def test_num_private_alleles(self):
stat_funct = partial(calc_number_of_private_alleles,
min_num_genotypes=0)
gts = numpy.array([[[0], [0], [0], [0], [-1]],
[[0], [0], [1], [1], [-1]],
[[0], [2], [0], [1], [-1]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0, 1, 1, 0], 2: [0, 1, 1, 0]}
self._check_function(stat_funct, varis, pops, expected)
# No missing alleles
gts = numpy.array([[[0], [0], [0], [0], [1]], [[0], [0], [1], [1],
[1]],
[[0], [2], [0], [1], [1]], [[1], [1], [0], [0],
[2]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0, 1, 1, 1], 2: [1, 1, 1, 2]}
self._check_function(stat_funct, varis, pops, expected)
# all missing
gts = numpy.array([[[0], [0], [0], [-1], [-1]],
[[0], [0], [1], [-1], [-1]],
[[0], [2], [-1], [-1], [-1]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0, 1, 2, 0], 2: [0, 1, 0, 0]}
self._check_function(stat_funct, varis, pops, expected)
# min_num_genotypes
stat_funct = partial(calc_number_of_private_alleles,
min_num_genotypes=2)
gts = numpy.array([[[0], [0], [0], [0], [1]], [[0], [0], [1], [1],
[1]],
[[0], [2], [0], [1], [1]], [[1], [-1], [0], [0],
[2]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0, 1, 1, 0], 2: [1, 1, 1, 0]}
self._check_function(stat_funct, varis, pops, expected)
def test_empty_pop(self):
missing = (-1, -1)
gts = [
[(1, 1), (1, 3), (1, 2), (1, 4), (3, 3), (3, 2), (3, 4), (2, 2),
(2, 4), (4, 4), (-1, -1)],
[(1, 3), (1, 1), (1, 1), (1, 3), (3, 3), (3, 2), (3, 4), (2, 2),
(2, 4), (4, 4), (-1, -1)],
[
missing, missing, missing, missing, missing, (3, 2), (3, 4),
(2, 2), (2, 4), (4, 4), (-1, -1)
],
]
samples = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
pops = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]
snps = VariationsArrays()
snps['/calls/GT'] = numpy.array(gts)
snps.samples = samples
dists = calc_pop_distance(snps,
populations=pops,
method='dest',
min_num_genotypes=0)
assert numpy.allclose(dists, [0.65490196])
gts = [
[
missing, missing, missing, missing, missing, (3, 2), (3, 4),
(2, 2), (2, 4), (4, 4), (-1, -1)
],
[
missing, missing, missing, missing, missing, (3, 2), (3, 4),
(2, 2), (2, 4), (4, 4), (-1, -1)
],
[
missing, missing, missing, missing, missing, (3, 2), (3, 4),
(2, 2), (2, 4), (4, 4), (-1, -1)
],
]
samples = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
pops = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]
snps = VariationsArrays()
snps['/calls/GT'] = numpy.array(gts)
snps.samples = samples
dists = calc_pop_distance(snps,
populations=pops,
method='dest',
min_num_genotypes=0)
assert numpy.isnan(dists[0])
def test_num_alleles(self):
stat_funct = partial(calc_number_of_alleles, min_num_genotypes=0)
gts = numpy.array([[[0], [0], [0], [0], [-1]],
[[0], [0], [1], [1], [-1]],
[[0], [0], [0], [1], [-1]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [1, 1, 1, 0], 2: [1, 1, 2, 0]}
self._check_function(stat_funct, varis, pops, expected)
# a population empty
gts = numpy.array([[[-1], [-1], [0], [0], [-1]],
[[-1], [-1], [1], [1], [-1]],
[[-1], [-1], [0], [1], [-1]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0, 0, 0, 0], 2: [1, 1, 2, 0]}
self._check_function(stat_funct, varis, pops, expected)
# only one pop
gts = numpy.array([[[1], [-1], [0], [0], [-1]],
[[-1], [-1], [1], [1], [-1]],
[[-1], [-1], [0], [1], [-1]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2]}
expected = {1: [1, 0, 0, 0]}
self._check_function(stat_funct, varis, pops, expected)
# min num genotypes
stat_funct = partial(calc_number_of_alleles, min_num_genotypes=3)
gts = numpy.array([[[1], [-1], [0], [0], [-1]],
[[-1], [-1], [1], [1], [-1]],
[[-1], [-1], [0], [1], [-1]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2, 3, 4, 5]}
expected = {1: [2, 0, 0, 0]}
self._check_function(stat_funct, varis, pops, expected)
def test_nei_dist(self):
gts = numpy.array([[[1, 1], [5, 2], [2, 2], [3, 2]],
[[1, 1], [1, 2], [2, 2], [2, 1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4]
pops = [[1, 2], [3, 4]]
dists = _calc_pop_pairwise_unbiased_nei_dists(varis,
populations=pops,
min_num_genotypes=1)
assert math.isclose(dists[0], 0.3726315908494797)
dists = _calc_pop_pairwise_unbiased_nei_dists(varis,
populations=pops,
min_num_genotypes=1,
chunk_size=1)
assert math.isclose(dists[0], 0.3726315908494797)
# all missing
gts = numpy.array([[[-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4]
pops = [[1, 2], [3, 4]]
dists = _calc_pop_pairwise_unbiased_nei_dists(varis,
populations=pops,
min_num_genotypes=1)
assert math.isnan(dists[0])
# min_num_genotypes
gts = numpy.array([[[1, 1], [5, 2], [2, 2], [3, 2]],
[[1, 1], [1, 2], [2, 2], [2, 1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4]
pops = [[1, 2], [3, 4]]
dists = _calc_pop_pairwise_unbiased_nei_dists(varis,
populations=pops,
min_num_genotypes=1)
assert math.isclose(dists[0], 0.3726315908494797)
dists = _calc_pop_pairwise_unbiased_nei_dists(varis,
populations=pops,
chunk_size=1)
assert math.isnan(dists[0])
def test_dest_jost_distance(self):
gts = [[(1, 1), (1, 3), (1, 2), (1, 4), (3, 3), (3, 2), (3, 4), (2, 2),
(2, 4), (4, 4), (-1, -1)],
[(1, 3), (1, 1), (1, 1), (1, 3), (3, 3), (3, 2), (3, 4), (2, 2),
(2, 4), (4, 4), (-1, -1)]]
samples = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
pops = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]
snps = VariationsArrays()
snps['/calls/GT'] = numpy.array(gts)
snps.samples = samples
dists = calc_pop_distance(snps,
populations=pops,
method='dest',
min_num_genotypes=0)
assert numpy.allclose(dists, [0.65490196])
dists = calc_pop_distance(snps,
populations=pops,
method='dest',
min_num_genotypes=0,
chunk_size=1)
assert numpy.allclose(dists, [0.65490196])
dists = calc_pop_distance(snps,
populations=pops,
method='dest',
min_num_genotypes=6,
chunk_size=1)
assert numpy.all(numpy.isnan(dists))
def test_excel(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 1], [1, 1], [0, 0], [0, 0]],
[[2, 2], [2, 0], [2, 1], [0, 0], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1]],
[[0, 0], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
variations[GT_FIELD] = gts
variations.samples = list(range(gts.shape[1]))
fhand = NamedTemporaryFile(suffix='.xlsx')
write_excel(variations, fhand)
# chrom pos
variations[CHROM_FIELD] = numpy.array([1, 1, 2, 2])
variations[POS_FIELD] = numpy.array([10, 20, 10, 20])
fhand = NamedTemporaryFile(suffix='.xlsx')
write_excel(variations, fhand)
# REF, ALT
variations[REF_FIELD] = numpy.array([b'A', b'A', b'A', b'A'])
variations[ALT_FIELD] = numpy.array([[b'T'], [b'T'], [b'T'], [b'T']])
write_excel(variations, fhand)
# with classifications
classes = [1, 1, 1, 2, 2]
write_excel(variations, fhand, classes)
def test_report(self):
gts = numpy.array([[[0, 0], [0, 1], [0, 0], [0, 0], [0, -1]],
[[0, 0], [0, 0], [0, 1], [1, 0], [-1, -1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
out_dir = tempfile.TemporaryDirectory()
create_pop_stats_report(varis,
pops,
out_dir.name,
min_num_genotypes=1,
min_call_dp_for_obs_het=0,
violin_ylimits={
'observed_heterozigosity': {
'bottom': 0,
'top': 0.5
}
})
stats_csv_fpath = os.path.join(out_dir.name, 'pop_stats.csv')
assert os.path.exists(stats_csv_fpath)
stats_csv_fpath = os.path.join(out_dir.name,
'pop_stats_violin_plots.svg')
# input(out_dir.name)
out_dir.cleanup()
def test_calc_obs_het(self):
stat_funct = calc_obs_het
gts = numpy.array([[[0, 0], [0, 1], [0, 0], [0, 0], [0, -1]],
[[0, 0], [0, 0], [0, 1], [1, 0], [-1, -1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
dps = numpy.array([[20, 15, 20, 20, 20], [20, 20, 20, 20, 20],
[20, 20, 20, 20, 20]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis[DP_FIELD] = dps
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0.5, 0, math.nan], 2: [0, 1., math.nan]}
partial_stat_funct = partial(stat_funct,
min_num_genotypes=1,
min_call_dp=0)
self._check_function(partial_stat_funct, varis, pops, expected)
# now setting a depth_threshold
expected = {1: [0, 0, math.nan], 2: [0, 1., math.nan]}
partial_stat_funct = partial(stat_funct,
min_call_dp=20,
min_num_genotypes=1)
self._check_function(partial_stat_funct, varis, pops, expected)
def test_calc_distrib_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
distrib, _ = calc_field_distrib_for_a_sample(hdf5, field='/calls/DP',
sample='1_17_1_gbs',
n_bins=15)
assert distrib.shape == (15,)
distrib2, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=None)
assert numpy.all(distrib == distrib2)
distrib3, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=50)
assert numpy.all(distrib3 == distrib2)
vars_ = VariationsArrays()
vars_['/calls/DP'] = numpy.array([[10, 5, 15],
[0, 15, 10]])
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [1, 1]],
[[0, 0], [0, 1], [1, 1]]])
vars_.samples = list(range(3))
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_het)
expec = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_hom)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
def test_calc_distrib_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
distrib, _ = calc_field_distrib_for_a_sample(hdf5, field='/calls/DP',
sample='1_17_1_gbs',
n_bins=15)
assert distrib.shape == (15,)
distrib2, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=None)
assert numpy.all(distrib == distrib2)
distrib3, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=50)
assert numpy.all(distrib3 == distrib2)
vars_ = VariationsArrays()
vars_['/calls/DP'] = numpy.array([[10, 5, 15],
[0, 15, 10]])
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [1, 1]],
[[0, 0], [0, 1], [1, 1]]])
vars_.samples = list(range(3))
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_het)
expec = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_hom)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
def test_gst_basic(self):
ad = [[[10, 3, -1], [11, 2, -1]], [[10, 0, -1], [10, 0, -1]],
[[10, 10, -1], [11, 11, -1]], [[-1, 2, 10], [-1, 10, 2]]]
snps = VariationsArrays()
snps.samples = [1, 2]
populations = [[1], [2]]
snps[AD_FIELD] = numpy.array(ad)
dist = calc_gst_per_loci(snps, populations)
expected = numpy.array([0.00952381, 0, 0, 0.44444444])
numpy.testing.assert_almost_equal(dist, expected)
def test_is_variable_func(self):
variations = VariationsArrays()
gts = numpy.array([[[-1, -1], [1, 1], [0, 1], [1, 1], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [0, 0]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5]
expected_variable = [MISSING_INT, TRUE_INT, TRUE_INT, FALSE_INT]
variable = is_variable(variations, samples=[1, 5])
assert numpy.all(variable == expected_variable)
def test_calc_exp_het(self):
stat_funct = calc_exp_het
gts = numpy.array([[[0, 0], [0, 1], [0, 0], [0, 0], [0, -1]],
[[0, 0], [0, 0], [0, 1], [1, 0], [-1, -1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [0.5, 0, math.nan], 2: [0, 0.6, math.nan]}
partial_stat_funct = partial(stat_funct, min_num_genotypes=1)
self._check_function(partial_stat_funct, varis, pops, expected)
def test_calc_major_allele_freq(self):
stat_funct = calc_major_allele_freq
gts = numpy.array([[[0], [0], [0], [0], [-1]],
[[0], [0], [1], [1], [-1]], [[0], [2], [0], [1],
[2]],
[[-1], [-1], [-1], [-1], [-1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4, 5]
pops = {1: [1, 2], 2: [3, 4, 5]}
expected = {1: [1., 1., 0.5, math.nan], 2: [1., 1., 1 / 3, math.nan]}
stat_funct = partial(stat_funct, min_num_genotypes=1)
self._check_function(stat_funct, varis, pops, expected)
def test_nei_dist(self):
gts = [[[0, 0], [0, 0], [0, 0], [1, 1], [1, 1], [1, 1], [1, 0]],
[[0, 0], [0, 0], [0, 0], [1, 1], [1, 1], [1, 1], [1, 1]],
[[0, 0], [0, 0], [0, 0], [1, 1], [1, 1], [1, 1], [1, 1]]]
snps = VariationsArrays()
snps['/calls/GT'] = numpy.array(gts)
snps.samples = [1, 2, 3, 4, 5, 6, 7]
pops = [[1, 2, 3], [4, 5, 6, 7]]
dists = calc_pop_distance(snps,
populations=pops,
method='nei',
min_num_genotypes=0)
assert dists[0] - 3.14019792 < 0.001
pops = [[1, 2, 3], [1, 2, 3]]
dists = calc_pop_distance(snps,
populations=pops,
method='nei',
min_num_genotypes=0)
assert dists[0] - 0 < 0.001
pops = [[1, 2, 3], [1, 4, 5, 6, 7]]
dists = calc_pop_distance(snps,
populations=pops,
method='nei',
min_num_genotypes=0)
assert dists[0] - 1.23732507 < 0.001
# by chunk
pops = [[1, 2, 3], [4, 5, 6, 7]]
dists = calc_pop_distance(snps,
populations=pops,
method='nei',
chunk_size=2,
min_num_genotypes=0)
assert dists[0] - 3.14019792 < 0.001
pops = [[1, 2, 3], [1, 2, 3]]
dists = calc_pop_distance(snps,
populations=pops,
method='nei',
chunk_size=2,
min_num_genotypes=0)
assert dists[0] - 0 < 0.001
pops = [[1, 2, 3], [1, 4, 5, 6, 7]]
dists = calc_pop_distance(snps,
populations=pops,
method='nei',
chunk_size=2,
min_num_genotypes=0)
assert dists[0] - 1.23732507 < 0.001
def test_pca(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
do_pca(hdf5)
varis = VariationsArrays()
gts = [[[0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [1, 1]],
]
gts = numpy.array(gts)
varis[GT_FIELD] = gts
varis.samples = ['a', 'b', 'c']
res = do_pca(varis)
projs = res['projections']
assert projs.shape[0] == gts.shape[1]
assert numpy.allclose(projs[0], projs[1])
assert not numpy.allclose(projs[0], projs[2])
def test_pca(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
do_pca(hdf5)
varis = VariationsArrays()
gts = [
[[0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [1, 1]],
]
gts = numpy.array(gts)
varis[GT_FIELD] = gts
varis.samples = ['a', 'b', 'c']
res = do_pca(varis)
projs = res['projections']
assert projs.shape[0] == gts.shape[1]
assert numpy.allclose(projs[0], projs[1])
assert not numpy.allclose(projs[0], projs[2])
def test_samples(self):
gts = numpy.array([[[0, 0], [0, 1], [2, 2], [-1, 3]],
[[0, 0], [0, 0], [1, 1], [2, 2]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4]
assert varis.samples == [1, 2, 3, 4]
# With another file
tmp_fhand = NamedTemporaryFile()
path = tmp_fhand.name
tmp_fhand.close()
fhand = open(join(TEST_DATA_DIR, 'phylome.sample.vcf'), 'rb')
vcf_parser = VCFParser(fhand=fhand, pre_read_max_size=1000)
h5 = VariationsH5(path, mode='w', ignore_undefined_fields=True)
h5.put_vars(vcf_parser)
fhand.close()
samples = h5.samples
samples[0] = '0'
h5.samples = samples
def test_samples(self):
gts = numpy.array([[[0, 0], [0, 1], [2, 2], [-1, 3]],
[[0, 0], [0, 0], [1, 1], [2, 2]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
varis = VariationsArrays()
varis[GT_FIELD] = gts
varis.samples = [1, 2, 3, 4]
assert varis.samples == [1, 2, 3, 4]
# With another file
tmp_fhand = NamedTemporaryFile()
path = tmp_fhand.name
tmp_fhand.close()
fhand = open(join(TEST_DATA_DIR, 'phylome.sample.vcf'), 'rb')
vcf_parser = VCFParser(fhand=fhand)
h5 = VariationsH5(path, mode='w', ignore_undefined_fields=True)
h5.put_vars(vcf_parser)
fhand.close()
samples = h5.samples
samples[0] = '0'
h5.samples = samples
def test_excel(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 1], [1, 1], [0, 0], [0, 0]],
[[2, 2], [2, 0], [2, 1], [0, 0], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1]],
[[0, 0], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
variations[GT_FIELD] = gts
variations.samples = list(range(gts.shape[1]))
fhand = NamedTemporaryFile(suffix='.xlsx')
write_excel(variations, fhand)
# chrom pos
variations[CHROM_FIELD] = numpy.array([1, 1, 2, 2])
variations[POS_FIELD] = numpy.array([10, 20, 10, 20])
fhand = NamedTemporaryFile(suffix='.xlsx')
write_excel(variations, fhand)
# REF, ALT
variations[REF_FIELD] = numpy.array(['A', 'A', 'A', 'A'])
variations[ALT_FIELD] = numpy.array([['T'], ['T'], ['T'], ['T']])
write_excel(variations, fhand)
def _filter_samples_by_index(variations,
sample_cols,
filtered_vars=None,
reverse=False):
if filtered_vars is None:
filtered_vars = VariationsArrays()
samples = variations.samples
try:
dtype = sample_cols.dtype
is_bool = numpy.issubdtype(dtype, numpy.dtype(bool))
except AttributeError:
item = first(iter(sample_cols))
is_bool = isinstance(item, bool)
if not is_bool:
sample_cols = [idx in sample_cols for idx in range(len(samples))]
if 'shape' not in dir(sample_cols):
sample_cols = numpy.array(sample_cols, dtype=numpy.bool)
if reverse:
sample_cols = numpy.logical_not(sample_cols)
for path in variations.keys():
matrix = variations[path]
if is_dataset(matrix):
matrix = matrix[:]
if 'calls' in path:
flt_data = matrix[:, sample_cols]
# flt_data = numpy.compress(sample_cols, , axis=1)
filtered_vars[path] = flt_data
else:
filtered_vars[path] = matrix
filtered_vars.metadata = variations.metadata
kept_samples = [
samples[idx] for idx, keep in enumerate(sample_cols) if keep
]
filtered_vars.samples = kept_samples
return filtered_vars
def test_kosman_pairwise_between_pops_by_chunk(self):
a = numpy.array([[-1, -1], [0, 0], [0, 1], [0, 0], [0, 0], [0, 1],
[0, 1], [0, 1], [0, 0], [0, 0], [0, 1]])
b = numpy.array([[1, 1], [-1, -1], [0, 0], [0, 0], [1, 1], [0, 1],
[1, 0], [1, 0], [1, 0], [0, 1], [1, 1]])
c = numpy.full(shape=(11, 2), fill_value=1, dtype=numpy.int16)
d = numpy.full(shape=(11, 2), fill_value=1, dtype=numpy.int16)
gts = numpy.stack((a, b, c, d), axis=0)
gts = numpy.transpose(gts, axes=(1, 0, 2)).astype(numpy.int16)
variations = VariationsArrays()
variations.samples = [1, 2, 3, 4]
variations['/calls/GT'] = gts
expected = [[0., 0.33333333, 0.75, 0.75], [0.33333333, 0., 0.45, 0.45],
[0.75, 0.45, 0., 0.], [0.75, 0.45, 0., 0.]]
distance = calc_pairwise_distances_between_pops(
variations,
chunk_size=None,
min_num_snps=1,
pop1_samples=[1, 2, 3, 4],
pop2_samples=[1, 2, 3, 4])
assert numpy.allclose(distance, expected)
expected = [[0., 0.33333333, 0.75, 0.75]]
distance = calc_pairwise_distances_between_pops(
variations,
chunk_size=None,
min_num_snps=1,
pop1_samples=[1],
pop2_samples=[1, 2, 3, 4])
assert numpy.allclose(distance, expected)
expected = [[0.75, 0.75], [0.45, 0.45]]
distance = calc_pairwise_distances_between_pops(variations,
chunk_size=None,
min_num_snps=1,
pop1_samples=[1, 2],
pop2_samples=[3, 4])
def test_calc_called_gts_distribution_per_depth(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
dist, _ = calc_called_gts_distrib_per_depth(hdf5, depths=range(30),
chunk_size=10)
assert dist[1, 1] == 1
dist2, _ = calc_called_gts_distrib_per_depth(hdf5, depths=range(30),
chunk_size=None)
assert numpy.all(dist == dist2)
vars_ = VariationsArrays()
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [0, 1],
[0, 0], [0, 1], [0, 0],
[0, 0], [0, 1], [1, 1],
[0, 0]]])
vars_['/calls/DP'] = numpy.array([[10, 5, 15, 7, 10,
0, 0, 25, 20, 10]])
vars_.samples = list(range(10))
dist, _ = calc_called_gts_distrib_per_depth(vars_, depths=[0, 5, 10,
30])
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(dist == expected)
def test_calc_maf_depth_distribs_per_sample(self):
variations = VariationsArrays()
variations['/calls/AO'] = numpy.array([])
variations['/calls/RO'] = numpy.array([])
distribs, bins = calc_maf_depth_distribs_per_sample(variations,
chunk_size=None)
assert distribs is None
assert bins is None
variations = VariationsArrays()
variations['/calls/AO'] = numpy.array([[[0, 0], [0, 0], [15, -1]]])
variations['/calls/RO'] = numpy.array([[10, 5, 15]])
variations.samples = list(range(3))
distribs, _ = calc_maf_depth_distribs_per_sample(variations, n_bins=4,
min_depth=6,
chunk_size=None)
expected = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 1, 0]]
assert numpy.all(distribs == expected)
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
distribs1, _ = calc_maf_depth_distribs_per_sample(hdf5, min_depth=6,
chunk_size=None)
distribs2, _ = calc_maf_depth_distribs_per_sample(hdf5, min_depth=6)
assert numpy.all(distribs1 == distribs2)
def _filter_samples_by_index(variations, sample_cols, filtered_vars=None,
reverse=False):
if filtered_vars is None:
filtered_vars = VariationsArrays()
samples = variations.samples
try:
dtype = sample_cols.dtype
is_bool = numpy.issubdtype(dtype, numpy.bool)
except AttributeError:
item = first(iter(sample_cols))
is_bool = isinstance(item, bool)
if not is_bool:
sample_cols = [idx in sample_cols for idx in range(len(samples))]
if 'shape' not in dir(sample_cols):
sample_cols = numpy.array(sample_cols, dtype=numpy.bool)
if reverse:
sample_cols = numpy.logical_not(sample_cols)
for path in variations.keys():
matrix = variations[path]
if is_dataset(matrix):
matrix = matrix[:]
if 'calls' in path:
flt_data = matrix[:, sample_cols]
# flt_data = numpy.compress(sample_cols, , axis=1)
filtered_vars[path] = flt_data
else:
filtered_vars[path] = matrix
filtered_vars.metadata = variations.metadata
kept_samples = [samples[idx] for idx, keep in enumerate(sample_cols)
if keep]
filtered_vars.samples = kept_samples
return filtered_vars
def test_calc_called_gts_distribution_per_depth(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
dist, _ = calc_called_gts_distrib_per_depth(hdf5, depths=range(30),
chunk_size=10)
assert dist[1, 1] == 1
dist2, _ = calc_called_gts_distrib_per_depth(hdf5, depths=range(30),
chunk_size=None)
assert numpy.all(dist == dist2)
vars_ = VariationsArrays()
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [0, 1],
[0, 0], [0, 1], [0, 0],
[0, 0], [0, 1], [1, 1],
[0, 0]]])
vars_['/calls/DP'] = numpy.array([[10, 5, 15, 7, 10,
0, 0, 25, 20, 10]])
vars_.samples = list(range(10))
dist, _ = calc_called_gts_distrib_per_depth(vars_, depths=[0, 5, 10,
30])
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(dist == expected)
def test_calc_maf_depth_distribs_per_sample(self):
variations = VariationsArrays()
variations['/calls/AO'] = numpy.array([])
variations['/calls/RO'] = numpy.array([])
distribs, bins = calc_maf_depth_distribs_per_sample(variations,
chunk_size=None)
assert distribs is None
assert bins is None
variations = VariationsArrays()
variations['/calls/AO'] = numpy.array([[[0, 0], [0, 0], [15, -1]]])
variations['/calls/RO'] = numpy.array([[10, 5, 15]])
variations.samples = list(range(3))
distribs, _ = calc_maf_depth_distribs_per_sample(variations, n_bins=4,
min_depth=6,
chunk_size=None)
expected = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 1, 0]]
assert numpy.all(distribs == expected)
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
distribs1, _ = calc_maf_depth_distribs_per_sample(hdf5, min_depth=6,
chunk_size=None)
distribs2, _ = calc_maf_depth_distribs_per_sample(hdf5, min_depth=6)
assert numpy.all(distribs1 == distribs2)
def test_diploid_writing(self):
variations = VariationsArrays()
gts = numpy.array([
[[0, 0], [2, 2], [1, 1], [0, 0], [0, 0]],
[[2, 2], [1, 1], [-1, 2], [0, 0], [-1, -1]],
[[0, 1], [0, 0], [0, 0], [1, 1], [0, 0]],
[[0, 0], [1, -1], [1, 1], [1, -1], [1, 1]],
[[0, 1], [0, 1], [-1, -1], [-1, 1], [1, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]],
])
ref = numpy.array([b'C', b'G', b'A', b'T', b'T', b'C', b'G'])
alt = numpy.array([[b'CT', b'CTT'], [b'GA', b'GAT'], [b'C', b''],
[b'G', b''], [b'A', b''], [b'G', b''], [b'C', b'']])
variations[GT_FIELD] = gts
variations[ALT_FIELD] = alt
variations[REF_FIELD] = ref
variations[CHROM_FIELD] = numpy.array(
['ch1', 'ch2', 'ch2', 'ch2', 'ch2', 'ch3', 'ch3'])
variations[POS_FIELD] = numpy.array([10, 20, 30, 40, 50, 10, 15])
variations.samples = list(map(str, range(gts.shape[1])))
fhand = io.BytesIO()
write_fasta(variations,
fhand,
remove_invariant_snps=True,
remove_indels=False,
try_to_align_easy_indels=True,
write_one_seq_per_sample_setting_hets_to_missing=False)
# SNPS
# C-- C-T CTT
# G-- GA- GAT
# A C
# T G
# T A
# haps
# 00 22 11 00 00
# 22 11 N2 00 NN
# 01 00 00 11 00
# 00 1N 11 1N 11
# 01 01 NN N1 10
#
# indi0_h1> C--GATATT
# indi0_h2> C--GATCTA
# indi1_h1> CTTGA-ATT
# indi1_h2> CTTGA-ANA
# indi2_h1> C-TNNNAGN
# indi2_h2> C-TGATAGN
# indi3_h1> C--G--CGN
# indi3_h2> C--G--CNA
# indi4_h1> C--NNNAGA
# indi4_h2> C--NNNAGT
result = fhand.getvalue().splitlines()
assert b'>0_hap1' in result[0]
assert result[1] == b'C--GATATT'
assert b'>0_hap2' in result[2]
assert result[3] == b'C--GATCTA'
assert b'>1_hap1' in result[4]
assert result[5] == b'CTTGA-AGT'
assert b'>1_hap2' in result[6]
assert result[7] == b'CTTGA-ANA'
assert b'>2_hap1' in result[8]
assert result[9] == b'C-TNNNAGN'
assert b'>2_hap2' in result[10]
assert result[11] == b'C-TGATAGN'
assert b'>3_hap1' in result[12]
assert result[13] == b'C--G--CGN'
assert b'>3_hap2' in result[14]
assert result[15] == b'C--G--CNA'
assert b'>4_hap1' in result[16]
assert result[17] == b'C--NNNAGA'
assert b'>4_hap2' in result[18]
assert result[19] == b'C--NNNAGT'
def test_fasta_writer(self):
variations = VariationsArrays()
gts = numpy.array([
[[0, 0], [0, 1], [1, 1], [0, 0], [0, 0]],
[[2, 2], [1, 1], [-1, 2], [0, 0], [-1, -1]],
[[0, 1], [0, 0], [0, 0], [1, 1], [0, 0]],
[[0, 0], [1, -1], [1, 1], [1, -1], [1, 1]],
[[0, 1], [0, 1], [-1, -1], [-1, 1], [1, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]],
])
ref = numpy.array(['C', 'G', 'A', 'T', 'T', 'C', 'C'])
alt = numpy.array([['A', 'TT'], ['A', 'T'], ['C', ''], ['G', ''],
['A', 'T'], ['G', ''], ['G', '']])
variations[GT_FIELD] = gts
variations[ALT_FIELD] = alt
variations[REF_FIELD] = ref
variations[CHROM_FIELD] = numpy.array(
['ch1', 'ch2', 'ch2', 'ch2', 'ch2', 'ch3', 'ch3'])
variations[POS_FIELD] = numpy.array([10, 20, 30, 40, 50, 10, 15])
variations.samples = list(map(str, range(gts.shape[1])))
fhand = io.BytesIO()
write_fasta(variations,
fhand,
remove_invariant_snps=True,
write_one_seq_per_sample_setting_hets_to_missing=True)
# SNPS
# C A TT
# G A T
# A C
# T G
# T A T
# C G
# N
# indi1> TNT
# indi2> AAN
# indi3> NAG
# indi4> GCN
# indi5> NAG
result = fhand.getvalue().splitlines()
assert b'>0' in result[0]
assert result[1] == b'TNT'
assert b'>1' in result[2]
assert result[3] == b'AAN'
assert b'>2' in result[4]
assert result[5] == b'NAG'
assert b'>3' in result[6]
assert result[7] == b'GCN'
assert b'>4' in result[8]
assert result[9] == b'NAG'
fhand = io.BytesIO()
write_fasta(variations,
fhand,
remove_invariant_snps=True,
write_one_seq_per_sample_setting_hets_to_missing=True)
result = fhand.getvalue().splitlines()
assert b'>0' in result[0]
assert result[1] == b'TNT'
fhand = io.BytesIO()
write_fasta(variations,
fhand,
remove_sites_all_N=True,
write_one_seq_per_sample_setting_hets_to_missing=True)
result = fhand.getvalue().decode().splitlines()
assert '>0' in result[0]
assert result[1] == 'TNTC'
def test_fasta_writer_with_indels(self):
variations = VariationsArrays()
gts = numpy.array([
[[0, 0], [2, 2], [1, 1], [0, 0], [0, 0]],
[[2, 2], [1, 1], [-1, 2], [0, 0], [-1, -1]],
[[0, 1], [0, 0], [0, 0], [1, 1], [0, 0]],
[[0, 0], [1, -1], [1, 1], [1, -1], [1, 1]],
[[0, 1], [0, 1], [-1, -1], [-1, 1], [1, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]],
])
ref = numpy.array([b'C', b'G', b'A', b'T', b'T', b'C', b'G'])
alt = numpy.array([[b'CT', b'CTT'], [b'GA', b'GAT'], [b'C', b''],
[b'G', b''], [b'A', b'T'], [b'G', b''], [b'C',
b'']])
variations[GT_FIELD] = gts
variations[ALT_FIELD] = alt
variations[REF_FIELD] = ref
variations[CHROM_FIELD] = numpy.array(
['ch1', 'ch2', 'ch2', 'ch2', 'ch2', 'ch3', 'ch3'])
variations[POS_FIELD] = numpy.array([10, 20, 30, 40, 50, 10, 15])
variations.samples = list(map(str, range(gts.shape[1])))
fhand = io.BytesIO()
write_fasta(variations,
fhand,
remove_invariant_snps=True,
remove_indels=False,
try_to_align_easy_indels=True,
write_one_seq_per_sample_setting_hets_to_missing=True)
# SNPS
# C-- C-T CTT
# G-- GA- GAT
# A C
# T G
# haps
# 0 2 1 0 0
# 2 1 H 0 N
# H 0 0 1 0
# 0 H 1 H 1
# indi1> C--GATNT
# indi2> CTTGA-AN
# indi3> C-TNNNAG
# indi4> C--G--CN
# indi5> C--NNNAG
result = fhand.getvalue().splitlines()
assert b'>0' in result[0]
assert result[1] == b'C--GATNT'
assert b'>1' in result[2]
assert result[3] == b'CTTGA-AN'
assert b'>2' in result[4]
assert result[5] == b'C-TNNNAG'
assert b'>3' in result[6]
assert result[7] == b'C--G--CN'
assert b'>4' in result[8]
assert result[9] == b'C--NNNAG'
fhand = io.BytesIO()
write_fasta(variations,
fhand,
remove_invariant_snps=True,
remove_indels=False,
put_hyphens_in_indels=False,
write_one_seq_per_sample_setting_hets_to_missing=True)
result = fhand.getvalue().splitlines()
assert b'>0' in result[0]
assert result[1] == b'CGATNT'
assert b'>1' in result[2]
assert result[3] == b'CTTGAAN'
assert b'>2' in result[4]
assert result[5] == b'CTNNNAG'
assert b'>3' in result[6]
assert result[7] == b'CGCN'
assert b'>4' in result[8]
assert result[9] == b'CNNNAG'