test: update control genes test case

tests/test_pydeseq2.py

@@ -73,27 +73,22 @@ def test_size_factors_poscounts(counts_df, metadata):
 def test_size_factors_control_genes(counts_df, metadata):
     """Test that the size_factors calculation properly takes control_genes"""
 
-    dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition")
-
-    dds.fit_size_factors(control_genes=["gene4"])
-
-    np.testing.assert_almost_equal(
-        dds.obsm["size_factors"].ravel(),
-        counts_df["gene4"] / np.exp(np.log(counts_df["gene4"]).mean()),
+    dds = DeseqDataSet(
+        counts=counts_df, metadata=metadata, design="~condition", control_genes=["gene4"]
     )
 
-    dds.fit_size_factors(fit_type="poscounts", control_genes=[3])
+    dds.fit_size_factors()
 
     np.testing.assert_almost_equal(
         dds.obsm["size_factors"].ravel(),
         counts_df["gene4"] / np.exp(np.log(counts_df["gene4"]).mean()),
     )
 
+    # We should have gene4 as control gene again.
     dds.fit_size_factors(fit_type="poscounts")
 
-    np.testing.assert_raises(
-        AssertionError,
-        np.testing.assert_array_equal,
+    # Gene 4 has no zero counts, so we should get the same as before
+    np.testing.assert_almost_equal(
         dds.obsm["size_factors"].ravel(),
         counts_df["gene4"] / np.exp(np.log(counts_df["gene4"]).mean()),
     )