Apply functions like min() and count() to intersecting intervals. bed_map() uses bed_intersect() to identify intersecting intervals, so output columns will be suffixed with .x and .y. Expressions that refer to input columns from x and y columns must take these suffixes into account.

bed_map(x, y, ..., min_overlap = 1)

concat(.data, sep = ",")

values_unique(.data, sep = ",")

values(.data, sep = ",")

Arguments

x

tbl_interval()

y

tbl_interval()

...

name-value pairs specifying column names and expressions to apply

min_overlap

minimum overlap for intervals.

.data

data

sep

separator character

Value

tbl_interval()

Details

Book-ended intervals can be included by setting min_overlap = 0. Non-intersecting intervals from x are included in the result with NA values

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

Examples

x <- trbl_interval( ~chrom, ~start, ~end, 'chr1', 100, 250, 'chr2', 250, 500 ) y <- trbl_interval( ~chrom, ~start, ~end, ~value, 'chr1', 100, 250, 10, 'chr1', 150, 250, 20, 'chr2', 250, 500, 500 ) bed_glyph(bed_map(x, y, value = sum(value)), label = 'value')
# summary examples bed_map(x, y, .sum = sum(value))
#> # A tibble: 2 x 4 #> chrom start end .sum #> <chr> <dbl> <dbl> <dbl> #> 1 chr1 100 250 30 #> 2 chr2 250 500 500
bed_map(x, y, .min = min(value), .max = max(value))
#> # A tibble: 2 x 5 #> chrom start end .min .max #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 chr1 100 250 10 20 #> 2 chr2 250 500 500 500
# identify non-intersecting intervals to include in the result res <- bed_map(x, y, .sum = sum(value)) x_not <- bed_intersect(x, y, invert = TRUE) dplyr::bind_rows(res, x_not)
#> # A tibble: 2 x 4 #> chrom start end .sum #> <chr> <dbl> <dbl> <dbl> #> 1 chr1 100 250 30 #> 2 chr2 250 500 500
# create a list-column bed_map(x, y, .values = list(value))
#> # A tibble: 2 x 4 #> chrom start end .values #> <chr> <dbl> <dbl> <list> #> 1 chr1 100 250 <dbl [2]> #> 2 chr2 250 500 <dbl [1]>
# use `nth` family from dplyr bed_map(x, y, .first = dplyr::first(value))
#> # A tibble: 2 x 4 #> chrom start end .first #> <chr> <dbl> <dbl> <dbl> #> 1 chr1 100 250 10 #> 2 chr2 250 500 500
bed_map(x, y, .absmax = abs(max(value)))
#> # A tibble: 2 x 4 #> chrom start end .absmax #> <chr> <dbl> <dbl> <dbl> #> 1 chr1 100 250 20 #> 2 chr2 250 500 500
bed_map(x, y, .count = length(value))
#> # A tibble: 2 x 4 #> chrom start end .count #> <chr> <dbl> <dbl> <int> #> 1 chr1 100 250 2 #> 2 chr2 250 500 1
bed_map(x, y, .vals = values(value))
#> # A tibble: 2 x 4 #> chrom start end .vals #> <chr> <dbl> <dbl> <chr> #> 1 chr1 100 250 10,20 #> 2 chr2 250 500 500
# count defaults are NA not 0; differs from bedtools2 ... bed_map(x, y, .counts = n())
#> # A tibble: 2 x 4 #> chrom start end .counts #> <chr> <dbl> <dbl> <int> #> 1 chr1 100 250 2 #> 2 chr2 250 500 1
# ... but NA counts can be coverted to 0's dplyr::mutate(bed_map(x, y, .counts = n()), .counts = ifelse(is.na(.counts), 0, .counts))
#> # A tibble: 2 x 4 #> chrom start end .counts #> <chr> <dbl> <dbl> <int> #> 1 chr1 100 250 2 #> 2 chr2 250 500 1