import unittest
from unittest import mock
from gn2.wqflask import app
from collections import OrderedDict
from gn2.wqflask.api.correlation import init_corr_params
from gn2.wqflask.api.correlation import convert_to_mouse_gene_id
from gn2.wqflask.api.correlation import do_literature_correlation_for_all_traits
from gn2.wqflask.api.correlation import get_sample_r_and_p_values
from gn2.wqflask.api.correlation import calculate_results
class AttributeSetter:
def __init__(self, obj):
for k, v in obj.items():
setattr(self, k, v)
class MockDataset(AttributeSetter):
def get_trait_data(self):
return None
def retrieve_genes(self, id=None):
return {"TT-1": "GH-1", "TT-2": "GH-2", "TT-3": "GH-3"}
class TestCorrelations(unittest.TestCase):
def setUp(self):
self.app_context = app.app_context()
self.app_context.push()
def tearDown(self):
self.app_context.pop()
def test_init_corr_params(self):
start_vars = {"return_count": "3", "type": "T1", "method": "spearman"}
corr_params_results = init_corr_params(start_vars=start_vars)
expected_results = {"return_count": 3, "type": "T1", "method": "spearman"}
self.assertEqual(corr_params_results, expected_results)
@mock.patch("gn2.wqflask.api.correlation.database_connection")
def test_convert_to_mouse_gene_id(self, mock_db):
conn = mock.MagicMock()
mock_db.return_value.__enter__.return_value = conn
with conn.cursor() as cursor:
cursor.fetchone.side_effect = [("MG-1",), ("MG-2",)]
self.assertEqual(
convert_to_mouse_gene_id(species="Other", gene_id=""), None
)
self.assertEqual(
convert_to_mouse_gene_id(species="mouse", gene_id="MG-4"), "MG-4"
)
self.assertEqual(
convert_to_mouse_gene_id(species="rat", gene_id="R1"), "MG-1"
)
self.assertEqual(
convert_to_mouse_gene_id(species="human", gene_id="H1"), "MG-2"
)
@mock.patch("gn2.wqflask.api.correlation.database_connection")
@mock.patch("gn2.wqflask.api.correlation.convert_to_mouse_gene_id")
def test_do_literature_correlation_for_all_traits(
self, mock_convert_to_mouse_geneid, mock_db
):
mock_convert_to_mouse_geneid.side_effect = ["MG-1", "MG-2;", "MG-3", "MG-4"]
trait_geneid_dict = {"TT-1": "GH-1", "TT-2": "GH-2", "TT-3": "GH-3"}
conn = mock.MagicMock()
mock_db.return_value.__enter__.return_value = conn
with conn.cursor() as cursor:
cursor.fetchone.side_effect = [("V1",), ("V2",), ("V3",)]
this_trait = AttributeSetter({"geneid": "GH-1"})
target_dataset = AttributeSetter(
{"group": AttributeSetter({"species": "rat"})}
)
results = do_literature_correlation_for_all_traits(
this_trait=this_trait,
target_dataset=target_dataset,
trait_geneid_dict=trait_geneid_dict,
corr_params={},
)
expected_results = {
"TT-1": ["GH-1", 0],
"TT-2": ["GH-2", "V1"],
"TT-3": ["GH-3", "V2"],
}
self.assertEqual(results, expected_results)
@mock.patch("gn2.wqflask.api.correlation.corr_result_helpers.normalize_values")
def test_get_sample_r_and_p_values(self, mock_normalize):
group = AttributeSetter(
{"samplelist": ["S1", "S2", "S3", "S4", "S5", "S6", "S7"]}
)
target_dataset = AttributeSetter({"group": group})
target_vals = [3.4, 6.2, 4.1, 3.4, 1.2, 5.6]
trait_data = {
"S1": AttributeSetter({"value": 2.3}),
"S2": AttributeSetter({"value": 1.1}),
"S3": AttributeSetter({"value": 6.3}),
"S4": AttributeSetter({"value": 3.6}),
"S5": AttributeSetter({"value": 4.1}),
"S6": AttributeSetter({"value": 5.0}),
}
this_trait = AttributeSetter({"data": trait_data})
mock_normalize.return_value = (
[2.3, 1.1, 6.3, 3.6, 4.1, 5.0],
[3.4, 6.2, 4.1, 3.4, 1.2, 5.6],
6,
)
mock_normalize.side_effect = [
([2.3, 1.1, 6.3, 3.6, 4.1, 5.0], [3.4, 6.2, 4.1, 3.4, 1.2, 5.6], 6),
([2.3, 1.1, 6.3, 3.6, 4.1, 5.0], [3.4, 6.2, 4.1, 3.4, 1.2, 5.6], 6),
([2.3, 1.1, 1.4], [3.4, 6.2, 4.1], 3),
]
results_pearsonr = get_sample_r_and_p_values(
this_trait=this_trait,
this_dataset={},
target_vals=target_vals,
target_dataset=target_dataset,
type="pearson",
)
results_spearmanr = get_sample_r_and_p_values(
this_trait=this_trait,
this_dataset={},
target_vals=target_vals,
target_dataset=target_dataset,
type="spearman",
)
results_num_overlap = get_sample_r_and_p_values(
this_trait=this_trait,
this_dataset={},
target_vals=target_vals,
target_dataset=target_dataset,
type="pearson",
)
expected_pearsonr = [-0.21618688834430866, 0.680771605997119, 6]
expected_spearmanr = [-0.11595420713048969, 0.826848213385815, 6]
for i, val in enumerate(expected_pearsonr):
self.assertAlmostEqual(val, results_pearsonr[i], 4)
for i, val in enumerate(expected_spearmanr):
self.assertAlmostEqual(val, results_spearmanr[i], 4)
self.assertEqual(results_num_overlap, None)
@mock.patch("gn2.wqflask.api.correlation.do_literature_correlation_for_all_traits")
def test_calculate_results(self, literature_correlation):
literature_correlation.return_value = {
"TT-1": ["GH-1", 0],
"TT-2": ["GH-2", 3],
"TT-3": ["GH-3", 1],
}
this_dataset = MockDataset({"group": AttributeSetter({"species": "rat"})})
target_dataset = MockDataset({"group": AttributeSetter({"species": "rat"})})
this_trait = AttributeSetter({"geneid": "GH-1"})
corr_params = {"type": "literature"}
sorted_results = calculate_results(
this_trait=this_trait,
this_dataset=this_dataset,
target_dataset=target_dataset,
corr_params=corr_params,
)
expected_results = {
"TT-2": ["GH-2", 3],
"TT-3": ["GH-3", 1],
"TT-1": ["GH-1", 0],
}
self.assertTrue(isinstance(sorted_results, OrderedDict))
self.assertEqual(dict(sorted_results), expected_results)
