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implicit class GenomicOperations extends AnyRef

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Instance Constructors

  1. new GenomicOperations(df: DataFrame)

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  8. final def getClass(): Class[_]
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  9. def getCompoundHet(patientIdColumnName: String, geneSymbolsColumnName: String): DataFrame
  10. def getPossiblyCompoundHet(patientIdColumnName: String, geneSymbolsColumnName: String): DataFrame
  11. def groupByLocus(cols: Column*): RelationalGroupedDataset
  12. def hashCode(): Int
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  13. val isHeterozygote: Column
  14. final def isInstanceOf[T0]: Boolean
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  15. val isSexualGenotype: Column
  16. val isWildType: Column
  17. def joinAndMerge(other: DataFrame, outputColumnName: String, joinType: String = "inner"): DataFrame
  18. def joinByLocus(other: DataFrame, joinType: String): DataFrame
  19. final def ne(arg0: AnyRef): Boolean
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  20. val normalizeCall: (Column) ⇒ Column
  21. val normalizeMonosomy: (Column) ⇒ Column
  22. final def notify(): Unit
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  23. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  24. def pickRandomCsqPerLocus(transcriptIdColumnName: String = "ensembl_transcript_id"): DataFrame
  25. def selectLocus(cols: Column*): DataFrame
  26. final def synchronized[T0](arg0: ⇒ T0): T0
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  27. def toString(): String
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  28. final def wait(arg0: Long, arg1: Int): Unit
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  29. final def wait(arg0: Long): Unit
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  30. final def wait(): Unit
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  31. def withAlleleDepths(adCol: Column = col("ad")): DataFrame
  32. def withCompoundHeterozygous(patientIdColumnName: String = "patient_id", geneSymbolsColumnName: String = "symbols", additionalFilter: Option[Column] = None): DataFrame
  33. def withGenotypeTransmission(as: String, calls_name: String = "calls", gender_name: String = "gender", affected_status_name: String = "affected_status", father_calls_name: String = "father_calls", father_affected_status_name: String = "father_affected_status", mother_calls_name: String = "mother_calls", mother_affected_status_name: String = "mother_affected_status"): DataFrame

    Add a column with genotype transmission.

    Add a column with genotype transmission. This column is named with as parameter

    as

    column name added to this datafarame

    gender_name

    name of the column thant contains the gender

  34. def withNormalizedVariants(referenceGenomePath: String = ...): DataFrame
  35. def withParentalOrigin(as: String, calls: Column, fth_calls: Column, mth_calls: Column): DataFrame
  36. def withPickedCsqPerLocus(genes: DataFrame, impactScoreColumnName: String = "impact_score", transcriptIdColumnName: String = "ensembl_transcript_id", geneSymbolColumnName: String = "symbol", omimGeneIdColumnName: String = "omim_gene_id", biotypeColumnName: String = "biotype", maneSelectColumnName: String = "mane_select", canonicalColumnName: String = "canonical", manePlusColumnName: String = "mane_plus", pickedColumnName: String = "picked"): DataFrame

    Pick a consequence with maximum impact for each locus, according to prioritization algorithm.

  37. def withRefseqMrnaId(): DataFrame
  38. def withRelativesGenotype(columnNames: Seq[String] = ..., participantIdColumn: Column = col("participant_id"), familyIdColumn: Column = col("family_id")): DataFrame
  39. def withSplitMultiAllelic: DataFrame
  40. def withTransmissionPerLocus(locusColumnNames: Seq[String], transmissionColumnName: String, resultColumnName: String): DataFrame

    Compute transmission per locus given an existing column containing the transmission for this particular occurrence.

    Compute transmission per locus given an existing column containing the transmission for this particular occurrence.

    locusColumnNames

    list of locus columns

    transmissionColumnName

    name of the transmission column

    resultColumnName

    name of the resulting computation

    returns

    a dataframe with a new column containing the number of transmission per locus as a Map of transmission type -> count per type.

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