as_granges(df) rng %>% summarise(gc = mean(gc)) rng %>% group_by(strand) %>% summarise(gc = mean(gc))
Slide or tile over a Ranges object
tile_ranges(x, width) slide_ranges(x, width, step)tile_ranges(x, width) slide_ranges(x, width, step)
x |
a Ranges object |
width |
the maximum width of each window/tile (integer vector of length 1) |
step |
the distance between start position of each sliding window (integer vector of length 1) |
The tile_ranges() function paritions a Ranges object x by the given the
width over all ranges in x, truncated by the sequence end.
The slide_ranges() function makes sliding windows within each range of x
of size width and sliding by step.
Both slide_ranges() and tile_ranges() return a new Ranges object
with a metadata column called "partition" which contains the index of the
input range x that a parition belongs to.
a Ranges object
GenomicRanges::tile()
gr <- data.frame(seqnames = c("chr1", rep("chr2", 3), rep("chr1", 2), rep("chr3", 4)), start = 1:10, end = 11, strand = c("-", rep("+", 2), rep("*", 2), rep("+", 3), rep("-", 2))) %>% as_granges() %>% set_genome_info(seqlengths = c(11,12,13)) # partition ranges into subranges of width 2, odd width ranges # will have one subrange of width 1 tile_ranges(gr, width = 2) # make sliding windows of width 3, moving window with step size of 2 slide_ranges(gr, width = 3, step = 2)gr <- data.frame(seqnames = c("chr1", rep("chr2", 3), rep("chr1", 2), rep("chr3", 4)), start = 1:10, end = 11, strand = c("-", rep("+", 2), rep("*", 2), rep("+", 3), rep("-", 2))) %>% as_granges() %>% set_genome_info(seqlengths = c(11,12,13)) # partition ranges into subranges of width 2, odd width ranges # will have one subrange of width 1 tile_ranges(gr, width = 2) # make sliding windows of width 3, moving window with step size of 2 slide_ranges(gr, width = 3, step = 2)
This is a lightweight wrapper to the export family of functions defined in rtracklayer.
write_bed(x, file, index = FALSE) write_bed_graph(x, file, index = FALSE) write_narrowpeaks(x, file)write_bed(x, file, index = FALSE) write_bed_graph(x, file, index = FALSE) write_narrowpeaks(x, file)
x |
A GRanges object |
file |
File name, URL or connection specifying a file to write x to.
Compressed files with extensions such as '.gz' are handled
automatically. If you want to index the file with tabix use the
|
index |
Compress and index the output file with bgzf and tabix (default = FALSE). Note that tabix indexing will sort the data by chromosome and start. |
The write functions return a BED(Graph)File invisibly
rtracklayer::BEDFile()
## Not run: test_path <- system.file("tests", package = "rtracklayer") bed_file <- file.path(test_path, "test.bed") gr <- read_bed(bed_file) bed_file_out <- file.path(tempdir(), "new.bed") write_bed(gr, bed_file_out) read_bed(bed_file_out) #' bedgraph bg_file <- file.path(test_path, "test.bedGraph") gr <- read_bed_graph(bg_file) bg_file_out <- file.path(tempdir(), "new.bg") write_bed(gr, bg_file_out) read_bed(bg_file_out) # narrowpeaks np_file <- system.file("extdata", "demo.narrowPeak.gz",package="rtracklayer") gr <- read_narrowpeaks(np_file, genome_info = "hg19") np_file_out <- file.path(tempdir(), "new.bg") write_narrowpeaks(gr, np_file_out) read_narrowpeaks(np_file_out) ## End(Not run)## Not run: test_path <- system.file("tests", package = "rtracklayer") bed_file <- file.path(test_path, "test.bed") gr <- read_bed(bed_file) bed_file_out <- file.path(tempdir(), "new.bed") write_bed(gr, bed_file_out) read_bed(bed_file_out) #' bedgraph bg_file <- file.path(test_path, "test.bedGraph") gr <- read_bed_graph(bg_file) bg_file_out <- file.path(tempdir(), "new.bg") write_bed(gr, bg_file_out) read_bed(bg_file_out) # narrowpeaks np_file <- system.file("extdata", "demo.narrowPeak.gz",package="rtracklayer") gr <- read_narrowpeaks(np_file, genome_info = "hg19") np_file_out <- file.path(tempdir(), "new.bg") write_narrowpeaks(gr, np_file_out) read_narrowpeaks(np_file_out) ## End(Not run)
This is a lightweight wrapper to the export family of functions defined in rtracklayer.
write_bigwig(x, file)write_bigwig(x, file)
x |
A GRanges object |
file |
File name, URL or connection specifying a file to write x to. Compressed files with extensions such as '.gz' are handled automatically. |
The write functions return a BigWigFile invisibly
rtracklayer::BigWigFile()
## Not run: if (.Platform$OS.type != "windows") { test_path <- system.file("tests", package = "rtracklayer") bw_file <- file.path(test_path, "test.bw") gr <- read_bigwig(bw_file) gr bw_out <- file.path(tempdir(), "test_out.bw") write_bigwig(gr ,bw_out) read_bigwig(bw_out) } ## End(Not run)## Not run: if (.Platform$OS.type != "windows") { test_path <- system.file("tests", package = "rtracklayer") bw_file <- file.path(test_path, "test.bw") gr <- read_bigwig(bw_file) gr bw_out <- file.path(tempdir(), "test_out.bw") write_bigwig(gr ,bw_out) read_bigwig(bw_out) } ## End(Not run)
This is a lightweight wrapper to the export family of functions defined in rtracklayer.
write_gff(x, file, index = FALSE) write_gff1(x, file, index = FALSE) write_gff2(x, file, index = FALSE) write_gff3(x, file, index = FALSE)write_gff(x, file, index = FALSE) write_gff1(x, file, index = FALSE) write_gff2(x, file, index = FALSE) write_gff3(x, file, index = FALSE)
x |
A GRanges object |
file |
Path or connection to write to |
index |
If TRUE the output file will be compressed and indexed using bgzf and tabix. |
The write function returns a GFFFile object invisibly
rtracklayer::GFFFile()
## Not run: test_path <- system.file("tests", package = "rtracklayer") test_gff3 <- file.path(test_path, "genes.gff3") gr <- read_gff3(test_gff3) out_gff3 <- file.path(tempdir(), "test.gff3") write_gff3(gr, out_gff3) read_gff3(out_gff3) ## End(Not run)## Not run: test_path <- system.file("tests", package = "rtracklayer") test_gff3 <- file.path(test_path, "genes.gff3") gr <- read_gff3(test_gff3) out_gff3 <- file.path(tempdir(), "test.gff3") write_gff3(gr, out_gff3) read_gff3(out_gff3) ## End(Not run)
Write a WIG file
write_wig(x, file)write_wig(x, file)
x |
A GRanges object |
file |
File name, URL or connection specifying a file to write x to. Compressed files with extensions such as '.gz' are handled automatically. |
The write function returns a WIGFile invisibly.
rtracklayer::WIGFile()