Rapgef2_simon_loo_data.ipynb

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   "source": [
    "Press the double arrow above and enjoy! This program allows you to examine Rapgef2 expression in all cell clusters identified in Loo, L., Simon, J. M., Xing, L., McCoy, E. S., Niehaus, J. K., Guo, J., ... & Zylka, M. J. (2019). Single-cell transcriptomic analysis of mouse neocortical development. Nature communications, 10(1), 1-11.\n",
    "\n"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "import matplotlib \n",
    "import ipywidgets as widgets\n",
    "import pandas as pd\n",
    "import io\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "from ipywidgets import interact_manual, interactive,GridspecLayout, AppLayout, Button, Layout, Widget\n",
    "import plotly.express as px\n",
    "\n",
    "data = pd.read_csv(\"LooSimon_MouseCortexDropSeq_E14P0_avgExpByCellType.csv\")\n",
    "# Get column names \n",
    "listOfColumnNames = list(data.columns.values[1:])\n",
    "idx = list(data['GeneID'])\n",
    "\n",
    "\n",
    "\n",
    "genes = ['Rapgef2']\n",
    "cells = listOfColumnNames\n",
    "data_gene_cell = data[cells]\n",
    "data_gene_cell.index= data['GeneID']\n",
    "data_gene_cell = data_gene_cell.loc[genes]\n",
    "data_gene_cell = data_gene_cell.reset_index()\n",
    "\n",
    "data_gene_cell2 = pd.melt(data_gene_cell, id_vars =['GeneID'],\n",
    "                              var_name ='Cluster', value_name ='Expression')\n",
    "\n",
    "ages = []\n",
    "cell_type = []\n",
    "supergroup = []\n",
    "import re\n",
    "\n",
    "for i in data_gene_cell2['Cluster']:    \n",
    "    if '-P]' in i:\n",
    "        ages.append('p0')\n",
    "    if '-E]' in i:\n",
    "        ages.append('e14.5')\n",
    "    if 'Layer' in i:\n",
    "        supergroup.append(\"Cortical Layers\")\n",
    "    if any(x in i for x in ['Endothelial','Choroid','Astrocyte','Oligo','Ganglionic','Micro']): \n",
    "        supergroup.append(\"Non-Neuronal\")\n",
    "    if any(x in i for x in ['SVZ','RG']): \n",
    "        supergroup.append(\"Proliferative\")\n",
    "    if \"Int\" in i:\n",
    "        supergroup.append(\"Inhibitory\")\n",
    "    if any(x in i for x in ['Thalamic','Striatal']): \n",
    "        supergroup.append(\"Midbrain\")\n",
    "    j = re.sub(\"[\\[].*?[\\]]\", \"\", i)\n",
    "    cell_type.append(j)\n",
    "\n",
    "data_gene_cell2['Cell_Type'] = cell_type\n",
    "data_gene_cell2['Age'] = ages\n",
    "data_gene_cell2['Category'] = supergroup\n",
    "\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "fig = px.bar(data_gene_cell2, x=\"Category\", y=\"Expression\", color = \"Age\", \n",
    "             #facet_col = \"Category\",\n",
    "             hover_name = \"Cell_Type\",\n",
    "             hover_data = [\"Age\", \"Cluster\"]\n",
    "            )\n",
    "fig.update_layout(barmode='group')\n",
    "\n",
    "fig.show()\n",
    "\n",
    "fig2 = go.Figure(data=[go.Table(\n",
    "        header=dict(values=list(data_gene_cell2.columns),\n",
    "                fill_color='paleturquoise',\n",
    "                align='left'),\n",
    "        cells=dict(values=[data_gene_cell2.GeneID, data_gene_cell2.Cluster, data_gene_cell2.Expression,\n",
    "                          data_gene_cell2.Cell_Type, data_gene_cell2.Age, data_gene_cell2.Category],\n",
    "                fill_color='lavender',\n",
    "                align='left'))\n",
    "            ])\n",
    "\n",
    "fig2.show()"
   ]
  }
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