Identify closest intervals.

Identify closest intervals.

Usage

bed_closest(x, y, overlap = TRUE, suffix = c(".x", ".y"))

Arguments

x

ivl_df

y

ivl_df

overlap

report overlapping intervals

suffix

colname suffixes in output

Value

ivl_df with additional columns:

• .overlap amount of overlap with overlapping interval. Non-overlapping or adjacent intervals have an overlap of 0. .overlap will not be included in the output if overlap = FALSE.

• .dist distance to closest interval. Negative distances denote upstream intervals. Book-ended intervals have a distance of 1.

Details

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().

Note

For each interval in x bed_closest() returns overlapping intervals from y and the closest non-intersecting y interval. Setting overlap = FALSE will report the closest non-intersecting y intervals, ignoring any overlapping y intervals.

See also

https://bedtools.readthedocs.io/en/latest/content/tools/closest.html

Other multiple set operations: bed_coverage(), bed_intersect(), bed_map(), bed_subtract(), bed_window()

Examples

x <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1", 100,    125
)

y <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1", 25,     50,
  "chr1", 140,    175
)

bed_glyph(bed_closest(x, y))


x <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1", 500,    600,
  "chr2", 5000,   6000
)

y <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1", 100,    200,
  "chr1", 150,    200,
  "chr1", 550,    580,
  "chr2", 7000,   8500
)

bed_closest(x, y)
#> # A tibble: 4 × 7
#>   chrom start.x end.x start.y end.y .overlap .dist
#>   <chr>   <dbl> <dbl>   <dbl> <dbl>    <int> <int>
#> 1 chr1      500   600     550   580       30     0
#> 2 chr1      500   600     100   200        0  -301
#> 3 chr1      500   600     150   200        0  -301
#> 4 chr2     5000  6000    7000  8500        0  1001

bed_closest(x, y, overlap = FALSE)
#> # A tibble: 3 × 6
#>   chrom start.x end.x start.y end.y .dist
#>   <chr>   <dbl> <dbl>   <dbl> <dbl> <int>
#> 1 chr1      500   600     100   200  -301
#> 2 chr1      500   600     150   200  -301
#> 3 chr2     5000  6000    7000  8500  1001

# Report distance based on strand
x <- tibble::tribble(
  ~chrom, ~start, ~end, ~name, ~score, ~strand,
  "chr1", 10, 20, "a", 1, "-"
)

y <- tibble::tribble(
  ~chrom, ~start, ~end, ~name, ~score, ~strand,
  "chr1", 8, 9, "b", 1, "+",
  "chr1", 21, 22, "b", 1, "-"
)

res <- bed_closest(x, y)

# convert distance based on strand
res$.dist_strand <- ifelse(res$strand.x == "+", res$.dist, -(res$.dist))
res
#> # A tibble: 2 × 14
#>   chrom start.x end.x name.x score.x strand.x start.y end.y name.y score.y
#>   <chr>   <dbl> <dbl> <chr>    <dbl> <chr>      <dbl> <dbl> <chr>    <dbl>
#> 1 chr1       10    20 a            1 -             21    22 b            1
#> 2 chr1       10    20 a            1 -              8     9 b            1
#> # ℹ 4 more variables: strand.y <chr>, .overlap <int>, .dist <int>,
#> #   .dist_strand <int>

# report absolute distances
res$.abs_dist <- abs(res$.dist)
res
#> # A tibble: 2 × 15
#>   chrom start.x end.x name.x score.x strand.x start.y end.y name.y score.y
#>   <chr>   <dbl> <dbl> <chr>    <dbl> <chr>      <dbl> <dbl> <chr>    <dbl>
#> 1 chr1       10    20 a            1 -             21    22 b            1
#> 2 chr1       10    20 a            1 -              8     9 b            1
#> # ℹ 5 more variables: strand.y <chr>, .overlap <int>, .dist <int>,
#> #   .dist_strand <int>, .abs_dist <int>