def test_two_rsids_for_one_gene(self):
# Prepare
tmpdir = tempfile.mkdtemp()
output_file = os.path.join(tmpdir, 'output.hdf5')
model_name = 'model00'
model_path = _create_model(
model_name,
[
['rs1', 'gene00', 0.3712, 'A', 'G'], # ambiguous
['rs2', 'gene00', 0.0807, 'G', 'C'], # non-ambiguous
])
options = [
self.python_path,
self.predixcan_path,
'--bgens-dir',
get_full_path('tests/data/set00/'),
'--bgens-prefix',
'chr',
'--bgens-sample-file',
get_full_path('tests/data/set00/impv1.sample'),
'--weights-file',
model_path,
'--output-file',
output_file,
]
return_code = call(options)
assert return_code == 0
assert os.path.isfile(output_file)
with h5py.File(output_file, 'r') as hdf5_file:
assert len(hdf5_file.keys()) == 3
assert 'genes' in hdf5_file.keys()
assert hdf5_file['genes'].shape == (1, )
genes = [x.decode() for x in hdf5_file['genes']]
assert genes[0] == 'gene00'
assert 'pred_expr' in hdf5_file.keys()
preds = hdf5_file['pred_expr']
assert preds.shape == (1, 300)
assert preds.chunks == (1, 300)
assert truncate(preds[0, 0]) == truncate(
0.3712 * (2 - np.dot([0.74909, 0.01339, 0.23758], [0, 1, 2])) +
0.0807 *
(np.dot([0.75232, 0.11729, 0.13050], [0, 1, 2]))), preds[0, 0]
assert truncate(preds[0, 299]) == truncate(
0.3712 * (2 - np.dot([0.05763, 0.77338, 0.16910], [0, 1, 2])) +
0.0807 *
(np.dot([0.00937, 0.13421, 0.85658], [0, 1, 2]))), preds[0,
299]
def test_alleles_in_bgen_order_gene(self):
# Prepare
tmpdir = tempfile.mkdtemp()
output_file = os.path.join(tmpdir, 'output.hdf5')
model_name = 'model00'
model_path = _create_model(model_name,
[['rs1', 'gene00', 0.3712, 'G', 'A']])
options = [
self.python_path,
self.predixcan_path,
'--bgens-dir',
get_full_path('tests/data/set00/'),
'--bgens-prefix',
'chr',
'--bgens-sample-file',
get_full_path('tests/data/set00/impv1.sample'),
'--weights-file',
model_path,
'--output-file',
output_file,
]
return_code = call(options)
assert return_code == 0
assert os.path.isfile(output_file)
with h5py.File(output_file, 'r') as hdf5_file:
assert len(hdf5_file.keys()) == 3
assert 'pred_expr' in hdf5_file.keys()
preds = hdf5_file['pred_expr']
assert preds.shape == (1, 300)
assert preds.dtype == np.dtype('float32')
assert preds.scaleoffset == 4
assert preds.chunks == (1, 300)
assert truncate(preds[0, 0]) == truncate(
0.3712 *
(np.dot([0.74909, 0.01333, 0.23758], [0, 1, 2]))), preds[0, 0]
def _create_model(model_name, values):
model_path = get_full_path('tests/data/models/{}.db'.format(model_name))
if os.path.exists(model_path): os.remove(model_path)
with sqlite3.connect(model_path) as conn:
conn.execute("""
CREATE TABLE weights
( "rsid" TEXT,
"gene" TEXT,
"weight" REAL,
"ref_allele" TEXT,
"eff_allele" TEXT
);
""")
for val in values:
conn.execute("""
insert into weights (rsid, gene, weight, ref_allele, eff_allele) values
('{}', '{}', {}, '{}', '{}');
""".format(val[0], val[1], val[2], val[3], val[4]))
return model_path
def setUp(self):
self.load_data_path = get_full_path(os.path.join('ukbrest', 'load_data.py'))
super(LoadDataTest, self).setUp()
def setUp(self):
self.predixcan_path = get_full_path(os.path.join('.', 'predict.py'))
self.python_path = '/home/miltondp/software/miniconda3/envs/predixcan_prediction/bin/python'
def test_many_dosages_files(self):
# Prepare
tmpdir = tempfile.mkdtemp()
output_file = os.path.join(tmpdir, 'output.hdf5')
model_name = 'model00'
alleles = {
0: ('A', 'G'),
1: ('C', 'A'),
9: ('C', 'T'),
10: ('G', 'T'),
11: ('C', 'G'),
21: ('G', 'A'),
}
weights = {
0: 0.1158,
1: 0.5455,
9: 0.9876,
10: 0.1755,
11: 0.2754,
21: 0.6855,
}
model_path = _create_model(
model_name,
[[
'rs{}'.format(i + 1), 'gene{:0>3d}'.format(j),
weights.get(j, 0.5),
alleles.get(j, ('G', ))[0],
alleles.get(j, ('', 'C'))[1]
] for j in range(21 + 1) for i in range(j * 10, j * 10 + 2)] + [[
'rs{}'.format(i + 1), 'gene2{:0>2d}'.format(j),
weights.get(j, 0.5),
alleles.get(j, ('G', ))[0],
alleles.get(j, ('', 'C'))[1]
] for j in range(11 + 1) for i in range(2000000 + j * 10, 2000000 +
j * 10 + 2)],
)
options = [
self.python_path,
self.predixcan_path,
'--bgens-dir',
get_full_path('tests/data/set00/'),
'--bgens-prefix',
'chr',
'--bgens-sample-file',
get_full_path('tests/data/set00/impv1.sample'),
'--weights-file',
model_path,
'--output-file',
output_file,
]
return_code = call(options)
assert return_code == 0
assert os.path.isfile(output_file)
with h5py.File(output_file, 'r') as hdf5_file:
assert len(hdf5_file.keys()) == 3
assert 'samples' in hdf5_file.keys()
samples = hdf5_file['samples']
assert samples.shape == (300, )
assert all(samples[:].astype(str) == np.array(
[str(x) for x in range(1, 300 + 1)]))
assert 'genes' in hdf5_file.keys()
assert hdf5_file['genes'].shape == (22 + 12, )
genes = [x.decode() for x in hdf5_file['genes']]
assert genes[0] == 'gene000'
assert genes[1] == 'gene001'
assert genes[20] == 'gene020'
assert genes[21] == 'gene021'
assert genes[22] == 'gene200'
assert genes[23] == 'gene201'
assert genes[32] == 'gene210'
assert genes[-1] == 'gene211'
assert 'pred_expr' in hdf5_file.keys()
preds = hdf5_file['pred_expr']
assert preds.shape == (22 + 12, 300)
assert preds.chunks == (10, 300)
# genes from chr1
# gene00
assert truncate(preds[0, 0]) == truncate(
0.1158 *
(2 - np.dot([0.74909, 0.01333, 0.23740], [0, 1, 2])) + 0.1158 *
(np.dot([0.75232, 0.11725, 0.13030], [0, 1, 2]))
) == 0.2188, preds[0, 0]
assert truncate(preds[0, 299]) == truncate(
0.1158 *
(2 - np.dot([0.05763, 0.77328, 0.16910], [0, 1, 2])) + 0.1158 *
(np.dot([0.00937, 0.13421, 0.85640], [0, 1, 2]))
) == 0.3167, preds[0, 299]
# gene01
assert truncate(preds[1, 0]) == truncate(
0.5455 *
(2 - np.dot([0.96807, 0.01962, 0.01231], [0, 1, 2])) + 0.5455 *
(np.dot([0.00190, 0.00429, 0.99381], [0, 1, 2]))
) == 2.1534, preds[1, 0]
assert truncate(preds[1, 1]) == truncate(
0.5455 *
(2 - np.dot([0.91510, 0.06826, 0.01669], [0, 1, 2])) + 0.5455 *
(np.dot([0.70937, 0.04896, 0.24177], [0, 1, 2]))
) == 1.3260, preds[1, 1]
# gene09
assert truncate(preds[9, 0]) == truncate(
0.9876 *
(2 - np.dot([0.74754, 0.13307, 0.11935], [0, 1, 2])) + 0.9876 *
(2 - np.dot([0.03755, 0.78400, 0.17849], [0, 1, 2]))
) == 2.4564, preds[9, 0]
assert truncate(preds[9, 298]) == truncate(
0.9876 *
(2 - np.dot([0.71102, 0.00968, 0.27929], [0, 1, 2])) + 0.9876 *
(2 - np.dot([0.08631, 0.77275, 0.14089], [0, 1, 2]))
) == 2.3477, preds[9, 298]
# gene10
assert truncate(preds[10, 0]) == truncate(
0.1755 *
(2 - np.dot([0.05931, 0.08242, 0.85827], [0, 1, 2])) + 0.1755 *
(np.dot([0.83525, 0.01184, 0.15291], [0, 1, 2]))
) == 0.091, preds[10, 0]
assert truncate(preds[10, 2]) == truncate(
0.1755 *
(2 - np.dot([0.61247, 0.22145, 0.16605], [0, 1, 2])) + 0.1755 *
(np.dot([0.09727, 0.77103, 0.13170], [0, 1, 2]))
) == 0.4353, preds[10, 2]
# gene11
assert truncate(preds[11, 0]) == truncate(
0.2754 *
(np.dot([0.83211, 0.12816, 0.03970], [0, 1, 2])) + 0.2754 *
(np.dot([0.96441, 0.02577, 0.00990], [0, 1, 2]))
) == 0.0697, preds[11, 0]
assert truncate(preds[11, 299]) == truncate(
0.2754 *
(np.dot([0.04018, 0.84357, 0.11625], [0, 1, 2])) + 0.2754 *
(np.dot([0.07541, 0.11284, 0.81175], [0, 1, 2]))
) == 0.7745, preds[11, 299]
# gene21
assert truncate(preds[21, 0]) == truncate(
0.6855 *
(np.dot([0.73030, 0.13711, 0.13255], [0, 1, 2])) + 0.6855 *
(2 - np.dot([0.11456, 0.04225, 0.84315], [0, 1, 2]))
) == 0.4617, preds[21, 0]
assert truncate(preds[21, 299]) == truncate(
0.6855 *
(np.dot([0.13023, 0.18599, 0.68379], [0, 1, 2])) + 0.6855 *
(2 - np.dot([0.85909, 0.07115, 0.06976], [0, 1, 2]))
) == 2.2915, preds[21, 299]
# genes from chr 2
# gene200
assert truncate(preds[22, 0]) == truncate(
0.1158 *
(np.dot([0.96459, 0.02124, 0.01418], [0, 1, 2])) + 0.1158 *
(2 - np.dot([0.91804, 0.01235, 0.06966], [0, 1, 2]))
) == 0.2197, preds[22, 0]
assert truncate(preds[22, 299]) == truncate(
0.1158 *
(np.dot([0.15472, 0.80145, 0.04384], [0, 1, 2])) + 0.1158 *
(2 - np.dot([0.95387, 0.00694, 0.03919], [0, 1, 2]))
) == 0.3246, preds[22, 299]
# gene211
assert truncate(preds[33, 0]) == truncate(
0.2754 *
(np.dot([0.07602, 0.84035, 0.08360], [0, 1, 2])) + 0.2754 *
(np.dot([0.79911, 0.18546, 0.01550], [0, 1, 2]))
) == 0.337, preds[33, 0]
assert truncate(preds[33, 299]) == truncate(
0.2754 *
(np.dot([0.18570, 0.81029, 0.00390], [0, 1, 2])) + 0.2754 *
(np.dot([0.21121, 0.01581, 0.77310], [0, 1, 2]))
) == 0.6554, preds[33, 299]
def test_two_rsids_for_one_gene_one_rsid_for_another_gene(self):
# Prepare
tmpdir = tempfile.mkdtemp()
output_file = os.path.join(tmpdir, 'output.hdf5')
model_name = 'model00'
model_path = _create_model(
model_name,
[
['rs1', 'gene00', 0.3712, 'A', 'G'], # ambiguous
['rs2', 'gene00', 0.0807, 'G', 'C'], # non-ambiguous
['rs10', 'gene01', 0.6188, 'A', 'T'], # non-ambiguous
])
options = [
self.python_path,
self.predixcan_path,
'--bgens-dir',
get_full_path('tests/data/set00/'),
'--bgens-prefix',
'chr',
'--bgens-sample-file',
get_full_path('tests/data/set00/impv1.sample'),
'--weights-file',
model_path,
'--output-file',
output_file,
]
return_code = call(options)
assert return_code == 0
assert os.path.isfile(output_file)
with h5py.File(output_file, 'r') as hdf5_file:
assert len(hdf5_file.keys()) == 3
assert 'samples' in hdf5_file.keys()
samples = hdf5_file['samples']
assert samples.shape == (300, )
assert all(samples[:].astype(str) == np.array(
[str(x) for x in range(1, 300 + 1)]))
assert 'genes' in hdf5_file.keys()
assert hdf5_file['genes'].shape == (2, )
genes = [x.decode() for x in hdf5_file['genes']]
assert genes[0] == 'gene00'
assert genes[1] == 'gene01'
assert 'pred_expr' in hdf5_file.keys()
preds = hdf5_file['pred_expr']
assert preds.shape == (2, 300)
assert preds.chunks == (2, 300)
# gene00
assert truncate(preds[0, 0]) == truncate(
0.3712 *
(2 - np.dot([0.74909, 0.01333, 0.23758], [0, 1, 2])) + 0.0807 *
(np.dot([0.75232, 0.11749, 0.13040], [0, 1, 2]))
) == 0.5916, preds[0, 0]
assert truncate(preds[0, 299]) == truncate(
0.3712 *
(2 - np.dot([0.05763, 0.77328, 0.16910], [0, 1, 2])) + 0.0807 *
(np.dot([0.00937, 0.13421, 0.85650], [0, 1, 2]))
) == 0.4788, preds[0, 299]
# gene01
assert truncate(preds[1, 0]) == truncate(0.6188 * (np.dot(
[0.11764, 0.86431, 0.01805], [0, 1, 2]))) == 0.5571, preds[1,
0]
assert truncate(preds[1, 298]) == truncate(0.6188 * (np.dot(
[0.03509, 0.82789, 0.13705], [0, 1, 2]))) == 0.6819, preds[1,
298]