Count occurrences around a genomic region given in a data frame - r

Question

I need to count mutations in the genome that occur at certain spots or rather ranges. The mutations have a genomic position (chromosome and basepair, e.g. Chr1, 10658324). The range or spot, respectively, is defined as 10000 basepairs up- and downstream (+-) of a given position in the genome. Both, positions of mutations and position of "spots" are stored in data frames.
Example:
set.seed(1)
Chr <- 1
Pos <- as.integer(runif(5000 , 0, 1e8))
mutations <- data.frame(Pos, Chr)
Chr <- 1
Pos <- as.integer(runif(50 , 0, 1e8))
spots <- data.frame(Pos, Chr)
So the question I am asking is: How many mutations are present +-10k basepairs around the positions given in "spots". (e.g. if the spot is 100k, the range would be 90k-110k)
The real data would of course contain all 24 chromosomes, but for the sake of simplicity we can focus on one chromosome for now.
The final data should contain the "spot" and the number of mutations in it's vicinity, ideally in a data frame or matrix.
Many thanks in advance for any suggestions or help!
Here's a first attempt, but I am pretty shure there is a way more elegant way of doing it.
w <- 10000 #setting range to 10k basepairs
loop <- spots$Pos #creating vector of positions to loop through
out <- data.frame(0,0)
colnames(out) <- c("Pos", "Count")
for (l in loop) {
temp <- nrow(filter(mutations, Pos>=l-w, Pos<=l+w))
temp2 <- cbind(l,temp)
colnames(temp2) <- c("Pos", "Count")
out <- rbind(out, temp2)
}
out <- out[-1,]

Answer 1

Using data.table foverlaps, then aggregate:
library(data.table)
#set the flank
myFlank <- 100000
#convert to ranges with flank
spotsRange <- data.table(
chr = spots$Chr,
start = spots$Pos - myFlank,
end = spots$Pos + myFlank,
posSpot = spots$Pos,
key = c("chr", "start", "end"))
#convert to ranges start end same as pos
mutationsRange <- data.table(
chr = mutations$Chr,
start = mutations$Pos,
end = mutations$Pos,
key = c("chr", "start", "end"))
#merge by overlap
res <- foverlaps(mutationsRange, spotsRange, nomatch = 0)
#count mutations
resCnt <- data.frame(table(res$posSpot))
colnames(resCnt) <- c("Pos", "MutationCount")
merge(spots, resCnt, by = "Pos")
# Pos Chr MutationCount
# 1 3439618 1 10
# 2 3549952 1 15
# 3 4375314 1 11
# 4 7337370 1 13
# ...

Answer 2

I'm not familiar with bed manipulations in R, so I'm going propose an answer with bedtools and someone here can try to convert to GRanges or other R bioinformatics library.
Essentially, you have two bed files, one with your spots and other with your mutations (I'm assuming a 1bp coordinate for each in the latter). In this case, you'd use closestBed to get the closest spot and the distance in bp of each mutation and then filter those that are 10KB from the spots. The code in a UNIX environment would look something like this:
# Assuming 4-column file structure (chr start end name)
closestBed -d -a mutations.bed -b spots.bed | awk '$9 <= 10000 {print}'
Where column 9 ($9) will be the distance in bp from the closest spot. Depending on how more specific you want to be, you can check the manual page at http://bedtools.readthedocs.io/en/latest/content/tools/closest.html. I'm pretty sure there's at least one bedtools-like package in R. If the functionality is similar, you can apply this exact same solution.
Hope that helps!