IRanges-class {IRanges} | R Documentation

## IRanges and NormalIRanges objects

### Description

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.

### Constructor

See `?`IRanges-constructor``.

### Coercion

  `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.

### Concatenation

  `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.

### Methods for NormalIRanges objects

  `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`.

### Author(s)

Hervé Pagès

### See Also

- 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.

### Examples

    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)

[Package IRanges version 2.24.1 Index]