[Bioc-devel] GenomicFiles: chunking

[Kasper Daniel Hansen]

I am testing GenomicFiles.

My use case: I have 231k ranges of average width 1.9kb and total width 442
MB.  I also have 38 BigWig files.  I want to compute the average coverage
of the 38 BigWig files inside each range.  This is similar to wanting to
get coverage of - say - all promoters in the human genome.

My data is residing on a file server which is connected to the compute node
through ethernet, so basically I have very slow file access.  Also. the
BigWig files are (perhaps unusually) big: ~2GB / piece.

Below I have two approaches, one using basically straightforward code and
the other using GenomicFiles (examples are 10 / 100 ranges on 5 files).
Basically GenomicFiles is 10-20x slower than the straightforward approach.
This is most likely because reduceByRange/reduceByFile processes each
(range, file) separately.

It seems naturally (to me) to allow some chunking of the mapping of the
ranges.  My naive approach is fast (I assume) because I read multiple
ranges through one import() command.  I know I have random access to the
BigWig files, but there must still be some overhead of seeking and perhaps
more importantly instantiating/moving stuff back and forth to R.  So
basically I would like to be able to write a MAP function which takes
  ranges, file
instead of just
  range, file
And then chunk over say 1,000s of ranges.  I could then have an argument to
reduceByXX called something like rangeSize, which is kind of yieldSize.

Perhaps this is what is intended for the reduceByYield on BigWig files?

In a way, this is what is done in the vignette with the coverage(BAMFILE)
example where tileGenome is essentially constructed by the user to chunk
the coverage computation.

I think the example of a set of regions I am querying on the files, will be
an extremely common usecase going forward. The raw data for all the regions
together is "too big" but I do a computation on each region to reduce the
size.  In this situation, all the focus is on the MAP step.  I see the need
for REDUCE in the case of the t-test example in the vignette, where the
return object is a single "thing" for each base.  But in general, I think
we will use these file structures a lot to construct things without REDUCE
(across neither files nor ranges).

Also, something completely different, it seems like it would be convenient
for stuff like BigWigFileViews to not have to actually parse the file in
the MAP step.  Somehow I would envision some kind of reading function,
stored inside the object, which just returns an Rle when I ask for a
(range, file).  Perhaps this is better left for later.

Best,
Kasper

Examples

approach1 <- function(ranges, files) {
    ## By hand
    all.Rle <- lapply(files, function(file) {
        rle <- import(file, as = "Rle", which = ranges, format =
"bw")[ranges]
        rle
    })
    print(object.size(all.Rle), units = "auto")
    mat <- do.call(cbind, lapply(all.Rle, function(xx) {
        sapply(xx, mean)
    }))
    invisible(mat)
}

system.time({
mat1 <- approach1(all.grs[1:10], all.files[1:5])
})

160.9 Kb
    user  system elapsed
   1.109   0.001   1.113

system.time({
mat1 <- approach1(all.grs[1:100], all.files[1:5])
}) # less than 4x slower than previous call

3 Mb
    user  system elapsed
   4.101   0.019   4.291


approach2 <- function(ranges, files) {
    gf <- GenomicFiles(rowData = ranges, files = files)
    MAPPER <- function(range, file, ....) {
        library(rtracklayer)
        rle <- import(file, which = range, as = "Rle", format = "bw")[range]
        mean(rle)
    }
    sExp <- reduceByRange(gf, MAP = MAPPER, summarize = TRUE, BPPARAM =
SerialParam())
    sExp
}

system.time({
mat2 <- approach2(all.grs[1:10], all.files[1:5])
})  # 8-9x slower than approach1

   user  system elapsed
   9.349   0.280   9.581

system.time({
mat2 <- approach2(all.grs[1:100], all.files[1:5])
})  # 9x slower than previous call, 20x slow than approach1 on same input

   user  system elapsed
  89.310   0.627  91.044

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