Skip to contents

Partition intervals into elemental intervals

Source: R/bed_partition.r
bed_partition.Rd

Convert a set of intervals into elemental intervals that contain each start and end position in the set.

Usage

bed_partition(x, ...)

Arguments

x

ivl_df

...

name-value pairs specifying column names and expressions to apply

Value

ivl_df()

Details

Summary operations, such as min() or max() can be performed on elemental intervals by specifying name-value pairs.

This function is useful for calculating summaries across overlapping intervals without merging the intervals.

input tbls are grouped by chrom by default, and additional groups can be added using dplyr::group_by(). For example, grouping by strand will constrain analyses to the same strand. To compare opposing strands across two tbls, strands on the y tbl can first be inverted using flip_strands().

See also

https://bedops.readthedocs.io/en/latest/content/reference/set-operations/bedops.html#partition-p-partition

Other single set operations: bed_cluster(), bed_complement(), bed_flank(), bed_genomecov(), bed_merge(), bed_shift(), bed_slop()

Examples

x <- tibble::tribble(
  ~chrom, ~start, ~end, ~value, ~strand,
  "chr1", 100, 500, 10, "+",
  "chr1", 200, 400, 20, "-",
  "chr1", 300, 550, 30, "+",
  "chr1", 550, 575, 2, "+",
  "chr1", 800, 900, 5, "+"
)


bed_glyph(bed_partition(x))

bed_glyph(bed_partition(x, value = sum(value)), label = "value")


bed_partition(x)
#> # A tibble: 7 × 3
#>   chrom start   end
#>   <chr> <int> <int>
#> 1 chr1    100   200
#> 2 chr1    200   300
#> 3 chr1    300   400
#> 4 chr1    400   500
#> 5 chr1    500   550
#> 6 chr1    550   575
#> 7 chr1    800   900

# compute summary over each elemental interval
bed_partition(x, value = sum(value))
#> # A tibble: 7 × 4
#>   chrom start   end value
#>   <chr> <int> <int> <dbl>
#> 1 chr1    100   200    10
#> 2 chr1    200   300    30
#> 3 chr1    300   400    60
#> 4 chr1    400   500    40
#> 5 chr1    500   550    30
#> 6 chr1    550   575     2
#> 7 chr1    800   900     5

# partition and compute summaries based on group
x <- dplyr::group_by(x, strand)
bed_partition(x, value = sum(value))
#> # A tibble: 6 × 5
#>   chrom start   end strand value
#>   <chr> <int> <int> <chr>  <dbl>
#> 1 chr1    100   300 +         10
#> 2 chr1    200   400 -         20
#> 3 chr1    300   500 +         40
#> 4 chr1    500   550 +         30
#> 5 chr1    550   575 +          2
#> 6 chr1    800   900 +          5

# combine values across multiple tibbles
y <- tibble::tribble(
  ~chrom, ~start, ~end, ~value, ~strand,
  "chr1", 10, 500, 100, "+",
  "chr1", 250, 420, 200, "-",
  "chr1", 350, 550, 300, "+",
  "chr1", 550, 555, 20, "+",
  "chr1", 800, 900, 50, "+"
)

x <- dplyr::bind_rows(x, y)
bed_partition(x, value = sum(value))
#> # A tibble: 11 × 5
#>    chrom start   end strand value
#>    <chr> <int> <int> <chr>  <dbl>
#>  1 chr1     10   100 +        100
#>  2 chr1    100   300 +        110
#>  3 chr1    200   250 -         20
#>  4 chr1    250   400 -        220
#>  5 chr1    300   350 +        140
#>  6 chr1    350   500 +        440
#>  7 chr1    400   420 -        200
#>  8 chr1    500   550 +        330
#>  9 chr1    550   555 +         22
#> 10 chr1    555   575 +          2
#> 11 chr1    800   900 +         55