Supp Figure 5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Tabulating ribosome densities for each dataset</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in list of datasets to be processed and context information (length) for genes\n",
    "Datasets = pd.read_csv('Data/yeast_datasets.csv')\n",
    "genes = pd.read_csv('Data/sacCer3 genes.csv')[['name','length']]; genes.columns = ['ORF','length']\n",
    "#prepare a dataframe to receive the results\n",
    "densities = pd.DataFrame({'ORF':genes['ORF']})\n",
    "#process each dataset file listed in the Datasets table\n",
    "for Dataset in Datasets['SeriesID']:\n",
    "    #read in dataset\n",
    "    dats = pd.read_csv(\"Data/\" + Dataset + '.csv')\n",
    "    #add reference RNA data if RNA data not included in dataset\n",
    "    if 'RNA_Prints' not in dats.columns:\n",
    "        RNA_dats = pd.read_csv('Data/RNA_reference.csv')\n",
    "        dats=dats.merge(RNA_dats, how='inner',on='ORF')\n",
    "    #remove all datapoints where RNA = 0 to avoid dividing by zero\n",
    "    dats = dats.loc[dats['RNA_Prints'] > 0]\n",
    "    #add information on gene lengths to enable calculation of RPK values\n",
    "    dats = dats.merge(genes, how='inner',on='ORF')\n",
    "    #calculate conversion factors from RNA RPK / Ribo Prints numbers of RNAs / Ribosomes\n",
    "    RNA_Conv = 60000 / (dats['RNA_Prints']/dats['length']/1000).sum()\n",
    "    Ribo_Conv = 170000 / dats['Ribo_Prints'].sum()\n",
    "    #calculate ribosome densities\n",
    "    dats[Dataset] = (dats['Ribo_Prints'] * Ribo_Conv) / ((dats['RNA_Prints'] / dats['length']/1000)*RNA_Conv)\n",
    "    #discard unnecessary columns\n",
    "    dats = dats[['ORF',Dataset]]\n",
    "    #merge data for this dataset into the main table\n",
    "    densities = densities.merge(dats, how='left',on='ORF')\n",
    "    densities = densities.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Comparing average ribosome densities across clusters</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-d538d66b81ac>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  these_densities[cluster] = these_densities.mean(axis=1)\n",
      "<ipython-input-3-d538d66b81ac>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  these_densities[cluster] = these_densities.mean(axis=1)\n",
      "<ipython-input-3-d538d66b81ac>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  these_densities[cluster] = these_densities.mean(axis=1)\n"
     ]
    }
   ],
   "source": [
    "by_cluster = pd.DataFrame({'ORF':genes['ORF']})\n",
    "for cluster in Datasets['cluster'].unique():\n",
    "    this_set = Datasets.loc[Datasets['cluster'] == cluster]\n",
    "    cols = list(this_set['SeriesID'])\n",
    "    cols.append('ORF')\n",
    "    these_densities = densities[cols]\n",
    "    these_densities[cluster] = these_densities.mean(axis=1)\n",
    "    these_densities = these_densities[['ORF',cluster]]\n",
    "    by_cluster = by_cluster.merge(these_densities, how = 'left', on='ORF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "medianprops = dict(linewidth=2.5, color='black')\n",
    "boxwidth = (0.5,)\n",
    "fig,ax = plt.subplots()\n",
    "ax.boxplot(by_cluster[1].values,positions = (1,),widths=boxwidth,boxprops={'color':'#4682b4','linewidth':2.5},medianprops = medianprops)\n",
    "ax.boxplot(by_cluster[2].values,positions = (2,),widths=boxwidth,boxprops={'color':'#9f935e','linewidth':2.5},medianprops = medianprops)\n",
    "ax.boxplot(by_cluster[3].values,positions = (3,),widths=boxwidth,boxprops={'color':'#ffa500','linewidth':2.5},medianprops = medianprops)\n",
    "ax.set_ylim((-10,300))\n",
    "ax.set_ylabel('Ribosomes per mRNA')\n",
    "ax.set_xlabel('Cluster Number')\n",
    "#plt.savefig('Supp Figure 2.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>mRNAs exceeding physically possible Ribosome loads</h2>\n",
    "<p>Based on the published physical dimension of a translating ribosome of 30 nt, the maximum ribosome capacity of an mRNA can be calcualted from its length. This analysis asks how many mRNAs exceed this physically possible limit in each dataset.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mRNA_dats = pd.read_csv('Data/sacCer3 genes.csv')[['name','length']]\n",
    "by_cluster = by_cluster.merge(mRNA_dats,how='inner',left_on='ORF',right_on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tvon-\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "percentage_max = by_cluster[['ORF',3,1,2]]\n",
    "for col in [1,2,3]:\n",
    "    for row in range(by_cluster.shape[0]):\n",
    "        percentage_max.iloc[row,col] = by_cluster.iloc[row,col] / (by_cluster.loc[row, 'length']/30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "fig,ax = plt.subplots()\n",
    "colors = ['#4682b4','#9f935e','#ffa500']\n",
    "for cluster in [1,2,3]:\n",
    "    this_cluster = percentage_max.loc[:,cluster]\n",
    "    this_cluster= sorted(this_cluster)\n",
    "    this_cluster = this_cluster[:-2]\n",
    "    x = range(len(this_cluster))\n",
    "    color_vec = []\n",
    "    this_color = colors[cluster-1]\n",
    "    ax.scatter(x,this_cluster,s=2,c=this_color)\n",
    "ax.plot((0,7000),(1,1),linestyle='--',color='red')\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim((0.01,1000))\n",
    "ax.set_xlabel('Rank')\n",
    "ax.set_ylabel('Ribosome Load (relative to max)')\n",
    "ax.set_yticks([0.01,1,100])\n",
    "ax.set_yticklabels([0.01,1,100])\n",
    "#plt.savefig('Rel_load.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}