notebooks/Example_ionic_currents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation of other standard toolboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install -q matplotlib seaborn gitpython --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from git import Repo\n",
    "import os\n",
    "\n",
    "git_url = \"https://github.com/BlueBrain/corpus-thalamus.git\"\n",
    "repo_dir = os.path.join(os.getcwd(), \"neurocuratorDB\")\n",
    "if not os.path.isdir(repo_dir):\n",
    "    Repo.clone_from(git_url, repo_dir)\n",
    "else:\n",
    "    Repo(repo_dir).remotes.origin.pull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stadard imports\n",
    "import sys, os\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "import quantities as pq\n",
    "import numpy as np\n",
    "\n",
    "# NAT imports\n",
    "from nat.annotationSearch import ParameterSearch, ConditionAtom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Searching for annotated maximal ionic current conductances and showing the three first records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeCategories</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Context</th>\n",
       "      <th>Parameter instance ID</th>\n",
       "      <th>Parameter name</th>\n",
       "      <th>Parameter type ID</th>\n",
       "      <th>Result type</th>\n",
       "      <th>Species</th>\n",
       "      <th>Text</th>\n",
       "      <th>Transmembrane ionic current</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Values</th>\n",
       "      <th>neuron part</th>\n",
       "      <th>obj_annotation</th>\n",
       "      <th>obj_parameter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td>Thalamus interneuron small</td>\n",
       "      <td></td>\n",
       "      <td>e3a5d1d8-061c-11e6-bd98-c869cd917532</td>\n",
       "      <td>conductance_ion_curr_max</td>\n",
       "      <td>BBP-030003</td>\n",
       "      <td>pointValue</td>\n",
       "      <td>[{'id': 'NIFORG:birnlex_167', 'name': 'Mouse'}]</td>\n",
       "      <td>Table 1</td>\n",
       "      <td>Low threshold calcium T-current</td>\n",
       "      <td>S/cm^2</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>Neuronal Cell Body</td>\n",
       "      <td>\"18b96b36-061c-11e6-b997-c869cd917532\";\"FIrst ...</td>\n",
       "      <td>{'isExperimentProperty': False, 'id': 'e3a5d1d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[]</td>\n",
       "      <td>Thalamus relay cell</td>\n",
       "      <td></td>\n",
       "      <td>f7145738-ed12-11e5-b708-64006a4c56ef</td>\n",
       "      <td>conductance_ion_curr_max</td>\n",
       "      <td>BBP-030003</td>\n",
       "      <td>pointValue</td>\n",
       "      <td>[{'id': 'NIFORG:birnlex_167', 'name': 'Mouse'}]</td>\n",
       "      <td>Table 2</td>\n",
       "      <td>Low threshold calcium T-current</td>\n",
       "      <td>nS</td>\n",
       "      <td>49.100000</td>\n",
       "      <td></td>\n",
       "      <td>\"71b9d726-ecee-11e5-b708-64006a4c56ef\";\"T-curr...</td>\n",
       "      <td>{'isExperimentProperty': False, 'id': 'f714573...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td>Thalamus relay cell</td>\n",
       "      <td>(Ba2+), and was readily blocked by\\naddition o...</td>\n",
       "      <td>58bea82e-a129-11e6-98a4-64006a4c56ef</td>\n",
       "      <td>conductance_ion_curr_max</td>\n",
       "      <td>BBP-030003</td>\n",
       "      <td>pointValue</td>\n",
       "      <td>[{'id': 'NIFORG:birnlex_160', 'name': 'Rat'}]</td>\n",
       "      <td>lthough we observed a typical T-type Ca2`\\ncur...</td>\n",
       "      <td>Low threshold calcium T-current</td>\n",
       "      <td>pA</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>neuron part</td>\n",
       "      <td>\"3c90e752-a129-11e6-98a4-64006a4c56ef\";\"\";\"&lt;cl...</td>\n",
       "      <td>{'isExperimentProperty': False, 'id': '58bea82...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AgeCategories                        Cell  \\\n",
       "0            []  Thalamus interneuron small   \n",
       "1            []         Thalamus relay cell   \n",
       "2            []         Thalamus relay cell   \n",
       "\n",
       "                                             Context  \\\n",
       "0                                                      \n",
       "1                                                      \n",
       "2  (Ba2+), and was readily blocked by\\naddition o...   \n",
       "\n",
       "                  Parameter instance ID            Parameter name  \\\n",
       "0  e3a5d1d8-061c-11e6-bd98-c869cd917532  conductance_ion_curr_max   \n",
       "1  f7145738-ed12-11e5-b708-64006a4c56ef  conductance_ion_curr_max   \n",
       "2  58bea82e-a129-11e6-98a4-64006a4c56ef  conductance_ion_curr_max   \n",
       "\n",
       "  Parameter type ID Result type  \\\n",
       "0        BBP-030003  pointValue   \n",
       "1        BBP-030003  pointValue   \n",
       "2        BBP-030003  pointValue   \n",
       "\n",
       "                                           Species  \\\n",
       "0  [{'id': 'NIFORG:birnlex_167', 'name': 'Mouse'}]   \n",
       "1  [{'id': 'NIFORG:birnlex_167', 'name': 'Mouse'}]   \n",
       "2    [{'id': 'NIFORG:birnlex_160', 'name': 'Rat'}]   \n",
       "\n",
       "                                                Text  \\\n",
       "0                                            Table 1   \n",
       "1                                            Table 2   \n",
       "2  lthough we observed a typical T-type Ca2`\\ncur...   \n",
       "\n",
       "       Transmembrane ionic current    Unit      Values         neuron part  \\\n",
       "0  Low threshold calcium T-current  S/cm^2    0.000012  Neuronal Cell Body   \n",
       "1  Low threshold calcium T-current      nS   49.100000                       \n",
       "2  Low threshold calcium T-current      pA  109.000000         neuron part   \n",
       "\n",
       "                                      obj_annotation  \\\n",
       "0  \"18b96b36-061c-11e6-b997-c869cd917532\";\"FIrst ...   \n",
       "1  \"71b9d726-ecee-11e5-b708-64006a4c56ef\";\"T-curr...   \n",
       "2  \"3c90e752-a129-11e6-98a4-64006a4c56ef\";\"\";\"<cl...   \n",
       "\n",
       "                                       obj_parameter  \n",
       "0  {'isExperimentProperty': False, 'id': 'e3a5d1d...  \n",
       "1  {'isExperimentProperty': False, 'id': 'f714573...  \n",
       "2  {'isExperimentProperty': False, 'id': '58bea82...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searcher = ParameterSearch(repo_dir)\n",
    "searcher.setSearchConditions(ConditionAtom(\"Parameter name\", \"conductance_ion_curr_max\"))\n",
    "searcher.expandRequiredTags = True\n",
    "searcher.onlyCentralTendancy = True\n",
    "resultDF = searcher.search()\n",
    "resultDF[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking which conductances have been specified as \"specific conductances\" (i.e., conductance densities) and normalizing the units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "isSpecific = []\n",
    "allConductances = []\n",
    "for v, u in zip(resultDF[\"Values\"], resultDF[\"Unit\"]):\n",
    "    try:\n",
    "        allConductances.append(float(pq.Quantity(v, u).rescale('S/cm**2')))\n",
    "        isSpecific.append(True)\n",
    "    except:\n",
    "        isSpecific.append(False)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the result of our query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultDF = resultDF[isSpecific]\n",
    "resultDF[\"Values\"] = np.log10(allConductances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ac9280438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Plotting a violin plot for annotated values for specific conductances\n",
    "# of the various kind of ionic currents \n",
    "plt.figure(figsize=(6, 8))\n",
    "g = sns.violinplot(y=\"Transmembrane ionic current\", x=\"Values\", data=resultDF, bw=0.25)\n",
    "g = sns.swarmplot( y=\"Transmembrane ionic current\", x=\"Values\", data=resultDF)\n",
    "g.set_ylabel(\"\")\n",
    "g.set_xlabel(\"Specific conductance ($S/cm^2$)\")\n",
    "g.set_xticklabels([('%.0e' % 10**nb) for nb in g.get_xticks()]) \n",
    "\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')\n",
    "#plt.plot()\n",
    "plt.savefig('example_currents.png', bbox_inches='tight', transparent=True, dpi=200)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Printing the JSON representation of a specific annotation of the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"annotId\": \"8e3edae6-ef34-11e5-ba5d-c869cd917532\",\n",
      "    \"authors\": [\n",
      "        \"iavarone\",\n",
      "        \"oreilly\"\n",
      "    ],\n",
      "    \"comment\": \"Fast potassium current parameters\",\n",
      "    \"experimentProperties\": [],\n",
      "    \"localizer\": {\n",
      "        \"location\": 26404,\n",
      "        \"text\": \"The gKIR and the\\nfast voltage-gated potassium channel ( gKf) had conductances of 20 \\u0002s/\\ncm2 and 50 mS/cm2, respectively. Both potassium channels had reversal\\npotentials of 100 mV.\",\n",
      "        \"type\": \"text\"\n",
      "    },\n",
      "    \"parameters\": [\n",
      "        {\n",
      "            \"description\": {\n",
      "                \"depVar\": {\n",
      "                    \"typeId\": \"BBP-030003\",\n",
      "                    \"values\": {\n",
      "                        \"statistic\": \"raw\",\n",
      "                        \"type\": \"simple\",\n",
      "                        \"unit\": \"mS/cm^2\",\n",
      "                        \"values\": [\n",
      "                            50.0\n",
      "                        ]\n",
      "                    }\n",
      "                },\n",
      "                \"type\": \"pointValue\"\n",
      "            },\n",
      "            \"id\": \"e47c93ba-ffdc-11e5-8b78-64006a4c56ef\",\n",
      "            \"isExperimentProperty\": false,\n",
      "            \"requiredTags\": [\n",
      "                {\n",
      "                    \"id\": \"BBP_nlx_0020\",\n",
      "                    \"name\": \"Fast voltage-gated potassium (Kf) current\",\n",
      "                    \"rootId\": \"nifext_8054\"\n",
      "                },\n",
      "                {\n",
      "                    \"id\": \"NIFCELL:nifext_41\",\n",
      "                    \"name\": \"Thalamocortical cell\",\n",
      "                    \"rootId\": \"sao1813327414\"\n",
      "                }\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"description\": {\n",
      "                \"depVar\": {\n",
      "                    \"typeId\": \"BBP-011001\",\n",
      "                    \"values\": {\n",
      "                        \"statistic\": \"raw\",\n",
      "                        \"type\": \"simple\",\n",
      "                        \"unit\": \"mV\",\n",
      "                        \"values\": [\n",
      "                            -100.0\n",
      "                        ]\n",
      "                    }\n",
      "                },\n",
      "                \"type\": \"pointValue\"\n",
      "            },\n",
      "            \"id\": \"e47c95c2-ffdc-11e5-8b78-64006a4c56ef\",\n",
      "            \"isExperimentProperty\": false,\n",
      "            \"requiredTags\": [\n",
      "                {\n",
      "                    \"id\": \"BBP_nlx_0020\",\n",
      "                    \"name\": \"Fast voltage-gated potassium (Kf) current\",\n",
      "                    \"rootId\": \"nifext_8054\"\n",
      "                },\n",
      "                {\n",
      "                    \"id\": \"NIFCELL:nifext_41\",\n",
      "                    \"name\": \"Thalamocortical cell\",\n",
      "                    \"rootId\": \"sao1813327414\"\n",
      "                }\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"pubId\": \"10.1523/JNEUROSCI.2740-15.2015\",\n",
      "    \"tags\": [\n",
      "        {\n",
      "            \"id\": \"NIFINV:birnlex_2300\",\n",
      "            \"name\": \"Computational model\"\n",
      "        },\n",
      "        {\n",
      "            \"id\": \"NIFCELL:nifext_46\",\n",
      "            \"name\": \"Thalamus interneuron small\"\n",
      "        },\n",
      "        {\n",
      "            \"id\": \"NIFORG:birnlex_160\",\n",
      "            \"name\": \"Rat\"\n",
      "        }\n",
      "    ],\n",
      "    \"version\": \"1\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "record = resultDF[resultDF[\"Transmembrane ionic current\"] == \"Fast voltage-gated potassium (Kf) current\"]\n",
    "print(json.dumps(record[\"obj_annotation\"].values[0].toJSON(), \n",
    "                 sort_keys=True, indent=4, separators=(',', ': ')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing the annotated text in its context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "st.\n",
       "Thus, our final model comprised the following: gLEAK was modeled\n",
       "with a reversal potential of ⫺79 mV and conductance of 150 ␮s/cm 2, and\n",
       "gH had a reversal potential of ⫺45 mV and peak conductance of 150\n",
       "␮s/cm 2. T-type Ca2⫹ channels ( gT) were modeled with a reversal potential of 120 mV and a permeability of 0.7 ␮m/s. The gCAN had a conductance of 250 ␮S/cm 2 and reversal potential of ⫺20 mV. <b>The gKIR and the\n",
       "fast voltage-gated potassium channel ( gKf) had conductances of 20 ␮s/\n",
       "cm 2 and 50 mS/cm 2, respectively. Both potassium channels had reversal\n",
       "potentials of ⫺100 </b>mV. The gNa had conductance of 50 mS/cm 2 and\n",
       "reversal potential of 50 mV. Throughout this manuscript, when referring\n",
       "to the ability of gT to provide current, we use the term “conductance”\n",
       "rather than “permeability.” Whereas the model is in actuality based on\n",
       "permeability rather than conductance, we use this naming convention to\n",
       "simplify the text.\n",
       "Simulations were solved with a fixed time step of "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbContextChar = 400\n",
    "context = record[\"obj_annotation\"].values[0].getContext(nbContextChar)\n",
    "from IPython.display import HTML\n",
    "HTML(context[:nbContextChar] +  '<b>' + context[nbContextChar:-nbContextChar]\n",
    "      + '</b>' + context[-nbContextChar:])"
   ]
  }
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