[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

docs/notebooks/breast_cancer.ipynb

@@ -27,7 +27,6 @@
     "%autoreload 2\n",
     "from multi_condition_comparisions.tl.de import StatsmodelsDE\n",
     "\n",
-    "import scanpy as sc\n",
     "import decoupler as dc"
    ]
   },
@@ -44,11 +43,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#import cellxgene_census\n",
-    "#cellxgene_census.download_source_h5ad('cd74e95e-6583-4875-a0ba-f2eae5a1e5a6.h5ad',\n",
-    "                                        #to_path=\"breast_cancer.h5ad\")\n",
+    "# import cellxgene_census\n",
+    "# cellxgene_census.download_source_h5ad('cd74e95e-6583-4875-a0ba-f2eae5a1e5a6.h5ad',\n",
+    "# to_path=\"breast_cancer.h5ad\")\n",
     "\n",
-    "#adata = sc.read_h5ad('./cd74e95e-6583-4875-a0ba-f2eae5a1e5a6.h5ad')"
+    "# adata = sc.read_h5ad('./cd74e95e-6583-4875-a0ba-f2eae5a1e5a6.h5ad')"
    ]
   },
   {
@@ -57,8 +56,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adata.layers['counts'] = adata.raw.X\n",
-    "adata.layers['normalised_counts'] = adata.X\n"
+    "adata.layers[\"counts\"] = adata.raw.X\n",
+    "adata.layers[\"normalised_counts\"] = adata.X"
    ]
   },
   {
@@ -116,13 +115,14 @@
     }
    ],
    "source": [
-    "pbulk = dc.get_pseudobulk(adata,\n",
-    "                            sample_col='donor_id',\n",
-    "                            groups_col='cell_type',\n",
-    "                            layer='normalised_counts',\n",
-    "                            mode='sum',\n",
-    "                            min_cells=10,\n",
-    "                            min_counts=1000,\n",
+    "pbulk = dc.get_pseudobulk(\n",
+    "    adata,\n",
+    "    sample_col=\"donor_id\",\n",
+    "    groups_col=\"cell_type\",\n",
+    "    layer=\"normalised_counts\",\n",
+    "    mode=\"sum\",\n",
+    "    min_cells=10,\n",
+    "    min_counts=1000,\n",
     ")\n",
     "\n",
     "pbulk"
@@ -713,8 +713,13 @@
     }
    ],
    "source": [
-    "mod.test_contrasts({\"T cells\": mod.cond(disease=\"breast cancer\", cell_type=\"T cell\") - mod.cond(disease=\"normal\", cell_type=\"T cell\")},\n",
-    "                   {\"fibroblasts\": mod.cond(disease=\"breast cancer\", cell_type=\"fibroblast\") - mod.cond(disease=\"normal\", cell_type=\"fibroblast\")})"
+    "mod.test_contrasts(\n",
+    "    {\"T cells\": mod.cond(disease=\"breast cancer\", cell_type=\"T cell\") - mod.cond(disease=\"normal\", cell_type=\"T cell\")},\n",
+    "    {\n",
+    "        \"fibroblasts\": mod.cond(disease=\"breast cancer\", cell_type=\"fibroblast\")\n",
+    "        - mod.cond(disease=\"normal\", cell_type=\"fibroblast\")\n",
+    "    },\n",
+    ")"
    ]
   }
  ],

src/multi_condition_comparisions/tl/de.py

@@ -165,7 +165,9 @@ def cond(self, **kwargs) -> np.ndarray:
         """
         The intention is to make contrasts using this function as in glmGamPoi
 
-        >>> res <- test_de(fit, contrast = cond(cell = "B cells", condition = "stim") - cond(cell = "B cells", condition = "ctrl"))
+        >>> res < -test_de(
+        ...     fit, contrast=cond(cell="B cells", condition="stim") - cond(cell="B cells", condition="ctrl")
+        ... )
 
         Parameters
         ----------

tests/test_de.py

@@ -4,7 +4,7 @@
 from pandas import testing as tm
 
 import multi_condition_comparisions
-from multi_condition_comparisions.tl.de import PyDESeq2DE, StatsmodelsDE, EdgeRDE
+from multi_condition_comparisions.tl.de import EdgeRDE, PyDESeq2DE, StatsmodelsDE
 
 
 def test_package_has_version():
@@ -78,6 +78,7 @@ def test_pydeseq2_complex(test_adata):
     assert expected_columns.issubset(set(res_df.columns))
     assert np.all((0 <= res_df["pvals"]) & (res_df["pvals"] <= 1))
 
+
 def test_edger_complex(test_adata):
     """Check that the EdgeR method can be initialized with a different covariate name and fitted and that the test_contrast
     method returns a dataframe with the correct number of rows.
@@ -94,4 +95,3 @@ def test_edger_complex(test_adata):
     expected_columns = {"pvals", "pvals_adj", "logfoldchanges"}
     assert expected_columns.issubset(set(res_df.columns))
     assert np.all((0 <= res_df["pvals"]) & (res_df["pvals"] <= 1))
-