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class CountedKmers extends AnyRef

A collection of counted k-mers represented in encoded form. Each k-mer is represented individually, making this dataset large if cached or persisted.

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Instance Constructors

  1. new CountedKmers(counts: Dataset[(Array[Long], Abundance)], splitter: Broadcast[AnyMinSplitter])(implicit spark: SparkSession)

    counts

    Pairs of encoded k-mers and their abundances.

    splitter

    Splitter for constructing super-mers

    spark

    the Spark session

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  4. final def asInstanceOf[T0]: T0
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  5. def cache(): CountedKmers.this.type

    Cache this dataset.

    Cache this dataset. This may be expensive when a large amount of distinct k-mers are present.

  6. def clone(): AnyRef
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    @throws( ... ) @native()
  7. val counts: Dataset[(Array[Long], Abundance)]
  8. final def eq(arg0: AnyRef): Boolean
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  10. def finalize(): Unit
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  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def toString(): String
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  19. def unpersist(): CountedKmers.this.type

    Unpersist this dataset.

  20. final def wait(): Unit
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  21. final def wait(arg0: Long, arg1: Int): Unit
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  22. final def wait(arg0: Long): Unit
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  23. def withSequences: Dataset[(NTSeq, Abundance)]

    Obtain these counts as pairs of k-mer sequence strings and abundances.

  24. def writeFasta(output: String): Unit

    Write counted k-mers with sequences as FASTA files to HDFS.

    Write counted k-mers with sequences as FASTA files to HDFS. The count will be used as the sequence ID of each k-mer. This action triggers a computation.

    output

    Directory to write to (prefix name)

  25. def writeTSV(withKmers: Boolean, output: String): Unit

    Write a table as TSV.

    Write a table as TSV. This action triggers a computation.

    withKmers

    Should k-mer sequences be included in the tables?

    output

    Directory to write to (prefix name)

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