ET_Manuscript_Extended_Figure_7.ipynb

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   "metadata": {},
   "source": [
    "# EXTENDED FIGURE 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dfc382d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nglui\n",
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cloudvolume\n",
    "from caveclient import CAVEclient\n",
    "\n",
    "\n",
    "client = CAVEclient('minnie65_phase3_v1')\n",
    "client.info.get_datastack_info()\n",
    "\n",
    "\n",
    "pd.options.display.max_rows = None\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "#Load dataframe\n",
    "synapse_table = pd.read_feather(\"ET_extended_synapse_table.feather\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9951a53e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.000041</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "      <td>1.000000</td>\n",
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      "text/plain": [
       "          1         2         3             4             5             6\n",
       "1  1.000000  1.000000  1.000000  8.112115e-01  1.000000e+00  1.000000e+00\n",
       "2  1.000000  1.000000  0.928235  1.686539e-04  1.122352e-01  1.904448e-01\n",
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     "execution_count": 3,
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     "output_type": "execute_result"
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   ],
   "source": [
    "# Statistical analysis\n",
    "# Multiple comparisions\n",
    "\n",
    "data = [synapse_table.query(\"consensus_subclass == '5P-ET'\")['size'].values,\n",
    "              synapse_table.query(\"consensus_subclass == '5P-IT'\")['size'].values,\n",
    "              synapse_table.query(\"consensus_subclass == '5P-NP'\")['size'].values,\n",
    "              synapse_table.query(\"consensus_subclass == '6P'\")['size'].values,\n",
    "              synapse_table.query(\"consensus_subclass == 'BC'\")['size'].values,\n",
    "              synapse_table.query(\"consensus_subclass == 'MC'\")['size'].values]\n",
    "\n",
    "\n",
    "#perform Dunn's test using a Bonferonni correction for the p-values\n",
    "import scikit_posthocs as sp\n",
    "sp.posthoc_conover(data, p_adjust = 'bonferroni')\n",
    "\n",
    "\n"
   ]
  }
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