Merge pull request #485 from aristoteleo/update_pp_tests

Update pp tests

tests/test_preprocess.py

@@ -9,6 +9,7 @@
 # from utils import *
 import dynamo as dyn
 from dynamo.preprocessing import Preprocessor
+from dynamo.preprocessing.cell_cycle import get_cell_phase
 from dynamo.preprocessing.preprocessor_utils import (
     calc_mean_var_dispersion_sparse,
     is_float_integer_arr,
@@ -184,6 +185,7 @@ def test_pca():
     assert np.linalg.norm(pca.explained_variance_ratio_[:10] - adata.uns["explained_variance_ratio_"][:10]) < 1e-1
 
 
+
 def test_preprocessor_seurat(adata):
     adata = dyn.sample_data.zebrafish()
     preprocessor = dyn.pp.Preprocessor()
@@ -228,6 +230,121 @@ def test_is_nonnegative():
     assert not is_nonnegative_integer_arr(test_mat)
 
 
+def test_filter_genes_by_clusters_():
+    # Create test data
+    n_cells = 1000
+    n_genes = 500
+    data = np.random.rand(n_cells, n_genes)
+    layers = {
+        "spliced": csr_matrix(data),
+        "unspliced": csr_matrix(data),
+    }
+    adata = anndata.AnnData(X=data, layers=layers)
+
+    # Add cluster information
+    clusters = np.random.randint(low=0, high=3, size=n_cells)
+    adata.obs['clusters'] = clusters
+
+    # Filter genes by cluster
+    clu_avg_selected = dyn.pp.filter_genes_by_clusters_(adata, 'clusters')
+
+    # Check that the output is a numpy array
+    assert type(clu_avg_selected) == np.ndarray
+
+    # Check that the output has the correct shape
+    assert clu_avg_selected.shape == (n_genes,)
+
+    # Check that all genes with U and S average > min_avg_U and min_avg_S respectively are selected
+    U, S = adata.layers['unspliced'], adata.layers['spliced']
+    U_avgs = np.array([np.mean(U[clusters == i], axis=0) for i in range(3)])
+    S_avgs = np.array([np.mean(S[clusters == i], axis=0) for i in range(3)])
+    expected_clu_avg_selected = np.any((U_avgs.max(1) > 0.02) & (S_avgs.max(1) > 0.08), axis=0)
+    assert np.array_equal(clu_avg_selected, expected_clu_avg_selected)
+
+
+def test_filter_genes_by_outliers():
+    # create a small test AnnData object
+    data = np.array([[1, 0, 3, 0], [0, 1, 1, 0], [0, 1, 2, 1], [0, 0, 0, 0], [0, 1, 1, 1], [1, 0, 0, 1]])
+    adata = anndata.AnnData(data)
+    adata.obs_names = ["cell1", "cell2", "cell3", "cell4", "cell5", "cell6"]
+    adata.var_names = ["gene1", "gene2", "gene3", "gene4"]
+
+    filtered_adata = dyn.pp.filter_genes_by_outliers(
+        adata,
+        min_avg_exp_s=0.5,
+        min_cell_s=2,
+        max_avg_exp=2.5,
+        min_count_s=2,
+        inplace=False,
+    )
+
+    # check that the filtered object contains the correct values
+    assert np.all(filtered_adata.values == [False, True, True, True])
+
+    # check that the original object is unchanged
+    assert np.all(adata.var_names.values == ["gene1", "gene2", "gene3", "gene4"])
+
+    dyn.pp.filter_genes_by_outliers(adata,
+                                    min_avg_exp_s=0.5,
+                                    min_cell_s=2,
+                                    max_avg_exp=2.5,
+                                    min_count_s=2,
+                                    inplace=True)
+
+    # check that the adata has been updated
+    assert adata.shape == (6, 3)
+    assert np.all(adata.var_names.values == ["gene2", "gene3", "gene4"])
+
+
+def test_filter_cells_by_outliers():
+    # Create a test AnnData object with some example data
+    adata = anndata.AnnData(
+        X=np.array([[1, 0, 3], [4 ,0 ,0], [7, 8, 9], [10, 11, 12]]))
+    adata.var_names = ["gene1", "gene2", "gene3"]
+    adata.obs_names = ["cell1", "cell2", "cell3", "cell4"]
+
+    # Test the function with custom range values
+    dyn.pp.filter_cells_by_outliers(
+        adata, min_expr_genes_s=2, max_expr_genes_s=6)
+
+    assert np.array_equal(
+        adata.obs_names.values,
+        ["cell1", "cell3", "cell4"],
+    )
+
+    # Test the function with invalid layer value
+    try:
+        dyn.pp.filter_cells_by_outliers(adata, layer="invalid_layer")
+        assert False, "Expected ValueError"
+    except ValueError:
+        pass
+
+def test_get_cell_phase():
+    from collections import OrderedDict
+
+    # create a mock anndata object with mock data
+    adata = anndata.AnnData(
+        X=pd.DataFrame(
+            [[1, 2, 7, 4, 1], [4, 3, 3, 5, 16], [5, 26, 7, 18, 9], [8, 39, 1, 1, 12]],
+            columns=["arglu1", "dynll1", "cdca5", "cdca8", "ckap2"],
+        )
+    )
+
+    # expected output
+    expected_output = pd.DataFrame(
+        {
+            "G1-S": [0.52330,-0.28244,-0.38155,-0.13393],
+            "S": [-0.77308, 0.98018, 0.39221, -0.38089],
+            "G2-M": [-0.33656, -0.27547, 0.70090, 0.35216],
+            "M": [0.07714, -0.36019, -0.67685, 0.77044],
+            "M-G1": [0.50919, -0.06209, -0.03472, -0.60778],
+        },
+    )
+
+    # test the function output against the expected output
+    np.allclose(get_cell_phase(adata).iloc[:, :5], expected_output)
+
+
 def test_gene_selection_method():
     adata = dyn.sample_data.zebrafish()
     dyn.pl.basic_stats(adata)
@@ -357,7 +474,11 @@ def test_regress_out():
     # test_highest_frac_genes_plot(adata.copy())
     # test_highest_frac_genes_plot_prefix_list(adata.copy())
     # test_recipe_monocle_feature_selection_layer_simple0()
+    # test_filter_genes_by_clusters_()
+    # test_filter_genes_by_outliers()
+    # test_filter_cells_by_outliers()
+    # test_get_cell_phase()
     # test_gene_selection_method()
-    test_normalize()
+    # test_normalize()
     # test_regress_out()
     pass