{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Open TG-GATEs clinical pathology data from ChEMBL using web services</h1>\n",
    "\n",
    "<h2>Introduction</h2>\n",
    "\n",
    "Open TG-GATEs is a toxicogenomics database that stores gene expression profiles and traditional toxicological data derived from in vivo (rat) and in vitro (primary rat hepatocytes, primary human hepatocytes) exposure to 170 compounds at multiple dosages and time points. The toxicology data is composed of biochemistry, hematology and histopathology findings with pathology imaging from the in vivo studies and cytotoxicity from the in vitro studies. The 170 compounds include representative known liver- and kidney-injuring pharmaceuticals, compounds and chemicals.<sup>(1)</sup> The data can be browsed <a href='http://toxico.nibiohn.go.jp/open-tggates/english/search.html'>here</a>.\n",
    "\n",
    "Biochemistry, hematology, organ weight and histopathology findings from the Open TG-GATEs dataset have been integrated into ChEMBL and can be found under src_id 11 and document_chembl_ids CHEMBL2766011 (biochemistry), CHEMBL2766014 (hematology), CHEMBL2766012 (organ weight) and CHEMBL2766013 (histopathology). Doses, time points, regimens and study_ids are stored in the assay description. For each biochemistry, hematology and organ weight endpoint, the average value across the individual animal samples has been calculated. The averaged treatment value is recorded as an activity standard_value while the  averaged controls for those endpoints can be found in the activity_comment.\n",
    "\n",
    "Detailed information regarding how to use the ChEMBL web services can be found <a href='https://www.ebi.ac.uk/chembl/ws'>here</a>.\n",
    "\n",
    "<sup>(1)</sup> Igarashi, Y., Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T., & Yamada, H. (2015). Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Research, 43(Database issue), D921–7.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Configuration and setup</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from chembl_webresource_client.new_client import new_client\n",
    "from chembl_webresource_client.settings import Settings\n",
    "Settings.Instance().MAX_LIMIT = 1000\n",
    "import requests\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "pd.set_option('max_colwidth',300)\n",
    "pd.set_option(\"display.max_rows\",999)\n",
    "pd.set_option(\"display.max_columns\",150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Retrieve Open TG-GATEs data from ChEMBL</h2>\n",
    "\n",
    "Requests to ChEMBL can be made either by \n",
    "\n",
    "1\\. Using the API provided by the Python package chembl_webresource_client:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158199"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity = new_client.activity\n",
    "activity.set_format('json')\n",
    "\n",
    "tggates_act_api = activity.get(document_chembl_id=(['CHEMBL2766011', 'CHEMBL2766014', 'CHEMBL2766012', 'CHEMBL2766013']))\n",
    "\n",
    "len(tggates_act_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2\\. Directly <i>via</i> URLs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-944438d87f82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0murl_stem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"https://www.ebi.ac.uk\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtggates_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_stem\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/chembl/api/data/activity.json?assay_description__icontains=TG-GATES&limit=1000\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtggates_act_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtggates_url\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'activities'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mtggates_url\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'page_meta'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'next'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/requests/models.py\u001b[0m in \u001b[0;36mjson\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    797\u001b[0m                     \u001b[0;31m# used.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 799\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    800\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    317\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 319\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \"\"\"\n\u001b[0;32m--> 339\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "url_stem = \"https://www.ebi.ac.uk\"\n",
    "tggates_url = requests.get(url_stem + \"/chembl/api/data/activity.json?assay_description__icontains=TG-GATES&limit=1000\").json()\n",
    "tggates_act_url = tggates_url['activities']\n",
    "\n",
    "while tggates_url['page_meta']['next']:\n",
    "    tggates_url = requests.get(url_stem + tggates_url['page_meta']['next']).json()\n",
    "    tggates_act_url += tggates_url['activities']\n",
    "\n",
    "len(tggates_act_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, the retrieved data will be converted to a dataframe: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158199, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_act_api_list = list(tggates_act_api.all())\n",
    "\n",
    "tggates_df = pd.DataFrame(tggates_act_api_list)\n",
    "\n",
    "tggates_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, doses, time points, regimens and study_ids are stored in the assay description. For each biochemistry, hematology and organ weight endpoint, the average value across the individual animal samples has been calculated. The averaged treatment value is recorded as an activity standard_value while the averaged controls for those endpoints can be found in the activity_comment. Here is an example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_comment</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>assay_type</th>\n",
       "      <th>bao_endpoint</th>\n",
       "      <th>bao_format</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>data_validity_comment</th>\n",
       "      <th>document_chembl_id</th>\n",
       "      <th>document_journal</th>\n",
       "      <th>document_year</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>pchembl_value</th>\n",
       "      <th>potential_duplicate</th>\n",
       "      <th>published_relation</th>\n",
       "      <th>published_type</th>\n",
       "      <th>published_units</th>\n",
       "      <th>published_value</th>\n",
       "      <th>qudt_units</th>\n",
       "      <th>record_id</th>\n",
       "      <th>src_id</th>\n",
       "      <th>standard_flag</th>\n",
       "      <th>standard_relation</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_organism</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>uo_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SD=2.07; p-value=0.44; N=5 || Control_MEAN=141.60; Control_SD=1.82; Control_N=5</td>\n",
       "      <td>13518617</td>\n",
       "      <td>CHEMBL2810656</td>\n",
       "      <td>Open TG-GATES: Regimen: Single; Time: 6 hr; Dose: High; Route: Gavage | Dataset: Biochemistry; Assay: SODIUM (Sodium); Study_ID: 183/14</td>\n",
       "      <td>T</td>\n",
       "      <td>BAO_0000179</td>\n",
       "      <td>BAO_0000218</td>\n",
       "      <td>Cc1ccc(C)c(OCCCC(C)(C)C(=O)O)c1</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL2766011</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL457</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>SODIUM</td>\n",
       "      <td>meq/L</td>\n",
       "      <td>142.6</td>\n",
       "      <td>None</td>\n",
       "      <td>1933591</td>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>SODIUM</td>\n",
       "      <td>mEq.L-1</td>\n",
       "      <td>142.6</td>\n",
       "      <td>CHEMBL376</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  activity_comment  \\\n",
       "0  SD=2.07; p-value=0.44; N=5 || Control_MEAN=141.60; Control_SD=1.82; Control_N=5   \n",
       "\n",
       "   activity_id assay_chembl_id  \\\n",
       "0     13518617   CHEMBL2810656   \n",
       "\n",
       "                                                                                                                         assay_description  \\\n",
       "0  Open TG-GATES: Regimen: Single; Time: 6 hr; Dose: High; Route: Gavage | Dataset: Biochemistry; Assay: SODIUM (Sodium); Study_ID: 183/14   \n",
       "\n",
       "  assay_type bao_endpoint   bao_format                 canonical_smiles  \\\n",
       "0          T  BAO_0000179  BAO_0000218  Cc1ccc(C)c(OCCCC(C)(C)C(=O)O)c1   \n",
       "\n",
       "  data_validity_comment document_chembl_id document_journal document_year  \\\n",
       "0                  None      CHEMBL2766011             None          None   \n",
       "\n",
       "  molecule_chembl_id pchembl_value potential_duplicate published_relation  \\\n",
       "0          CHEMBL457          None               False               None   \n",
       "\n",
       "  published_type published_units published_value qudt_units  record_id  \\\n",
       "0         SODIUM           meq/L           142.6       None    1933591   \n",
       "\n",
       "   src_id standard_flag standard_relation standard_type standard_units  \\\n",
       "0      11          True              None        SODIUM        mEq.L-1   \n",
       "\n",
       "  standard_value target_chembl_id    target_organism   target_pref_name  \\\n",
       "0          142.6        CHEMBL376  Rattus norvegicus  Rattus norvegicus   \n",
       "\n",
       "  uo_units  \n",
       "0     None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open TG-GATEs data is now ready to be used.\n",
    "\n",
    "Further processing of the data might be useful. In the next step, parameters such as dose_level, time, regimen, route, study_id were extracted from the assay_description. p-values and standard_values for the controls can also be extracted from the activity_comment for further comparison with the treatment standard_values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158199, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get fields of interest\n",
    "tggates = tggates_df[['assay_chembl_id', 'assay_description', 'molecule_chembl_id', 'standard_type', 'standard_value', 'standard_units', 'activity_comment']]\n",
    "\n",
    "tggates.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>activity_comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL2810656</td>\n",
       "      <td>Open TG-GATES: Regimen: Single; Time: 6 hr; Dose: High; Route: Gavage | Dataset: Biochemistry; Assay: SODIUM (Sodium); Study_ID: 183/14</td>\n",
       "      <td>CHEMBL457</td>\n",
       "      <td>SODIUM</td>\n",
       "      <td>142.6</td>\n",
       "      <td>mEq.L-1</td>\n",
       "      <td>SD=2.07; p-value=0.44; N=5 || Control_MEAN=141.60; Control_SD=1.82; Control_N=5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  assay_chembl_id  \\\n",
       "0   CHEMBL2810656   \n",
       "\n",
       "                                                                                                                         assay_description  \\\n",
       "0  Open TG-GATES: Regimen: Single; Time: 6 hr; Dose: High; Route: Gavage | Dataset: Biochemistry; Assay: SODIUM (Sodium); Study_ID: 183/14   \n",
       "\n",
       "  molecule_chembl_id standard_type standard_value standard_units  \\\n",
       "0          CHEMBL457        SODIUM          142.6        mEq.L-1   \n",
       "\n",
       "                                                                  activity_comment  \n",
       "0  SD=2.07; p-value=0.44; N=5 || Control_MEAN=141.60; Control_SD=1.82; Control_N=5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter on endpoints of interest, e.g. clinical chemistry and hematology data\n",
    "h_cc = tggates[tggates['assay_description'].str.contains('Hematology', 'Biochemistry')]\n",
    "\n",
    "h_cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# information regarding dose, time, regimen is store in assay_description\n",
    "tggates_ap_split = h_cc['assay_description'].str.split(' \\| |; ', 0, expand=True).add_prefix('assay_description_part')\n",
    "\n",
    "tggates_ap_split.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assay_description_part0</th>\n",
       "      <th>assay_description_part1</th>\n",
       "      <th>assay_description_part2</th>\n",
       "      <th>assay_description_part3</th>\n",
       "      <th>assay_description_part4</th>\n",
       "      <th>assay_description_part5</th>\n",
       "      <th>assay_description_part6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Open TG-GATES: Regimen: Single</td>\n",
       "      <td>Time: 6 hr</td>\n",
       "      <td>Dose: Low</td>\n",
       "      <td>Route: Gavage</td>\n",
       "      <td>Dataset: Hematology</td>\n",
       "      <td>Assay: RBC (Erythrocytes)</td>\n",
       "      <td>Study_ID: 542/6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            assay_description_part0 assay_description_part1  \\\n",
       "589  Open TG-GATES: Regimen: Single              Time: 6 hr   \n",
       "\n",
       "    assay_description_part2 assay_description_part3 assay_description_part4  \\\n",
       "589               Dose: Low           Route: Gavage     Dataset: Hematology   \n",
       "\n",
       "       assay_description_part5 assay_description_part6  \n",
       "589  Assay: RBC (Erythrocytes)         Study_ID: 542/6  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_ap_split.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regimen    = tggates_ap_split['assay_description_part0'].str.split(':', 0,   expand=True).rename(columns={2:'regimen'}             ).drop([0,1], 1)\n",
    "time       = tggates_ap_split['assay_description_part1'].str.split(':', 0,   expand=True).rename(columns={1:'time'}                ).drop([0],   1)\n",
    "dose_level = tggates_ap_split['assay_description_part2'].str.split(':', 0,   expand=True).rename(columns={1:'dose_level'}          ).drop([0],   1)\n",
    "route      = tggates_ap_split['assay_description_part3'].str.split(':', 0,   expand=True).rename(columns={1:'route'}               ).drop([0],   1)\n",
    "dataset    = tggates_ap_split['assay_description_part4'].str.split(':', 0,   expand=True).rename(columns={1:'dataset'}             ).drop([0],   1)\n",
    "assay_test = tggates_ap_split['assay_description_part5'].str.split(':', 0,   expand=True).rename(columns={1:'assay_test'}          ).drop([0],   1)\n",
    "exp_group  = tggates_ap_split['assay_description_part6'].str.split(':|/', 0, expand=True).rename(columns={1:'exp_id', 2:'group_id'}).drop([0],   1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regimen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     regimen\n",
       "589   Single"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regimen.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    exp_id group_id\n",
       "589    542        6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_group.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tggates_ap = (\n",
    "    regimen\n",
    "    .merge(time,       left_index=True, right_index=True, how='inner')\n",
    "    .merge(dose_level, left_index=True, right_index=True, how='inner')\n",
    "    .merge(route,      left_index=True, right_index=True, how='inner')\n",
    "    .merge(dataset,    left_index=True, right_index=True, how='inner')\n",
    "    .merge(assay_test, left_index=True, right_index=True, how='inner')\n",
    "    .merge(exp_group,  left_index=True, right_index=True, how='inner')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_ap.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regimen</th>\n",
       "      <th>time</th>\n",
       "      <th>dose_level</th>\n",
       "      <th>route</th>\n",
       "      <th>dataset</th>\n",
       "      <th>assay_test</th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Single</td>\n",
       "      <td>6 hr</td>\n",
       "      <td>Low</td>\n",
       "      <td>Gavage</td>\n",
       "      <td>Hematology</td>\n",
       "      <td>RBC (Erythrocytes)</td>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     regimen   time dose_level    route      dataset           assay_test  \\\n",
       "589   Single   6 hr        Low   Gavage   Hematology   RBC (Erythrocytes)   \n",
       "\n",
       "    exp_id group_id  \n",
       "589    542        6  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_ap.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tggates_ap = tggates_ap.replace('^\\s', '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# information regarding treatment and control is store in activity_comment:\n",
    "tggates_split = h_cc['activity_comment'].str.split(' \\|\\| ', expand=True).add_prefix('act_comment_part')\n",
    "\n",
    "tggates_split.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_comment_part0</th>\n",
       "      <th>act_comment_part1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>SD=26.81; p-value=0.81; N=5</td>\n",
       "      <td>Control_MEAN=643.60; Control_SD=39.44; Control_N=5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               act_comment_part0  \\\n",
       "589  SD=26.81; p-value=0.81; N=5   \n",
       "\n",
       "                                      act_comment_part1  \n",
       "589  Control_MEAN=643.60; Control_SD=39.44; Control_N=5  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_split.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60540, 3), (60540, 3))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_treat   = tggates_split['act_comment_part0'].str.split(';', expand=True).add_prefix('treatment_part')\n",
    "tggates_control = tggates_split['act_comment_part1'].str.split(';', expand=True).add_prefix('control_part')\n",
    "\n",
    "tggates_treat.shape, tggates_control.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment_part0</th>\n",
       "      <th>treatment_part1</th>\n",
       "      <th>treatment_part2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>SD=26.81</td>\n",
       "      <td>p-value=0.81</td>\n",
       "      <td>N=5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    treatment_part0 treatment_part1 treatment_part2\n",
       "589        SD=26.81    p-value=0.81             N=5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_treat.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_sd_t = tggates_treat['treatment_part0'].str.split('=', expand=True).rename(columns={1:'SD'}).drop([0], 1)\n",
    "t_sd_t.fillna(value=np.nan, inplace=True)\n",
    "t_sd_t['SD'] = t_sd_t['SD'].map(lambda x: x.strip()).replace('nan', np.nan)\n",
    "\n",
    "t_sd_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>26.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SD\n",
       "589  26.81"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_sd_t.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_p_t = tggates_treat['treatment_part1'].str.split('=', expand=True).rename(columns={1:'p-value'}).drop([0], 1)\n",
    "t_p_t.fillna(value=np.nan, inplace=True)\n",
    "\n",
    "t_p_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p-value\n",
       "589    0.81"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_p_t.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_n_t = tggates_treat['treatment_part2'].str.split('=', expand=True).rename(columns={1:'N'}).drop([0], 1)\n",
    "t_n_t.fillna(value=np.nan, inplace=True)\n",
    "\n",
    "t_n_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     N\n",
       "589  5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_n_t.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control_part0</th>\n",
       "      <th>control_part1</th>\n",
       "      <th>control_part2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Control_MEAN=643.60</td>\n",
       "      <td>Control_SD=39.44</td>\n",
       "      <td>Control_N=5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           control_part0      control_part1 control_part2\n",
       "589  Control_MEAN=643.60   Control_SD=39.44   Control_N=5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_control.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_mean_c = tggates_control['control_part0'].str.split('=', expand=True).rename(columns={1:'control_value'}).drop([0], 1)\n",
    "t_mean_c.fillna(value=np.nan, inplace=True)\n",
    "\n",
    "t_mean_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>643.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    control_value\n",
       "589        643.60"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_mean_c.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_sd_c = tggates_control['control_part1'].str.split('=', 0, expand=True).rename(columns={1:'control_SD'}).drop([0], 1)\n",
    "t_sd_c.fillna(value=np.nan, inplace=True)\n",
    "\n",
    "t_sd_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control_SD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>39.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    control_SD\n",
       "589      39.44"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_sd_c.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_n_c = tggates_control['control_part2'].str.split('=', 0, expand=True).rename(columns={1:'control_N'}).drop([0], 1)\n",
    "t_n_c.fillna(value=np.nan, inplace=True)\n",
    "\n",
    "t_n_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    control_N\n",
       "589         5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_n_c.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 6)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_comment = (\n",
    "    t_sd_t[['SD']]\n",
    "    .merge(t_p_t.loc[:,   ['p-value']],       left_index=True, right_index=True, how='inner')\n",
    "    .merge(t_n_t.loc[:,   ['N']],             left_index=True, right_index=True, how='inner')\n",
    "    .merge(t_mean_c.loc[:,['control_value']], left_index=True, right_index=True, how='inner')\n",
    "    .merge(t_sd_c.loc[:,  ['control_SD']],    left_index=True, right_index=True, how='inner')\n",
    "    .merge(t_n_c.loc[: ,  ['control_N']],     left_index=True, right_index=True, how='inner')\n",
    ")\n",
    "\n",
    "tggates_comment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SD</th>\n",
       "      <th>p-value</th>\n",
       "      <th>N</th>\n",
       "      <th>control_value</th>\n",
       "      <th>control_SD</th>\n",
       "      <th>control_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>26.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5</td>\n",
       "      <td>643.60</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SD p-value  N control_value control_SD control_N\n",
       "589  26.81    0.81  5        643.60      39.44         5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_comment.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60540, 21)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the assay parameters, with the activity data for treatment and control assays\n",
    "tggates_f = (\n",
    "    tggates_ap\n",
    "    .merge(h_cc,            left_index=True, right_index=True, how='inner')\n",
    "    .merge(tggates_comment, left_index=True, right_index=True, how='inner')\n",
    ")\n",
    "\n",
    "tggates_f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regimen</th>\n",
       "      <th>time</th>\n",
       "      <th>dose_level</th>\n",
       "      <th>route</th>\n",
       "      <th>dataset</th>\n",
       "      <th>assay_test</th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>activity_comment</th>\n",
       "      <th>SD</th>\n",
       "      <th>p-value</th>\n",
       "      <th>N</th>\n",
       "      <th>control_value</th>\n",
       "      <th>control_SD</th>\n",
       "      <th>control_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Single</td>\n",
       "      <td>6 hr</td>\n",
       "      <td>Low</td>\n",
       "      <td>Gavage</td>\n",
       "      <td>Hematology</td>\n",
       "      <td>RBC (Erythrocytes)</td>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "      <td>CHEMBL2884443</td>\n",
       "      <td>Open TG-GATES: Regimen: Single; Time: 6 hr; Dose: Low; Route: Gavage | Dataset: Hematology; Assay: RBC (Erythrocytes); Study_ID: 542/6</td>\n",
       "      <td>CHEMBL1697830</td>\n",
       "      <td>RBC</td>\n",
       "      <td>6382000</td>\n",
       "      <td>cells.uL-1</td>\n",
       "      <td>SD=26.81; p-value=0.81; N=5 || Control_MEAN=643.60; Control_SD=39.44; Control_N=5</td>\n",
       "      <td>26.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5</td>\n",
       "      <td>643.60</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    regimen  time dose_level   route     dataset          assay_test exp_id  \\\n",
       "589  Single  6 hr        Low  Gavage  Hematology  RBC (Erythrocytes)    542   \n",
       "\n",
       "    group_id assay_chembl_id  \\\n",
       "589        6   CHEMBL2884443   \n",
       "\n",
       "                                                                                                                          assay_description  \\\n",
       "589  Open TG-GATES: Regimen: Single; Time: 6 hr; Dose: Low; Route: Gavage | Dataset: Hematology; Assay: RBC (Erythrocytes); Study_ID: 542/6   \n",
       "\n",
       "    molecule_chembl_id standard_type standard_value standard_units  \\\n",
       "589      CHEMBL1697830           RBC        6382000     cells.uL-1   \n",
       "\n",
       "                                                                      activity_comment  \\\n",
       "589  SD=26.81; p-value=0.81; N=5 || Control_MEAN=643.60; Control_SD=39.44; Control_N=5   \n",
       "\n",
       "        SD p-value  N control_value control_SD control_N  \n",
       "589  26.81    0.81  5        643.60      39.44         5  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tggates_f.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>dataset</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_test</th>\n",
       "      <th>regimen</th>\n",
       "      <th>dose_level</th>\n",
       "      <th>time</th>\n",
       "      <th>route</th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>SD</th>\n",
       "      <th>p-value</th>\n",
       "      <th>N</th>\n",
       "      <th>control_value</th>\n",
       "      <th>control_SD</th>\n",
       "      <th>control_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>CHEMBL1697830</td>\n",
       "      <td>Hematology</td>\n",
       "      <td>CHEMBL2884443</td>\n",
       "      <td>RBC (Erythrocytes)</td>\n",
       "      <td>Single</td>\n",
       "      <td>Low</td>\n",
       "      <td>6 hr</td>\n",
       "      <td>Gavage</td>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "      <td>RBC</td>\n",
       "      <td>6382000</td>\n",
       "      <td>cells.uL-1</td>\n",
       "      <td>26.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5</td>\n",
       "      <td>643.60</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    molecule_chembl_id     dataset assay_chembl_id          assay_test  \\\n",
       "589      CHEMBL1697830  Hematology   CHEMBL2884443  RBC (Erythrocytes)   \n",
       "\n",
       "    regimen dose_level  time   route exp_id group_id standard_type  \\\n",
       "589  Single        Low  6 hr  Gavage    542        6           RBC   \n",
       "\n",
       "    standard_value standard_units     SD p-value  N control_value control_SD  \\\n",
       "589        6382000     cells.uL-1  26.81    0.81  5        643.60      39.44   \n",
       "\n",
       "    control_N  \n",
       "589         5  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder columns and select fields of interest\n",
    "tggates_f = tggates_f[['molecule_chembl_id', 'dataset', 'assay_chembl_id', 'assay_test', 'regimen', 'dose_level', 'time', 'route', 'exp_id', 'group_id', 'standard_type', 'standard_value', 'standard_units', 'SD', 'p-value', 'N', 'control_value', 'control_SD', 'control_N']]\n",
    "\n",
    "tggates_f.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Tox Flag</h1>\n",
    "\n",
    "We are always interested in understanding how these datasets are used by our collaborators.\n",
    "<br>For these type of data, we believe it would be useful to the user to have a simple flag that could summarise the observations across treatment and controls.</br>\n",
    "\n",
    "Here we propose a way of providing this summary based on very simple and intuitive rules:\n",
    "\n",
    "For each study and endpoint, a tox_flag is calculated based on comparisons between \n",
    "\n",
    "1\\. the treatment values and the controls:\n",
    "<br>- if there's a significant difference between treatment values and controls (p-value <= 0.05) and there's an increase above controls: tox_flag = 0.5</br>\n",
    "<br>- if there's a significant difference between treatment values and controls (p-value <= 0.05) and there's a decrease below controls: tox_flag = -0.5</br>\n",
    "\n",
    "2\\. the treatment_values and normal ranges of historical controls\n",
    "<br>- if there's a significant difference between treatment values and controls (p-value <= 0.05) and there's an increase above the normal range: tox_flag = 1</br>\n",
    "<br>- if there's a significant difference between treatment values and controls (p-value <= 0.05) and there's a decrease below the normal range: tox_flag = -1</br>\n",
    "\n",
    "Normal ranges available in the DrugMatrix<sup>2</sup> <i>in vivo</i> dataset were used as reference. The DrugMatrix dataset can also be found in CHEMBL under src_id 15 and document_chembl_ids CHEMBL2924216 (biochemistry), CHEMBL2924217 (hematology) and CHEMBL2924218 (pathology).\n",
    "\n",
    "<sup>2</sup>Ganter, B., Tugendreich, S., Pearson, C. I., Ayanoglu, E., Baumhueter, S., Bostian, K. A., et al. (2005). Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. Journal of Biotechnology, 119(3), 219–244."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define how to calculate tox_flag\n",
    "def tox_flag(row):\n",
    "    if (float(row['standard_value']) > float(row['control_value'])) & (float(row['standard_value']) > float(row['nrange_uvalue'])) & (float(row['p-value']) <= 0.05) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True) & (pd.isnull(row['p-value']) != True):\n",
    "        val = 1\n",
    "    elif (float(row['standard_value']) > float(row['control_value'])) & (float(row['standard_value']) <= float(row['nrange_uvalue'])) & (float(row['p-value']) <= 0.05) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True) & (pd.isnull(row['p-value']) != True):\n",
    "        val = 0.5\n",
    "    elif (float(row['standard_value']) > float(row['control_value'])) & (float(row['p-value']) > 0.05) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True) & (pd.isnull(row['p-value']) != True):\n",
    "        val = 0\n",
    "    elif (float(row['standard_value']) < float(row['control_value'])) & (float(row['p-value']) <= 0.05) & (float(row['standard_value']) < float(row['nrange_lvalue'])) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True) & (pd.isnull(row['p-value']) != True):\n",
    "        val = -1\n",
    "    elif (float(row['standard_value']) < float(row['control_value'])) & (float(row['p-value']) <= 0.05) & (float(row['standard_value']) >= float(row['nrange_lvalue'])) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True) & (pd.isnull(row['p-value']) != True):\n",
    "        val = -0.5\n",
    "    elif (float(row['standard_value']) < float(row['control_value'])) & (float(row['p-value']) > 0.05) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True) & (pd.isnull(row['p-value']) != True):\n",
    "        val = 0\n",
    "    elif (float(row['standard_value']) == float(row['control_value'])) & (pd.isnull(row['standard_value']) != True) & (pd.isnull(row['control_value']) != True):\n",
    "        val = 0\n",
    "    else:\n",
    "        val = 0\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# retrieve normal ranges from the drugmatrix data in ChEMBL for the biochemistry and hematology endpoints\n",
    "drugmatrix_act_api      = activity.get(document_chembl_id=(['CHEMBL2924216', 'CHEMBL2924217']))\n",
    "drugmatrix_act_api_list = list(drugmatrix_act_api.all())\n",
    "drugmatrix_df           = pd.DataFrame(drugmatrix_act_api_list)\n",
    "dmatrix                 = drugmatrix_df[['standard_type', 'activity_comment']]\n",
    "\n",
    "# extract normal range from activity_comment\n",
    "dmatrix_split  = dmatrix['activity_comment'].str.split('\\ \\|\\|\\ ', 0, expand=True).add_prefix('act_comment_part')\n",
    "dmatrix_nrange = dmatrix_split['act_comment_part2'].str.split(':', 0, expand=True).add_prefix('nrange_part')\n",
    "nrange         = dmatrix_nrange['nrange_part1'].str.split('-', 0, expand=True).rename(columns={0: 'nrange_lvalue',1:'nrange_uvalue'})\n",
    "\n",
    "# for each endpoint, merge upper and lower values for the normal range\n",
    "dmatrix.drop('activity_comment', axis=1, inplace=True)\n",
    "dmatrix_nrange = dmatrix.merge(nrange, left_index = True, right_index = True, how='inner').drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>standard_type</th>\n",
       "      <th>nrange_lvalue</th>\n",
       "      <th>nrange_uvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALT</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  standard_type nrange_lvalue nrange_uvalue\n",
       "0           ALT            29            75"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmatrix_nrange.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>dataset</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_test</th>\n",
       "      <th>regimen</th>\n",
       "      <th>dose_level</th>\n",
       "      <th>time</th>\n",
       "      <th>route</th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>SD</th>\n",
       "      <th>p-value</th>\n",
       "      <th>N</th>\n",
       "      <th>control_value</th>\n",
       "      <th>control_SD</th>\n",
       "      <th>control_N</th>\n",
       "      <th>nrange_lvalue</th>\n",
       "      <th>nrange_uvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL1697830</td>\n",
       "      <td>Hematology</td>\n",
       "      <td>CHEMBL2884443</td>\n",
       "      <td>RBC (Erythrocytes)</td>\n",
       "      <td>Single</td>\n",
       "      <td>Low</td>\n",
       "      <td>6 hr</td>\n",
       "      <td>Gavage</td>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "      <td>RBC</td>\n",
       "      <td>6382000</td>\n",
       "      <td>cells.uL-1</td>\n",
       "      <td>26.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5</td>\n",
       "      <td>643.60</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "      <td>4.92</td>\n",
       "      <td>7.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  molecule_chembl_id     dataset assay_chembl_id          assay_test regimen  \\\n",
       "0      CHEMBL1697830  Hematology   CHEMBL2884443  RBC (Erythrocytes)  Single   \n",
       "\n",
       "  dose_level  time   route exp_id group_id standard_type standard_value  \\\n",
       "0        Low  6 hr  Gavage    542        6           RBC        6382000   \n",
       "\n",
       "  standard_units     SD p-value  N control_value control_SD control_N  \\\n",
       "0     cells.uL-1  26.81    0.81  5        643.60      39.44         5   \n",
       "\n",
       "  nrange_lvalue nrange_uvalue  \n",
       "0          4.92          7.53  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge dmatrix_nrange to tggates_all\n",
    "tggates_all = tggates_f.merge(dmatrix_nrange, on='standard_type', how='inner')\n",
    "\n",
    "tggates_all.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>dataset</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_test</th>\n",
       "      <th>regimen</th>\n",
       "      <th>dose_level</th>\n",
       "      <th>time</th>\n",
       "      <th>route</th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>SD</th>\n",
       "      <th>p-value</th>\n",
       "      <th>N</th>\n",
       "      <th>control_value</th>\n",
       "      <th>control_SD</th>\n",
       "      <th>control_N</th>\n",
       "      <th>nrange_lvalue</th>\n",
       "      <th>nrange_uvalue</th>\n",
       "      <th>tox_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL1697830</td>\n",
       "      <td>Hematology</td>\n",
       "      <td>CHEMBL2884443</td>\n",
       "      <td>RBC (Erythrocytes)</td>\n",
       "      <td>Single</td>\n",
       "      <td>Low</td>\n",
       "      <td>6 hr</td>\n",
       "      <td>Gavage</td>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "      <td>RBC</td>\n",
       "      <td>6382000</td>\n",
       "      <td>cells.uL-1</td>\n",
       "      <td>26.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5</td>\n",
       "      <td>643.60</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "      <td>4.92</td>\n",
       "      <td>7.53</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  molecule_chembl_id     dataset assay_chembl_id          assay_test regimen  \\\n",
       "0      CHEMBL1697830  Hematology   CHEMBL2884443  RBC (Erythrocytes)  Single   \n",
       "\n",
       "  dose_level  time   route exp_id group_id standard_type standard_value  \\\n",
       "0        Low  6 hr  Gavage    542        6           RBC        6382000   \n",
       "\n",
       "  standard_units     SD p-value  N control_value control_SD control_N  \\\n",
       "0     cells.uL-1  26.81    0.81  5        643.60      39.44         5   \n",
       "\n",
       "  nrange_lvalue nrange_uvalue  tox_flag  \n",
       "0          4.92          7.53       0.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate tox_flag\n",
    "tggates_all['tox_flag'] = tggates_all.apply(tox_flag, axis=1)\n",
    "\n",
    "tggates_all.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>dataset</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_test</th>\n",
       "      <th>regimen</th>\n",
       "      <th>dose_level</th>\n",
       "      <th>route</th>\n",
       "      <th>exp_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>SD</th>\n",
       "      <th>p-value</th>\n",
       "      <th>N</th>\n",
       "      <th>control_value</th>\n",
       "      <th>control_SD</th>\n",
       "      <th>control_N</th>\n",
       "      <th>nrange_lvalue</th>\n",
       "      <th>nrange_uvalue</th>\n",
       "      <th>tox_flag</th>\n",
       "      <th>time_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL1697830</td>\n",
       "      <td>Hematology</td>\n",
       "      <td>CHEMBL2884443</td>\n",
       "      <td>RBC (Erythrocytes)</td>\n",
       "      <td>Single</td>\n",
       "      <td>1_Low</td>\n",
       "      <td>Gavage</td>\n",
       "      <td>542</td>\n",
       "      <td>6</td>\n",
       "      <td>RBC</td>\n",
       "      <td>6382000</td>\n",
       "      <td>cells.uL-1</td>\n",
       "      <td>26.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5</td>\n",
       "      <td>643.60</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "      <td>4.92</td>\n",
       "      <td>7.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  molecule_chembl_id     dataset assay_chembl_id          assay_test regimen  \\\n",
       "0      CHEMBL1697830  Hematology   CHEMBL2884443  RBC (Erythrocytes)  Single   \n",
       "\n",
       "  dose_level   route exp_id group_id standard_type standard_value  \\\n",
       "0      1_Low  Gavage    542        6           RBC        6382000   \n",
       "\n",
       "  standard_units     SD p-value  N control_value control_SD control_N  \\\n",
       "0     cells.uL-1  26.81    0.81  5        643.60      39.44         5   \n",
       "\n",
       "  nrange_lvalue nrange_uvalue  tox_flag  time_day  \n",
       "0          4.92          7.53       0.0      0.25  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# format time and dose_level to be used to sort data\n",
    "\n",
    "tggates_all_sorted = tggates_all.copy()\n",
    "tggates_all_sorted['time_day'] = np.where(tggates_all_sorted['time'].str.split(' ').str[1] =='hr', round(tggates_all_sorted['time'].str.split(' ').str[0].astype(int)/int(24), 2),tggates_all_sorted['time'].str.split(' ').str[0].astype(int))\n",
    "tggates_all_sorted['dose_level'] = np.where((tggates_all_sorted['dose_level']== 'Low') & (tggates_all_sorted['dose_level']!= '2_Middle') & (tggates_all_sorted['dose_level']!= '3_High'), '1_Low', tggates_all_sorted['dose_level'])\n",
    "tggates_all_sorted['dose_level'] = np.where((tggates_all_sorted['dose_level']== 'Middle') & (tggates_all_sorted['dose_level']!= '1_Low') & (tggates_all_sorted['dose_level']!= '3_High'), '2_Middle', tggates_all_sorted['dose_level'])\n",
    "tggates_all_sorted['dose_level'] = np.where((tggates_all_sorted['dose_level']== 'High') & (tggates_all_sorted['dose_level']!= '1_Low') & (tggates_all_sorted['dose_level']!= '2_Middle'), '3_High', tggates_all_sorted['dose_level'])\n",
    "tggates_all_sorted.drop(['time'], axis = 1, inplace=True)\n",
    "\n",
    "tggates_all_sorted.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>standard_type</th>\n",
       "      <th colspan=\"3\" halign=\"left\">BASOLE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">EOSLE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">HCT</th>\n",
       "      <th colspan=\"3\" halign=\"left\">HGB</th>\n",
       "      <th colspan=\"3\" halign=\"left\">LYMLE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">MCH</th>\n",
       "      <th colspan=\"3\" halign=\"left\">MCHC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">MCV</th>\n",
       "      <th colspan=\"3\" halign=\"left\">MONOLE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">NEUTLE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">PLAT</th>\n",
       "      <th colspan=\"3\" halign=\"left\">RBC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">WBC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>dose_level</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "      <th>1_Low</th>\n",
       "      <th>2_Middle</th>\n",
       "      <th>3_High</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>time_day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">CHEMBL101</th>\n",
       "      <th>0.12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.38</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "standard_type               BASOLE                 EOSLE                  \\\n",
       "dose_level                   1_Low 2_Middle 3_High 1_Low 2_Middle 3_High   \n",
       "molecule_chembl_id time_day                                                \n",
       "CHEMBL101          0.12        0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   0.25        0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   0.38        0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   1.00        0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   4.00        0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   8.00        0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   15.00       0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "                   29.00       0.0      0.0    0.0   0.0      0.0    0.0   \n",
       "\n",
       "standard_type                 HCT                   HGB                 LYMLE  \\\n",
       "dose_level                  1_Low 2_Middle 3_High 1_Low 2_Middle 3_High 1_Low   \n",
       "molecule_chembl_id time_day                                                     \n",
       "CHEMBL101          0.12       0.0      0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   0.25       0.0      0.0    0.0   0.0      0.0    0.0  -0.5   \n",
       "                   0.38       0.0      0.5    0.5   0.0      1.0    1.0   0.0   \n",
       "                   1.00       0.0      0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   4.00       0.0      0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   8.00       0.0      0.0   -0.5   0.0      0.0    0.0   0.0   \n",
       "                   15.00      0.0      0.0   -0.5   0.0      0.0    1.0   0.0   \n",
       "                   29.00      0.0      0.0   -0.5   0.0      0.0    1.0   0.0   \n",
       "\n",
       "standard_type                                 MCH                  MCHC  \\\n",
       "dose_level                  2_Middle 3_High 1_Low 2_Middle 3_High 1_Low   \n",
       "molecule_chembl_id time_day                                               \n",
       "CHEMBL101          0.12          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   0.25          0.0   -0.5   0.0      0.0    0.0   0.0   \n",
       "                   0.38          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   1.00          0.0   -1.0   0.0      0.0    0.0   0.0   \n",
       "                   4.00          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   8.00          0.0   -0.5   0.0      0.0    0.0   0.0   \n",
       "                   15.00         0.0   -0.5   0.0      0.0    0.0   0.0   \n",
       "                   29.00         0.0    0.0   0.5      0.5    0.5   0.0   \n",
       "\n",
       "standard_type                                 MCV                 MONOLE  \\\n",
       "dose_level                  2_Middle 3_High 1_Low 2_Middle 3_High  1_Low   \n",
       "molecule_chembl_id time_day                                                \n",
       "CHEMBL101          0.12          0.0   -0.5   0.0      0.0    0.0    1.0   \n",
       "                   0.25          0.0    0.0   0.0      0.0    0.0    0.0   \n",
       "                   0.38          0.0    0.0   0.0      0.0    0.0    0.0   \n",
       "                   1.00          0.0    0.0   0.0      0.0    0.0    0.0   \n",
       "                   4.00          0.0    0.0   0.0      0.0    0.0    0.0   \n",
       "                   8.00          0.5    0.0   0.0      0.0    0.0    0.0   \n",
       "                   15.00         0.0    0.0   0.0      0.0   -0.5    0.0   \n",
       "                   29.00         0.0    0.0   0.5      0.5    0.5    0.0   \n",
       "\n",
       "standard_type                               NEUTLE                  PLAT  \\\n",
       "dose_level                  2_Middle 3_High  1_Low 2_Middle 3_High 1_Low   \n",
       "molecule_chembl_id time_day                                                \n",
       "CHEMBL101          0.12          0.0    0.0    0.0      0.0    0.0   0.0   \n",
       "                   0.25          0.0    0.0    0.5      0.0    0.0   0.0   \n",
       "                   0.38          0.0    0.0    0.0      0.0    0.0   0.0   \n",
       "                   1.00          0.0    0.0    0.0      0.0    0.5   0.0   \n",
       "                   4.00          0.0    0.0    0.0      0.0    0.0   0.0   \n",
       "                   8.00          0.0    0.0    0.0      0.0    0.5   0.0   \n",
       "                   15.00         0.0    0.0    0.0      0.0    0.5   1.0   \n",
       "                   29.00        -0.5    0.0    0.0      0.0    0.0   0.0   \n",
       "\n",
       "standard_type                                 RBC                   WBC  \\\n",
       "dose_level                  2_Middle 3_High 1_Low 2_Middle 3_High 1_Low   \n",
       "molecule_chembl_id time_day                                               \n",
       "CHEMBL101          0.12          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   0.25          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   0.38          0.0    0.0   0.0      1.0    0.0   0.0   \n",
       "                   1.00          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   4.00          1.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   8.00          0.0    0.0   0.0      0.0    0.0   0.0   \n",
       "                   15.00         0.0    1.0   0.0      0.0    0.0   0.0   \n",
       "                   29.00         0.0    1.0   1.0      0.0    1.0   0.0   \n",
       "\n",
       "standard_type                                \n",
       "dose_level                  2_Middle 3_High  \n",
       "molecule_chembl_id time_day                  \n",
       "CHEMBL101          0.12          0.0    0.0  \n",
       "                   0.25          0.0    0.0  \n",
       "                   0.38          0.0    0.0  \n",
       "                   1.00          0.0    0.0  \n",
       "                   4.00          0.0    0.0  \n",
       "                   8.00          0.0    0.0  \n",
       "                   15.00         0.0    0.0  \n",
       "                   29.00         0.0    0.0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for clear visualisation, build matrix with rows of compounds and time points and columns of end poinst and dose_levels\n",
    "tggates_all_sorted_pv = pd.pivot_table(tggates_all_sorted, index=['molecule_chembl_id','time_day'], columns=['standard_type', 'dose_level'], values='tox_flag')\n",
    "\n",
    "tggates_all_sorted_pv.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that Open TG-GATEs gene expression profiles can be found in <a href='https://www.ebi.ac.uk/arrayexpress/search.html?query=Open+TG-GATEs'>ArrayExpress</a> and be linked to the Open TG-GATEs clinical pathology studies in ChEMBL.\n",
    "<br>Taking as an example the experiment <a href='https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-800/?query=Open+TG-GATEs'>E-MTAB-800</a> from ArrayExpress,\n",
    "\n",
    "1\\. the 'Term Accession Number' in E-MTAB-800 corresponds to the molecule_chembl_id\n",
    "<br>2\\. the 'Source Name' in E-MTAB-800 has some correspondance to the exp_id and group_id. For example, the source name 182051 in E-MTAB-800 corresponds to exp_id = 182 and group_id = 5 (study_id = 182/5). The last digit in the source name refers to the animal_id.</br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eZmgKNkmeqn5a6XU8p/L1zLXYDa81dVtTT2VfzczW2E133AA+u9s2XbcxNO6M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gzH2lR7q7bDxvhBUK1Du/fCod2+tBmpIUnmmcwrc3VFbmUIP5DaVdJ4M/sU3o67KULqaFJg6KO+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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16991f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap using seaborn\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "sns.heatmap(tggates_all_sorted_pv.head(8), vmax=.8, square=True)\n",
    "\n",
    "compounds = tggates_all_sorted_pv.head(8).index.get_level_values(\"molecule_chembl_id\")\n",
    "endpoints = tggates_all_sorted_pv.head(8).columns.get_level_values(\"standard_type\")\n",
    "doses = tggates_all_sorted_pv.head(8).columns.get_level_values(\"dose_level\")\n",
    "for j, compound in enumerate(compounds):\n",
    "    for i, endpoint in enumerate(endpoints):\n",
    "        if i and endpoint != endpoints[i - 1]:\n",
    "            for z, doses in enumerate(doses):\n",
    "                ax.axhline(j, c=\"w\")\n",
    "                ax.axhline(j*8, c=\"w\",linewidth=10)\n",
    "                ax.axvline(i, c=\"w\",linewidth=10)\n",
    "                ax.axvline(z, c=\"w\")\n",
    "                ax.set_xlabel(\"endpoint_dose\", fontsize=12)\n",
    "                ax.set_ylabel(\"compound_time\", fontsize=12)\n",
    "                ax.tick_params(axis='x', labelsize=14)\n",
    "                ax.tick_params(axis='y', labelsize=14)\n",
    "            f.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}