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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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
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final
def
asInstanceOf[T0]: T0
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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.
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def
clone(): AnyRef
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- val counts: Dataset[(Array[Long], Abundance)]
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def
toString(): String
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def
unpersist(): CountedKmers.this.type
Unpersist this dataset.
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def
wait(): Unit
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def
withSequences: Dataset[(NTSeq, Abundance)]
Obtain these counts as pairs of k-mer sequence strings and abundances.
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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)
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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)