{
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  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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."
   ]
  }
 ],
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