Computes the absolute distance between the midpoint of each x interval and
the midpoints of each closest y interval.
Value
ivl_df with .absdist and .absdist_scaled columns.
Details
Absolute distances are scaled by the inter-reference gap for the
chromosome as follows. For Q query points and R reference
points on a chromosome, scale the distance for each query point i to
the closest reference point by the inter-reference gap for each chromosome.
If an x interval has no matching y chromosome,
.absdist is NA.
$$d_i(x,y) = min_k(|q_i - r_k|)\frac{R}{Length\ of\ chromosome}$$
Both absolute and scaled distances are reported as .absdist and
.absdist_scaled.
Interval statistics can be used in combination with
dplyr::group_by() and dplyr::do() to calculate
statistics for subsets of data. See vignette('interval-stats') for
examples.
See also
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002529
Other interval statistics:
bed_fisher(),
bed_jaccard(),
bed_projection(),
bed_reldist()
Examples
genome <- read_genome(valr_example("hg19.chrom.sizes.gz"))
x <- bed_random(genome, seed = 1010486)
y <- bed_random(genome, seed = 9203911)
bed_absdist(x, y, genome)
#> # A tibble: 1,000,000 × 5
#> chrom start end .absdist .absdist_scaled
#> <chr> <int> <int> <dbl> <dbl>
#> 1 chr1 5184 6184 1392 0.448
#> 2 chr1 7663 8663 1087 0.350
#> 3 chr1 9858 10858 1526 0.491
#> 4 chr1 13805 14805 2421 0.779
#> 5 chr1 14081 15081 2697 0.868
#> 6 chr1 16398 17398 1700 0.547
#> 7 chr1 17486 18486 612 0.197
#> 8 chr1 22063 23063 466 0.150
#> 9 chr1 22494 23494 897 0.289
#> 10 chr1 29351 30351 1143 0.368
#> # ℹ 999,990 more rows