SuppNote4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enhanced protein isoform characterization through long-read proteogenomics \n",
    "## Supplementary Note 4\n",
    "---\n",
    "Ben Jordan | Gloria Sheynkman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns \n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import os, sys\n",
    "import matplotlib\n",
    "\n",
    "import config, data_loader\n",
    "matplotlib.rc('font', **config.font)\n",
    "\n",
    "# all plots go into a directory\n",
    "if not os.path.exists('plot'):\n",
    "    os.makedirs('plot')\n",
    "\n",
    "# all statistics go into a directory\n",
    "if not os.path.exists('stats'):\n",
    "    os.makedirs('stats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "PG = data_loader.ProteinGroup()\n",
    "protein_group = PG.pacbio_hybrid_protein_group\n",
    "ms_found_accs = (\n",
    "    protein_group['protein_group']\n",
    "    .str.split('|')\n",
    "    .explode()\n",
    "    .unique()\n",
    ")\n",
    "# ms_found_accs = [a for a in ms_found_accs if a.startswith('PB.')]\n",
    "# pacbio_database = pd.read_table(f'{config.PIPELINE_RESULTS_DIRECTORY}/protein_filter/{config.EXPERIMENT_NAME}.classification_filtered.tsv', usecols =['pb','CPM', 'pr_gene'])\n",
    "pacbio_database = pd.read_table(f'{config.PIPELINE_RESULTS_DIRECTORY}/protein_gene_rename/{config.EXPERIMENT_NAME}_orf_refined_gene_update.tsv', usecols =['base_acc','CPM', 'pr_gene'])\n",
    "\n",
    "pacbio_database['MS Detected'] = pacbio_database['base_acc'].isin(ms_found_accs)\n",
    "pacbio_database['log2(CPM+1)'] = np.log2(pacbio_database['CPM']+1)\n",
    "# pacbio_database = pacbio_database[pacbio_database['base_acc'].isin(data_loader.accs_in_hiconf_space)]\n",
    "pacbio_database.to_csv('stats/FigS4.2_hybrid_db_isoforms_found_vs_not_found_in_mass_spec.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pacbio_database = pacbio_database[pacbio_database['CPM'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for zoom_level in ['zoom','full']:\n",
    "#     fig, ax = plt.subplots(figsize=(4,3))\n",
    "#     ax.hist(pacbio_database[pacbio_database['MS Detected']==True]['log2(CPM+1)'].to_list(), color='lightblue', alpha=0.6, edgecolor='black', bins=np.arange(0,15,0.5), label='MS detected')\n",
    "#     ax.hist(pacbio_database[pacbio_database['MS Detected']==False]['log2(CPM+1)'].to_list(), color='orange', alpha=0.6, edgecolor='black', bins=np.arange(0,15,0.5), label='MS not detected')\n",
    "#     ax.legend(prop={'size':12})\n",
    "#     ax.set_xlabel('Transcript abundance ($log_2$(CPM+1))')\n",
    "#     # ax.set_xlim(0, 13)\n",
    "#     ax.set_ylabel('Number of protein isoforms')\n",
    "#     if zoom_level == 'zoom':\n",
    "#         ax.set_ylim(0, 1000)\n",
    "\n",
    "#     plt.savefig(f\"plot/FigS4.2_overlapped_hist_{zoom_level}_plot_cpm_distribution_isoforms_found_vs_not_found_in_mass_spec.pdf\", bbox_inches='tight')\n",
    "#     plt.axvline(x=np.log2(25+1), color='red')\n",
    "#     plt.savefig(f\"plot/FigS4.2_overlapped_hist_{zoom_level}_plot_cpm_distribution_isoforms_found_vs_not_found_in_mass_spec_with_cpm_vline.pdf\", bbox_inches='tight')\n",
    "#     plt.show()\n",
    "#     plt.clf()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# gene_detect = pacbio_database.groupby('pr_gene').sum().reset_index()\n",
    "# gene_detect['log2(CPM+1)'] = np.log2(gene_detect['CPM']+1)\n",
    "\n",
    "# gene_detect['MS Detected'] = gene_detect['pr_gene'].isin(ms_found_genes)\n",
    "# gene_detect.to_csv('stats/FigS4.2_genes_found_vs_not_found_in_mass_spec_with_cpm.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_found_genes = (\n",
    "    protein_group['pg_gene']\n",
    "    .str.split('|')\n",
    "    .explode()\n",
    "    .unique()\n",
    ")\n",
    "gene_detect = pacbio_database.groupby('pr_gene').sum().reset_index()\n",
    "gene_detect['log2(CPM+1)'] = np.log2(gene_detect['CPM']+1)\n",
    "\n",
    "gene_detect['MS Detected'] = gene_detect['pr_gene'].isin(ms_found_genes)\n",
    "gene_detect.to_csv('stats/FigS4.2_genes_found_vs_not_found_in_mass_spec_with_cpm_all.tsv',sep='\\t')\n",
    "\n",
    "for zoom_level in ['zoom','full']:\n",
    "    fig, ax = plt.subplots(figsize=(4,3))\n",
    "    ax.hist(gene_detect[gene_detect['MS Detected']==True]['log2(CPM+1)'].to_list(), color='lightblue', alpha=0.6, edgecolor='black', bins=np.arange(0,15,0.5), label='MS detected')\n",
    "    ax.hist(gene_detect[gene_detect['MS Detected']==False]['log2(CPM+1)'].to_list(), color='orange', alpha=0.6, edgecolor='black', bins=np.arange(0,15,0.5), label='MS not detected')\n",
    "    ax.legend(prop={'size':12})\n",
    "    ax.set_xlabel('Transcript abundance ($log_2$(CPM+1))')\n",
    "    # ax.set_xlim(0, 13)\n",
    "    ax.set_ylabel('Number of genes')\n",
    "    if zoom_level == 'zoom':\n",
    "        ax.set_ylim(0, 1000)\n",
    "\n",
    "    plt.savefig(f\"plot/FigS4.2_overlapped_hist_{zoom_level}_plot_cpm_distribution_genes_found_vs_not_found_in_mass_spec_all_genes.pdf\", bbox_inches='tight')\n",
    "    # plt.axvline(x=np.log2(25+1), color='red')\n",
    "    # plt.savefig(f\"plot/FigS4.2_overlapped_hist_{zoom_level}_plot_cpm_distribution_genes_found_vs_not_found_in_mass_spec_with_cpm_vline.pdf\", bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2n1v4Y/wmIHO41/mCsnSwiZH0kGgw3Mg/KuPECKp6t4g8ChyHc1VZD8xQ1ZSGBVbV2SLyGvCEiByAGxM8CdcKfExVC0XkcWCQiBTjWoP9cUbwWX+MxSIyCnhGROoCa3HO0nOBManU1zCMyk25Ri/VrcZ4M026BLkIt2KkP87gLgJuBJ729f1wrbo+uHG+acBVumu1CEAP4BFgMG7scyJu2ZytFjEMo4RIRlBE9sMtTfs1bh1ueEJFVbVFqpRSN47X22/x6n/ELZu7LckxNuNcXnqmSi/DMHKPqC3Bv+ICEYwHCkngZmIYZaGqpVwxjPxDNbs80KIawfNwkw7D0qmMkdvUrFmT1atXU79+fTOEeYqqsnr1amrWrJlpVUqIagR/BL4oU8owktCoUSMKCwsrj+uEkRZq1qxJo0aNMq1GCVGN4EhcOKuJZQkaRiKqV69Os2aWk8vILqIawSVAPxGZi4vvtyVUr6p6U0o1MwzDqACiGsE/AOtwS9G6xqlXXKQXwzCMSkXUyNLWhzEMIycpl7O0iDTG5RpuCPwdt853nqpuS4NuhmEYaSeqs3QVXNDS64GquO7vBFzO4SYicrKqLk+bloZhGGkiaiitu3DL0LoDB+GiuIBbtlaNXdnnDMMwKhVRjeDVuJSXLwJrYoWqOhcXUfrUNOhmGIaRdqIawfrsitUXZiW7wtobhmFUKqIawbm4NJjxOA+XftMwDKPSEXV2+E5c4vJGwGu4iZFzRORm4BJcLl/DMIxKR6SWoKpOwOXr3Qe4FzcxcifQFjhPVV9Lm4aGYRhppDyRpScBk0SkFj6cvapuKmM3wzCMrCaqn2C8RLwH+PD3xcAmVV2XSsUMwzAqgqgtwaXEz+NbgoisAR5X1T/vrVKGYRgVRVQj2B2X32MCMApYARQA5/rtHqAmcKuIbFDVR1Osp2EYRlqI6iJzBfC8qp6tqv9U1bdV9UVVvQiXk/jnqtoPt7IkJTk9RKSriMwQka0iskxE7haRqr5ORKS/iHwtIltEZIKItArtX0NEHhGR70Vko4i8LCKHpEI3wzByh6hG8H+A0QnqXgNO9O9nAk33UidE5HjgDVy6zTNwhvZWXLIngAH+/UM4F526uEmbuoHDPAX8FpeMqQfQHufmU3Vv9TMMI3eI2h1ejosjOCFOXVfge/++AS7u4N5yP/C2qnb3n98RkfrAL0VkCG7N8kBVHQogIlOBZcA1wBARaYEzgJep6kgv8wlu1cs5JDbohmHkGVGN4CPA4yJSAIzFLZUrAM7GrSTpIyJNgYHA23ujkD/H8bixxhJU9TZf/ytcruFxgbq1IjIF58s4BDjZV40PyCwSkflexoygYRhAdGfpJ4Df4wIljAHe968nAz1V9THgONyEyS17qdOROGfszSLyqohsE5EfRGSgD+nV0sstDu33VaCuJfC9zz2cSMYwDKNcztLDgeEi8lNct/db4Bv1SURV9d/Av1OgU4F//QfwL1zL7kTcGOBWnOHerqo7QvttZFcgh/395zAbcYFgDcMwgHJGlgZQ1S+BL9OgS4zq/vUtVe3r378rIg1whvB+4vssCruSwksEmdIVIj3xM9uNG8fzDTcMIxeJOjtckcSW4r0ZKp+AGwtcB9QQkeqh+trAev9+PS4pVJigTClUdbiqdlLVTgUFBfFEDMPIQbLRCMZamfuEymNGbyeuRRdO/tScXTEPFwEN/TrnRDKGYRiJjaCIZGrs7DOcS85FofIzcOOQLwHbCMwei0g93LjhJF80CZcL5ayAzOG4qDcxGcMwjKRjgnNE5GxV/UBE/gbco6pL0q2QqhaLSD/g7yLyJPAycArOFef3qrpBRB4HBolIMbAQ6A9sAJ71x1gsIqOAZ7wD9VpcHpS5uFltwzAMILkRrAEcJyJf4NYO/0tE4o6nAajqmkR15UVV/yEiO4F+uNUe3wDX+xlqfHkxzmm6NjANuEpVg/r1wPk3Dsa1eCcCN6pqUar0NAyj8iPew2X3CpEXgMsoI3pMDFXNmeVonTp10lmzZmVaDcPIO0Rktqp2qshzJmsJdseNv9UHRgCD2N1B2TAMo1KT0Aiq6o/4ZWcichIwoiLGBA3DMCqSSM7SqtoDQETOwM3C7g+sxi2fe1MT9akNwzCynKjh9ffDtQpPxM20/gA0BG4HPhCRbnHW6RqGYWQ9UZ2l78cFNuimqvVVtbWq1gNOB1oBf0mXgoZhGOkk6trhi4B+qloqTJaqviUi/XERpf+UYt2MHKTPrbexYuWquHUHFTTgocH3V7BGRr4T1QjWwgUtjccy4IDUqGPkOitWrqLXvQ/HrfvdOd248urfxa0zA2mki6hGcA5uxcZbcep6AJ+mSiEjfykq1oQG8pF+vStYGyNfiGoE78SFuG+MW8a2AjgI100+llAUaMMwjMpCVBeZ90TkLJzD9MPsitc3BzhXVccn2d0wDCNrKU9k6TeAN7y7zE+AdeYWYxhGZWdPIktvBsz4GYaRE2RjUFXDMIwKw4ygYRh5jRlBwzDymqhrh6cC9/rJEcOocObPn2+O1EZaiDox0g6X18MwMoI5UhvpImp3eARwt4gcHSeDm2EYRqUlakvwBKA9MAtARMIuMqqqdVOpmJHdJAuE8NXixTRv0SJu3RcLF6ZTLcMoN1GN4Hi/GQaQPBBCj7NOS1pnGNlE1GVzd6dbkXiISA3c0rwZqtrdlwku29x1QAPgA+AGVf0itN/9wKXAfrjADzeq6rcVqb9hGNlPZBcZEakrIneIyLsi8rmItBWRW0UknX/td+GCtgYZANwBPARcAtQFJvn8wjGeAn4L3IaLctMeeF1EciYjnmEYqSGSERSRpsA84GZckvOWuLzERwHjReT0VCsmIkcDNwKrAmV1cLmGB6rqUFUdB5wG1AGu8TItcAbwD6r6vKq+DPza63pOqvU0DKNyE7Ul+BjwHdAYuAAXRQZVvRz4D651ljJEpBrwN+BBYHmgqgsu2fq4WIGqrgWmAN180cn+dXxAZhEwPyBjGIYBRDeCJ+OcpTexezL2p3F+hKnkVmAf4L5QeUv/Gs5//FWgriXwfZwIN0EZwzAMIPrs8A5ciP14HABsT406ICKtgP5AV1Xd4eZBStgf2K6qO0K7bfR1MZmNcQ69ETgsVXoahpEbRG0JvgYMEpHDA2UqIgfg0m7GC7tfbkSkCvAc8JyqTo8nwu4t0Vh5cTlk4p27p4jMEpFZK1euLJ/ihmFUWqIawd641t58YK4vew7XLa0L9E2RPjcATYABIlLNjw2C84ypBqwHaohI9dB+tX0d/rVOnGMHZXZDVYeraidV7VRQULBXF2EYRuUhkhFU1ZVAR5yRmgtMxBnAgUCHFPrfnQccCqwBdvqtPW62N/ZZgGah/ZoDC/z7RUDDOMv7gjKGYRhA+cLrb8NNgjydPnW4jt1bcS8CC4G7/etjuMRODwCISD3gRF8PMAmoCpwF/J+XORxoizPahmEYJUQ2giJyBM5HrzNwCLAaeBd4QFWXpEIZVd2tpSYiW4HVqhpbt/w4bnyyGGcU++N8F5/1x1gsIqOAZ7wD9VrcLPNcYEwq9DSyCwuzZewNUeMJnoKbHFmBmwRZiUu5eS5wqYicpKpz0qRjmH64CY4+uHG+acBVqhoc7+sBPAIMxnX5J+KWzRVVkI5GBWJhtoy9IWpL8H7gbeB8Vd0ZK/TjbuOBocAvUq8eqGqH0Ocfccvhbkuyz2agp98MwzASEnV2uC0wLGgAAVR1K25VR6dUK2YYhlERRDWC84FjEtQ1ZfcVHIZhGJWChN1hEQkaveHAIz5E1Wjc2GA93Frc24Hr06mkYRhGukg2JjiL0isvBBco4c5QGcDLOLcUwzCMSkUyI/jLCtPCyEqShdC3MPlGrpDQCKrqlIpUxMg+ygqhbxi5QFQ/wZrATcCxwE/iiKiqdk2hXoZhGBVCVD/BZ4DLcPk8VqdPHcMwjIolqhE8C7hdVR9IpzKGYRgVTVQjuB74NJ2KGJkh1yc/bF2xURZRjeC9wJ0iMldVC9OpkFGx5Prkh60rNsoiqhEci8s0t0xEVgJbQvWqqi1SqplhGEYFENUI/hMXPmskbrWIYRhGThDVCB4PXKOq/06nMoZhGBVN1AAKy4FwCkvDMIxKT1Qj+GfgLyLSSUI5MA3DMCozUbvDN+KywM3ApdqMNzFSN6WaGYZhVABRjeB4vxmVkFz3BTSMvSGSEVTVu8uWMrKVXPcFNIy9IWoAhd+WJaOq/9h7dQzDMCqWqN3h5xOUK7Ad2ASkzAiKSFVc1JprgcbAMuAJ4K+qqn5yph8uT3EDXGCHG1T1i8AxauASRF0K7IfLkndjChPFG4aRA0SdHa4XZzsMuALnPH1eivW6E7dU7wXgbFwS9UeBvr5+AHAH8BBwCVAXmOTzDMd4CvgtLitdD6A98Lo3sIZhGED0McH1cYrXA/8WkX2Bx0hRxjkRqYJbovegqv7FF08SkQKgj4g8ics5PFBVh/p9puJai9cAQ0SkBc4AXqaqI73MJ8AC4BxcnhTDMIzILcFkLMOl5EwVdXFd67ChWgAUACfjkq6Pi1Wo6lpgCi7xE14GAjPaqroIlzUvJmMYhhF5YuSAOMVVcOuJ+5PClJveoP1vnKqzgEKgkf8cPudXuFYeQEvge5+EPSzTMkWqVjj9el/P6hVL49bVP6gp9z78VLmPuWXVVzx4wwUJ6wwj14k6MbKK0pnngmwHLkqNOvERkd8Bp+CctvcHtqvqjpDYRl+Hf90Y51AbcWOZ8c7RE+gJ0Lhx4xRonXpWr1jK0zc2iVt33dCle3TMGlV2MPSPcW8Jv7xm1h4ds7JgsQYNiG4Er2Z3I6jABuDdBGOGKUFELsdNcrwMDMPlOY5nkAUoDrwvS6YUqjocl1+ZTp06JTL4Wcun8+dz3RXxe/ofvf8hD96wNG5d0Y7w4p/8wWINGhB9YuT5NOsRFxHpBTyMG/+73LvHrAdqiEh1Vd0ZEK+Nm6zBv9aJc8igTE4hRVsTthL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      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
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       "<Figure size 432x288 with 0 Axes>"
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   ],
   "source": [
    "pacbio_database_hc = pacbio_database[pacbio_database['base_acc'].isin(data_loader.accs_in_hiconf_space)]\n",
    "\n",
    "gene_detect_hc = pacbio_database_hc.groupby('pr_gene').sum().reset_index()\n",
    "gene_detect_hc['log2(CPM+1)'] = np.log2(gene_detect_hc['CPM']+1)\n",
    "\n",
    "gene_detect_hc['MS Detected'] = gene_detect_hc['pr_gene'].isin(ms_found_genes)\n",
    "gene_detect_hc.to_csv('stats/FigS4.2_genes_found_vs_not_found_in_mass_spec_with_cpm_high_confidence.tsv',sep='\\t')\n",
    "\n",
    "for zoom_level in ['zoom','full']:\n",
    "    fig, ax = plt.subplots(figsize=(4,3))\n",
    "    ax.hist(gene_detect_hc[gene_detect_hc['MS Detected']==True]['log2(CPM+1)'].to_list(), color='lightblue', alpha=0.6, edgecolor='black', bins=np.arange(0,15,0.5), label='MS detected')\n",
    "    ax.hist(gene_detect_hc[gene_detect_hc['MS Detected']==False]['log2(CPM+1)'].to_list(), color='orange', alpha=0.6, edgecolor='black', bins=np.arange(0,15,0.5), label='MS not detected')\n",
    "    ax.legend(prop={'size':12})\n",
    "    ax.set_xlabel('Transcript abundance ($log_2$(CPM+1))')\n",
    "    # ax.set_xlim(0, 13)\n",
    "    ax.set_ylabel('Number of genes')\n",
    "    if zoom_level == 'zoom':\n",
    "        ax.set_ylim(0, 1000)\n",
    "\n",
    "    plt.savefig(f\"plot/FigS4.2_overlapped_hist_{zoom_level}_plot_cpm_distribution_genes_found_vs_not_found_in_mass_spec_high_confidence.pdf\", bbox_inches='tight')\n",
    "    # plt.axvline(x=np.log2(25+1), color='red')\n",
    "    # plt.savefig(f\"plot/FigS4.2_overlapped_hist_{zoom_level}_plot_cpm_distribution_genes_found_vs_not_found_in_mass_spec_with_cpm_vline.pdf\", bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "over_cutoff = pacbio_database[pacbio_database['CPM'] > 25].groupby('pr_gene').sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "over_cpm_threshold = gene_detect[gene_detect['CPM']>25].groupby('MS Detected').size().reset_index(name='Number of genes')\n",
    "over_cpm_threshold['Percent'] = over_cpm_threshold['Number of genes'] / over_cpm_threshold['Number of genes'].sum() * 100\n",
    "over_cpm_threshold.to_csv('stats/FigS4.2_over_25CPM_genes_MS_detected.tsv', sep = '\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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