Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  def add_struct_fields(struct: Column, fields: Column*): Column

   Adds fields to a struct.

   Adds fields to a struct.

   struct

   The struct to which fields will be added

   fields

   The new fields to add. The arguments must alternate between string-typed literal field names and field values.

   returns

   A struct consisting of the input struct and the added fields

   Since

   0.3.0

5.  def aggregate_by_index(arr: Column, initialValue: Column, update: (Column, Column) ⇒ Column, merge: (Column, Column) ⇒ Column): Column
6.  def aggregate_by_index(arr: Column, initialValue: Column, update: (Column, Column) ⇒ Column, merge: (Column, Column) ⇒ Column, evaluate: (Column) ⇒ Column): Column

   Computes custom per-sample aggregates.

   Computes custom per-sample aggregates.

   arr

   array of values.

   initialValue

   the initial value

   update

   update function

   merge

   merge function

   evaluate

   evaluate function

   returns

   An array of aggregated values. The number of elements in the array is equal to the number of samples.

   Since

   0.3.0

7.  def array_summary_stats(arr: Column): Column

   Computes the minimum, maximum, mean, standard deviation for an array of numerics.

   Computes the minimum, maximum, mean, standard deviation for an array of numerics.

   arr

   An array of any numeric type

   returns

   A struct containing double mean, stdDev, min, and max fields

   Since

   0.3.0

8.  def array_to_dense_vector(arr: Column): Column

   Converts an array of numerics into a spark.ml DenseVector.

   Converts an array of numerics into a spark.ml DenseVector.

   arr

   The array of numerics

   returns

   A spark.ml DenseVector

   Since

   0.3.0

9.  def array_to_sparse_vector(arr: Column): Column

   Converts an array of numerics into a spark.ml SparseVector.

   Converts an array of numerics into a spark.ml SparseVector.

   arr

   The array of numerics

   returns

   A spark.ml SparseVector

   Since

   0.3.0

10.  final def asInstanceOf[T0]: T0

    Definition Classes

    Any

11.  def call_summary_stats(genotypes: Column): Column

    Computes call summary statistics for an array of genotype structs.

    Computes call summary statistics for an array of genotype structs. See :ref:variant-qc for more details.

    genotypes

    The array of genotype structs with calls field

    returns

    A struct containing callRate, nCalled, nUncalled, nHet, nHomozygous, nNonRef, nAllelesCalled, alleleCounts, alleleFrequencies fields. See :ref:variant-qc.

    Since

    0.3.0

12.  def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )

13.  def dp_summary_stats(genotypes: Column): Column

    Computes summary statistics for the depth field from an array of genotype structs.

    Computes summary statistics for the depth field from an array of genotype structs. See :ref:variant-qc.

    genotypes

    An array of genotype structs with depth field

    returns

    A struct containing mean, stdDev, min, and max of genotype depths

    Since

    0.3.0

14.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

15.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

16.  def expand_struct(struct: Column): Column

    Promotes fields of a nested struct to top-level columns similar to using struct.* from SQL, but can be used in more contexts.

    Promotes fields of a nested struct to top-level columns similar to using struct.* from SQL, but can be used in more contexts.

    struct

    The struct to expand

    returns

    Columns corresponding to fields of the input struct

    Since

    0.3.0

17.  def explode_matrix(matrix: Column): Column

    Explodes a spark.ml Matrix (sparse or dense) into multiple arrays, one per row of the matrix.

    Explodes a spark.ml Matrix (sparse or dense) into multiple arrays, one per row of the matrix.

    matrix

    The sparl.ml Matrix to explode

    returns

    An array column in which each row is a row of the input matrix

    Since

    0.3.0

18.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

19.  def genotype_states(genotypes: Column): Column

    Gets the number of alternate alleles for an array of genotype structs.

    Gets the number of alternate alleles for an array of genotype structs. Returns -1 if there are any -1 s (no-calls) in the calls array.

    genotypes

    An array of genotype structs with calls field

    returns

    An array of integers containing the number of alternate alleles in each call array

    Since

    0.3.0

20.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

21.  def gq_summary_stats(genotypes: Column): Column

    Computes summary statistics about the genotype quality field for an array of genotype structs.

    Computes summary statistics about the genotype quality field for an array of genotype structs. See :ref:variant-qc.

    genotypes

    The array of genotype structs with conditionalQuality field

    returns

    A struct containing mean, stdDev, min, and max of genotype qualities

    Since

    0.3.0

22.  def hard_calls(probabilities: Column, numAlts: Column, phased: Column): Column
23.  def hard_calls(probabilities: Column, numAlts: Column, phased: Column, threshold: Double): Column

    Converts an array of probabilities to hard calls.

    Converts an array of probabilities to hard calls. The probabilities are assumed to be diploid. See :ref:variant-data-transformations for more details.

    probabilities

    The array of probabilities to convert

    numAlts

    The number of alternate alleles

    phased

    Whether the probabilities are phased. If phased, we expect one 2 * numAlts values in the probabilities array. If unphased, we expect one probability per possible genotype.

    threshold

    The minimum probability to make a call. If no probability falls into the range of [0, 1 - threshold] or [threshold, 1], a no-call (represented by -1 s) will be emitted. If not provided, this parameter defaults to 0.9.

    returns

    An array of hard calls

    Since

    0.3.0

24.  def hardy_weinberg(genotypes: Column): Column

    Computes statistics relating to the Hardy Weinberg equilibrium.

    Computes statistics relating to the Hardy Weinberg equilibrium. See :ref:variant-qc for more details.

    genotypes

    The array of genotype structs with calls field

    returns

    A struct containing two fields, hetFreqHwe (the expected heterozygous frequency according to Hardy-Weinberg equilibrium) and pValueHwe (the associated p-value)

    Since

    0.3.0

25.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

26.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

27.  def lift_over_coordinates(contigName: Column, start: Column, end: Column, chainFile: String): Column
28.  def lift_over_coordinates(contigName: Column, start: Column, end: Column, chainFile: String, minMatchRatio: Double): Column

    Performs liftover for the coordinates of a variant.

    Performs liftover for the coordinates of a variant. To perform liftover of alleles and add additional metadata, see :ref:liftover.

    contigName

    The current contig name

    start

    The current start

    end

    The current end

    chainFile

    Location of the chain file on each node in the cluster

    minMatchRatio

    Minimum fraction of bases that must remap to do liftover successfully. If not provided, defaults to 0.95.

    returns

    A struct containing contigName, start, and end fields after liftover

    Since

    0.3.0

29.  def linear_regression_gwas(genotypes: Column, phenotypes: Column, covariates: Column): Column

    Performs a linear regression association test optimized for performance in a GWAS setting.

    Performs a linear regression association test optimized for performance in a GWAS setting. See :ref:linear-regression for details.

    genotypes

    A numeric array of genotypes

    phenotypes

    A numeric array of phenotypes

    covariates

    A spark.ml Matrix of covariates

    returns

    A struct containing beta, standardError, and pValue fields. See :ref:linear-regression.

    Since

    0.3.0

30.  def logistic_regression_gwas(genotypes: Column, phenotypes: Column, covariates: Column, test: String): Column

    Performs a logistic regression association test optimized for performance in a GWAS setting.

    Performs a logistic regression association test optimized for performance in a GWAS setting. See :ref:logistic-regression for more details.

    genotypes

    An numeric array of genotypes

    phenotypes

    A double array of phenotype values

    covariates

    A spark.ml Matrix of covariates

    test

    Which logistic regression test to use. Can be LRT or Firth

    returns

    A struct containing beta, oddsRatio, waldConfidenceInterval, and pValue fields. See :ref:logistic-regression.

    Since

    0.3.0

31.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

32.  def normalize_variant(contigName: Column, start: Column, end: Column, refAllele: Column, altAlleles: Column, refGenomePathString: String): Column

    Normalizes the variant with a behavior similar to vt normalize or bcftools norm.

    Normalizes the variant with a behavior similar to vt normalize or bcftools norm. Creates a StructType column including the normalized start, end, referenceAllele and alternateAlleles fields (whether they are changed or unchanged as the result of normalization) as well as a StructType field called normalizationStatus that contains the following fields:

    changed: A boolean field indicating whether the variant data was changed as a result of normalization

    errorMessage: An error message in case the attempt at normalizing the row hit an error. In this case, the changed field will be set to false. If no errors occur, this field will be null.

    In case of an error, the start, end, referenceAllele and alternateAlleles fields in the generated struct will be null.

    contigName

    The current contig name

    start

    The current start

    end

    The current end

    refAllele

    The current reference allele

    altAlleles

    The current array of alternate alleles

    refGenomePathString

    A path to the reference genome .fasta file. The .fasta file must be accompanied with a .fai index file in the same folder.

    returns

    A struct as explained above

    Since

    0.3.0

33.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

34.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

35.  def sample_call_summary_stats(genotypes: Column, refAllele: Column, alternateAlleles: Column): Column

    Computes per-sample call summary statistics.

    Computes per-sample call summary statistics. See :ref:sample-qc for more details.

    genotypes

    An array of genotype structs with calls field

    refAllele

    The reference allele

    alternateAlleles

    An array of alternate alleles

    returns

    A struct containing sampleId, callRate, nCalled, nUncalled, nHomRef, nHet, nHomVar, nSnp, nInsertion, nDeletion, nTransition, nTransversion, nSpanningDeletion, rTiTv, rInsertionDeletion, rHetHomVar fields. See :ref:sample-qc.

    Since

    0.3.0

36.  def sample_dp_summary_stats(genotypes: Column): Column

    Computes per-sample summary statistics about the depth field in an array of genotype structs.

    Computes per-sample summary statistics about the depth field in an array of genotype structs.

    genotypes

    An array of genotype structs with depth field

    returns

    An array of structs where each struct contains mean, stDev, min, and max of the genotype depths for a sample. If sampleId is present in a genotype, it will be propagated to the resulting struct as an extra field.

    Since

    0.3.0

37.  def sample_gq_summary_stats(genotypes: Column): Column

    Computes per-sample summary statistics about the genotype quality field in an array of genotype structs.

    Computes per-sample summary statistics about the genotype quality field in an array of genotype structs.

    genotypes

    An array of genotype structs with conditionalQuality field

    returns

    An array of structs where each struct contains mean, stDev, min, and max of the genotype qualities for a sample. If sampleId is present in a genotype, it will be propagated to the resulting struct as an extra field.

    Since

    0.3.0

38.  def subset_struct(struct: Column, fields: String*): Column

    Selects fields from a struct.

    Selects fields from a struct.

    struct

    Struct from which to select fields

    fields

    Fields to select

    returns

    A struct containing only the indicated fields

    Since

    0.3.0

39.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

40.  def toString(): String

    Definition Classes

    AnyRef → Any

41.  def vector_to_array(vector: Column): Column

    Converts a spark.ml Vector (sparse or dense) to an array of doubles.

    Converts a spark.ml Vector (sparse or dense) to an array of doubles.

    vector

    Vector to convert

    returns

    An array of doubles

    Since

    0.3.0

42.  final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

43.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

44.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )