scikit-allel - Explore and analyse genetic variation¶

GenotypeArray¶

class allel.model.GenotypeArray[source]¶

Array of discrete genotype calls.

Parameters:	data : array_like, int, shape (n_variants, n_samples, ploidy) Genotype data. **kwargs : keyword arguments All keyword arguments are passed through to numpy.array().

Notes

This class represents data on discrete genotype calls as a 3-dimensional numpy array of integers. By convention the first dimension corresponds to the variants genotyped, the second dimension corresponds to the samples genotyped, and the third dimension corresponds to the ploidy of the samples.

Each integer within the array corresponds to an allele index, where 0 is the reference allele, 1 is the first alternate allele, 2 is the second alternate allele, ... and -1 (or any other negative integer) is a missing allele call. A single byte integer dtype (int8) can represent up to 127 distinct alleles, which is usually sufficient. The actual alleles (i.e., the alternate nucleotide sequences) and the physical positions of the variants within the genome of an organism are stored in separate arrays, discussed elsewhere.

In many cases the number of distinct alleles for each variant is small, e.g., less than 10, or even 2 (all variants are biallelic). In these cases a genotype array is not the most compact way of storing genotype data in memory. This class defines functions for bit-packing diploid genotype calls into single bytes, and for transforming genotype arrays into sparse matrices, which can assist in cases where memory usage needs to be minimised. Note however that these more compact representations do not allow the same flexibility in terms of using numpy universal functions to access and manipulate data.

Arrays of this class can store either phased or unphased genotype calls. If the genotypes are phased (i.e., haplotypes have been resolved) then individual haplotypes can be extracted by converting to a HaplotypeArray then indexing the second dimension. If the genotype calls are unphased then the ordering of alleles along the third (ploidy) dimension is arbitrary. N.B., this means that an unphased diploid heterozygous call could be stored as (0, 1) or equivalently as (1, 0).

A genotype array can store genotype calls with any ploidy > 1. For haploid calls, use a HaplotypeArray. Note that genotype arrays are not capable of storing calls for samples with differing or variable ploidy.

Examples

Instantiate a genotype array:

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]], dtype='i1')
>>> g.dtype
dtype('int8')
>>> g.ndim
3
>>> g.shape
(3, 2, 2)
>>> g.n_variants
3
>>> g.n_samples
2
>>> g.ploidy
2

Genotype calls for a single variant at all samples can be obtained by indexing the first dimension, e.g.:

>>> g[1]
array([[0, 1],
       [1, 1]], dtype=int8)

Genotype calls for a single sample at all variants can be obtained by indexing the second dimension, e.g.:

>>> g[:, 1]
array([[ 0,  1],
       [ 1,  1],
       [-1, -1]], dtype=int8)

A genotype call for a single sample at a single variant can be obtained by indexing the first and second dimensions, e.g.:

>>> g[1, 0]
array([0, 1], dtype=int8)

A genotype array can store polyploid calls, e.g.:

>>> g = allel.model.GenotypeArray([[[0, 0, 0], [0, 0, 1]],
...                                [[0, 1, 1], [1, 1, 1]],
...                                [[0, 1, 2], [-1, -1, -1]]],
...                                dtype='i1')
>>> g.ploidy
3

n_variants[source]¶

Number of variants (length of first array dimension).

n_samples[source]¶

Number of samples (length of second array dimension).

ploidy[source]¶

Sample ploidy (length of third array dimension).

subset(variants=None, samples=None)[source]¶

Make a sub-selection of variants and/or samples.

Parameters:	variants : array_like Boolean array or list of indices. samples : array_like Boolean array or list of indices.
Returns:	out : GenotypeArray

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1], [1, 1]],
...                                [[0, 1], [1, 1], [1, 2]],
...                                [[0, 2], [-1, -1], [-1, -1]]])
>>> g.subset(variants=[0, 1], samples=[0, 2])
GenotypeArray((2, 2, 2), dtype=int64)
[[[0 0]
  [1 1]]
 [[0 1]
  [1 2]]]

is_called()[source]¶

Find non-missing genotype calls.

Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]])
>>> g.is_called()
array([[ True,  True],
       [ True,  True],
       [ True, False]], dtype=bool)

is_missing()[source]¶

Find missing genotype calls.

Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]])
>>> g.is_missing()
array([[False, False],
       [False, False],
       [False,  True]], dtype=bool)

is_hom(allele=None)[source]¶

Find genotype calls that are homozygous.

Parameters:	allele : int, optional Allele index.
Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> g.is_hom()
array([[ True, False],
       [False,  True],
       [ True, False]], dtype=bool)
>>> g.is_hom(allele=1)
array([[False, False],
       [False,  True],
       [False, False]], dtype=bool)

is_hom_ref()[source]¶

Find genotype calls that are homozygous for the reference allele.

Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]])
>>> g.is_hom_ref()
array([[ True, False],
       [False, False],
       [False, False]], dtype=bool)

is_hom_alt()[source]¶

Find genotype calls that are homozygous for any alternate (i.e., non-reference) allele.

Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> g.is_hom_alt()
array([[False, False],
       [False,  True],
       [ True, False]], dtype=bool)

is_het()[source]¶

Find genotype calls that are heterozygous.

Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]])
>>> g.is_het()
array([[False,  True],
       [ True, False],
       [ True, False]], dtype=bool)

is_call(call)[source]¶

Find genotypes with a given call.

Parameters:	call : array_like, int, shape (ploidy,) The genotype call to find.
Returns:	out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype is call.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]])
>>> g.is_call((0, 2))
array([[False, False],
       [False, False],
       [ True, False]], dtype=bool)

count_called(axis=None)[source]¶

count_missing(axis=None)[source]¶

count_hom(allele=None, axis=None)[source]¶

count_hom_ref(axis=None)[source]¶

count_hom_alt(axis=None)[source]¶

count_het(axis=None)[source]¶

count_call(call, axis=None)[source]¶

count_alleles(max_allele=None)[source]¶

Count the number of calls of each allele per variant.

Parameters:	max_allele : int, optional The highest allele index to count. Alleles above this will be ignored.
Returns:	ac : AlleleCountsArray

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> g.count_alleles()
AlleleCountsArray((3, 3), dtype=int32)
[[3 1 0]
 [1 2 1]
 [0 0 2]]
>>> g.count_alleles(max_allele=1)
AlleleCountsArray((3, 2), dtype=int32)
[[3 1]
 [1 2]
 [0 0]]

to_haplotypes(copy=False)[source]¶

Reshape a genotype array to view it as haplotypes by dropping the ploidy dimension.

Returns:	h : HaplotypeArray, shape (n_variants, n_samples * ploidy) Haplotype array. copy : bool, optional If True, make a copy of the data.

Notes

If genotype calls are unphased, the haplotypes returned by this function will bear no resemblance to the true haplotypes.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]])
>>> g.to_haplotypes()
HaplotypeArray((3, 4), dtype=int64)
[[ 0  0  0  1]
 [ 0  1  1  1]
 [ 0  2 -1 -1]]

to_n_alt(fill=0)[source]¶

Transform each genotype call into the number of non-reference alleles.

Parameters:	fill : int, optional Use this value to represent missing calls.
Returns:	out : ndarray, int, shape (n_variants, n_samples) Array of non-ref alleles per genotype call.

Notes

This function simply counts the number of non-reference alleles, it makes no distinction between different alternate alleles.

By default this function returns 0 for missing genotype calls and for homozygous reference genotype calls. Use the fill argument to change how missing calls are represented.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> g.to_n_alt()
array([[0, 1],
       [1, 2],
       [2, 0]], dtype=int8)
>>> g.to_n_alt(fill=-1)
array([[ 0,  1],
       [ 1,  2],
       [ 2, -1]], dtype=int8)

to_allele_counts(alleles=None)[source]¶

Transform genotype calls into allele counts per call.

Parameters:	alleles : sequence of ints, optional If not None, count only the given alleles. (By default, count all alleles.)
Returns:	out : ndarray, uint8, shape (n_variants, n_samples, len(alleles)) Array of allele counts per call.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                               [[0, 2], [1, 1]],
...                               [[2, 2], [-1, -1]]])
>>> g.to_allele_counts()
array([[[2, 0, 0],
        [1, 1, 0]],
       [[1, 0, 1],
        [0, 2, 0]],
       [[0, 0, 2],
        [0, 0, 0]]], dtype=uint8)
>>> g.to_allele_counts(alleles=(0, 1))
array([[[2, 0],
        [1, 1]],
       [[1, 0],
        [0, 2]],
       [[0, 0],
        [0, 0]]], dtype=uint8)

to_packed(boundscheck=True)[source]¶

Pack diploid genotypes into a single byte for each genotype, using the left-most 4 bits for the first allele and the right-most 4 bits for the second allele. Allows single byte encoding of diploid genotypes for variants with up to 15 alleles.

Parameters:	boundscheck : bool, optional If False, do not check that minimum and maximum alleles are compatible with bit-packing.
Returns:	packed : ndarray, uint8, shape (n_variants, n_samples) Bit-packed genotype array.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]], dtype='i1')
>>> g.to_packed()
array([[  0,   1],
       [  2,  17],
       [ 34, 239]], dtype=uint8)

static from_packed(packed)[source]¶

Unpack diploid genotypes that have been bit-packed into single bytes.

Parameters:	packed : ndarray, uint8, shape (n_variants, n_samples) Bit-packed diploid genotype array.
Returns:	g : GenotypeArray, shape (n_variants, n_samples, 2) Genotype array.

Examples

>>> import allel
>>> import numpy as np
>>> packed = np.array([[0, 1],
...                    [2, 17],
...                    [34, 239]], dtype='u1')
>>> allel.model.GenotypeArray.from_packed(packed)
GenotypeArray((3, 2, 2), dtype=int8)
[[[ 0  0]
  [ 0  1]]
 [[ 0  2]
  [ 1  1]]
 [[ 2  2]
  [-1 -1]]]

to_sparse(format='csr', **kwargs)[source]¶

Convert into a sparse matrix.

Parameters:	format : {‘coo’, ‘csc’, ‘csr’, ‘dia’, ‘dok’, ‘lil’} Sparse matrix format. kwargs : keyword arguments Passed through to sparse matrix constructor.
Returns:	m : scipy.sparse.spmatrix Sparse matrix

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 1], [0, 1]],
...                                [[1, 1], [0, 0]],
...                                [[0, 0], [-1, -1]]], dtype='i1')
>>> m = g.to_sparse(format='csr')
>>> m
<4x4 sparse matrix of type '<class 'numpy.int8'>'
    with 6 stored elements in Compressed Sparse Row format>
>>> m.data
array([ 1,  1,  1,  1, -1, -1], dtype=int8)
>>> m.indices
array([1, 3, 0, 1, 2, 3], dtype=int32)
>>> m.indptr
array([0, 0, 2, 4, 6], dtype=int32)

static from_sparse(m, ploidy, order=None, out=None)[source]¶

Construct a genotype array from a sparse matrix.

Parameters:	m : scipy.sparse.spmatrix Sparse matrix ploidy : int The sample ploidy. order : {‘C’, ‘F’}, optional Whether to store data in C (row-major) or Fortran (column-major) order in memory. out : ndarray, shape (n_variants, n_samples), optional Use this array as the output buffer.
Returns:	g : GenotypeArray, shape (n_variants, n_samples, ploidy) Genotype array.

Examples

>>> import allel
>>> import numpy as np
>>> import scipy.sparse
>>> data = np.array([ 1,  1,  1,  1, -1, -1], dtype=np.int8)
>>> indices = np.array([1, 3, 0, 1, 2, 3], dtype=np.int32)
>>> indptr = np.array([0, 0, 2, 4, 6], dtype=np.int32)
>>> m = scipy.sparse.csr_matrix((data, indices, indptr))
>>> g = allel.model.GenotypeArray.from_sparse(m, ploidy=2)
>>> g
GenotypeArray((4, 2, 2), dtype=int8)
[[[ 0  0]
  [ 0  0]]
 [[ 0  1]
  [ 0  1]]
 [[ 1  1]
  [ 0  0]]
 [[ 0  0]
  [-1 -1]]]

haploidify_samples()[source]¶

Construct a pseudo-haplotype for each sample by randomly selecting an allele from each genotype call.

Returns:	h : HaplotypeArray

Examples

>>> import allel
>>> import numpy as np
>>> np.random.seed(42)
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[1, 2], [2, 1]],
...                                [[2, 2], [-1, -1]]])
>>> g.haploidify_samples()
HaplotypeArray((4, 2), dtype=int64)
[[ 0  1]
 [ 0  1]
 [ 1  1]
 [ 2 -1]]
>>> g = allel.model.GenotypeArray([[[0, 0, 0], [0, 0, 1]],
...                                [[0, 1, 1], [1, 1, 1]],
...                                [[0, 1, 2], [-1, -1, -1]]])
>>> g.haploidify_samples()
HaplotypeArray((3, 2), dtype=int64)
[[ 0  0]
 [ 1  1]
 [ 2 -1]]

HaplotypeArray¶

class allel.model.HaplotypeArray[source]¶

Array of haplotypes.

Parameters:	data : array_like, int, shape (n_variants, n_haplotypes) Haplotype data. **kwargs : keyword arguments All keyword arguments are passed through to numpy.array().

Notes

This class represents haplotype data as a 2-dimensional numpy array of integers. By convention the first dimension corresponds to the variants genotyped, the second dimension corresponds to the haplotypes.

Each integer within the array corresponds to an allele index, where 0 is the reference allele, 1 is the first alternate allele, 2 is the second alternate allele, ... and -1 (or any other negative integer) is a missing allele call.

If adjacent haplotypes originate from the same sample, then a haplotype array can also be viewed as a genotype array. However, this is not a requirement.

Examples

Instantiate a haplotype array:

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 1],
...                                 [0, 1, 1, 1],
...                                 [0, 2, -1, -1]], dtype='i1')
>>> h.dtype
dtype('int8')
>>> h.ndim
2
>>> h.shape
(3, 4)
>>> h.n_variants
3
>>> h.n_haplotypes
4

Allele calls for a single variant at all haplotypes can be obtained by indexing the first dimension, e.g.:

>>> h[1]
array([0, 1, 1, 1], dtype=int8)

A single haplotype can be obtained by indexing the second dimension, e.g.:

>>> h[:, 1]
array([0, 1, 2], dtype=int8)

An allele call for a single haplotype at a single variant can be obtained by indexing the first and second dimensions, e.g.:

>>> h[1, 0]
0

View haplotypes as diploid genotypes:

>>> h.to_genotypes(ploidy=2)
GenotypeArray((3, 2, 2), dtype=int8)
[[[ 0  0]
  [ 0  1]]
 [[ 0  1]
  [ 1  1]]
 [[ 0  2]
  [-1 -1]]]

n_variants[source]¶

Number of variants (length of first dimension).

n_haplotypes[source]¶

Number of haplotypes (length of second dimension).

subset(variants=None, haplotypes=None)[source]¶

Make a sub-selection of variants and/or haplotypes.

Parameters:	variants : array_like Boolean array or list of indices. haplotypes : array_like Boolean array or list of indices.
Returns:	out : HaplotypeArray

is_called()[source]¶

is_missing()[source]¶

is_ref()[source]¶

is_alt(allele=None)[source]¶

is_call(allele)[source]¶

count_called(axis=None)[source]¶

count_missing(axis=None)[source]¶

count_ref(axis=None)[source]¶

count_alt(axis=None)[source]¶

count_call(allele, axis=None)[source]¶

count_alleles(max_allele=None)[source]¶

Count the number of calls of each allele per variant.

Parameters:	max_allele : int, optional The highest allele index to count. Alleles greater than this index will be ignored.
Returns:	ac : AlleleCountsArray, int, shape (n_variants, n_alleles)

Examples

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 1],
...                                 [0, 1, 1, 1],
...                                 [0, 2, -1, -1]], dtype='i1')
>>> ac = h.count_alleles()
>>> ac
AlleleCountsArray((3, 3), dtype=int32)
[[3 1 0]
 [1 3 0]
 [1 0 1]]

to_genotypes(ploidy, copy=False)[source]¶

Reshape a haplotype array to view it as genotypes by restoring the ploidy dimension.

Parameters:	ploidy : int The sample ploidy.
Returns:	g : ndarray, int, shape (n_variants, n_samples, ploidy) Genotype array (sharing same underlying buffer). copy : bool, optional If True, copy the data.

Examples

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 1],
...                                 [0, 1, 1, 1],
...                                 [0, 2, -1, -1]], dtype='i1')
>>> h.to_genotypes(ploidy=2)
GenotypeArray((3, 2, 2), dtype=int8)
[[[ 0  0]
  [ 0  1]]
 [[ 0  1]
  [ 1  1]]
 [[ 0  2]
  [-1 -1]]]

to_sparse(format='csr', **kwargs)[source]¶

Convert into a sparse matrix.

Parameters:	format : {‘coo’, ‘csc’, ‘csr’, ‘dia’, ‘dok’, ‘lil’} Sparse matrix format. kwargs : keyword arguments Passed through to sparse matrix constructor.
Returns:	m : scipy.sparse.spmatrix Sparse matrix

Examples

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 0],
...                                 [0, 1, 0, 1],
...                                 [1, 1, 0, 0],
...                                 [0, 0, -1, -1]], dtype='i1')
>>> m = h.to_sparse(format='csr')
>>> m
<4x4 sparse matrix of type '<class 'numpy.int8'>'
    with 6 stored elements in Compressed Sparse Row format>
>>> m.data
array([ 1,  1,  1,  1, -1, -1], dtype=int8)
>>> m.indices
array([1, 3, 0, 1, 2, 3], dtype=int32)
>>> m.indptr
array([0, 0, 2, 4, 6], dtype=int32)

static from_sparse(m, order=None, out=None)[source]¶

Construct a haplotype array from a sparse matrix.

Parameters:	m : scipy.sparse.spmatrix Sparse matrix order : {‘C’, ‘F’}, optional Whether to store data in C (row-major) or Fortran (column-major) order in memory. out : ndarray, shape (n_variants, n_samples), optional Use this array as the output buffer.
Returns:	h : HaplotypeArray, shape (n_variants, n_haplotypes) Haplotype array.

Examples

>>> import allel
>>> import numpy as np
>>> import scipy.sparse
>>> data = np.array([ 1,  1,  1,  1, -1, -1], dtype=np.int8)
>>> indices = np.array([1, 3, 0, 1, 2, 3], dtype=np.int32)
>>> indptr = np.array([0, 0, 2, 4, 6], dtype=np.int32)
>>> m = scipy.sparse.csr_matrix((data, indices, indptr))
>>> h = allel.model.HaplotypeArray.from_sparse(m)
>>> h
HaplotypeArray((4, 4), dtype=int8)
[[ 0  0  0  0]
 [ 0  1  0  1]
 [ 1  1  0  0]
 [ 0  0 -1 -1]]

AlleleCountsArray¶

class allel.model.AlleleCountsArray[source]¶

Array of allele counts.

Parameters:	data : array_like, int, shape (n_variants, n_alleles) Allele counts data. **kwargs : keyword arguments All keyword arguments are passed through to numpy.array().

Notes

This class represents allele counts as a 2-dimensional numpy array of integers. By convention the first dimension corresponds to the variants genotyped, the second dimension corresponds to the alleles counted.

Examples

Obtain allele counts from a genotype array:

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 1], [1, 1]],
...                                [[0, 2], [-1, -1]]], dtype='i1')
>>> ac = g.count_alleles()
>>> ac
AlleleCountsArray((3, 3), dtype=int32)
[[3 1 0]
 [1 3 0]
 [1 0 1]]
>>> ac.dtype
dtype('int32')
>>> ac.shape
(3, 3)
>>> ac.n_variants
3
>>> ac.n_alleles
3

Allele counts for a single variant can be obtained by indexing the first dimension, e.g.:

>>> ac[1]
array([1, 3, 0], dtype=int32)

Allele counts for a specific allele can be obtained by indexing the second dimension, e.g., reference allele counts:

>>> ac[:, 0]
array([3, 1, 1], dtype=int32)

Calculate the total number of alleles called for each variant:

>>> import numpy as np
>>> n = np.sum(ac, axis=1)
>>> n
array([4, 4, 2])

n_variants[source]¶

Number of variants (length of first array dimension).

n_alleles[source]¶

Number of alleles (length of second array dimension).

allelism()[source]¶

Determine the number of distinct alleles observed for each variant.

Returns:	n : ndarray, int, shape (n_variants,) Allelism array.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.allelism()
array([2, 3, 1])

is_variant()[source]¶

Find variants with at least one non-reference allele call.

Returns:	out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.is_variant()
array([False,  True,  True,  True], dtype=bool)

is_non_variant()[source]¶

Find variants with no non-reference allele calls.

Returns:	out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.is_non_variant()
array([ True, False, False, False], dtype=bool)

is_segregating()[source]¶

Find segregating variants (where more than one allele is observed).

Returns:	out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.is_segregating()
array([False,  True,  True, False], dtype=bool)

is_non_segregating(allele=None)[source]¶

Find non-segregating variants (where at most one allele is observed).

Parameters:	allele : int, optional Allele index.
Returns:	out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.is_non_segregating()
array([ True, False, False,  True], dtype=bool)
>>> ac.is_non_segregating(allele=2)
array([False, False, False,  True], dtype=bool)

is_singleton(allele)[source]¶

Find variants with a single call for the given allele.

Parameters:	allele : int, optional Allele index.
Returns:	out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [0, 1]],
...                                [[1, 1], [1, 2]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.is_singleton(allele=1)
array([False,  True, False, False], dtype=bool)
>>> ac.is_singleton(allele=2)
array([False, False,  True, False], dtype=bool)

is_doubleton(allele)[source]¶

Find variants with exactly two calls for the given allele.

Parameters:	allele : int, optional Allele index.
Returns:	out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [1, 1]],
...                                [[1, 1], [1, 2]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.is_doubleton(allele=1)
array([False,  True, False, False], dtype=bool)
>>> ac.is_doubleton(allele=2)
array([False, False, False,  True], dtype=bool)

count_variant()[source]¶

count_non_variant()[source]¶

count_segregating()[source]¶

count_non_segregating(allele=None)[source]¶

count_singleton(allele=1)[source]¶

count_doubleton(allele=1)[source]¶

to_frequencies(fill=nan)[source]¶

Compute allele frequencies.

Parameters:	fill : float, optional Value to use when number of allele calls is 0.
Returns:	af : ndarray, float, shape (n_variants, n_alleles)

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1]],
...                                [[0, 2], [1, 1]],
...                                [[2, 2], [-1, -1]]])
>>> ac = g.count_alleles()
>>> ac.to_frequencies()
array([[ 0.75,  0.25,  0.  ],
       [ 0.25,  0.5 ,  0.25],
       [ 0.  ,  0.  ,  1.  ]])

SortedIndex¶

class allel.model.SortedIndex[source]¶

Index of sorted values, e.g., positions from a single chromosome or contig.

Parameters:	data : array_like Values in ascending order. **kwargs : keyword arguments All keyword arguments are passed through to numpy.array().

Notes

Values must be given in ascending order, although duplicate values may be present (i.e., values must be monotonically increasing).

Examples

>>> import allel
>>> idx = allel.model.SortedIndex([2, 5, 14, 15, 42, 42, 77], dtype='i4')
>>> idx.dtype
dtype('int32')
>>> idx.ndim
1
>>> idx.shape
(7,)
>>> idx.is_unique
False

is_unique[source]¶

True if no duplicate entries.

locate_key(key)[source]¶

Get index location for the requested key.

Parameters:	key : int Value to locate.
Returns:	loc : int or slice Location of key (will be slice if there are duplicate entries).

Examples

>>> import allel
>>> idx = allel.model.SortedIndex([3, 6, 6, 11])
>>> idx.locate_key(3)
0
>>> idx.locate_key(11)
3
>>> idx.locate_key(6)
slice(1, 3, None)
>>> try:
...     idx.locate_key(2)
... except KeyError as e:
...     print(e)
...
2

locate_keys(keys, strict=True)[source]¶

Get index locations for the requested keys.

Parameters:	keys : array_like, int Array of keys to locate. strict : bool, optional If True, raise KeyError if any keys are not found in the index.
Returns:	loc : ndarray, bool Boolean array with location of values.

Examples

>>> import allel
>>> idx1 = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.model.SortedIndex([4, 6, 20, 39])
>>> loc = idx1.locate_keys(idx2, strict=False)
>>> loc
array([False,  True, False,  True, False], dtype=bool)
>>> idx1[loc]
SortedIndex(2, dtype=int64)
[ 6 20]

locate_intersection(other)[source]¶

Locate the intersection with another array.

Parameters:	other : array_like, int Array of values to intersect.
Returns:	loc : ndarray, bool Boolean array with location of intersection. loc_other : ndarray, bool Boolean array with location in other of intersection.

Examples

>>> import allel
>>> idx1 = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.model.SortedIndex([4, 6, 20, 39])
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False,  True, False,  True, False], dtype=bool)
>>> loc2
array([False,  True,  True, False], dtype=bool)
>>> idx1[loc1]
SortedIndex(2, dtype=int64)
[ 6 20]
>>> idx2[loc2]
SortedIndex(2, dtype=int64)
[ 6 20]

intersect(other)[source]¶

Intersect with other sorted index.

Parameters:	other : array_like, int Array of values to intersect with.
Returns:	out : SortedIndex Values in common.

Examples

>>> import allel
>>> idx1 = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> idx2 = allel.model.SortedIndex([4, 6, 20, 39])
>>> idx1.intersect(idx2)
SortedIndex(2, dtype=int64)
[ 6 20]

locate_range(start=None, stop=None)[source]¶

Locate slice of index containing all entries within start and stop values inclusive.

Parameters:	start : int, optional Start value. stop : int, optional Stop value.
Returns:	loc : slice Slice object.

Examples

>>> import allel
>>> idx = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> loc = idx.locate_range(4, 32)
>>> loc
slice(1, 4, None)
>>> idx[loc]
SortedIndex(3, dtype=int64)
[ 6 11 20]

intersect_range(start=None, stop=None)[source]¶

Intersect with range defined by start and stop values inclusive.

Parameters:	start : int, optional Start value. stop : int, optional Stop value.
Returns:	idx : SortedIndex

Examples

>>> import allel
>>> idx = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> idx.intersect_range(4, 32)
SortedIndex(3, dtype=int64)
[ 6 11 20]

locate_ranges(starts, stops, strict=True)[source]¶

Locate items within the given ranges.

Parameters:	starts : array_like, int Range start values. stops : array_like, int Range stop values. strict : bool, optional If True, raise KeyError if any ranges contain no entries.
Returns:	loc : ndarray, bool Boolean array with location of entries found.

Examples

>>> import allel
>>> import numpy as np
>>> idx = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
...                    [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc = idx.locate_ranges(starts, stops, strict=False)
>>> loc
array([False,  True,  True, False,  True], dtype=bool)
>>> idx[loc]
SortedIndex(3, dtype=int64)
[ 6 11 35]

locate_intersection_ranges(starts, stops)[source]¶

Locate the intersection with a set of ranges.

Parameters:	starts : array_like, int Range start values. stops : array_like, int Range stop values.
Returns:	loc : ndarray, bool Boolean array with location of entries found. loc_ranges : ndarray, bool Boolean array with location of ranges containing one or more entries.

Examples

>>> import allel
>>> import numpy as np
>>> idx = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
...                    [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> loc, loc_ranges = idx.locate_intersection_ranges(starts, stops)
>>> loc
array([False,  True,  True, False,  True], dtype=bool)
>>> loc_ranges
array([False,  True, False,  True, False], dtype=bool)
>>> idx[loc]
SortedIndex(3, dtype=int64)
[ 6 11 35]
>>> ranges[loc_ranges]
array([[ 6, 17],
       [31, 35]])

intersect_ranges(starts, stops)[source]¶

Intersect with a set of ranges.

Parameters:	starts : array_like, int Range start values. stops : array_like, int Range stop values.
Returns:	idx : SortedIndex

Examples

>>> import allel
>>> import numpy as np
>>> idx = allel.model.SortedIndex([3, 6, 11, 20, 35])
>>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35],
...                    [100, 120]])
>>> starts = ranges[:, 0]
>>> stops = ranges[:, 1]
>>> idx.intersect_ranges(starts, stops)
SortedIndex(3, dtype=int64)
[ 6 11 35]

UniqueIndex¶

class allel.model.UniqueIndex[source]¶

Array of unique values (e.g., variant or sample identifiers).

Parameters:	data : array_like Values. **kwargs : keyword arguments All keyword arguments are passed through to numpy.array().

Notes

This class represents an arbitrary set of unique values, e.g., sample or variant identifiers.

There is no need for values to be sorted. However, all values must be unique within the array, and must be hashable objects.

Examples

>>> import allel
>>> idx = allel.model.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx.dtype
dtype('<U1')
>>> idx.ndim
1
>>> idx.shape
(4,)

locate_key(key)[source]¶

Get index location for the requested key.

Parameters:	key : object Key to locate.
Returns:	loc : int Location of key.

Examples

>>> import allel
>>> idx = allel.model.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx.locate_key('A')
0
>>> idx.locate_key('B')
2
>>> try:
...     idx.locate_key('X')
... except KeyError as e:
...     print(e)
...
'X'

locate_keys(keys, strict=True)[source]¶

Get index locations for the requested keys.

Parameters:	keys : array_like Array of keys to locate. strict : bool, optional If True, raise KeyError if any keys are not found in the index.
Returns:	loc : ndarray, bool Boolean array with location of keys.

Examples

>>> import allel
>>> idx = allel.model.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx.locate_keys(['F', 'C'])
array([False,  True, False,  True], dtype=bool)
>>> idx.locate_keys(['X', 'F', 'G', 'C', 'Z'], strict=False)
array([False,  True, False,  True], dtype=bool)

locate_intersection(other)[source]¶

Locate the intersection with another array.

Parameters:	other : array_like Array to intersect.
Returns:	loc : ndarray, bool Boolean array with location of intersection. loc_other : ndarray, bool Boolean array with location in other of intersection.

Examples

>>> import allel
>>> idx1 = allel.model.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx2 = allel.model.UniqueIndex(['X', 'F', 'G', 'C', 'Z'])
>>> loc1, loc2 = idx1.locate_intersection(idx2)
>>> loc1
array([False,  True, False,  True], dtype=bool)
>>> loc2
array([False,  True, False,  True, False], dtype=bool)
>>> idx1[loc1]
UniqueIndex(2, dtype=<U1)
['C' 'F']
>>> idx2[loc2]
UniqueIndex(2, dtype=<U1)
['F' 'C']

intersect(other)[source]¶

Intersect with other.

Parameters:	other : array_like Array to intersect.
Returns:	out : UniqueIndex

Examples

>>> import allel
>>> idx1 = allel.model.UniqueIndex(['A', 'C', 'B', 'F'])
>>> idx2 = allel.model.UniqueIndex(['X', 'F', 'G', 'C', 'Z'])
>>> idx1.intersect(idx2)
UniqueIndex(2, dtype=<U1)
['C' 'F']
>>> idx2.intersect(idx1)
UniqueIndex(2, dtype=<U1)
['F' 'C']

SortedMultiIndex¶

class allel.model.SortedMultiIndex(l1, l2, copy=True)[source]¶

Two-level index of sorted values, e.g., variant positions from two or more chromosomes/contigs.

Parameters:	l1 : array_like First level values in ascending order. l2 : array_like Second level values, in ascending order within each sub-level. copy : bool, optional If True, inputs will be copied into new arrays.

Examples

>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.model.SortedMultiIndex(chrom, pos)
>>> len(idx)
6

locate_key(k1, k2=None)[source]¶

Get index location for the requested key.

Parameters:	k1 : object Level 1 key. k2 : object, optional Level 2 key.
Returns:	loc : int or slice Location of requested key (will be slice if there are duplicate entries).

Examples

>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.model.SortedMultiIndex(chrom, pos)
>>> idx.locate_key('chr1')
slice(0, 2, None)
>>> idx.locate_key('chr1', 4)
1
>>> idx.locate_key('chr2', 5)
slice(3, 5, None)
>>> try:
...     idx.locate_key('chr3', 4)
... except KeyError as e:
...     print(e)
...
('chr3', 4)

locate_range(k1, start=None, stop=None)[source]¶

Locate slice of index containing all entries within the range key:start-stop inclusive.

Parameters:	key : object Level 1 key value. start : object, optional Level 2 start value. stop : object, optional Level 2 stop value.
Returns:	loc : slice Slice object.

Examples

>>> import allel
>>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3']
>>> pos = [1, 4, 2, 5, 5, 3]
>>> idx = allel.model.SortedMultiIndex(chrom, pos)
>>> idx.locate_range('chr1')
slice(0, 2, None)
>>> idx.locate_range('chr1', 1, 4)
slice(0, 2, None)
>>> idx.locate_range('chr2', 3, 7)
slice(3, 5, None)
>>> try:
...     idx.locate_range('chr3', 4, 9)
... except KeyError as e:
...     print(e)
('chr3', 4, 9)

VariantTable¶

class allel.model.VariantTable[source]¶

TODO

n_variants[source]¶

Number of variants (length of first dimension).

names[source]¶

eval(expression, vm='numexpr')[source]¶

TODO doco

query(expression, vm='numexpr')[source]¶

TODO doco

GenotypeCArray¶

class allel.bcolz.GenotypeCArray(data=None, copy=True, **kwargs)[source]¶

Alternative implementation of the allel.model.GenotypeArray interface, using a bcolz.carray as the backing store.

Parameters:	data : array_like, int, shape (n_variants, n_samples, ploidy), optional Data to initialise the array with. May be a bcolz carray, which will not be copied if copy=False. May also be None, in which case rootdir must be provided (disk-based array). copy : bool, optional If True, copy the input data into a new bcolz carray. **kwargs : keyword arguments Passed through to the bcolz carray constructor.

Examples

Instantiate a compressed genotype array from existing data:

>>> import allel
>>> g = allel.bcolz.GenotypeCArray([[[0, 0], [0, 1]],
...                                 [[0, 1], [1, 1]],
...                                 [[0, 2], [-1, -1]]], dtype='i1')
>>> g
GenotypeCArray((3, 2, 2), int8)
  nbytes: 12; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[[ 0  0]
  [ 0  1]]
 [[ 0  1]
  [ 1  1]]
 [[ 0  2]
  [-1 -1]]]

Obtain a numpy ndarray from a compressed array by slicing:

>>> g[:]
GenotypeArray((3, 2, 2), dtype=int8)
[[[ 0  0]
  [ 0  1]]
 [[ 0  1]
  [ 1  1]]
 [[ 0  2]
  [-1 -1]]]

Build incrementally:

>>> import bcolz
>>> data = bcolz.zeros((0, 2, 2), dtype='i1')
>>> data.append([[0, 0], [0, 1]])
>>> data.append([[0, 1], [1, 1]])
>>> data.append([[0, 2], [-1, -1]])
>>> g = allel.bcolz.GenotypeCArray(data, copy=False)
>>> g
GenotypeCArray((3, 2, 2), int8)
  nbytes: 12; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[[ 0  0]
  [ 0  1]]
 [[ 0  1]
  [ 1  1]]
 [[ 0  2]
  [-1 -1]]]

Load from HDF5:

>>> import h5py
>>> with h5py.File('example.h5', mode='w') as h5f:
...     h5f.create_dataset('genotype',
...                        data=[[[0, 0], [0, 1]],
...                              [[0, 1], [1, 1]],
...                              [[0, 2], [-1, -1]]],
...                        dtype='i1',
...                        chunks=(2, 2, 2))
...
<HDF5 dataset "genotype": shape (3, 2, 2), type "|i1">
>>> g = allel.bcolz.GenotypeCArray.from_hdf5('example.h5', 'genotype')
>>> g
GenotypeCArray((3, 2, 2), int8)
  nbytes: 12; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[[ 0  0]
  [ 0  1]]
 [[ 0  1]
  [ 1  1]]
 [[ 0  2]
  [-1 -1]]]

Note that methods of this class will return bcolz carrays rather than numpy ndarrays where possible. E.g.:

>>> g.take([0, 2], axis=0)
GenotypeCArray((2, 2, 2), int8)
  nbytes: 8; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[[ 0  0]
  [ 0  1]]
 [[ 0  2]
  [-1 -1]]]
>>> g.is_called()
carray((3, 2), bool)
  nbytes: 6; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[ True  True]
 [ True  True]
 [ True False]]
>>> g.to_haplotypes()
HaplotypeCArray((3, 4), int8)
  nbytes: 12; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[ 0  0  0  1]
 [ 0  1  1  1]
 [ 0  2 -1 -1]]
>>> g.count_alleles()
AlleleCountsCArray((3, 3), int32)
  nbytes: 36; cbytes: 16.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[[3 1 0]
 [1 3 0]
 [1 0 1]]

HaplotypeCArray¶

class allel.bcolz.HaplotypeCArray(data=None, copy=True, **kwargs)[source]¶

Alternative implementation of the allel.model.HaplotypeArray interface, using a bcolz.carray as the backing store.

Parameters:	data : array_like, int, shape (n_variants, n_haplotypes), optional Data to initialise the array with. May be a bcolz carray, which will not be copied if copy=False. May also be None, in which case rootdir must be provided (disk-based array). copy : bool, optional If True, copy the input data into a new bcolz carray. **kwargs : keyword arguments Passed through to the bcolz carray constructor.

AlleleCountsCArray¶

class allel.bcolz.AlleleCountsCArray(data=None, copy=True, **kwargs)[source]¶

Alternative implementation of the allel.model.AlleleCountsArray interface, using a bcolz.carray as the backing store.

Parameters:	data : array_like, int, shape (n_variants, n_alleles), optional Data to initialise the array with. May be a bcolz carray, which will not be copied if copy=False. May also be None, in which case rootdir must be provided (disk-based array). copy : bool, optional If True, copy the input data into a new bcolz carray. **kwargs : keyword arguments Passed through to the bcolz carray constructor.

VariantCTable¶

class allel.bcolz.VariantCTable(data=None, copy=True, index=None, **kwargs)[source]¶

Alternative implementation of the allel.model.VariantTable interface, using a bcolz.ctable as the backing store.

Parameters:

data : tuple or list of column objects, optional The list of column data to build the ctable object. This can also be a pure NumPy structured array. May also be a bcolz ctable, which will not be copied if copy=False. May also be None, in which case rootdir must be provided (disk-based array). copy : bool, optional If True, copy the input data into a new bcolz ctable. index : string or pair of strings, optional If a single string, name of column to use for a sorted index. If a pair of strings, name of columns to use for a sorted multi-index. **kwargs : keyword arguments Passed through to the bcolz ctable constructor.

Examples

Instantiate from existing data:

>>> import allel
>>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3']
>>> pos = [2, 7, 3, 9, 6]
>>> dp = [35, 12, 78, 22, 99]
>>> qd = [4.5, 6.7, 1.2, 4.4, 2.8]
>>> vt = allel.bcolz.VariantCTable([chrom, pos, dp, qd],
...                                names=['CHROM', 'POS', 'DP', 'QD'],
...                                index=('CHROM', 'POS'))
>>> vt
VariantCTable((5,), [('CHROM', 'S4'), ('POS', '<i8'), ('DP', '<i8'), ('QD', '<f8')])
  nbytes: 140; cbytes: 64.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[(b'chr1', 2, 35, 4.5) (b'chr1', 7, 12, 6.7) (b'chr2', 3, 78, 1.2)
 (b'chr2', 9, 22, 4.4) (b'chr3', 6, 99, 2.8)]

Slicing rows returns allel.model.VariantTable:

>>> vt[:2]
VariantTable((2,), dtype=[('CHROM', 'S4'), ('POS', '<i8'), ('DP', '<i8'), ('QD', '<f8')])
[(b'chr1', 2, 35, 4.5) (b'chr1', 7, 12, 6.7)]

Accessing columns returns allel.bcolz.VariantCTable:

>>> vt[['DP', 'QD']]
VariantCTable((5,), [('DP', '<i8'), ('QD', '<f8')])
  nbytes: 80; cbytes: 32.00 KB; ratio: 0.00
  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
[(35, 4.5) (12, 6.7) (78, 1.2) (22, 4.4) (99, 2.8)]

Use the index to locate variants:

>>> loc = vt.index.locate_range(b'chr2', 1, 10)
>>> vt[loc]
VariantTable((2,), dtype=[('CHROM', 'S4'), ('POS', '<i8'), ('DP', '<i8'), ('QD', '<f8')])
[(b'chr2', 3, 78, 1.2) (b'chr2', 9, 22, 4.4)]

Utility functions¶

allel.bcolz.carray_block_map(carr, f, out=None, blen=None, **kwargs)[source]¶

allel.bcolz.carray_block_sum(carr, axis=None, blen=None, transform=None)[source]¶

allel.bcolz.carray_block_max(carr, axis=None, blen=None)[source]¶

allel.bcolz.carray_block_min(carr, axis=None, blen=None)[source]¶

allel.bcolz.carray_block_compress(carr, condition, axis, blen=None, **kwargs)[source]¶

allel.bcolz.carray_block_take(carr, indices, axis, **kwargs)[source]¶

allel.bcolz.carray_from_hdf5(*args, **kwargs)[source]¶

TODO

allel.bcolz.ctable_block_compress(ctbl, condition, blen=None, **kwargs)[source]¶

allel.bcolz.ctable_block_take(ctbl, indices, **kwargs)[source]¶

allel.bcolz.ctable_from_hdf5_group(*args, **kwargs)[source]¶

TODO

Diversity & divergence¶

allel.stats.mean_pairwise_diversity(ac, fill=nan)[source]¶

Calculate for each variant the mean number of pairwise differences between haplotypes within a single population.

Parameters:	ac : array_like, int, shape (n_variants, n_alleles) Allele counts array. fill : float Use this value where there are no pairs to compare (e.g., all allele calls are missing).
Returns:	mpd : ndarray, float, shape (n_variants,)

See also

windowed_diversity

Notes

The values returned by this function can be summed over a genome region and divided by the number of accessible bases to estimate nucleotide diversity, a.k.a. pi.

Examples

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 0],
...                                 [0, 0, 0, 1],
...                                 [0, 0, 1, 1],
...                                 [0, 1, 1, 1],
...                                 [1, 1, 1, 1],
...                                 [0, 0, 1, 2],
...                                 [0, 1, 1, 2],
...                                 [0, 1, -1, -1]])
>>> ac = h.count_alleles()
>>> allel.stats.mean_pairwise_diversity(ac)
array([ 0.        ,  0.5       ,  0.66666667,  0.5       ,  0.        ,
        0.83333333,  0.83333333,  1.        ])

allel.stats.windowed_diversity(pos, ac, size, start=None, stop=None, step=None, windows=None, is_accessible=None, fill=nan)[source]¶

Calculate nucleotide diversity in windows over a single chromosome/contig.

Parameters:

pos : array_like, int, shape (n_items,) Variant positions, using 1-based coordinates, in ascending order. ac : array_like, int, shape (n_variants, n_alleles) Allele counts array. size : int The window size (number of bases). start : int, optional The position at which to start (1-based). stop : int, optional The position at which to stop (1-based). step : int, optional The distance between start positions of windows. If not given, defaults to the window size, i.e., non-overlapping windows. windows : array_like, int, shape (n_windows, 2), optional Manually specify the windows to use as a sequence of (window_start, window_stop) positions, using 1-based coordinates. Overrides the size/start/stop/step parameters. is_accessible : array_like, bool, shape (len(contig),), optional Boolean array indicating accessibility status for all positions in the chromosome/contig. fill : object, optional The value to use where a window is empty, i.e., contains no items.

Returns:

pi : ndarray, float, shape (n_windows,) Nucleotide diversity in each window. windows : ndarray, int, shape (n_windows, 2) The windows used, as an array of (window_start, window_stop) positions, using 1-based coordinates. n_bases : ndarray, int, shape (n_windows,) Number of (accessible) bases in each window. counts : ndarray, int, shape (n_windows,) Number of variants in each window.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 0]],
...                                [[0, 0], [0, 1]],
...                                [[0, 0], [1, 1]],
...                                [[0, 1], [1, 1]],
...                                [[1, 1], [1, 1]],
...                                [[0, 0], [1, 2]],
...                                [[0, 1], [1, 2]],
...                                [[0, 1], [-1, -1]],
...                                [[-1, -1], [-1, -1]]])
>>> ac = g.count_alleles()
>>> pos = [2, 4, 7, 14, 15, 18, 19, 25, 27]
>>> pi, windows, n_bases, counts = allel.stats.windowed_diversity(
...     pos, ac, size=10, start=1, stop=31
... )
>>> pi
array([ 0.11666667,  0.21666667,  0.09090909])
>>> windows
array([[ 1, 10],
       [11, 20],
       [21, 31]])
>>> n_bases
array([10, 10, 11])
>>> counts
array([3, 4, 2])

allel.stats.mean_pairwise_divergence(ac1, ac2, fill=nan)[source]¶

Calculate for each variant the mean number of pairwise differences between haplotypes from two different populations.

Parameters:	ac1 : array_like, int, shape (n_variants, n_alleles) Allele counts array from the first population. ac2 : array_like, int, shape (n_variants, n_alleles) Allele counts array from the second population. fill : float Use this value where there are no pairs to compare (e.g., all allele calls are missing).
Returns:	mpd : ndarray, float, shape (n_variants,)

See also

windowed_divergence

Notes

The values returned by this function can be summed over a genome region and divided by the number of accessible bases to estimate nucleotide divergence between two populations, a.k.a. Dxy.

Examples

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 0],
...                                 [0, 0, 0, 1],
...                                 [0, 0, 1, 1],
...                                 [0, 1, 1, 1],
...                                 [1, 1, 1, 1],
...                                 [0, 0, 1, 2],
...                                 [0, 1, 1, 2],
...                                 [0, 1, -1, -1]])
>>> ac1 = h.take([0, 1], axis=1).count_alleles()
>>> ac2 = h.take([2, 3], axis=1).count_alleles()
>>> allel.stats.mean_pairwise_divergence(ac1, ac2)
array([ 0.  ,  0.5 ,  1.  ,  0.5 ,  0.  ,  1.  ,  0.75,   nan])

allel.stats.windowed_divergence(pos, ac1, ac2, size, start=None, stop=None, step=None, is_accessible=None, fill=nan)[source]¶

Calculate nucleotide divergence between two populations in windows over a single chromosome/contig.

Parameters:

pos : array_like, int, shape (n_items,) Variant positions, using 1-based coordinates, in ascending order. ac1 : array_like, int, shape (n_variants, n_alleles) Allele counts array for the first population. ac2 : array_like, int, shape (n_variants, n_alleles) Allele counts array for the second population. size : int The window size (number of bases). start : int, optional The position at which to start (1-based). stop : int, optional The position at which to stop (1-based). step : int, optional The distance between start positions of windows. If not given, defaults to the window size, i.e., non-overlapping windows. windows : array_like, int, shape (n_windows, 2), optional Manually specify the windows to use as a sequence of (window_start, window_stop) positions, using 1-based coordinates. Overrides the size/start/stop/step parameters. is_accessible : array_like, bool, shape (len(contig),), optional Boolean array indicating accessibility status for all positions in the chromosome/contig. fill : object, optional The value to use where a window is empty, i.e., contains no items.

Returns:

Dxy : ndarray, float, shape (n_windows,) Nucleotide divergence in each window. windows : ndarray, int, shape (n_windows, 2) The windows used, as an array of (window_start, window_stop) positions, using 1-based coordinates. n_bases : ndarray, int, shape (n_windows,) Number of (accessible) bases in each window. counts : ndarray, int, shape (n_windows,) Number of variants in each window.

Examples

Simplest case, two haplotypes in each population:

>>> import allel
>>> h = allel.model.HaplotypeArray([[0, 0, 0, 0],
...                                 [0, 0, 0, 1],
...                                 [0, 0, 1, 1],
...                                 [0, 1, 1, 1],
...                                 [1, 1, 1, 1],
...                                 [0, 0, 1, 2],
...                                 [0, 1, 1, 2],
...                                 [0, 1, -1, -1],
...                                 [-1, -1, -1, -1]])
>>> h1 = h.subset(haplotypes=[0, 1])
>>> h2 = h.subset(haplotypes=[2, 3])
>>> ac1 = h1.count_alleles()
>>> ac2 = h2.count_alleles()
>>> pos = [2, 4, 7, 14, 15, 18, 19, 25, 27]
>>> dxy, windows, n_bases, counts = windowed_divergence(
...     pos, ac1, ac2, size=10, start=1, stop=31
... )
>>> dxy
array([ 0.15 ,  0.225,  0.   ])
>>> windows
array([[ 1, 10],
       [11, 20],
       [21, 31]])
>>> n_bases
array([10, 10, 11])
>>> counts
array([3, 4, 2])

Pairwise distance¶

allel.stats.pairwise_distance(x, metric)[source]¶

Compute pairwise distance between individuals (samples or haplotypes).

Parameters:	x : array_like, shape (n_variants, n_individuals) Array of genotype data. metric : string or function Distance metric. See documentation for the function scipy.spatial.distance.pdist() for a list of built-in distance metrics.
Returns:	dist : ndarray, shape (n_individuals * (n_individuals - 1) / 2,) Distance matrix in condensed form.

See also

allel.plot.pairwise_distance

Notes

If x is a bcolz carray, a chunk-wise implementation will be used to avoid loading the entire input array into memory. This means that a distance matrix will be calculated for each chunk in the input array, and the results will be summed to produce the final output. For some distance metrics this will return a different result from the standard implementation, although the relative distances may be equivalent.

Examples

>>> import allel
>>> g = allel.model.GenotypeArray([[[0, 0], [0, 1], [1, 1]],
...                                [[0, 1], [1, 1], [1, 2]],
...                                [[0, 2], [2, 2], [-1, -1]]])
>>> d = allel.stats.pairwise_distance(g.to_n_alt(), metric='cityblock')
>>> d
array([ 3.,  4.,  3.])
>>> import scipy.spatial
>>> scipy.spatial.distance.squareform(d)
array([[ 0.,  3.,  4.],
       [ 3.,  0.,  3.],
       [ 4.,  3.,  0.]])