def xpehh(h1, h2, pos, min_ehh=0.05):
"""Compute the unstandardized cross-population extended haplotype
homozygosity score (XPEHH) for each variant.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
pos : array_like, int, shape (n_variants,)
Variant positions on physical or genetic map.
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPEHH scores.
Notes
-----
This function will calculate XPEHH for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype arrays
before passing to this function.
This function returns NaN for any EHH calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `min_ehh` to None.
This function currently does nothing to account for large gaps between
variants. There will be edge effects near any large gaps.
Note that the unstandardized score is returned. Usually these scores are
then normalised in different allele frequency bins.
Haplotype arrays from the two populations may have different numbers of
haplotypes.
"""
from allel.opt.stats import ihh_scan_int8
# scan forward
ihh1_fwd = ihh_scan_int8(h1, pos, min_ehh=min_ehh)
ihh2_fwd = ihh_scan_int8(h2, pos, min_ehh=min_ehh)
# scan backward
ihh1_rev = ihh_scan_int8(h1[::-1], pos[::-1], min_ehh=min_ehh)[::-1]
ihh2_rev = ihh_scan_int8(h2[::-1], pos[::-1], min_ehh=min_ehh)[::-1]
# compute unstandardized score
ihh1 = ihh1_fwd + ihh1_rev
ihh2 = ihh2_fwd + ihh2_rev
score = np.log(ihh1 / ihh2)
return score
def test_ihh_scan_int8_d():
# edge case: start from 0 haplotype homozygosity
gaps = np.array([10], dtype='f8')
h = np.array([[0, 1], [1, 0]], dtype='i1')
expect = [0, 0]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=False)
assert_array_nanclose(expect, actual)
expect = [0, 0]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=True)
assert_array_nanclose(expect, actual)
def test_ihh_scan_int8_d():
# edge case: start from 0 haplotype homozygosity
gaps = np.array([10], dtype='f8')
h = np.array([[0, 1],
[1, 0]], dtype='i1')
expect = [0, 0]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=False)
assert_array_nanclose(expect, actual)
expect = [0, 0]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=True)
assert_array_nanclose(expect, actual)
def test_ihh_scan_int8_c():
# simple case: 1 haplotype pair, haplotype homozygosity decays
gaps = np.array([10, 10], dtype='f8')
h = np.array([[0, 1], [0, 0], [0, 0]], dtype='i1')
# do not include edges
expect = [0, 5, 15]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=False)
assert_array_nanclose(expect, actual)
# include edges
expect = [0, 5, 15]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=True)
assert_array_nanclose(expect, actual)
def test_ihh_scan_int8_b():
# 1 haplotype pair, haplotype homozygosity over all variants
# handling of large gap (encoded as -1)
gaps = np.array([10, -1], dtype='f8')
h = np.array([[0, 0], [0, 0], [0, 0]], dtype='i1')
# do not include edges
expect = [np.nan, np.nan, np.nan]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=False)
assert_array_nanclose(expect, actual)
# include edges
expect = [0, 10, np.nan]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=True)
assert_array_nanclose(expect, actual)
def test_ihh_scan_int8_c():
# simple case: 1 haplotype pair, haplotype homozygosity decays
gaps = np.array([10, 10], dtype='f8')
h = np.array([[0, 1],
[0, 0],
[0, 0]], dtype='i1')
# do not include edges
expect = [0, 5, 15]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=False)
assert_array_nanclose(expect, actual)
# include edges
expect = [0, 5, 15]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=True)
assert_array_nanclose(expect, actual)
def test_ihh_scan_int8_b():
# 1 haplotype pair, haplotype homozygosity over all variants
# handling of large gap (encoded as -1)
gaps = np.array([10, -1], dtype='f8')
h = np.array([[0, 0],
[0, 0],
[0, 0]], dtype='i1')
# do not include edges
expect = [np.nan, np.nan, np.nan]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=False)
assert_array_nanclose(expect, actual)
# include edges
expect = [0, 10, np.nan]
actual = ihh_scan_int8(h, gaps, min_ehh=0, include_edges=True)
assert_array_nanclose(expect, actual)
def xpehh(
h1,
h2,
pos,
map_pos=None,
min_ehh=0.05,
include_edges=False,
gap_scale=20000,
max_gap=200000,
is_accessible=None,
use_threads=True,
):
"""Compute the unstandardized cross-population extended haplotype
homozygosity score (XPEHH) for each variant.
Parameters
----------
h1 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the first population.
h2 : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array for the second population.
pos : array_like, int, shape (n_variants,)
Variant positions on physical or genetic map.
map_pos : array_like, float, shape (n_variants,)
Variant positions (genetic map distance).
min_ehh: float, optional
Minimum EHH beyond which to truncate integrated haplotype
homozygosity calculation.
include_edges : bool, optional
If True, report scores even if EHH does not decay below `min_ehh`
before reaching the edge of the data.
gap_scale : int, optional
Rescale distance between variants if gap is larger than this value.
max_gap : int, optional
Do not report scores if EHH spans a gap larger than this number of
base pairs.
is_accessible : array_like, bool, optional
Genome accessibility array. If provided, distance between variants
will be computed as the number of accessible bases between them.
use_threads : bool, optional
If True use multiple threads to compute.
Returns
-------
score : ndarray, float, shape (n_variants,)
Unstandardized XPEHH scores.
Notes
-----
This function will calculate XPEHH for all variants. To exclude variants
below a given minor allele frequency, filter the input haplotype arrays
before passing to this function.
This function returns NaN for any EHH calculations where haplotype
homozygosity does not decay below `min_ehh` before reaching the first or
last variant. To disable this behaviour, set `include_edges` to True.
Note that the unstandardized score is returned. Usually these scores are
then standardized genome-wide.
Haplotype arrays from the two populations may have different numbers of
haplotypes.
See Also
--------
standardize
"""
from allel.opt.stats import ihh_scan_int8
# check inputs
h1 = HaplotypeArray(np.asarray(h1, dtype="i1"))
h2 = HaplotypeArray(np.asarray(h2, dtype="i1"))
pos = asarray_ndim(pos, 1)
check_dim0_aligned(h1, h2, pos)
# compute gaps between variants for integration
gaps = compute_ihh_gaps(pos, map_pos, gap_scale, max_gap, is_accessible)
# setup kwargs
kwargs = dict(min_ehh=min_ehh, include_edges=include_edges)
if use_threads and multiprocessing.cpu_count() > 1:
# use multiple threads
# setup threadpool
pool = ThreadPool(min(4, multiprocessing.cpu_count()))
# scan forward
res1_fwd = pool.apply_async(ihh_scan_int8, (h1, gaps), kwargs)
res2_fwd = pool.apply_async(ihh_scan_int8, (h2, gaps), kwargs)
# scan backward
res1_rev = pool.apply_async(ihh_scan_int8, (h1[::-1], gaps[::-1]), kwargs)
res2_rev = pool.apply_async(ihh_scan_int8, (h2[::-1], gaps[::-1]), kwargs)
# wait for both to finish
pool.close()
pool.join()
# obtain results
ihh1_fwd = res1_fwd.get()
ihh2_fwd = res2_fwd.get()
ihh1_rev = res1_rev.get()
ihh2_rev = res2_rev.get()
# cleanup
pool.terminate()
else:
# compute without threads
# scan forward
ihh1_fwd = ihh_scan_int8(h1, gaps, **kwargs)
ihh2_fwd = ihh_scan_int8(h2, gaps, **kwargs)
# scan backward
ihh1_rev = ihh_scan_int8(h1[::-1], gaps[::-1], **kwargs)
ihh2_rev = ihh_scan_int8(h2[::-1], gaps[::-1], **kwargs)
# handle reverse scans
ihh1_rev = ihh1_rev[::-1]
ihh2_rev = ihh2_rev[::-1]
# compute unstandardized score
ihh1 = ihh1_fwd + ihh1_rev
ihh2 = ihh2_fwd + ihh2_rev
score = np.log(ihh1 / ihh2)
return score
