Description Constructor Coercion Concatenation Methods for NormalIRanges objects Author(s) See Also Examples
The IRanges class is a simple implementation of the IntegerRanges container where 2 integer vectors of the same length are used to store the start and width values. See the IntegerRanges virtual class for a formal definition of IntegerRanges objects and for their methods (all of them should work for IRanges objects).
Some subclasses of the IRanges class are: NormalIRanges, Views, etc...
A NormalIRanges object is just an IRanges object that is guaranteed to be "normal". See the Normality section in the man page for IntegerRanges objects for the definition and properties of "normal" IntegerRanges objects.
See ?`IRanges-constructor`
.
ranges(x, use.names=FALSE, use.mcols=FALSE)
: Squeeze the ranges
out of IntegerRanges object x
and return them in an IRanges
object parallel to x
(i.e. same length as x
).
as(from, "IRanges")
: Creates an IRanges instance from an
IntegerRanges derivative, or from a logical or integer vector.
When from
is a logical vector, the resulting IRanges object
contains the indices for the runs of TRUE
values.
When from
is an integer vector, the elements are either
singletons or "increase by 1" sequences.
as(from, "NormalIRanges")
: Creates a NormalIRanges instance
from a logical or integer vector. When from
is an integer vector,
the elements must be strictly increasing.
c(x, ..., ignore.mcols=FALSE)
:
Concatenate IRanges object x
and the IRanges objects in
...
together.
See ?c
in the S4Vectors package for
more information about concatenating Vector derivatives.
max(x)
:
The maximum value in the finite set of integers represented by x
.
min(x)
:
The minimum value in the finite set of integers represented by x
.
Hervé Pagès
The GRanges class in the GenomicRanges package for storing a set of genomic ranges.
The IPos class for representing a set of integer positions (i.e. integer ranges of width 1).
IPosRanges-comparison for comparing and ordering integer ranges and/or positions.
IRanges-utils for some utility functions for creating or modifying IRanges objects.
findOverlaps-methods for finding overlapping integer ranges and/or positions.
intra-range-methods and inter-range-methods for intra range and inter range transformations.
coverage-methods for computing the coverage of a set of ranges and/or positions.
setops-methods for set operations on IRanges objects.
nearest-methods for finding the nearest integer range/position neighbor.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | showClass("IRanges") # shows the known subclasses
## ---------------------------------------------------------------------
## A. MANIPULATING IRanges OBJECTS
## ---------------------------------------------------------------------
## All the methods defined for IntegerRanges objects work on IRanges
## objects.
## See ?IntegerRanges for some examples.
## Also see ?`IRanges-utils` and ?`setops-methods` for additional
## operations on IRanges objects.
## Concatenating IRanges objects
ir1 <- IRanges(c(1, 10, 20), width=5)
mcols(ir1) <- DataFrame(score=runif(3))
ir2 <- IRanges(c(101, 110, 120), width=10)
mcols(ir2) <- DataFrame(score=runif(3))
ir3 <- IRanges(c(1001, 1010, 1020), width=20)
mcols(ir3) <- DataFrame(value=runif(3))
some.iranges <- c(ir1, ir2)
## all.iranges <- c(ir1, ir2, ir3) ## This will raise an error
all.iranges <- c(ir1, ir2, ir3, ignore.mcols=TRUE)
stopifnot(is.null(mcols(all.iranges)))
## ---------------------------------------------------------------------
## B. A NOTE ABOUT PERFORMANCE
## ---------------------------------------------------------------------
## Using an IRanges object for storing a big set of ranges is more
## efficient than using a standard R data frame:
N <- 2000000L # nb of ranges
W <- 180L # width of each range
start <- 1L
end <- 50000000L
set.seed(777)
range_starts <- sort(sample(end-W+1L, N))
range_widths <- rep.int(W, N)
## Instantiation is faster
system.time(x <- IRanges(start=range_starts, width=range_widths))
system.time(y <- data.frame(start=range_starts, width=range_widths))
## Subsetting is faster
system.time(x16 <- x[c(TRUE, rep.int(FALSE, 15))])
system.time(y16 <- y[c(TRUE, rep.int(FALSE, 15)), ])
## Internal representation is more compact
object.size(x16)
object.size(y16)
|
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Class "IRanges" [package "IRanges"]
Slots:
Name: start width NAMES elementType
Class: integer integer character_OR_NULL character
Name: elementMetadata metadata
Class: DataTable_OR_NULL list
Extends:
Class "IPosRanges", directly
Class "IntegerRanges", by class "IPosRanges", distance 2
Class "Ranges", by class "IPosRanges", distance 3
Class "IntegerRanges_OR_missing", by class "IntegerRanges", distance 3
Class "List", by class "IPosRanges", distance 4
Class "Vector", by class "IPosRanges", distance 5
Class "list_OR_List", by class "IPosRanges", distance 5
Class "Annotated", by class "IPosRanges", distance 6
Known Subclasses: "NormalIRanges", "GroupingIRanges"
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