CoverageRDD

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8. def coverage(halfWindowSize: Int, lociBroadcast: Broadcast[LociSet], explode: Boolean = false): RDD[(Position, Coverage)]

   Compute a PositionCoverage for every position in @loci, allowing a half-window of @halfWindowSize.

   Compute a PositionCoverage for every position in @loci, allowing a half-window of @halfWindowSize.

   halfWindowSize

   count bases as contributing coverage to a window that extends this many loci in either direction.

   lociBroadcast

   Spark Broadcast of a set of loci to compute depths for.

   explode

   If true, emit (locus, 1) tuples for every region-base before letting Spark do map-side, then reduce-side, reductions. Otherwise, traverse regions, emitting (locus, depth) tuples for all regions in a partition that overlap a current locus, effectively folding the map-side-reduction into application code, as an optimization.

   returns

   RDD of (Position, Coverage) tuples giving the total coverage, and number of region-starts, at each genomic position in lociBroadcast.

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15. def makeCappedLociSets(halfWindowSize: Int, loci: LociSet, maxRegionsPerPartition: Int, explode: Boolean, trimRanges: Boolean): RDD[LociSet]

    Break the input @loci into smaller LociSets such that the number of regions (with a @halfWindowSize grace-window) overlapping each set is ≤ @maxRegionsPerPartition.

    Break the input @loci into smaller LociSets such that the number of regions (with a @halfWindowSize grace-window) overlapping each set is ≤ @maxRegionsPerPartition.

    First obtains the "coverage" RDD, then takes regions greedily, meaning the end of each partition of the coverage-RDD will tend to have a "remainder" LociSet that has ≈half the maximum regions per partition.

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19. def partitionDepths(halfWindowSize: Int, lociBroadcast: Broadcast[LociSet], depthCutoff: Int): RDD[((ContigName, Boolean), Long)]

    Compute the depth at each locus in @rdd, then group loci into runs that are uniformly below (true) or above (false) depthCutoff.

    Compute the depth at each locus in @rdd, then group loci into runs that are uniformly below (true) or above (false) depthCutoff.

    Useful for getting a sense of which parts of the genome have exceedingly high coverage.

    halfWindowSize

    see coverage.

    lociBroadcast

    see coverage.

    depthCutoff

    separate runs of loci that are uniformly below (or equal to) vs. above (>) this cutoff.

    returns

    RDD whose elements have:

    • a key consisting of a contig name and a boolean indicating whether loci represented by this element have coverage depth ≤ depthCutoff, and
    • a value indicating the length of a run of loci with depth above or below depthCutoff, as described above.

20. val sc: SparkContext

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23. def validLociCounts(halfWindowSize: Int, lociBroadcast: Broadcast[LociSet], depthCutoff: Int): (RDD[((ContigName, Boolean), NumLoci)], NumLoci, NumLoci)

    Compute the coverage-depth at each locus, then aggregate loci into runs that are all above or below depthCutoff.

    Compute the coverage-depth at each locus, then aggregate loci into runs that are all above or below depthCutoff.

    returns

    tuple containing an RDD returned by partitionDepths as well as the total numbers of loci with depth below (or equal to) depthCutoff (resp. above depthCutoff).

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