{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intermine-Python: Tutorial 14 - Visualisation in Intermine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the great need for data visualisation in the present world, we have introduced a few visual features to Intermine as well. We have tried to cover the most common needs of visualisation and have explained their use in this tutorial.<br/>\n",
    "NOTE: This feature of Python Client is supported only on Python Versions>= 3.6 because of the dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'An exception of type KeyError occurred. Check token'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from intermine import bar_chart as b\n",
    "b.save_mine_and_token(\"humanmine\",\"<token>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'saves the given mine and token'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_go_vs_p(list name)` can be used to print GO Terms vs p-value, as the name suggests. Also each bar in the bar-chart is labelled by the gene count corresponding to the particular GO Term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b.plot_go_vs_p(\"PL_obesityMonogen_ORahilly09\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](images/Figure_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, `plot_go_vs_count(list name)` can be used to print GO Terms vs gene count. Again, each bar in the bar-chart is labelled by the annotation corresponding to the particular GO Term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.plot_go_vs_count(\"PL_obesityMonogen_ORahilly09\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](images/Figure_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`query_to_barchart_log(xml)` is used to plot the query given as an argument in xml format.<br/>\n",
    "Its important to note that the query should be in a format such that the first row contains the gene, the second row has content for x-axis and the third row consists if y-axis values.<br/>\n",
    "Also, only if the second argument is 'true', the y axis values are converted to their corresponding loge values. Its really useful if the values have a diverse range. If not needed, the second argument can be any string but 't"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.query_to_barchart_log('<query model=\"genomic\" view=\"Gene.name Gene.symbol Gene.length\" sortOrder=\"Gene.name ASC\" ><constraint path=\"Gene.length\" op=\"&lt;\" value=\"450074\" /><constraint path=\"Gene.name\" op=\"=\" value=\"translation initiation factor IF-2-like\" /></query>','true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](images/Figure_3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will soon be coming out with more plots. If you have any ideas feel free to open an issue in the Python Client repository."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}