def test_warn_on_sample_mismatch(self):
mantel(self.output_dir, self.dm1, self.mismatched_dm,
intersect_ids=True)
self.assertBasicVizValidity(self.output_dir, 3,
mismatched_ids={'foo', 'x'},
exp_test_stat=0.5, exp_p_value=1)
def test_warn_on_sample_mismatch(self):
mantel(self.output_dir, self.dm1, self.mismatched_dm,
intersect_ids=True)
self.assertBasicVizValidity(self.output_dir, 3,
mismatched_ids={'foo', 'x'},
exp_test_stat=0.5, exp_p_value=1)
def test_alt_labels(self):
mantel(self.output_dir, self.dm1, self.dm2, label1='Peanut',
label2='Milo')
self.assertBasicVizValidity(self.output_dir, 3, label1='Peanut',
label2='Milo', exp_test_stat=0.5,
exp_p_value=1)
def test_alt_labels(self):
mantel(self.output_dir, self.dm1, self.dm2, label1='Peanut',
label2='Milo')
self.assertBasicVizValidity(self.output_dir, 3, label1='Peanut',
label2='Milo', exp_test_stat=0.5,
exp_p_value=1)
def test_same_matrices(self):
mantel(self.output_dir, self.dm1, self.dm1)
self.assertBasicVizValidity(self.output_dir,
3,
exp_test_stat=1,
exp_p_value=1)
def test_defaults_positive_correlation(self):
mantel(self.output_dir, self.dm1, self.dm2)
self.assertBasicVizValidity(self.output_dir,
3,
exp_test_stat=0.5,
exp_p_value=1)
def test_zero_permutations(self):
mantel(self.output_dir, self.dm1, self.dm2, permutations=0)
self.assertBasicVizValidity(self.output_dir,
3,
permutations=0,
exp_test_stat=0.5,
exp_p_value='NaN')
def test_alt_permutations(self):
mantel(self.output_dir, self.dm1, self.dm2, permutations=42)
self.assertBasicVizValidity(self.output_dir,
3,
permutations=42,
exp_test_stat=0.5,
exp_p_value=1)
def test_pearson(self):
mantel(self.output_dir, self.dm1, self.dm2, method='pearson')
self.assertBasicVizValidity(self.output_dir,
3,
method='pearson',
exp_test_stat=0.5,
exp_p_value=1)
def test_defaults_reordered(self):
# Order of IDs in distance matrices shouldn't change the results.
mantel(self.output_dir, self.dm1, self.dm2_reordered)
self.assertBasicVizValidity(self.output_dir,
3,
exp_test_stat=0.5,
exp_p_value=1)
def test_same_filtered_matrices(self):
# These two matrices filter down to the same data.
mantel(self.output_dir, self.dm2, self.mismatched_dm,
intersect_ids=True)
self.assertBasicVizValidity(self.output_dir, 3,
mismatched_ids={'foo', 'x'},
exp_test_stat=1, exp_p_value=1)
def test_warn_on_sample_mismatch_reverse_comparison(self):
# Comparing X to Y should be the same as comparing Y to X.
mantel(self.output_dir, self.mismatched_dm, self.dm1,
intersect_ids=True)
self.assertBasicVizValidity(self.output_dir, 3,
mismatched_ids={'foo', 'x'},
exp_test_stat=0.5, exp_p_value=1)
def test_defaults_reverse_comparison(self):
# Comparing X to Y should be the same as comparing Y to X.
mantel(self.output_dir, self.dm2, self.dm1)
self.assertBasicVizValidity(self.output_dir,
3,
exp_test_stat=0.5,
exp_p_value=1)
def test_same_filtered_matrices(self):
# These two matrices filter down to the same data.
mantel(self.output_dir, self.dm2, self.mismatched_dm,
intersect_ids=True)
self.assertBasicVizValidity(self.output_dir, 3,
mismatched_ids={'foo', 'x'},
exp_test_stat=1, exp_p_value=1)
def test_warn_on_sample_mismatch_reverse_comparison(self):
# Comparing X to Y should be the same as comparing Y to X.
mantel(self.output_dir, self.mismatched_dm, self.dm1,
intersect_ids=True)
self.assertBasicVizValidity(self.output_dir, 3,
mismatched_ids={'foo', 'x'},
exp_test_stat=0.5, exp_p_value=1)
def test_zero_permutations(self):
mantel(self.output_dir, self.dm1, self.dm2, permutations=0)
self.assertBasicVizValidity(self.output_dir, 3, permutations=0,
exp_test_stat=0.5, exp_p_value='NaN')
def test_defaults_positive_correlation(self):
mantel(self.output_dir, self.dm1, self.dm2)
self.assertBasicVizValidity(self.output_dir, 3, exp_test_stat=0.5,
exp_p_value=1)
def test_error_on_sample_mismatch(self):
with self.assertRaisesRegex(ValueError,
'intersect_ids.*mismatches.*\n\nfoo, x'):
mantel(self.output_dir, self.dm1, self.mismatched_dm)
def test_defaults_negative_correlation(self):
mantel(self.output_dir, self.dm1, self.dm3)
# p-value will be stochastic with this dataset so not asserting its
# value.
self.assertBasicVizValidity(self.output_dir, 3, exp_test_stat=-1)
def test_defaults_negative_correlation(self):
mantel(self.output_dir, self.dm1, self.dm3)
# p-value will be stochastic with this dataset so not asserting its
# value.
self.assertBasicVizValidity(self.output_dir, 3, exp_test_stat=-1)
def test_same_matrices(self):
mantel(self.output_dir, self.dm1, self.dm1)
self.assertBasicVizValidity(self.output_dir, 3, exp_test_stat=1,
exp_p_value=1)
def test_defaults_reverse_comparison(self):
# Comparing X to Y should be the same as comparing Y to X.
mantel(self.output_dir, self.dm2, self.dm1)
self.assertBasicVizValidity(self.output_dir, 3, exp_test_stat=0.5,
exp_p_value=1)
def test_error_on_sample_mismatch(self):
with self.assertRaisesRegex(ValueError,
'intersect_ids.*mismatches.*\n\nfoo, x'):
mantel(self.output_dir, self.dm1, self.mismatched_dm)
def test_defaults_reordered(self):
# Order of IDs in distance matrices shouldn't change the results.
mantel(self.output_dir, self.dm1, self.dm2_reordered)
self.assertBasicVizValidity(self.output_dir, 3, exp_test_stat=0.5,
exp_p_value=1)
def test_alt_permutations(self):
mantel(self.output_dir, self.dm1, self.dm2, permutations=42)
self.assertBasicVizValidity(self.output_dir, 3, permutations=42,
exp_test_stat=0.5, exp_p_value=1)
def test_pearson(self):
mantel(self.output_dir, self.dm1, self.dm2, method='pearson')
self.assertBasicVizValidity(self.output_dir, 3, method='pearson',
exp_test_stat=0.5, exp_p_value=1)