errors_correction

notebooks/spatialdata_tutorials/1_run_segmentation_free.ipynb

@@ -190,7 +190,7 @@
     "\n",
     "\n",
     "input_data=sdata.points['transcripts'][['missegmentation_associated']].compute()\n",
-    "troutpy.pl.pie_of_positive(input_data,groupby='missegmentation_associated',figures_path=output_path,save=True)"
+    "troutpy.pl.pie_of_positive(input_data,groupby='missegmentation_associated',save=True)"
    ]
   }
  ],

notebooks/spatialdata_tutorials/4_explore_comunication.ipynb

@@ -267,23 +267,21 @@
    "outputs": [],
    "source": [
     "def communicating_genes_per_celltype(sdata,proportion_threshold: float = 0.2,cell_type_key='cell type'):\n",
-    "    \"\"\"\n",
-    "    Computes the number of significant genes exchanged between source and target cell types\n",
-    "    based on their proportions in the dataset.\n",
+    "    \"\"\"Computes the number of significant genes exchanged between source and target cell types based on their proportions in the dataset.\n",
     "\n",
     "    Parameters:\n",
     "    - sdata: AnnData object\n",
-    "        A spatial data object containing the 'table' and 'xrna_metadata' components.\n",
+    "    A spatial data object containing the 'table' and 'xrna_metadata' components.\n",
     "    - proportion_threshold: float, optional (default=0.2)\n",
-    "        The threshold for filtering significant gene proportions. Genes with proportions \n",
-    "        above this value are considered significant.\n",
+    "    The threshold for filtering significant gene proportions. Genes with proportions \n",
+    "    above this value are considered significant.\n",
     "    - cell_type_key: str, optional (default='cell type')\n",
-    "        The key in `adata.obs` that contains the cell type annotations.\n",
+    "    The key in `adata.obs` that contains the cell type annotations.\n",
     "\n",
     "    Returns:\n",
     "    - number_interactions_df: pandas.DataFrame\n",
-    "        A DataFrame where rows represent source cell types, columns represent target cell \n",
-    "        types, and values indicate the number of significant genes shared between them.\n",
+    "    A DataFrame where rows represent source cell types, columns represent target cell \n",
+    "    types, and values indicate the number of significant genes shared between them.\n",
     "    \"\"\"\n",
     "    adata=sdata['table']\n",
     "    source_proportions=pd.DataFrame(sdata['xrna_metadata'].varm['source'],index=sdata['xrna_metadata'].var.index,columns=adata.obs[cell_type_key].unique().dropna())\n",

notebooks/spatialdata_tutorials/7_image_params.ipynb

@@ -255,30 +255,6 @@
     "# The transcripts DataFrame now has a column for each channel's intensity\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['DAPI',\n",
-       " 'ATP1A1/CD45/E-Cadherin',\n",
-       " '18S',\n",
-       " 'AlphaSMA/Vimentin',\n",
-       " 'dummy',\n",
-       " 'feature_name',\n",
-       " 'feature_name']"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,