Implement parallelization, BAM usage and wrapper

[jleechung]

• Add Windows parallelization in getReducedExonRanges (annotation-data-helper.R) Detects user OS and calls BiocParallel if OS is Windows. Scales well (runtime approximately halved with number of cores = 2)

• Add BAM file usage for calculateJunctionReadCounts (junction-read-count.R) Reads BAM file using GenomicAlignments::readGAlignments and summarizeJunctions. Extra argument 'genome' is required to infer strand of junctions for downstream analysis ( i.e., to prevent ambiguous junctions returned when overlapped with intronRanges). Removes BAM files immediately after processing and calls garbage collection to free up memory. Examples for calculateJunctionReadCounts and calculatePromoterReadCounts with BAM usage added

• Add Windows parallelization for calculatePromoterReadCounts (junction-read-count.R) Detects user OS and calls BiocParallel if OS is Windows. Scales for large number of input junction files

• Allow for unspecified file labels by creating labels if user does not provide labels argument in calculatePromoterReadCounts (junction-read-count.R) If labels unprovided, simply creates labels 's1', 's2', ...

• Wrapper function proActiv.R Takes in minimally arguments promoterAnnotationData, junctionFilePaths and junctionType (and genome if input is a BAM file), returning a summarizedExperiment object giving promoter counts and activity, with promoter-gene mapping (stored as row data) and gene expression (stored as column data)

[jonathangoeke]

* Add Windows parallelization in `getReducedExonRanges` (`annotation-data-helper.R`)
  Detects user OS and calls `BiocParallel` if OS is Windows. Scales well (runtime approximately halved with number of cores = 2)

* Add BAM file usage for `calculateJunctionReadCounts` (`junction-read-count.R`)
  Reads BAM file using `GenomicAlignments::readGAlignments` and `summarizeJunctions`. Extra argument 'genome' is required to infer strand of junctions for downstream analysis ( i.e., to prevent ambiguous junctions returned when overlapped with `intronRanges`). Removes BAM files immediately after processing and calls garbage collection to free up memory. Examples for `calculateJunctionReadCounts` and `calculatePromoterReadCounts` with BAM usage added

* Add Windows parallelization for `calculatePromoterReadCounts` (`junction-read-count.R`)
  Detects user OS and calls `BiocParallel` if OS is Windows. Scales for large number of input junction files

* Allow for unspecified file labels by creating labels if user does not provide labels argument in `calculatePromoterReadCounts` (`junction-read-count.R`)
  If labels unprovided, simply creates labels 's1', 's2', ...

* Wrapper function `proActiv.R`
  Takes in minimally arguments `promoterAnnotationData`, `junctionFilePaths` and `junctionType` (and `genome` if input is a BAM file), returning a `summarizedExperiment` object giving promoter counts and activity, with promoter-gene mapping (stored as row data) and gene expression (stored as column data)

Thanks @jleechung , really a lot of very helpful additions. I made some comments regarding some of the code, please have a look.