Calculate number of singletons in R - r

Question

I have some RNA-seq data and I need to calculate the number of singletons. We define a singleton as a read that does not have any other reads mapped close by (in a distance of 100 bases to either side).
I have a dataframe with the begin coordinate and the end coordinate of each read. I'm using R to do this.
I have written this code for the moment, but the apply is not correct and therefore is giving an error.
begin_end <- data.frame(begin_coordinate, final_coordinate)
apply(begin_end, 1, function(x) x[,1]-(x-1)[,2])
The first lines of the dataframe are:
> head(begin_end)
begin final
1 60507 60551
2 60790 60840
3 62004 62051
4 62819 62868
5 65141 65187
The first one seems to be a singleton because the next reads starts more than 100 bases after it ends and so are the rest in the first lines of the dataset. But the dataframe is long and I hope not all the reads are singletons.

Answer 1

Here's the same thing #jeremycg did with dplyr's lag and lead, but in data.table:
library(data.table)
setDT(begin_end)
begin_end[{
d = begin - shift(final, type="lag")
pmin(d, shift(d, type="lead"), na.rm=TRUE) > 100
}]
Comment. The basic data.table syntax is DT[i,j]. i is for filtering the input while j is for modifying the output.
We used i above, but to examine how it works, we can toss the relevant vectors into j:
begin_end[,{
d = begin - shift(final, type="lag")
d_lead = shift(d, type="lead")
my_pmin = pmin(d, d_lead, na.rm=TRUE)
c(.SD, list(d = d, d_lead = d_lead, my_pmin = my_pmin))
}]
# begin final d d_lead my_pmin
# 1: 60507 60551 NA 239 239
# 2: 60790 60840 239 1164 239
# 3: 62004 62051 1164 768 768
# 4: 62819 62868 768 2273 768
# 5: 65141 65187 2273 NA 2273
.SD is a list of column vectors already in the table, short for Subset of Data.

Answer 2

You seem to be trying to get the previous end value out of the apply using (x-1). Unfortunately, you can't do this inside the apply family.
Luckily, there is a function called lag (there are several, so i'll use the one from dplyr). This lets us lag a column by a given number of entries:
begin_end$space <- begin_end$begin - dplyr::lag(begin_end$final)
here's the output:
begin final space
1 60507 60551 NA
2 60790 60840 239
3 62004 62051 1164
4 62819 62868 768
5 65141 65187 2273
Then you can try:
begin_end$issingle <- begin_end$space >= 100

Answer 3

Using Bioconductor's GenomicRanges I think the idea would be to create a GRanges() (maybe from reading the data using GenomicAlignments::readGAlignments() or makeGRangesFromDataFrame()) from your reads, extend them in each direction using resize(), then use findOverlaps() to identify singletons as the reads that only overlap themselves. Roughly
library(GenomicRanges)
gr = GRanges(seqnames="chr1",
IRanges(start=c(1000, 1150, 1500), width=100))
gr100 = resize(gr, width(gr) + 200, fix="center")
hits = findOverlaps(gr100)
gr100[tabulate(queryHits(hits), queryLength(hits)) == 1]
leading to
> gr100[tabulate(queryHits(hits), queryLength(hits)) == 1]
GRanges object with 1 range and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr1 [1400, 1699] *
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengths
This will be fast for millions of records.