From 0f31c0949b801f0bdeb4b812607d5408ee718375 Mon Sep 17 00:00:00 2001 From: Julia Date: Fri, 19 Apr 2024 10:12:26 +0200 Subject: [PATCH] add test for new MDS solver --- tests/data/mds/example_0_dataset.pckl | Bin 0 -> 377529 bytes tests/data/mds/example_0_mds.pckl | Bin 0 -> 5700 bytes tests/data/mds/example_1_dataset.pckl | Bin 0 -> 377529 bytes tests/data/mds/example_1_mds.pckl | Bin 0 -> 5700 bytes tests/data/mds/example_2_dataset.pckl | Bin 0 -> 377529 bytes tests/data/mds/example_2_mds.pckl | Bin 0 -> 5700 bytes tests/data/mds/example_3_dataset.pckl | Bin 0 -> 377529 bytes tests/data/mds/example_3_mds.pckl | Bin 0 -> 5700 bytes tests/test_dataset.py | 48 ++++++++++++++++++++++++-- 9 files changed, 45 insertions(+), 3 deletions(-) create mode 100644 tests/data/mds/example_0_dataset.pckl create mode 100644 tests/data/mds/example_0_mds.pckl create mode 100644 tests/data/mds/example_1_dataset.pckl create mode 100644 tests/data/mds/example_1_mds.pckl create mode 100644 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"data" / "smartpca" / "does_not_exist" +@pytest.fixture +def example_mds_data_dir() -> pathlib.Path: + return pathlib.Path(__file__).parent / "data" / "mds" + + class TestEigenDataset: def test_get_embedding_populations(self, example_population_list): populations = _get_embedding_populations(example_population_list) @@ -371,8 +382,6 @@ def _modify_population( f"{sample_id.strip()}\t{sex.strip()}\t{new_population}\n" ) - print(new_ind.open().read()) - class TestEigenDatasetMDS: def test_run_mds_with_dash_in_population_name( @@ -621,6 +630,39 @@ def test_numpy_dataset_from_eigenfiles_fails_for_plink_dataset( with pytest.raises(PandoraException, match="Not all required input files"): numpy_dataset_from_eigenfiles(example_ped_dataset_prefix) + @pytest.mark.parametrize("dataset_id", range(4)) + def test_mds_simulated_data(self, dataset_id, example_mds_data_dir): + """ + Since we experienced problems with MDS analyses in Pandora < 2.0.0, we decided to implement a sanity check + of the current state of MDS results when preparing version 2.0.0. + For this, we performed MDS analyses using the R cmdscale function as a reference for a set of 4 datasets. + The 4 datasets are simulated datasets with a varying fraction of missing data obtained from this publication: + + Yi, X. & Latch, E. K. (2022). Nonrandom missing data can bias Principal Component Analysis inference of + population genetic structure. Molecular Ecology Resources, 22, 602–611. https://doi.org/10.1111/1755-0998.13498. + + The data is publicly available on GitHub: https://github.com/xuelingyi/missing_data_PCA + + For the reference MDS results, we used the missing_corrected_hamming_distance distance metric. To compare the + MDS results, we use Pandora's EmbeddingComparison and expect the Pandora stability to be close to 1 + indicating that the MDS implementation in Pandora produces the same MDS results as the R cmdscale method. + """ + dataset: NumpyDataset = pickle.load( + (example_mds_data_dir / f"example_{dataset_id}_dataset.pckl").open("rb") + ) + expected_mds = pickle.load( + (example_mds_data_dir / f"example_{dataset_id}_mds.pckl").open("rb") + ) + + dataset.run_mds( + n_components=2, + distance_metric=missing_corrected_hamming_sample_distance, + imputation=None, + ) + + comparison = EmbeddingComparison(dataset.mds, expected_mds) + assert comparison.compare() == pytest.approx(1.0) + @pytest.mark.parametrize("dtype, expected_missing_value", DTYPES_AND_MISSING_VALUES) def test_process_input_data_no_missing_values(dtype, expected_missing_value):