realworld-benchmark-visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3\n",
    "### Master Thesis: Development and evaluation of Galaxy pipelines for detection of SARS-CoV-2 variants by genomic analysis of wastewater samples\n",
    "\n",
    "**Purpose of this jupyter notebook # 3:** \n",
    "- visualize real-world datasets for SARS-CoV-2 wastewater surveillance;\n",
    "- visualizations of Cojac results for coocurrance detection of SARS-CoV-2 lineages on readl-world datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import Circle, RegularPolygon\n",
    "from matplotlib.path import Path\n",
    "from matplotlib.projections.polar import PolarAxes\n",
    "from matplotlib.projections import register_projection\n",
    "from matplotlib.spines import Spine\n",
    "from matplotlib.transforms import Affine2D\n",
    "from matplotlib.colors import LogNorm, Normalize\n",
    "from pandas.plotting import parallel_coordinates\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "#plt.rcParams.update({'font.size': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dataframe with real datasets statistics\n",
    "df_rd = pd.read_csv(\"data/real/rd.tsv\", sep='\\t')\n",
    "df_rd = df_rd.pivot(\"Sequencing platform\", \"Library Strategy\", \"# of samples\")\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"# of samples\")\n",
    "ax = sns.heatmap(df_rd, cmap=\"YlGnBu\", norm=LogNorm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataframe extraction\n",
    "\n",
    "#real dataset cojac results\n",
    "df_coj_real =pd.read_csv(\"data/real/cojac_real.tsv\", sep='\\t').fillna(0)\n",
    "df_coj_real_ca = df_coj_real.loc[df_coj_real['Location'] == 'Canada']\n",
    "df_coj_real_us = df_coj_real.loc[df_coj_real['Location'] == 'US']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real worls datasets: Canada, Ontario (PRJNA824537), US (PRJNA765346)\n",
    "\n",
    "To look at Cojac results for real world datasets, barplots similar to Freyja barplots were generated using Python matplotlib library.\n",
    "\n",
    "#### Visualization of Cojac results for Canada, Ontario (PRJNA824537) dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the figure size\n",
    "plt.figure(figsize=(7.5, 10))\n",
    "\n",
    "# from raw value to percentage\n",
    "total = df_coj_real_ca.groupby('Date')['Proportion'].sum().reset_index()\n",
    "alpha = df_coj_real_ca[df_coj_real_ca.Lineage=='Alpha'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "delta = df_coj_real_ca[df_coj_real_ca.Lineage=='Delta'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "ba1 = df_coj_real_ca[df_coj_real_ca.Lineage=='Omicron (BA.1)'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "ba2 = df_coj_real_ca[df_coj_real_ca.Lineage=='Omicron (BA.2)'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "other = df_coj_real_ca[df_coj_real_ca.Lineage=='Other'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "\n",
    "alpha['Proportion'] = [i / j for i,j in zip(alpha['Proportion'], total['Proportion'])]\n",
    "delta['Proportion'] = alpha['Proportion'] + delta['Proportion']\n",
    "ba1['Proportion'] =delta['Proportion'] + ba1['Proportion']\n",
    "ba2['Proportion'] = ba1['Proportion'] + ba2['Proportion']\n",
    "total['Proportion'] = ba2['Proportion'] + other['Proportion']\n",
    "\n",
    "# bar charts\n",
    "bar1 = sns.barplot(x=\"Date\", y=\"Proportion\", data=total, color='#aec6e8', order=['Nov21', 'Dec21', 'Jan22','Feb22'])\n",
    "bar2 = sns.barplot(x=\"Date\", y=\"Proportion\", data=ba2, color='#f8bc83', order=['Nov21', 'Dec21', 'Jan22','Feb22'])\n",
    "bar4 = sns.barplot(x=\"Date\", y=\"Proportion\", data=ba1, color='#1f78b4', order=['Nov21', 'Dec21', 'Jan22','Feb22'])\n",
    "bar6 = sns.barplot(x=\"Date\", y=\"Proportion\", data=delta, color='#fa7d11', order=['Nov21', 'Dec21', 'Jan22','Feb22'])\n",
    "bar7 = sns.barplot(x=\"Date\", y=\"Proportion\", data=alpha, color='#2aa129', order=['Nov21', 'Dec21', 'Jan22','Feb22'])\n",
    "\n",
    "# add legend\n",
    "bar_floor5 = mpatches.Patch(color='#aec6e8', label='Other')\n",
    "bar_floor4 = mpatches.Patch(color='#f8bc83', label='Omicron (BA.2)')\n",
    "bar_floor3 = mpatches.Patch(color='#1f78b4', label='Omicron (BA.1)')\n",
    "bar_floor2 = mpatches.Patch(color='#fa7d11', label='Delta')\n",
    "bar_floor1 = mpatches.Patch(color='#2aa129', label='Alpha')\n",
    "plt.legend(handles=[bar_floor5, bar_floor4, bar_floor3, bar_floor2, bar_floor1], loc='upper left', bbox_to_anchor=(1.05, 1))\n",
    "plt.title(label='Cojac results on dataset from Canada, Ontario (PRJNA824537)')\n",
    "\n",
    "# show the graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of Cojac results for US (PRJNA765346) dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the figure size\n",
    "plt.figure(figsize=(15, 7.5))\n",
    "\n",
    "# from raw value to percentage\n",
    "total = df_coj_real_us.groupby('Date')['Proportion'].sum().reset_index()\n",
    "alpha = df_coj_real_us[df_coj_real_us.Lineage=='Alpha'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "delta = df_coj_real_us[df_coj_real_us.Lineage=='Delta'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "ba1 = df_coj_real_us[df_coj_real_us.Lineage=='Omicron (BA.1)'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "ba2 = df_coj_real_us[df_coj_real_us.Lineage=='Omicron (BA.2)'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "other = df_coj_real_us[df_coj_real_us.Lineage=='Other'].groupby('Date')['Proportion'].sum().reset_index()\n",
    "\n",
    "alpha['Proportion'] = [i / j for i,j in zip(alpha['Proportion'], total['Proportion'])]\n",
    "delta['Proportion'] = alpha['Proportion'] + delta['Proportion']\n",
    "ba1['Proportion'] =delta['Proportion'] + ba1['Proportion']\n",
    "ba2['Proportion'] = ba1['Proportion'] + ba2['Proportion']\n",
    "total['Proportion'] = ba2['Proportion'] + other['Proportion']\n",
    "\n",
    "# bar charts\n",
    "bar1 = sns.barplot(x=\"Date\", y=\"Proportion\", data=total, color='#aec6e8', order=['Nov21', 'Dec21', 'Jan22','Feb22','Mar22','Apr22','May22','Jun22','Jul22','Aug22'])\n",
    "bar2 = sns.barplot(x=\"Date\", y=\"Proportion\", data=ba2, color='#2aa129', order=['Nov21', 'Dec21', 'Jan22','Feb22','Mar22','Apr22','May22','Jun22','Jul22','Aug22'])\n",
    "bar4 = sns.barplot(x=\"Date\", y=\"Proportion\", data=ba1, color='#f8bc83', order=['Nov21', 'Dec21', 'Jan22','Feb22','Mar22','Apr22','May22','Jun22','Jul22','Aug22'])\n",
    "bar6 = sns.barplot(x=\"Date\", y=\"Proportion\", data=delta, color='#1f78b4', order=['Nov21', 'Dec21', 'Jan22','Feb22','Mar22','Apr22','May22','Jun22','Jul22','Aug22'])\n",
    "bar7 = sns.barplot(x=\"Date\", y=\"Proportion\", data=alpha, color='#fa7d11', order=['Nov21', 'Dec21', 'Jan22','Feb22','Mar22','Apr22','May22','Jun22','Jul22','Aug22'])\n",
    "\n",
    "# add legend\n",
    "bar_floor5 = mpatches.Patch(color='#aec6e8', label='Other')\n",
    "bar_floor4 = mpatches.Patch(color='#2aa129', label='Omicron (BA.2)')\n",
    "bar_floor3 = mpatches.Patch(color='#f8bc83', label='Omicron (BA.1)')\n",
    "bar_floor2 = mpatches.Patch(color='#1f78b4', label='Delta')\n",
    "bar_floor1 = mpatches.Patch(color='#fa7d11', label='Alpha')\n",
    "plt.legend(handles=[bar_floor5, bar_floor4, bar_floor3, bar_floor2, bar_floor1], loc='upper left', bbox_to_anchor=(1.05, 1))\n",
    "plt.title(label='Cojac results on dataset from US (PRJNA765346)')\n",
    "\n",
    "# show the graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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