I have two large data sets that look like this.
library(tidyverse)
dat1 <- tibble(chrom=c(rep(c("Chr1","Chr2"),each=5)),
start=c(9885,11944, 13271,15104,19059,25793,97514,104718,118862,120950),
end=c(11008,17644,20164,23807,25264,106001,119205, 121576,124981,138514)
)
head(dat1,n=4)
#> # A tibble: 10 × 3
#> chrom start end
#> <chr> <dbl> <dbl>
#> 1 Chr1 9885 11008
#> 2 Chr1 11944 17644
#> 3 Chr1 13271 20164
#> 4 Chr1 15104 23807
dat2 <- tibble(chrom=c(rep(c("Chr1","Chr2"),each=5)),
start=c(9885,11944, 13271,15104,19059,25793,97514,104718,118862,120950),
end=c(10203,12546,13669,15638,19283,26703,97773, 105102,119388,121331)
)
head(dat2, n=4)
#> # A tibble: 10 × 3
#> chrom start end
#> <chr> <dbl> <dbl>
#> 1 Chr1 9885 10203
#> 2 Chr1 11944 12546
#> 3 Chr1 13271 13669
#> 4 Chr1 15104 15638
Created on 2022-12-05 with reprex v2.0.2
I want to group my data based on the chrom and find which ranges [start-end], from the Chr1, from dat2 overlap with ranges [start-end] of Chr1 from dat1.
I have found a lovely package to work it around, but I feel I need to break the data sets into data frames of different chromosomes to compare them.
library(plyranges)
dat1 <- dat1 %>%
as_iranges()
dat2 <- dat2 %>%
as_iranges()
dat1 %>%
mutate(n_olap = count_overlaps(., dat2),
n_olap_within = count_overlaps_within(., dat2))
IRanges object with 10 ranges and 3 metadata columns:
start end width | chrom n_olap n_olap_within
<integer> <integer> <integer> | <character> <integer> <integer>
[1] 9885 11008 1124 | Chr1 1 0
[2] 11944 17644 5701 | Chr1 3 0
[3] 13271 20164 6894 | Chr1 3 0
[4] 15104 23807 8704 | Chr1 2 0
[5] 19059 25264 6206 | Chr1 1 0
[6] 25793 106001 80209 | Chr2 3 0
[7] 97514 119205 21692 | Chr2 3 0
[8] 104718 121576 16859 | Chr2 3 0
[9] 118862 124981 6120 | Chr2 2 0
[10] 120950 138514 17565 | Chr2 1 0
To get what I want from here, I need to filter my data and then compare it. But there should a way or dplyr trick to find a solution
dat1 <- dat1 %>%
as_iranges() %>%
filter(chrom=="Chr1")
dat2 <- dat2 %>%
as_iranges() %>%
filter(chrom=="Chr1")
dat1 %>%
mutate(n_olap = count_overlaps(., dat2),
n_olap_within = count_overlaps_within(., dat2))
Is there a way to compare only chromosomes with each other?
I'll stick with the data.table theme set forth by RicVillalba, but I think the foverlaps function is meant for things like this (especially with larger datasets).
library(data.table)
setDT(dat1)
setDT(dat2)
setkey(dat1, chrom, start, end)
setkey(dat2, chrom, start, end)
dat1[, id := .I]
foverlaps(dat1, dat2)
# chrom start end i.start i.end id
# <char> <num> <num> <num> <num> <int>
# 1: Chr1 9885 10203 9885 11008 1
# 2: Chr1 11944 12546 11944 17644 2
# 3: Chr1 13271 13669 11944 17644 2
# 4: Chr1 15104 15638 11944 17644 2
# 5: Chr1 13271 13669 13271 20164 3
# 6: Chr1 15104 15638 13271 20164 3
# 7: Chr1 19059 19283 13271 20164 3
# 8: Chr1 15104 15638 15104 23807 4
# 9: Chr1 19059 19283 15104 23807 4
# 10: Chr1 19059 19283 19059 25264 5
# ---
# 13: Chr2 104718 105102 25793 106001 6
# 14: Chr2 97514 97773 97514 119205 7
# 15: Chr2 104718 105102 97514 119205 7
# 16: Chr2 118862 119388 97514 119205 7
# 17: Chr2 104718 105102 104718 121576 8
# 18: Chr2 118862 119388 104718 121576 8
# 19: Chr2 120950 121331 104718 121576 8
# 20: Chr2 118862 119388 118862 124981 9
# 21: Chr2 120950 121331 118862 124981 9
# 22: Chr2 120950 121331 120950 138514 10
(Note that in addition to requiring keys, the order matters: the last two keys must be the "start" and "end" of the ranges to check for overlapping. I added chrom to ensure we do this by-chromosome.)
That's the start. I added the id column into dat1 so that we could efficiently return back to the original columns. If you inspect the columns closely, notice that the i.* columns are from dat1, so those are the ones we want to keep.
Extending that to do the aggregation you're hoping for,
overlaps <- foverlaps(dat1, dat2)[, .(n_olaps = .N, n_within = sum(between(i.start, start, end) & between(i.end, start, end))), by = .(id)]
overlaps
# id n_olaps n_within
# <int> <int> <int>
# 1: 1 1 0
# 2: 2 3 0
# 3: 3 3 0
# 4: 4 2 0
# 5: 5 1 0
# 6: 6 3 0
# 7: 7 3 0
# 8: 8 3 0
# 9: 9 2 0
# 10: 10 1 0
dat1 <- overlaps[dat1, on = .(id)]
dat1
# id n_olaps n_within chrom start end
# <int> <int> <int> <char> <num> <num>
# 1: 1 1 0 Chr1 9885 11008
# 2: 2 3 0 Chr1 11944 17644
# 3: 3 3 0 Chr1 13271 20164
# 4: 4 2 0 Chr1 15104 23807
# 5: 5 1 0 Chr1 19059 25264
# 6: 6 3 0 Chr2 25793 106001
# 7: 7 3 0 Chr2 97514 119205
# 8: 8 3 0 Chr2 104718 121576
# 9: 9 2 0 Chr2 118862 124981
# 10: 10 1 0 Chr2 120950 138514
I chose to create overlaps and join it back onto dat1 in case there were other columns that needed to be retained. It's certainly feasible to do this more in-place in dat1, especially helpful if your dataset is too big for a temporary copy.