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| author | Alexander Kabui | 2021-05-12 19:40:27 +0300 |
|---|---|---|
| committer | Alexander Kabui | 2021-05-12 19:40:27 +0300 |
| commit | beccacde5c9c7317bfe795e5c8c4ebe033f39f89 (patch) | |
| tree | 4255ae2277e31f2a8e7e51a4e40deb764aa2e7dc /tests/unit | |
| parent | 3728b26ed5bd3cbd384dab6907e2518c6c7cf30b (diff) | |
| download | genenetwork3-beccacde5c9c7317bfe795e5c8c4ebe033f39f89.tar.gz | |
rename p_val ro tissue_p_value for tissue_results
Diffstat (limited to 'tests/unit')
| -rw-r--r-- | tests/unit/computations/test_correlation.py | 12 |
1 files changed, 7 insertions, 5 deletions
diff --git a/tests/unit/computations/test_correlation.py b/tests/unit/computations/test_correlation.py index 6414c3b..42ab796 100644 --- a/tests/unit/computations/test_correlation.py +++ b/tests/unit/computations/test_correlation.py @@ -236,7 +236,7 @@ class TestCorrelation(TestCase): target_tissues_values = [1, 2, 3] mock_compute_corr_coeff.side_effect = [(0.4, 0.9), (-0.2, 0.91)] expected_tissue_results = {"1456_at": {"tissue_corr": 0.4, - "p_value": 0.9, "tissue_number": 3}} + "tissue_p_val": 0.9, "tissue_number": 3}} tissue_results = tissue_correlation_for_trait_list( primary_tissue_values, target_tissues_values, corr_method="pearson", trait_id="1456_at", @@ -417,13 +417,15 @@ class TestCorrelation(TestCase): target_tissue_data = {"trait_symbol_dict": target_trait_symbol, "symbol_tissue_vals_dict": target_symbol_tissue_vals} - mock_tissue_corr.side_effect = [{"tissue_corr": -0.5, "p_value": 0.9, "tissue_number": 3}, - {"tissue_corr": 1.11, "p_value": 0.2, "tissue_number": 3}] + mock_tissue_corr.side_effect = [{"tissue_corr": -0.5, "tissue_p_val": 0.9, + "tissue_number": 3}, + {"tissue_corr": 1.11, "tissue_p_val": 0.2, + "tissue_number": 3}] expected_results = [{"1412_at": - {"tissue_corr": 1.11, "p_value": 0.2, "tissue_number": 3}}, + {"tissue_corr": 1.11, "tissue_p_val": 0.2, "tissue_number": 3}}, {"1418702_a_at": - {"tissue_corr": -0.5, "p_value": 0.9, "tissue_number": 3}}] + {"tissue_corr": -0.5, "tissue_p_val": 0.9, "tissue_number": 3}}] results = compute_all_tissue_correlation( primary_tissue_dict=primary_tissue_dict, |