SuppNote1.4_appris.ipynb

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   "source": [
    "# Enhanced protein isoform characterization through long-read proteogenomics \n",
    "## Supplementary Note S1.4\n",
    "---\n",
    "Ben Jordan | Gloria Sheynkman"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import config, appris_helper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpm_threshold = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "appris_info_file = '0_pre_analysis/appris_transcripts.tsv'\n",
    "appris_isonames = pd.read_table(appris_info_file)['isoname'].to_list()\n",
    "\n",
    "sqanti_info_file = f\"{config.PIPELINE_RESULTS_DIRECTORY}/transcriptome_summary/sqanti_isoform_info.tsv\"\n",
    "sqanti_info = pd.read_table(sqanti_info_file)\n",
    "\n",
    "def get_is_appris(row, appris_isonames):\n",
    "    return row['cat'] =='FSM' and row['isoname'] in appris_isonames\n",
    "sqanti_info['isoname'] = sqanti_info['transcript'].str.replace('_', '-')\n",
    "  \n",
    "sqanti_info['is_appris'] = sqanti_info.apply(lambda row: get_is_appris(row, appris_isonames), axis=1)\n",
    "sqanti_info = sqanti_info.groupby('gene').filter(lambda x: len(x) > 1)\n",
    "\n",
    "major_isoform = appris_helper.get_major_isoform(sqanti_info)\n",
    "\n",
    "\n",
    "appris_helper.plot_appris_donut(major_isoform, 'FigS1.4')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}