Pre-commit fixes

scbs/MethylVI_batch.ipynb

@@ -54,16 +54,16 @@
     }
    ],
    "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import mudata\n",
-    "import scanpy as sc\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
+    "import scanpy as sc\n",
     "import scvi\n",
     "import seaborn as sns\n",
     "import torch\n",
-    "import os\n",
-    "import tempfile\n",
-    "\n",
     "from scvi.external import METHYLVI"
    ]
   },
@@ -155,9 +155,7 @@
     "if not os.path.exists(mdata_path):\n",
     "    os.system(f\"wget -q -O {mdata_path} https://figshare.com/ndownloader/files/49632108\")\n",
     "\n",
-    "mdata = mudata.read_h5mu(\n",
-    "    mdata_path\n",
-    ")\n",
+    "mdata = mudata.read_h5mu(mdata_path)\n",
     "mdata.mod"
    ]
   },
@@ -185,7 +183,7 @@
     }
    ],
    "source": [
-    "mdata['mCG'].layers"
+    "mdata[\"mCG\"].layers"
    ]
   },
   {
@@ -224,7 +222,7 @@
     }
    ],
    "source": [
-    "mdata['mCG'].X"
+    "mdata[\"mCG\"].X"
    ]
   },
   {
@@ -240,11 +238,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sc.tl.pca(mdata['mCG'])\n",
-    "sc.tl.pca(mdata['mCH'])\n",
+    "sc.tl.pca(mdata[\"mCG\"])\n",
+    "sc.tl.pca(mdata[\"mCH\"])\n",
     "\n",
-    "ch_pcs = mdata['mCH'].obsm['X_pca']\n",
-    "cg_pcs = mdata['mCG'].obsm['X_pca']\n",
+    "ch_pcs = mdata[\"mCH\"].obsm[\"X_pca\"]\n",
+    "cg_pcs = mdata[\"mCG\"].obsm[\"X_pca\"]\n",
     "\n",
     "# standardize the values of PCs from both modalities\n",
     "cg_pcs = cg_pcs / cg_pcs.std()\n",
@@ -253,7 +251,7 @@
     "# total_pcs\n",
     "total_pcs = np.hstack([ch_pcs, cg_pcs])\n",
     "\n",
-    "mdata.obsm['X_pca'] = total_pcs"
+    "mdata.obsm[\"X_pca\"] = total_pcs"
    ]
   },
   {
@@ -290,8 +288,8 @@
     "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(11, 5))\n",
     "\n",
-    "sc.pl.umap(mdata, color='mCG:Platform', ax=ax[0], show=False, title=\"Sequencing protocol\")\n",
-    "sc.pl.umap(mdata, color='mCG:CoarseType', ax=ax[1], show=False, title=\"Cell type\")\n",
+    "sc.pl.umap(mdata, color=\"mCG:Platform\", ax=ax[0], show=False, title=\"Sequencing protocol\")\n",
+    "sc.pl.umap(mdata, color=\"mCG:CoarseType\", ax=ax[1], show=False, title=\"Cell type\")\n",
     "\n",
     "plt.subplots_adjust(wspace=0.5)"
    ]
@@ -316,7 +314,7 @@
    "source": [
     "Before training our model, we'll use methylVI's `setup_mudata` function to prepare our `MuData` object for training. \n",
     "\n",
-    "First, we need to tell methylVI which modalities in our MuData object to consider via the `methylation_contexts` argument. Here we'll jointly model both CpG and non-CpG methylation features, so we'll set this argument to a list containing the names of both modalities. Next, methylVI directly models the total coverage and number of methylated cytosines in each region. Thus, for each modality in our `MuData` object, we need layers containing the coverage in each region (specified by `cov_layer`) and layers with the number of methylated cytosines (specified by `mc_layer`). Finally, we'll provide methylVI with a categorical covariate specifying the sequencing protocol used for each cell."
+    "First, we need to tell methylVI which modalities in our MuData object to consider via the `methylation_contexts` argument. Here we'll jointly model both CpG and non-CpG methylation features, so we'll set this argument to a list containing the names of both modalities. Next, methylVI directly models the total coverage and number of methylated cytosines in each region. Thus, for each modality in our `MuData` object, we need layers containing the coverage in each region (specified by `cov_layer`) and layers with the number of methylated cytosines (specified by `mc_layer`). Finally, we'll provide methylVI with a categorical covariate specifying the sequencing protocol used for each cell.\n"
    ]
   },
   {
@@ -336,11 +334,11 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "```{note}\n",
-    "Specify the modality of each argument via the `modalities` dictionary, which maps layer/key arguments to MuData modalities. In our case, both the `mCG` and `mCH` modalities contain the all of the fields specified in the `categorical_covariate_keys` argument (i.e., `Protocol`) in their respective `.obs`, so we arbitrarily choose `mCG` here.\n",
+    "Specify the modality of each argument via the `modalities` dictionary, which maps layer/key arguments to MuData modalities. In our case, both the `mCG` and `mCH` modalities contain the all of the fields specified in the `categorical_covariate_keys` argument (i.e., `Protocol`) in their respective `.obs`, so we arbitrarily choose `mCG` here\n",
     "```"
    ]
   },
@@ -360,7 +358,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[34mINFO    \u001B[0m The model has been initialized                                                                            \n"
+      "\u001b[34mINFO    \u001b[0m The model has been initialized                                                                            \n"
      ]
     },
     {
@@ -407,7 +405,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mdata.obsm['methylVI'] = model.get_latent_representation()"
+    "mdata.obsm[\"methylVI\"] = model.get_latent_representation()"
    ]
   },
   {
@@ -439,13 +437,13 @@
     }
    ],
    "source": [
-    "sc.pp.neighbors(mdata, use_rep='methylVI')\n",
+    "sc.pp.neighbors(mdata, use_rep=\"methylVI\")\n",
     "sc.tl.umap(mdata)\n",
     "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(11, 5))\n",
     "\n",
-    "sc.pl.umap(mdata, color='mCG:Platform', ax=ax[0], show=False, title=\"Sequencing protocol\")\n",
-    "sc.pl.umap(mdata, color='mCG:CoarseType', ax=ax[1], show=False, title=\"Cell type\")\n",
+    "sc.pl.umap(mdata, color=\"mCG:Platform\", ax=ax[0], show=False, title=\"Sequencing protocol\")\n",
+    "sc.pl.umap(mdata, color=\"mCG:CoarseType\", ax=ax[1], show=False, title=\"Cell type\")\n",
     "\n",
     "plt.subplots_adjust(wspace=0.5)"
    ]