def test_test_multiple_features_kruskal_test(self):
test_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
expected_object = pd.DataFrame.from_dict(
{
0: ['Season', 'specie_1', 0.890909, 0.640533],
1: ['Season', 'specie_2', 0.336364, 0.845200],
2: ['Season', 'specie_3', 1.518519, 0.468013],
3: ['Season', 'specie_4', 7.436364, 0.024278],
4: ['Season', 'specie_5', 2.142857, 0.342519],
},
orient='index',
columns=['features', 'taxons', 'static_value', 'p-value'])
pd.testing.assert_frame_equal(
test_object.test_multiple_features(self.multiple_option_feature,
'kruskal_test'),
expected_object)
def test_test_multiple_features_one_way_anova(self):
test_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
expected_object = pd.DataFrame.from_dict(
{
0: ['Season', 'specie_1', 0.064622, 0.937974],
1: ['Season', 'specie_2', 0.401588, 0.683744],
2: ['Season', 'specie_3', 1.208941, 0.354003],
3: ['Season', 'specie_4', 15.370573, 0.002748],
4: ['Season', 'specie_5', 1.817575, 0.231335],
},
orient='index',
columns=['features', 'taxons', 'static_value', 'p-value'])
pd.testing.assert_frame_equal(
test_object.test_multiple_features(self.multiple_option_feature,
'one_way_anova'),
expected_object)
def test_corrected_p_values(self):
test_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
tested_object = pd.DataFrame.from_dict(
{
0: ['Season', 'specie_1', 0.890909, 0.640533],
1: ['Season', 'specie_2', 0.336364, 0.845200],
2: ['Season', 'specie_3', 1.518519, 0.468013],
3: ['Season', 'specie_4', 7.436364, 0.024278],
4: ['Season', 'specie_5', 2.142857, 0.342519],
},
orient='index',
columns=['features', 'taxons', 'static_value', 'p-value'])
expected_object = np.array(
[0.8006663, 0.8452, 0.7800217, 0.12139, 0.7800217])
np.testing.assert_almost_equal(
test_object.corrected_p_values(tested_object['p-value'], 'fdr_bh'),
expected_object)
def test_test_dichotomic_features_t_test(self):
test_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
expected_object = pd.DataFrame.from_dict(
{
0: ['SEX', 'specie_1', 2.580936, 0.032569, 64219.3, 925.7],
1:
['SEX', 'specie_2', 1.353146, 0.213002, 4611894.3, 432948.3],
2: ['SEX', 'specie_3', 0.984586, 0.353664, 769104.2, 180.0],
3: ['SEX', 'specie_4', 1.788817, 0.111441, 915345.3, 227463.2],
4: ['SEX', 'specie_5', 0.485682, 0.640215, 102820.3, 39705.3],
},
orient='index',
columns=[
'features', 'taxons', 'static_value', 'p-value',
'variance_group1', 'variance_group2'
])
pd.testing.assert_frame_equal(
test_object.test_dichotomic_features(self.dicotomic_feature,
't_test'), expected_object)
def test_test_dichotomic_features_wilcoxon_rank_test(self):
test_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
expected_object = pd.DataFrame.from_dict(
{
0: ['SEX', 'specie_1', 2.611165, 0.009023, 64219.3, 925.7],
1:
['SEX', 'specie_2', 0.522233, 0.601508, 4611894.3, 432948.3],
2: ['SEX', 'specie_3', 0.104447, 0.916815, 769104.2, 180.0],
3: ['SEX', 'specie_4', 1.148913, 0.250592, 915345.3, 227463.2],
4: ['SEX', 'specie_5', -0.104447, 0.916815, 102820.3, 39705.3],
},
orient='index',
columns=[
'features', 'taxons', 'static_value', 'p-value',
'variance_group1', 'variance_group2'
])
pd.testing.assert_frame_equal(
test_object.test_dichotomic_features(self.dicotomic_feature,
'wilcoxon_rank_test'),
expected_object)
def test_number_columns_to_skip(self):
tested_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
expected_object = 2
self.assertEqual(tested_object.number_columns_to_skip, expected_object)
def test_test_default(self):
test_object = DifferentialAnalysis(self.tested_object_metadata,
self.tested_object_reads)
with pytest.raises(Exception):
assert test_object.test_default()