finish cell2location

Author: adamgayoso

cell2location_lymph_node_spatial_tutorial.ipynb

@@ -165,11 +165,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>Note!</b>\n",
+    "<div class=\"alert alert-info\">\n",
+    "Note\n",
     "Here we rename genes to ENSEMBL ID for correct matching between single cell and spatial data.\n",
-    "</div>\n",
-    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "You can still plot gene expression by name using standard scanpy functions as follows: \n",
     "```python\n",
     "sc.pl.spatial(color='PTPRC', gene_symbols='SYMBOL', ...)\n",
@@ -190,8 +195,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-danger\">\n",
-    "<b>Note!</b>\n",
+    "<div class=\"alert alert-info\">\n",
+    "Note\n",
+    "    \n",
     "Mitochondia-encoded genes (gene names start with prefix mt- or MT-) are irrelevant for spatial mapping because their expression represents technical artifacts in the single cell and nucleus data rather than biological abundance of mitochondria. Yet these genes compose 15-40% of mRNA in each location. Hence, to avoid mapping artifacts we strongly recommend removing mitochondrial genes.\n",
     "</div>"
    ]
@@ -255,7 +261,6 @@
     "Warning\n",
     "    \n",
     "Here we rename genes to ENSEMBL ID for correct matching between single cell and spatial data.\n",
-    "    \n",
     "</div>"
    ]
   },
@@ -276,11 +281,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<b>Note!</b>\n",
+    "<div class=\"alert alert-info\">\n",
+    "Note\n",
+    "    \n",
     "Before we estimate the reference cell type signature we recommend to perform very permissive genes selection. We prefer this to standard highly-variable-gene selection because our procedure keeps markers of rare genes while removing most of the uninformative genes.\n",
-    "</div>\n",
-    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "In this 2D histogram, orange rectangle highlights genes excluded based on the combination of number of cells expressing that gene (Y-axis) and average RNA count for cells where the gene was detected (X-axis).\n",
     "\n",
     "In this case, the downloaded dataset was already filtered using this method, hence no density under the orange rectangle (to be changed in the future version of the tutorial)."