bpiterate {BiocParallel} | R Documentation

## Parallel iteration over an indeterminate number of data chunks

### Description

`bpiterate` iterates over an indeterminate number of data chunks (e.g., records in a file). Each chunk is processed by parallel workers in an asynchronous fashion; as each worker finishes it receives a new chunk. Data are traversed a single time.

### Usage

    bpiterate(ITER, FUN, ..., BPPARAM=bpparam())

    ## S4 method for signature 'ANY,ANY,missing'
    bpiterate(ITER, FUN, ..., BPPARAM=bpparam())

    ## S4 method for signature 'ANY,ANY,BiocParallelParam'
    bpiterate(ITER, FUN, ..., BPPARAM=bpparam())

### Arguments

`ITER` | A function with no arguments that returns an object to process, generally a chunk of data from a file. When no objects are left (i.e., end of file) it should return NULL and continue to return NULL regardless of the number of times it is invoked after reaching the end of file. This function is run on the master.
`FUN` | A function to process the object returned by `ITER`; run on parallel workers separate from the master. When BPPARAM is a MulticoreParam, FUN is 'decorated' with additional arguments and therefore must have ... in the signature.
`BPPARAM` | An optional `BiocParallelParam` instance determining the parallel back-end to be used during evaluation, or a `list` of `BiocParallelParam` instances, to be applied in sequence for nested calls to `bpiterate`.
`...` | Arguments to other methods, specifically named arguments for `FUN`, or `REDUCE` or `init`. `REDUCE`: Optional function that combines (reduces) output from `FUN`. As each worker returns, the data are combined with the `REDUCE` function. `REDUCE` takes 2 arguments; one is the current result and the other is the output of `FUN` from a worker that just finished. `init`: Optional initial value for `REDUCE`; must be of the same type as the object returned from `FUN`. When supplied, `reduce.in.order` is set to TRUE. `reduce.in.order`: Logical. When TRUE, REDUCE is applied to the results from the workers in the same order the tasks were sent out.

### Details

Supported for `SnowParam` and `MulticoreParam`.

`bpiterate` iterates through an unknown number of data chunks, dispatching chunks to parallel workers as they become available. In contrast, other `bp*apply` functions such as `bplapply` or `bpmapply` require the number of data chunks to be specified ahead of time. This quality makes `bpiterate` useful for iterating through files of unknown length.

`ITER` serves up chunks of data until the end of the file is reached at which point it returns NULL. Note that `ITER` should continue to return NULL reguardless of the number of times it is invoked after reaching the end of the file. `FUN` is applied to each object (data chunk) returned by `ITER`.

### Value

By default, a `list` the same length as the number of chunks in `ITER()`. When `REDUCE` is used, the return is consistent with application of the reduction.

### Author(s)

Valerie Obenchain mailto:vobencha@fhcrc.org.

### See Also

- `bpvec` for parallel, vectorized calculations.

- `bplapply` for parallel, lapply-like calculations.

- `BiocParallelParam` for details of `BPPARAM`.

### Examples

    ## Not run:
    if (require(Rsamtools) && require(RNAseqData.HNRNPC.bam.chr14) &&
        require(GenomicAlignments) && require(ShortRead)) {

      ## ----------------------------------------------------------------------
      ## Iterate through a BAM file
      ## ----------------------------------------------------------------------

      ## Select a single file and set 'yieldSize' in the BamFile object.
      fl <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[[1]]
      bf <- BamFile(fl, yieldSize = 300000)

      ## bamIterator() is initialized with a BAM file and returns a function.
      ## The return function requires no arguments and iterates through the
      ## file returning data chunks the size of yieldSize.
      bamIterator <- function(bf) {
          done <- FALSE
          if (!isOpen( bf))
              open(bf)

          function() {
              if (done)
                  return(NULL)
              yld <- readGAlignments(bf)
              if (length(yld) == 0L) {
                  close(bf)
                  done <<- TRUE
                  NULL
              } else yld
          }
      }

      ## FUN counts reads in a region of interest.
      roi <- GRanges("chr14", IRanges(seq(19e6, 107e6, by = 10e6), width = 10e6))
      counter <- function(reads, roi, ...) {
          countOverlaps(query = roi, subject = reads)
      }

      ## Initialize the iterator.
      ITER <- bamIterator(bf)

      ## The number of chunks returned by ITER() determines the result length.
      bpparam <- MulticoreParam(workers = 3)
      bpiterate(ITER, counter, roi = roi, BPPARAM = bpparam)

      ## Re-initialize the iterator and combine on the fly with REDUCE:
      ITER <- bamIterator(bf)
      bpparam <- MulticoreParam(workers = 3)
      bpiterate(ITER, counter, REDUCE = sum, roi = roi, BPPARAM = bpparam)

      ## ----------------------------------------------------------------------
      ## Iterate through a FASTA file
      ## ----------------------------------------------------------------------

      ## Set data chunk size with 'n' in the FastqStreamer object.
      sp <- SolexaPath(system.file('extdata', package = 'ShortRead'))
      fl <- file.path(analysisPath(sp), "s_1_sequence.txt")

      ## Create an iterator that returns data chunks the size of 'n'.
      fastqIterator <- function(fqs) {
          done <- FALSE
          if (!isOpen(fqs))
              open(fqs)

          function() {
              if (done)
                  return(NULL)
              yld <- yield(fqs)
              if (length(yld) == 0L) {
                  close(fqs)
                  done <<- TRUE
                  NULL
              } else yld
          }
      }

      ## The process function summarizes the number of times each sequence occurs.
      summary <- function(reads, ...) {
           ShortRead::tables(reads, n = 0)$distribution
      }

      ## Create a param.
      bpparam <- SnowParam(workers = 2)

      ## Initialize the streamer and iterator.
      fqs <- FastqStreamer(fl, n = 100)
      ITER <- fastqIterator(fqs)
      bpiterate(ITER, summary, BPPARAM = bpparam)

      ## Results from the workers are combined on the fly when REDUCE is used.
      ## Collapsing the data in this way can substantially reduce memory
      ## requirements.
      fqs <- FastqStreamer(fl, n = 100)
      ITER <- fastqIterator(fqs)
      bpiterate(ITER, summary, REDUCE = merge, all = TRUE, BPPARAM = bpparam)

      }

    ## End(Not run)

[Package BiocParallel version 1.14.2 Index]