Backport categorical ComBat reference coding to 1.12.x

docs/release-notes/4496.fix.md

@@ -0,0 +1 @@
+Fix {func}`~scanpy.pp.combat` with categorical `covariates`, which produced a singular design matrix and either raised or silently returned wrong values {smaller}`L Heumos`

src/scanpy/preprocessing/_combat.py

@@ -51,8 +51,8 @@ def _design_matrix(
     if other_cols:
         col_repr = " + ".join(f"Q('{x}')" for x in other_cols)
         factor_matrix = patsy.dmatrix(
-            f"~ 0 + {col_repr}", model[other_cols], return_type="dataframe"
-        )
+            f"~ {col_repr}", model[other_cols], return_type="dataframe"
+        ).drop(columns="Intercept")
 
         design = pd.concat((design, factor_matrix), axis=1)
         logg.info(f"Found {len(other_cols)} categorical variables:")

tests/test_combat_categorical.py

@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pytest
+from anndata import AnnData
+from scipy.sparse import csr_matrix  # noqa: TID251
+
+import scanpy as sc
+from scanpy.preprocessing._combat import _design_matrix
+
+
+def _categorical_example(n_covariates):
+    combinations = list(product(["batch_a", "batch_b"], ["a", "b", "c"], ["x", "y"]))
+    obs = pd.DataFrame(combinations * 4, columns=["batch", "condition", "donor"])
+    obs.index = obs.index.astype(str)
+    for key in obs:
+        obs[key] = obs[key].astype("category")
+    rng = np.random.default_rng(4454)
+    obs["age"] = rng.normal(size=len(obs))
+    x = rng.normal(size=(len(obs), 24))
+    x += obs["batch"].cat.codes.to_numpy()[:, None] * np.linspace(0.5, 2, 24)
+    x += obs["condition"].cat.codes.to_numpy()[:, None] * np.linspace(0.3, 1, 24)
+    categories = ["condition", "donor"][:n_covariates]
+    numeric = pd.concat(
+        [
+            obs[["batch", "age"]],
+            pd.get_dummies(obs[categories], drop_first=True, dtype=float),
+        ],
+        axis=1,
+    )
+    return AnnData(x, obs=obs), numeric, categories
+
+
+@pytest.mark.parametrize("n_covariates", [1, 2])
+def test_categorical_design_is_full_rank(n_covariates):
+    adata, numeric, categories = _categorical_example(n_covariates)
+    model = adata.obs[["batch", *categories, "age"]]
+    levels = adata.obs["batch"].cat.categories
+    design = _design_matrix(model, "batch", levels)
+    expected = _design_matrix(numeric, "batch", levels)
+    assert design.shape == expected.shape
+    assert np.linalg.matrix_rank(design) == design.shape[1]
+    expected_columns = [*expected.columns[:2], *numeric.columns[2:], "age"]
+    np.testing.assert_allclose(design.to_numpy(), expected[expected_columns].to_numpy())
+
+
+@pytest.mark.parametrize("n_covariates", [1, 2])
+@pytest.mark.parametrize("sparse", [False, True])
+def test_categorical_combat_matches_reference_coding(n_covariates, sparse):
+    adata, numeric, categories = _categorical_example(n_covariates)
+    x = adata.X.copy()
+    if sparse:
+        adata.X = csr_matrix(adata.X)
+    reference = AnnData(x.copy(), obs=numeric)
+    expected = sc.pp.combat(
+        reference, key="batch", covariates=list(numeric.columns[1:]), inplace=False
+    )
+    actual = sc.pp.combat(
+        adata, key="batch", covariates=[*categories, "age"], inplace=False
+    )
+    assert np.isfinite(actual).all()
+    np.testing.assert_allclose(actual, expected, rtol=1e-10, atol=1e-10)
+    np.testing.assert_array_equal(adata.X.toarray() if sparse else adata.X, x)
+    sc.pp.combat(adata, key="batch", covariates=[*categories, "age"])
+    np.testing.assert_allclose(adata.X, expected, rtol=1e-10, atol=1e-10)
+
+
+def test_single_level_categorical_covariate_matches_batch_only():
+    adata, _, _ = _categorical_example(1)
+    adata.obs["constant"] = pd.Categorical(["same"] * adata.n_obs)
+    expected = sc.pp.combat(adata, key="batch", inplace=False)
+    actual = sc.pp.combat(adata, key="batch", covariates=["constant"], inplace=False)
+    np.testing.assert_allclose(actual, expected, rtol=1e-10, atol=1e-10)