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An open-source toolkit for large-scale genomic analysis
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# This file contains the manifest of SparkSQL functions provided by Glow. The definitions in this
# file are used to generate Scala and Python APIs.
#
# The top level names are groups of functions. These groups primarily matter for documentation. Each
# group contains an ordered list of functions. Most of the parameters are self-evident.
#
# Potentially confusing function options are:
# - expr_class: The class name of the expression in Glow that implements the function. This value is
# *not* user-visible. It's used only for client generation.
# - exclude_python: If true, the function is not exposed via the Python API
# - exclude_scala: If true, the function is not exposed via the Scala API.
#
# Potentially confusing argument options are:
# - type: The argument's type. This can currently be one of [str, double, lambda1, lambda2]. These
# types do not correspond to any real programming language; they are just placeholders used for
# client generation. More types can be added as needed. If an argument does not have an explicit
# type, it is assumed to be of Spark SQL Column type.
# - is_var_args: If true, the parameters can be repeated. Only the last argument in the args list
# can be var args.
# - is_optional: If true, the parameter can be omitted. Only the last argument in the args list can
# be optional.
#
# Warning: Spark version changes can modify the expected output. Known changes:
# - PYSPARK_ROW_FIELD_SORTING_ENABLED was introduced in Spark 3.0, changing the schema ordering
#
# For more details about how the definitions in this file are eventually turned into clients,
# consult the generator script at project/render_template.py.
complex_type_manipulation:
functions:
- name: add_struct_fields
doc: Adds fields to a struct.
since: 0.3.0
examples:
python: |
>>> df = spark.createDataFrame([Row(struct=Row(a=1))])
>>> df.select(glow.add_struct_fields('struct', lit('b'), lit(2)).alias('struct')).collect()
[Row(struct=Row(a=1, b=2))]
expr_class: io.projectglow.sql.expressions.AddStructFields
args:
- name: struct
doc: The struct to which fields will be added
- name: fields
doc: The new fields to add. The arguments must alternate between string-typed literal field names and field values.
is_var_args: true
returns: A struct consisting of the input struct and the added fields
- name: array_summary_stats
doc: Computes the minimum, maximum, mean, standard deviation for an array of numerics.
since: 0.3.0
examples:
python: |
>>> df = spark.createDataFrame([Row(arr=[1, 2, 3])])
>>> df.select(glow.expand_struct(glow.array_summary_stats('arr'))).collect()
[Row(mean=2.0, stdDev=1.0, min=1.0, max=3.0)]
expr_class: io.projectglow.sql.expressions.ArrayStatsSummary
args:
- name: arr
doc: An array of any numeric type
returns: A struct containing double ``mean``, ``stdDev``, ``min``, and ``max`` fields
- name: array_to_dense_vector
doc: Converts an array of numerics into a ``spark.ml`` ``DenseVector``.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.ArrayToDenseVector
examples:
python: |
>>> from pyspark.ml.linalg import DenseVector
>>> df = spark.createDataFrame([Row(arr=[1, 2, 3])])
>>> df.select(glow.array_to_dense_vector('arr').alias('v')).collect()
[Row(v=DenseVector([1.0, 2.0, 3.0]))]
args:
- name: arr
doc: The array of numerics
returns: A ``spark.ml`` ``DenseVector``
- name: array_to_sparse_vector
doc: Converts an array of numerics into a ``spark.ml`` ``SparseVector``.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.ArrayToSparseVector
examples:
python: |
>>> from pyspark.ml.linalg import SparseVector
>>> df = spark.createDataFrame([Row(arr=[1, 0, 2, 0, 3, 0])])
>>> df.select(glow.array_to_sparse_vector('arr').alias('v')).collect()
[Row(v=SparseVector(6, {0: 1.0, 2: 2.0, 4: 3.0}))]
args:
- name: arr
doc: The array of numerics
returns: A ``spark.ml`` ``SparseVector``
- name: expand_struct
doc: Promotes fields of a nested struct to top-level columns similar to using ``struct.*`` from SQL, but can be used in more contexts.
since: 0.3.0
examples:
python: |
>>> df = spark.createDataFrame([Row(struct=Row(a=1, b=2))])
>>> df.select(glow.expand_struct(col('struct'))).collect()
[Row(a=1, b=2)]
expr_class: io.projectglow.sql.expressions.ExpandStruct
args:
- name: struct
doc: The struct to expand
returns: Columns corresponding to fields of the input struct
- name: explode_matrix
doc: Explodes a ``spark.ml`` ``Matrix`` (sparse or dense) into multiple arrays, one per row of the matrix.
since: 0.3.0
examples:
python: |
>>> from pyspark.ml.linalg import DenseMatrix
>>> m = DenseMatrix(numRows=3, numCols=2, values=[1, 2, 3, 4, 5, 6])
>>> df = spark.createDataFrame([Row(matrix=m)])
>>> df.select(glow.explode_matrix('matrix').alias('row')).collect()
[Row(row=[1.0, 4.0]), Row(row=[2.0, 5.0]), Row(row=[3.0, 6.0])]
expr_class: io.projectglow.sql.expressions.ExplodeMatrix
args:
- name: matrix
doc: The ``sparl.ml`` ``Matrix`` to explode
returns: An array column in which each row is a row of the input matrix
- name: subset_struct
doc: Selects fields from a struct.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.SubsetStruct
examples:
python: |
>>> df = spark.createDataFrame([Row(struct=Row(a=1, b=2, c=3))])
>>> df.select(glow.subset_struct('struct', 'a', 'c').alias('struct')).collect()
[Row(struct=Row(a=1, c=3))]
args:
- name: struct
doc: Struct from which to select fields
- name: fields
doc: Fields to select
type: str
is_var_args: true
returns: A struct containing only the indicated fields
- name: vector_to_array
doc: Converts a ``spark.ml`` ``Vector`` (sparse or dense) to an array of doubles.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.VectorToArray
examples:
python: |
>>> from pyspark.ml.linalg import DenseVector, SparseVector
>>> df = spark.createDataFrame([Row(v=SparseVector(3, {0: 1.0, 2: 2.0})), Row(v=DenseVector([3.0, 4.0]))])
>>> df.select(glow.vector_to_array('v').alias('arr')).collect()
[Row(arr=[1.0, 0.0, 2.0]), Row(arr=[3.0, 4.0])]
args:
- name: vector
doc: Vector to convert
returns: An array of doubles
etl:
functions:
- name: hard_calls
doc: Converts an array of probabilities to hard calls. The probabilities are assumed to be diploid. See :ref:`variant-data-transformations`
for more details.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.HardCalls
examples:
python: |
>>> df = spark.createDataFrame([Row(probs=[0.95, 0.05, 0.0])])
>>> df.select(glow.hard_calls('probs', numAlts=lit(1), phased=lit(False)).alias('calls')).collect()
[Row(calls=[0, 0])]
>>> df = spark.createDataFrame([Row(probs=[0.05, 0.95, 0.0])])
>>> df.select(glow.hard_calls('probs', numAlts=lit(1), phased=lit(False)).alias('calls')).collect()
[Row(calls=[0, 1])]
>>> # Use the threshold parameter to change the minimum probability required for a call
>>> df = spark.createDataFrame([Row(probs=[0.05, 0.95, 0.0])])
>>> df.select(glow.hard_calls('probs', numAlts=lit(1), phased=lit(False), threshold=0.99).alias('calls')).collect()
[Row(calls=[-1, -1])]
args:
- name: probabilities
doc: The array of probabilities to convert
- name: numAlts
doc: The number of alternate alleles
- name: phased
doc: 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.
- name: threshold
doc: 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``.
type: double
is_optional: true
returns: An array of hard calls
- name: lift_over_coordinates
doc: Performs liftover for the coordinates of a variant. To perform liftover of alleles and add additional metadata, see :ref:`liftover`.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.LiftOverCoordinatesExpr
examples:
python: |
>>> df = spark.read.format('vcf').load('test-data/liftover/unlifted.test.vcf').where('start = 18210071')
>>> chain_file = 'test-data/liftover/hg38ToHg19.over.chain.gz'
>>> reference_file = 'test-data/liftover/hg19.chr20.fa.gz'
>>> df.select('contigName', 'start', 'end').head()
Row(contigName='chr20', start=18210071, end=18210072)
>>> lifted_df = df.select(glow.expand_struct(glow.lift_over_coordinates('contigName', 'start', 'end', chain_file)))
>>> lifted_df.head()
Row(contigName='chr20', start=18190715, end=18190716)
args:
- name: contigName
doc: The current contig name
- name: start
doc: The current start
- name: end
doc: The current end
- name: chainFile
doc: Location of the chain file on each node in the cluster
type: str
- name: minMatchRatio
doc: Minimum fraction of bases that must remap to do liftover successfully. If not provided, defaults to ``0.95``.
type: double
is_optional: true
returns: A struct containing ``contigName``, ``start``, and ``end`` fields after liftover
- name: normalize_variant
doc: |
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``.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.NormalizeVariantExpr
examples:
python: |
>>> df = spark.read.format('vcf').load('test-data/variantsplitternormalizer-test/test_left_align_hg38_altered.vcf')
>>> ref_genome = 'test-data/variantsplitternormalizer-test/Homo_sapiens_assembly38.20.21_altered.fasta'
>>> df.select('contigName', 'start', 'end', 'referenceAllele', 'alternateAlleles').head()
Row(contigName='chr20', start=400, end=401, referenceAllele='G', alternateAlleles=['GATCTTCCCTCTTTTCTAATATAAACACATAAAGCTCTGTTTCCTTCTAGGTAACTGGTTTGAG'])
>>> normalized_df = df.select('contigName', glow.expand_struct(glow.normalize_variant('contigName', 'start', 'end', 'referenceAllele', 'alternateAlleles', ref_genome)))
>>> normalized_df.head()
Row(contigName='chr20', start=268, end=269, referenceAllele='A', alternateAlleles=['ATTTGAGATCTTCCCTCTTTTCTAATATAAACACATAAAGCTCTGTTTCCTTCTAGGTAACTGG'], normalizationStatus=Row(changed=True, errorMessage=None))
args:
- name: contigName
doc: The current contig name
- name: start
doc: The current start
- name: end
doc: The current end
- name: refAllele
doc: The current reference allele
- name: altAlleles
doc: The current array of alternate alleles
- name: refGenomePathString
doc: |
A path to the reference genome ``.fasta`` file. The ``.fasta`` file must be accompanied with a ``.fai`` index file in the same folder.
type: str
returns: A struct as explained above
- name: mean_substitute
doc: Substitutes the missing values of a numeric array using the mean of the non-missing values. Any values that
are NaN, null or equal to the missing value parameter are considered missing. See
:ref:`variant-data-transformations` for more details.
since: 0.4.0
expr_class: io.projectglow.sql.expressions.MeanSubstitute
examples:
python: |
>>> df = spark.createDataFrame([Row(unsubstituted_values=[float('nan'), None, 0.0, 1.0, 2.0, 3.0, 4.0])])
>>> df.select(glow.mean_substitute('unsubstituted_values', lit(0.0)).alias('substituted_values')).collect()
[Row(substituted_values=[2.5, 2.5, 2.5, 1.0, 2.0, 3.0, 4.0])]
>>> df = spark.createDataFrame([Row(unsubstituted_values=[0, 1, 2, 3, -1, None])])
>>> df.select(glow.mean_substitute('unsubstituted_values').alias('substituted_values')).collect()
[Row(substituted_values=[0.0, 1.0, 2.0, 3.0, 1.5, 1.5])]
args:
- name: array
doc: A numeric array that may contain missing values
- name: missingValue
doc: A value that should be considered missing. If not provided, this parameter defaults to ``-1``.
is_optional: true
returns: A numeric array with substituted missing values
quality_control:
functions:
- name: aggregate_by_index
doc: Computes custom per-sample aggregates.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.UnwrappedAggregateByIndex
exclude_python: true
args:
- name: arr
doc: array of values.
- name: initialValue
doc: the initial value
- name: update
doc: update function
type: lambda2
- name: merge
doc: merge function
type: lambda2
- name: evaluate
doc: evaluate function
type: lambda1
is_optional: true
returns: An array of aggregated values. The number of elements in the array is equal to the number of samples.
- name: call_summary_stats
doc: Computes call summary statistics for an array of genotype structs. See :ref:`variant-qc` for more details.
since: 0.3.0
examples:
python: |
>>> schema = 'genotypes: array>>'
>>> df = spark.createDataFrame([Row(genotypes=[Row(calls=[0, 0]), Row(calls=[1, 0]), Row(calls=[1, 1])])], schema)
>>> df.select(glow.expand_struct(glow.call_summary_stats('genotypes'))).collect()
[Row(callRate=1.0, nCalled=3, nUncalled=0, nHet=1, nHomozygous=[1, 1], nNonRef=2, nAllelesCalled=6, alleleCounts=[3, 3], alleleFrequencies=[0.5, 0.5])]
expr_class: io.projectglow.sql.expressions.CallStats
args:
- name: genotypes
doc: 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`.
- name: dp_summary_stats
doc: Computes summary statistics for the depth field from an array of genotype structs. See :ref:`variant-qc`.
examples:
python: |
>>> df = spark.createDataFrame([Row(genotypes=[Row(depth=1), Row(depth=2), Row(depth=3)])], 'genotypes: array>')
>>> df.select(glow.expand_struct(glow.dp_summary_stats('genotypes'))).collect()
[Row(mean=2.0, stdDev=1.0, min=1.0, max=3.0)]
expr_class: io.projectglow.sql.expressions.DpSummaryStats
since: 0.3.0
args:
- name: genotypes
doc: An array of genotype structs with ``depth`` field
returns: A struct containing ``mean``, ``stdDev``, ``min``, and ``max`` of genotype depths
- name: hardy_weinberg
doc: Computes statistics relating to the Hardy Weinberg equilibrium. See :ref:`variant-qc` for more details.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.HardyWeinberg
examples:
python: |
>>> genotypes = [
... Row(calls=[1, 1]),
... Row(calls=[1, 0]),
... Row(calls=[0, 0])]
>>> df = spark.createDataFrame([Row(genotypes=genotypes)], 'genotypes: array>>')
>>> df.select(glow.expand_struct(glow.hardy_weinberg('genotypes'))).collect()
[Row(hetFreqHwe=0.6, pValueHwe=0.7)]
args:
- name: genotypes
doc: 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)
- name: gq_summary_stats
doc: Computes summary statistics about the genotype quality field for an array of genotype structs. See :ref:`variant-qc`.
since: 0.3.0
examples:
python: |
>>> genotypes = [
... Row(conditionalQuality=1),
... Row(conditionalQuality=2),
... Row(conditionalQuality=3)]
>>> df = spark.createDataFrame([Row(genotypes=genotypes)], 'genotypes: array>')
>>> df.select(glow.expand_struct(glow.gq_summary_stats('genotypes'))).collect()
[Row(mean=2.0, stdDev=1.0, min=1.0, max=3.0)]
expr_class: io.projectglow.sql.expressions.GqSummaryStats
args:
- name: genotypes
doc: The array of genotype structs with ``conditionalQuality`` field
returns: A struct containing ``mean``, ``stdDev``, ``min``, and ``max`` of genotype qualities
- name: sample_call_summary_stats
doc: Computes per-sample call summary statistics. See :ref:`sample-qc` for more details.
since: 0.3.0
examples:
python: |
>>> sites = [
... {'refAllele': 'C', 'alternateAlleles': ['G'], 'genotypes': [{'sampleId': 'NA12878', 'calls': [0, 0]}]},
... {'refAllele': 'A', 'alternateAlleles': ['G'], 'genotypes': [{'sampleId': 'NA12878', 'calls': [1, 1]}]},
... {'refAllele': 'AG', 'alternateAlleles': ['A'], 'genotypes': [{'sampleId': 'NA12878', 'calls': [1, 0]}]}]
>>> df = spark.createDataFrame(sites, 'refAllele: string, alternateAlleles: array, genotypes: array>>')
>>> df.select(glow.sample_call_summary_stats('genotypes', 'refAllele', 'alternateAlleles').alias('stats')).collect()
[Row(stats=[Row(sampleId='NA12878', callRate=1.0, nCalled=3, nUncalled=0, nHomRef=1, nHet=1, nHomVar=1, nSnp=2, nInsertion=0, nDeletion=1, nTransition=2, nTransversion=0, nSpanningDeletion=0, rTiTv=inf, rInsertionDeletion=0.0, rHetHomVar=1.0)])]
expr_class: io.projectglow.sql.expressions.CallSummaryStats
args:
- name: genotypes
doc: An array of genotype structs with ``calls`` field
- name: refAllele
doc: The reference allele
- name: alternateAlleles
doc: 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`.
- name: sample_dp_summary_stats
doc: Computes per-sample summary statistics about the depth field in an array of genotype structs.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.SampleDpSummaryStatistics
examples:
python: |
>>> sites = [
... {'genotypes': [{'sampleId': 'NA12878', 'depth': 1}]},
... {'genotypes': [{'sampleId': 'NA12878', 'depth': 2}]},
... {'genotypes': [{'sampleId': 'NA12878', 'depth': 3}]}]
>>> df = spark.createDataFrame(sites, 'genotypes: array>')
>>> df.select(glow.sample_dp_summary_stats('genotypes').alias('stats')).collect()
[Row(stats=[Row(sampleId='NA12878', mean=2.0, stdDev=1.0, min=1.0, max=3.0)])]
args:
- name: genotypes
doc: 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.
- name: sample_gq_summary_stats
doc: Computes per-sample summary statistics about the genotype quality field in an array of genotype structs.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.SampleGqSummaryStatistics
examples:
python: |
>>> sites = [
... Row(genotypes=[Row(sampleId='NA12878', conditionalQuality=1)]),
... Row(genotypes=[Row(sampleId='NA12878', conditionalQuality=2)]),
... Row(genotypes=[Row(sampleId='NA12878', conditionalQuality=3)])]
>>> df = spark.createDataFrame(sites, 'genotypes: array>')
>>> df.select(glow.sample_gq_summary_stats('genotypes').alias('stats')).collect()
[Row(stats=[Row(sampleId='NA12878', mean=2.0, stdDev=1.0, min=1.0, max=3.0)])]
args:
- name: genotypes
doc: 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.
- name: assert_true_or_error
doc: Asserts a boolean condition is true.
since: 0.5.0
expr_class: io.projectglow.sql.expressions.AssertTrueOrError
exclude_python: true
exclude_scala: true
examples:
python: |
>>> df = spark.createDataFrame([Row(v=1), Row(v=1)])
>>> df.select(glow.assert_true_or_error(df.v == 1, 'the value is not one!').alias('is_one')).collect()
[Row(is_one=None), Row(is_one=None)]
args:
- name: condition
doc: Boolean condition to check
- name: errMsg
doc: Error message if condition fails
type: str
returns: Null if true, or throws an exception if not true
gwas_functions:
functions:
- name: linear_regression_gwas
doc: Performs a linear regression association test optimized for performance in a GWAS setting.
See :ref:`linear-regression` for details.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.LinearRegressionExpr
examples:
python: |
>>> from pyspark.ml.linalg import DenseMatrix
>>> phenotypes = [2, 3, 4]
>>> genotypes = [0, 1, 2]
>>> covariates = DenseMatrix(numRows=3, numCols=1, values=[1, 1, 1])
>>> df = spark.createDataFrame([Row(genotypes=genotypes, phenotypes=phenotypes, covariates=covariates)])
>>> df.select(glow.expand_struct(glow.linear_regression_gwas('genotypes', 'phenotypes', 'covariates'))).collect()
[Row(beta=0.9999999999999998, standardError=1.4901161193847656e-08, pValue=9.486373847239922e-09)]
args:
- name: genotypes
doc: A numeric array of genotypes
- name: phenotypes
doc: A numeric array of phenotypes
- name: covariates
doc: A ``spark.ml`` ``Matrix`` of covariates
returns: A struct containing ``beta``, ``standardError``, and ``pValue`` fields. See :ref:`linear-regression`.
- name: logistic_regression_gwas
doc: Performs a logistic regression association test optimized for performance in a GWAS setting.
See :ref:`logistic-regression` for more details.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.LogisticRegressionExpr
examples:
python: |
>>> from pyspark.ml.linalg import DenseMatrix
>>> phenotypes = [1, 0, 0, 1, 1]
>>> genotypes = [0, 0, 1, 2, 2]
>>> covariates = DenseMatrix(numRows=5, numCols=1, values=[1, 1, 1, 1, 1])
>>> offset = [1, 0, 1, 0, 1]
>>> df = spark.createDataFrame([Row(genotypes=genotypes, phenotypes=phenotypes, covariates=covariates, offset=offset)])
>>> df.select(glow.expand_struct(glow.logistic_regression_gwas('genotypes', 'phenotypes', 'covariates', 'Firth'))).collect()
[Row(beta=0.7418937644793101, oddsRatio=2.09990848346903, waldConfidenceInterval=[0.2509874689201784, 17.569066925598555], pValue=0.3952193664793294)]
>>> df.select(glow.expand_struct(glow.logistic_regression_gwas('genotypes', 'phenotypes', 'covariates', 'LRT'))).collect()
[Row(beta=1.1658962684583645, oddsRatio=3.208797538802116, waldConfidenceInterval=[0.29709600522888285, 34.65674887513274], pValue=0.2943946848756769)]
>>> df.select(glow.expand_struct(glow.logistic_regression_gwas('genotypes', 'phenotypes', 'covariates', 'Firth', 'offset'))).collect()
[Row(beta=0.8024832156793392, oddsRatio=2.231074294047771, waldConfidenceInterval=[0.2540891981649045, 19.590334974925725], pValue=0.3754070658316332)]
>>> df.select(glow.expand_struct(glow.logistic_regression_gwas('genotypes', 'phenotypes', 'covariates', 'LRT', 'offset'))).collect()
[Row(beta=1.1996041727573317, oddsRatio=3.3188029900720117, waldConfidenceInterval=[0.3071189078535928, 35.863807161497334], pValue=0.2857137988674153)]
args:
- name: genotypes
doc: An numeric array of genotypes
- name: phenotypes
doc: A double array of phenotype values
- name: covariates
doc: A ``spark.ml`` ``Matrix`` of covariates
- name: test
doc: Which logistic regression test to use. Can be ``LRT`` or ``Firth``
type: str
- name: offset
doc: An optional double array of offset values. The offset vector is added with coefficient 1 to the linear predictor term X*b.
is_optional: true
returns: A struct containing ``beta``, ``oddsRatio``, ``waldConfidenceInterval``, and ``pValue`` fields. See :ref:`logistic-regression`.
- name: genotype_states
doc: 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.
since: 0.3.0
expr_class: io.projectglow.sql.expressions.GenotypeStates
examples:
python: |
>>> genotypes = [
... Row(calls=[1, 1]),
... Row(calls=[1, 0]),
... Row(calls=[0, 0]),
... Row(calls=[-1, -1])]
>>> df = spark.createDataFrame([Row(genotypes=genotypes)], 'genotypes: array>>')
>>> df.select(glow.genotype_states('genotypes').alias('states')).collect()
[Row(states=[2, 1, 0, -1])]
args:
- name: genotypes
doc: An array of genotype structs with ``calls`` field
returns: An array of integers containing the number of alternate alleles in each call array
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