add unittest

setup.py

@@ -29,6 +29,7 @@
         "typing_extensions",
         "rich",
         "dill",
+        "pytest",
     ],
     python_requires=">=3.5, <3.12",
     classifiers=[

tests/conftest.py

@@ -0,0 +1,40 @@
+import pytest
+import numpy as np
+import pandas as pd
+import networkx as nx
+import scanpy as sc
+
+
+@pytest.fixture(scope="session")
+def fake_grn():
+    G = nx.barbell_graph(5, 1)
+    G = nx.relabel_nodes(G, {i: str(i) for i in G})
+    return G
+
+
+@pytest.fixture(scope="session")
+def fake_snp_score():
+    snp_score = {"6": 1, "7": 1, "8": 1, "3": 1, "5": 0}
+    return snp_score
+
+
+@pytest.fixture(scope="session")
+def fake_adata(fake_grn):
+    df = pd.DataFrame(
+        0,
+        index=[str(i) for i in np.arange(90)],
+        columns=[str(i) for i in np.arange(len(fake_grn))],
+    )
+    df.iloc[:30, [0, 2, 3]] = 100
+    df.iloc[30:60, [2, 5, 9]] = 100
+    df.iloc[60:90, [8, 9, 10]] = 100
+
+    ad = sc.AnnData(df)
+    ad.obs["cell type"] = ["A"] * 30 + ["B"] * 30 + ["C"] * 30
+
+    sc.pp.normalize_total(ad)
+    sc.pp.log1p(ad)
+    ad.raw = ad
+    sc.pp.scale(ad)
+
+    return ad

tests/test_toy_example.py

@@ -0,0 +1,67 @@
+import snp2cell
+
+snp2cell.util.set_num_cpu(1)
+
+
+def test_toy_example(fake_grn, fake_adata, fake_snp_score):
+    s2c = snp2cell.SNP2CELL()
+    s2c.add_grn_from_networkx(fake_grn)
+
+    assert s2c is not None, "snp2cell object was not created"
+    assert s2c.grn is not None, "networkx object was not added to snp2cell object"
+
+    # add SNP score, propagate, calculate statistics
+    s2c.add_score(score_dct=fake_snp_score, score_key="snp_score")
+
+    assert (
+        "snp_score" in s2c.scores
+    ), "original snp_score was not added to snp2cell object"
+    assert (
+        "snp_score" in s2c.scores_prop
+    ), "propagated snp_score was not added to snp2cell object"
+    assert (
+        "snp_score" in s2c.scores_rand
+    ), "permuted snp_scores were not added to snp2cell object"
+    assert (
+        "snp_score__pval" in s2c.scores_prop
+    ), "statistics were not calculated for snp_score"
+
+    # add DE scores, propagate, calculate statistics
+    s2c.link_adata(fake_adata)
+    s2c.adata_add_de_scores(groupby="cell type", check=False)
+
+    assert s2c.adata is not None, "anndata object was not added to snp2cell object"
+    for grp in fake_adata.obs["cell type"].unique().tolist():
+        assert (
+            grp in s2c.de_groups["cell type"]
+        ), f"cell type {grp} was not added to snp2cell object"
+        assert (
+            f"DE_{grp}__score" in s2c.scores
+        ), f"original DE score for {grp} was not added to snp2cell object"
+        assert (
+            f"DE_{grp}__score" in s2c.scores_prop
+        ), f"propagated DE score for {grp} was not added to snp2cell object"
+
+    assert (
+        f"DE_cell type__score" in s2c.scores_rand
+    ), f"permuted DE scores for 'cell type' were not added to snp2cell object"
+
+    for grp in fake_adata.obs["cell type"].unique().tolist():
+        assert (
+            f"DE_{grp}__score__pval" in s2c.scores_prop
+        ), f"statistics were not calculated for {grp}"
+
+    # combine each DE score with the SNP score
+    s2c.adata_combine_de_scores(
+        group_key="cell type", score_key="snp_score", suffix="__zscore"
+    )
+
+    assert (
+        "min(DE_C__score__zscore,snp_score__zscore)" in s2c.scores_prop
+    ), "DE score and SNP score were not combined"
+
+    sum_a = s2c.scores_prop["min(DE_A__score__zscore,snp_score__zscore)"].sum()
+    sum_b = s2c.scores_prop["min(DE_B__score__zscore,snp_score__zscore)"].sum()
+    sum_c = s2c.scores_prop["min(DE_C__score__zscore,snp_score__zscore)"].sum()
+
+    assert sum_c > sum_b > sum_a, "computed values are not correct"