SuppFig2c-d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enhanced protein isoform characterization through long-read proteogenomics \n",
    "## Supplementary Figure 2c-d\n",
    "---\n",
    "Ben Jordan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import config\n",
    "import matplotlib\n",
    "matplotlib.rc('font', **config.font)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supplementary Fig. 2: Generation and characterization of candidate protein isoform sequences from long-read RNA-seq data. \n",
    "\n",
    " c, Distribution of the number of protein isoforms per gene. d, Bar chart showing the number of genes that contain all known protein isoforms. Data based on the filtered PacBio database (~45K protein isoforms). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "filtered_database_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/protein_filter/{config.EXPERIMENT_NAME}.classification_filtered.tsv' # ~45K prot\n",
    "\n",
    "# plot the distribution of protein number per gene, for filtered protein database\n",
    "db_type = 'filtered'\n",
    "db_file = filtered_database_file\n",
    "db = pd.read_table(db_file)\n",
    "db['gene'] = db['pr_gene']\n",
    "# only consider entries at 1 CPM or higher\n",
    "db = db[~db['gene'].str.startswith('novel')]\n",
    "genes = db.groupby('pr_gene').size().reset_index()\n",
    "genes.columns = ['gene', 'num_transcripts']\n",
    "num_iso = genes.groupby('num_transcripts').size().reset_index(name='count')\n",
    "fig = plt.subplots(figsize=(6,3))\n",
    "plt.bar(num_iso['num_transcripts'][0:20], num_iso['count'][0:20], edgecolor='black')\n",
    "plt.xticks(range(1, 21, 2))\n",
    "plt.xlabel('Number of protein isoforms per gene')\n",
    "plt.ylabel('Number of genes')\n",
    "plt.savefig(f'plot/SuppFig2c_hist_num_prot_iso_per_gene_{db_type}.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "num_iso.to_csv(f'stats/SuppFig2c_hist_num_prot_iso_per_gene_{db_type}.tsv', sep='\\t',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supplementary Fig. 2: Generation and characterization of candidate protein isoform sequences from long-read RNA-seq data. \n",
    "\n",
    "d, Bar chart showing the number of genes that contain all known protein isoforms. Data based on the filtered PacBio database (~45K protein isoforms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 252x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
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   "source": [
    "\n",
    "\n",
    "filtered_database_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/protein_filter/{config.EXPERIMENT_NAME}.classification_filtered.tsv' # ~45K prot\n",
    "\n",
    "prots = pd.read_table(filtered_database_file)\n",
    "prots['gene'] = prots['pr_gene']\n",
    "\n",
    "# plot distribution of genes with all known isoforms\n",
    "def all_proteins_are_known_for_gene(row):\n",
    "    if set(row['pclass'].to_list()) == set(['pFSM']):\n",
    "        return True\n",
    "    return False\n",
    "prots_novel_status = prots.groupby('gene').apply(all_proteins_are_known_for_gene).reset_index()\n",
    "prots_novel_status.columns = ['gene', 'all_protein_isoforms_known']\n",
    "prots_novel_status = prots_novel_status.groupby('all_protein_isoforms_known').size().reset_index()\n",
    "prots_novel_status.columns = ['All known', 'Count']\n",
    "prots_novel_status = prots_novel_status.sort_values('All known', ascending=False)\n",
    "prots_novel_status.plot.bar(x='All known', y='Count', rot=0, edgecolor='black', legend=False, figsize=(3.5,4), zorder=10)\n",
    "plt.xlabel('All protein isoforms detected \\nfor gene are in the reference', labelpad=10)\n",
    "plt.xticks([0, 1], ['Yes', 'No'])\n",
    "plt.ylabel('Number of genes')\n",
    "# plt.grid(zorder=0, axis='y')\n",
    "plt.savefig('plot/SuppFig2d_bar_gene_and_prot_iso_novelty_cat.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.clf()\n",
    "# output source data table\n",
    "prots_novel_status.to_csv('stats/SuppFig2d_bar_gene_and_prot_iso_novelty_cat.tsv', sep='\\t', index=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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