# Copyright 2019 The Glow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The Glow Python functions
# Note that this file is generated from the definitions in functions.yml.
from pyspark import SparkContext
from pyspark.sql.column import Column, _to_java_column, _to_seq
from typeguard import check_argument_types, check_return_type
from typing import Union
__all__ = [] # Extended within each group
def sc():
return SparkContext._active_spark_context
########### complex_type_manipulation
__all__.append('add_struct_fields')
[docs]def add_struct_fields(struct: Union[Column, str], *fields: Union[Column, str]) -> Column:
"""
Adds fields to a struct.
Added in version 0.3.0.
Examples:
>>> 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))]
Args:
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
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.add_struct_fields(_to_java_column(struct), _to_seq(sc(), fields, _to_java_column)))
assert check_return_type(output)
return output
__all__.append('array_summary_stats')
[docs]def array_summary_stats(arr: Union[Column, str]) -> Column:
"""
Computes the minimum, maximum, mean, standard deviation for an array of numerics.
Added in version 0.3.0.
Examples:
>>> 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)]
Args:
arr : An array of any numeric type
Returns:
A struct containing double ``mean``, ``stdDev``, ``min``, and ``max`` fields
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.array_summary_stats(_to_java_column(arr)))
assert check_return_type(output)
return output
__all__.append('array_to_dense_vector')
[docs]def array_to_dense_vector(arr: Union[Column, str]) -> Column:
"""
Converts an array of numerics into a ``spark.ml`` ``DenseVector``.
Added in version 0.3.0.
Examples:
>>> 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:
arr : The array of numerics
Returns:
A ``spark.ml`` ``DenseVector``
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.array_to_dense_vector(_to_java_column(arr)))
assert check_return_type(output)
return output
__all__.append('array_to_sparse_vector')
[docs]def array_to_sparse_vector(arr: Union[Column, str]) -> Column:
"""
Converts an array of numerics into a ``spark.ml`` ``SparseVector``.
Added in version 0.3.0.
Examples:
>>> 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:
arr : The array of numerics
Returns:
A ``spark.ml`` ``SparseVector``
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.array_to_sparse_vector(_to_java_column(arr)))
assert check_return_type(output)
return output
__all__.append('expand_struct')
[docs]def expand_struct(struct: Union[Column, str]) -> Column:
"""
Promotes fields of a nested struct to top-level columns similar to using ``struct.*`` from SQL, but can be used in more contexts.
Added in version 0.3.0.
Examples:
>>> df = spark.createDataFrame([Row(struct=Row(a=1, b=2))])
>>> df.select(glow.expand_struct(col('struct'))).collect()
[Row(a=1, b=2)]
Args:
struct : The struct to expand
Returns:
Columns corresponding to fields of the input struct
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.expand_struct(_to_java_column(struct)))
assert check_return_type(output)
return output
__all__.append('explode_matrix')
[docs]def explode_matrix(matrix: Union[Column, str]) -> Column:
"""
Explodes a ``spark.ml`` ``Matrix`` (sparse or dense) into multiple arrays, one per row of the matrix.
Added in version 0.3.0.
Examples:
>>> 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])]
Args:
matrix : The ``sparl.ml`` ``Matrix`` to explode
Returns:
An array column in which each row is a row of the input matrix
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.explode_matrix(_to_java_column(matrix)))
assert check_return_type(output)
return output
__all__.append('subset_struct')
[docs]def subset_struct(struct: Union[Column, str], *fields: str) -> Column:
"""
Selects fields from a struct.
Added in version 0.3.0.
Examples:
>>> 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:
struct : Struct from which to select fields
fields : Fields to select
Returns:
A struct containing only the indicated fields
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.subset_struct(_to_java_column(struct), _to_seq(sc(), fields)))
assert check_return_type(output)
return output
__all__.append('vector_to_array')
[docs]def vector_to_array(vector: Union[Column, str]) -> Column:
"""
Converts a ``spark.ml`` ``Vector`` (sparse or dense) to an array of doubles.
Added in version 0.3.0.
Examples:
>>> 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:
vector : Vector to convert
Returns:
An array of doubles
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.vector_to_array(_to_java_column(vector)))
assert check_return_type(output)
return output
########### etl
__all__.append('hard_calls')
[docs]def hard_calls(probabilities: Union[Column, str], numAlts: Union[Column, str], phased: Union[Column, str], threshold: float = None) -> Column:
"""
Converts an array of probabilities to hard calls. The probabilities are assumed to be diploid. See :ref:`variant-data-transformations` for more details.
Added in version 0.3.0.
Examples:
>>> 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:
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
"""
assert check_argument_types()
if threshold is None:
output = Column(sc()._jvm.io.projectglow.functions.hard_calls(_to_java_column(probabilities), _to_java_column(numAlts), _to_java_column(phased)))
else:
output = Column(sc()._jvm.io.projectglow.functions.hard_calls(_to_java_column(probabilities), _to_java_column(numAlts), _to_java_column(phased), threshold))
assert check_return_type(output)
return output
__all__.append('lift_over_coordinates')
[docs]def lift_over_coordinates(contigName: Union[Column, str], start: Union[Column, str], end: Union[Column, str], chainFile: str, minMatchRatio: float = None) -> Column:
"""
Performs liftover for the coordinates of a variant. To perform liftover of alleles and add additional metadata, see :ref:`liftover`.
Added in version 0.3.0.
Examples:
>>> 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:
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
"""
assert check_argument_types()
if minMatchRatio is None:
output = Column(sc()._jvm.io.projectglow.functions.lift_over_coordinates(_to_java_column(contigName), _to_java_column(start), _to_java_column(end), chainFile))
else:
output = Column(sc()._jvm.io.projectglow.functions.lift_over_coordinates(_to_java_column(contigName), _to_java_column(start), _to_java_column(end), chainFile, minMatchRatio))
assert check_return_type(output)
return output
__all__.append('normalize_variant')
[docs]def normalize_variant(contigName: Union[Column, str], start: Union[Column, str], end: Union[Column, str], refAllele: Union[Column, str], altAlleles: Union[Column, str], refGenomePathString: str) -> Column:
"""
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``.
Added in version 0.3.0.
Examples:
>>> 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:
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
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.normalize_variant(_to_java_column(contigName), _to_java_column(start), _to_java_column(end), _to_java_column(refAllele), _to_java_column(altAlleles), refGenomePathString))
assert check_return_type(output)
return output
__all__.append('mean_substitute')
[docs]def mean_substitute(array: Union[Column, str], missingValue: Union[Column, str] = None) -> Column:
"""
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.
Added in version 0.4.0.
Examples:
>>> 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:
array : A numeric array that may contain missing values
missingValue : A value that should be considered missing. If not provided, this parameter defaults to ``-1``.
Returns:
A numeric array with substituted missing values
"""
assert check_argument_types()
if missingValue is None:
output = Column(sc()._jvm.io.projectglow.functions.mean_substitute(_to_java_column(array)))
else:
output = Column(sc()._jvm.io.projectglow.functions.mean_substitute(_to_java_column(array), _to_java_column(missingValue)))
assert check_return_type(output)
return output
########### quality_control
__all__.append('call_summary_stats')
[docs]def call_summary_stats(genotypes: Union[Column, str]) -> Column:
"""
Computes call summary statistics for an array of genotype structs. See :ref:`variant-qc` for more details.
Added in version 0.3.0.
Examples:
>>> schema = 'genotypes: array<struct<calls: array<int>>>'
>>> 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])]
Args:
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`.
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.call_summary_stats(_to_java_column(genotypes)))
assert check_return_type(output)
return output
__all__.append('dp_summary_stats')
[docs]def dp_summary_stats(genotypes: Union[Column, str]) -> Column:
"""
Computes summary statistics for the depth field from an array of genotype structs. See :ref:`variant-qc`.
Added in version 0.3.0.
Examples:
>>> df = spark.createDataFrame([Row(genotypes=[Row(depth=1), Row(depth=2), Row(depth=3)])], 'genotypes: array<struct<depth: int>>')
>>> df.select(glow.expand_struct(glow.dp_summary_stats('genotypes'))).collect()
[Row(mean=2.0, stdDev=1.0, min=1.0, max=3.0)]
Args:
genotypes : An array of genotype structs with ``depth`` field
Returns:
A struct containing ``mean``, ``stdDev``, ``min``, and ``max`` of genotype depths
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.dp_summary_stats(_to_java_column(genotypes)))
assert check_return_type(output)
return output
__all__.append('hardy_weinberg')
[docs]def hardy_weinberg(genotypes: Union[Column, str]) -> Column:
"""
Computes statistics relating to the Hardy Weinberg equilibrium. See :ref:`variant-qc` for more details.
Added in version 0.3.0.
Examples:
>>> genotypes = [
... Row(calls=[1, 1]),
... Row(calls=[1, 0]),
... Row(calls=[0, 0])]
>>> df = spark.createDataFrame([Row(genotypes=genotypes)], 'genotypes: array<struct<calls: array<int>>>')
>>> df.select(glow.expand_struct(glow.hardy_weinberg('genotypes'))).collect()
[Row(hetFreqHwe=0.6, pValueHwe=0.7)]
Args:
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)
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.hardy_weinberg(_to_java_column(genotypes)))
assert check_return_type(output)
return output
__all__.append('gq_summary_stats')
[docs]def gq_summary_stats(genotypes: Union[Column, str]) -> Column:
"""
Computes summary statistics about the genotype quality field for an array of genotype structs. See :ref:`variant-qc`.
Added in version 0.3.0.
Examples:
>>> genotypes = [
... Row(conditionalQuality=1),
... Row(conditionalQuality=2),
... Row(conditionalQuality=3)]
>>> df = spark.createDataFrame([Row(genotypes=genotypes)], 'genotypes: array<struct<conditionalQuality: int>>')
>>> df.select(glow.expand_struct(glow.gq_summary_stats('genotypes'))).collect()
[Row(mean=2.0, stdDev=1.0, min=1.0, max=3.0)]
Args:
genotypes : The array of genotype structs with ``conditionalQuality`` field
Returns:
A struct containing ``mean``, ``stdDev``, ``min``, and ``max`` of genotype qualities
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.gq_summary_stats(_to_java_column(genotypes)))
assert check_return_type(output)
return output
__all__.append('sample_call_summary_stats')
[docs]def sample_call_summary_stats(genotypes: Union[Column, str], refAllele: Union[Column, str], alternateAlleles: Union[Column, str]) -> Column:
"""
Computes per-sample call summary statistics. See :ref:`sample-qc` for more details.
Added in version 0.3.0.
Examples:
>>> 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<string>, genotypes: array<struct<sampleId: string, calls: array<int>>>')
>>> 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)])]
Args:
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`.
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.sample_call_summary_stats(_to_java_column(genotypes), _to_java_column(refAllele), _to_java_column(alternateAlleles)))
assert check_return_type(output)
return output
__all__.append('sample_dp_summary_stats')
[docs]def sample_dp_summary_stats(genotypes: Union[Column, str]) -> Column:
"""
Computes per-sample summary statistics about the depth field in an array of genotype structs.
Added in version 0.3.0.
Examples:
>>> sites = [
... {'genotypes': [{'sampleId': 'NA12878', 'depth': 1}]},
... {'genotypes': [{'sampleId': 'NA12878', 'depth': 2}]},
... {'genotypes': [{'sampleId': 'NA12878', 'depth': 3}]}]
>>> df = spark.createDataFrame(sites, 'genotypes: array<struct<depth: int, sampleId: string>>')
>>> 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:
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.
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.sample_dp_summary_stats(_to_java_column(genotypes)))
assert check_return_type(output)
return output
__all__.append('sample_gq_summary_stats')
[docs]def sample_gq_summary_stats(genotypes: Union[Column, str]) -> Column:
"""
Computes per-sample summary statistics about the genotype quality field in an array of genotype structs.
Added in version 0.3.0.
Examples:
>>> 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<struct<sampleId: string, conditionalQuality: int>>')
>>> 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:
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.
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.sample_gq_summary_stats(_to_java_column(genotypes)))
assert check_return_type(output)
return output
########### gwas_functions
__all__.append('linear_regression_gwas')
[docs]def linear_regression_gwas(genotypes: Union[Column, str], phenotypes: Union[Column, str], covariates: Union[Column, str]) -> Column:
"""
Performs a linear regression association test optimized for performance in a GWAS setting. See :ref:`linear-regression` for details.
Added in version 0.3.0.
Examples:
>>> 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:
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`.
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.linear_regression_gwas(_to_java_column(genotypes), _to_java_column(phenotypes), _to_java_column(covariates)))
assert check_return_type(output)
return output
__all__.append('logistic_regression_gwas')
[docs]def logistic_regression_gwas(genotypes: Union[Column, str], phenotypes: Union[Column, str], covariates: Union[Column, str], test: str, offset: Union[Column, str] = None) -> Column:
"""
Performs a logistic regression association test optimized for performance in a GWAS setting. See :ref:`logistic-regression` for more details.
Added in version 0.3.0.
Examples:
>>> 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:
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``
offset : An optional double array of offset values. The offset vector is added with coefficient 1 to the linear predictor term X*b.
Returns:
A struct containing ``beta``, ``oddsRatio``, ``waldConfidenceInterval``, and ``pValue`` fields. See :ref:`logistic-regression`.
"""
assert check_argument_types()
if offset is None:
output = Column(sc()._jvm.io.projectglow.functions.logistic_regression_gwas(_to_java_column(genotypes), _to_java_column(phenotypes), _to_java_column(covariates), test))
else:
output = Column(sc()._jvm.io.projectglow.functions.logistic_regression_gwas(_to_java_column(genotypes), _to_java_column(phenotypes), _to_java_column(covariates), test, _to_java_column(offset)))
assert check_return_type(output)
return output
__all__.append('genotype_states')
[docs]def genotype_states(genotypes: Union[Column, str]) -> Column:
"""
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.
Added in version 0.3.0.
Examples:
>>> 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<struct<calls: array<int>>>')
>>> df.select(glow.genotype_states('genotypes').alias('states')).collect()
[Row(states=[2, 1, 0, -1])]
Args:
genotypes : An array of genotype structs with ``calls`` field
Returns:
An array of integers containing the number of alternate alleles in each call array
"""
assert check_argument_types()
output = Column(sc()._jvm.io.projectglow.functions.genotype_states(_to_java_column(genotypes)))
assert check_return_type(output)
return output