alexlenail/Canonicity_Update.ipynb

## Canonicity_Update.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib_venn import venn2, venn3\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene_id\n",
       "ENSG00000000003.14    ENST00000373020.8\n",
       "ENSG00000000005.5     ENST00000373031.4\n",
       "ENSG00000000419.12    ENST00000371588.9\n",
       "ENSG00000000457.13    ENST00000367772.8\n",
       "ENSG00000000460.16    ENST00000359326.8\n",
       "Name: transcript_id, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ucsc_canonical = pd.read_csv('/Users/alex/Desktop/knownCanonical.txt', sep='\\t', header=None, names=['chr', 'start', 'stop', 'idk', 'transcript_id', 'gene_id'])[['transcript_id', 'gene_id']].set_index('gene_id').transcript_id\n",
    "ucsc_canonical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ucsc_canonical.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene_id\n",
       "ENSG00000121410.11     ENST00000263100.7\n",
       "ENSG00000268895.6      ENST00000594950.5\n",
       "ENSG00000148584.15     ENST00000373997.8\n",
       "ENSG00000175899.14    ENST00000318602.11\n",
       "ENSG00000245105.4      ENST00000499762.2\n",
       "Name: transcript_id, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_canonical = pd.read_csv('./processed/genome.csv')\n",
    "my_canonical = my_canonical[my_canonical.canonicity == 0][['gene_id', 'transcript_id']].set_index('gene_id').transcript_id\n",
    "my_canonical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_canonical.index.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many gene_id's are shared? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib_venn._common.VennDiagram at 0x1365d40b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kPfM/P1TTXBcAGqBOpBNkk7Xa/6nKb3o6MNOZGqVFnO+d6z5Abf9nk+syymnPBHpd16DCKW6IHpcOxN9fBGjwQhGujcUbdZTNnomuK1BhNr87MNOZxrouwAvBFarDd4D9TYGZTqJ8aG7SdQUl4/zI1QsBGqrVR50j6dWd55VLx6cC8wauLVCg0XUB5dTWGJhpJMqnRuSJje0NxB60Na53Z3IboHb7uqC8Gw7IgfpAjIAqn5ufDESAguPDeNct0DGOH7/sDo7S0xYr945LB+aN3OlhvAZomXWN1P5P5d7ktPP//VIJdQu03vHjl1U6Tk4HkJQXjM8E5kioQVrEWY65DtBQtUAPjg5Mv5PyudFZKgJynqQYDnPEXYDaFUgjnT2+Ax31uoGI8oYoyITewMwIqXP1wC5boLWEbAPlA/Xher7K26amA7MiydlUJtcBGiqddc67TJR6xdReDdChcvkPPcLhYzuRqtYBJOUdU9OBOSIKZYCGqgWaF0wuFpiRTxUAYzOBOSLSAA26dEIHkJS31OYCE6DOskQDtEzSVYEZ8VQBUZMPTIBWuZoL6vIFrHb42GWXqtIWqPKWRD4wXUqCozxxGaChOqVFqioQk5ZVgERBqnM6Ej8ULgM0VLswpTVAlQfV5QJzZBSiALWrkIJy+DAgqSrXFSj1eqOyGqBD4aoFGqrWJ0A6EZg5dypARgWnBRqqPtDQBWivBqjyoFHZwHQtOTmidbUyZkgB+iSM/gBc11XYjORSePKX8OiF8K4XYW4UcmOg9Xfw0+Ohp+9+j0P9pXDj2+B398LDh/s5fd9/NVz4IFwgYObC8sfg3sHWnBcN0EPpzSEf/A1frK6g40dv5bv/+AAfau1makTINdWw+esX8/OqCnI/W8a8hzbydgEjQu49J3HX22axHmD5Hur/axEf6M5QD5jPns0t88exP2+g+THesa6N00XInzqOJ/757Fd/vwoSxxKfP+RaWplDBZ18lpZXrr+HC1nPBQiGsSznbwv/Jy8xkYd5P1mqEPL8AzdTTZY/sJAVXAFAnA6u4jbG0vXKz7uLS1nJu/kYn3rN9UfmpDHoKkCH9LhxyN8I91wHW7dAfD586W5YdTGsuh/uq4b8RfDXN8AVj8Kv+u73d/CeE2DF0X7Oe2DXv8GsZ2H+BvjqaMguHeLSUxOUGXcl9q1nuXhUgl29OaoAzprEovedwq0AH3+AD//3Es755Bt44i0nsPoD83gxIvD4Zib+8C985G2zaAb41nNcd/lM/nDlyazalyQeFduq+t5i3nggzeif/zX/EotgNrSHb/nw0cSOJUDn8mcSPMZDXPfKdc8wi+3M5xN8lSqy7Cq8xhkiPMiHuIzbmMd29lJDJTkyRHiBK7meGxlLFz/lXTzMhbyf3wGwmdHs5CQqaTvGp+KkgeLq33pIT/ZMOHAdbAWYCukm2LUWRn0JVlZj+3QWwMZ9MLrvPp+D+WNh3xTYdbSfA/AzOP96eGA0dgL8qdA5lJqNtj9f5+VWRq1rY84F03i677pr5rAiIhARmDySTe099nfYUE06UngNu3tfnQL39FbGG0PkypNZ1fd9o6voBVi8g/M/MI/fxyI2UGeMHtrvMIgix7In6BtYRx3dr7luGedzOg/Qt1BkfOE1fpqTGMF25rEdgCa6iWHII4CQpJI8kCFBLR2v/Lzf814u5F445q6FULVAS+YRGLMTJl8Nm4qv/zWcfQksAdgG8dvhsufg2x+BNw3k57TC2Cfh+O/CO2KQ/TLcfT1sGWydRgKz6qNkvvc8V151Mvd29pLof1tPhujyvZz1npP4v77rfrKM+Q9t4J3pLCM/dCq3AKxvY2xllJ6P3c9HO9M0TK5jVcsF/KoyiunqpfGBdSz4ziJOTcTouv40fnnGRPaW8zl63ZD/KLsZyxaOZxHvIEKW87mb09nCvsK5ir7NJ8hQy2QWcxUPESfHQn7B7TQTJU0Ve7mOOwB4hHlU0cE8tnN/+Z/KYPiyBdpnG8SvgY9+FO6aDqm+698KV0QgfwssAvgAvPW98MhkSA/05+Qh0gk12+AbX4R7Pg03DGXGsZHAdNaXxO0vMae6gs5LZ9gjgP6+8ChXTxzJurfPtv2cANfNZ9md76L5b+bx/V+t5u0AuTyR1iQzrz+Ne259Gzd3pGj8wRLeCJA3xCqiZG9/Jze/cRJPfX8x15bn2fnHkAPAECFNDZ/kG5zHPTzEDeSBPBE6mMn7uJWP8e/s4FSeYjZpoqzkfN7HTXyef2YkO7iLK+iikmVcwTv57SArCdUh/JDDpBOi58NHz4NF/w5L+67/CLzhBZj7ONzaNyy3AabfCu8aCTc/ChffD2++Bi440s+pg/a3w9IocANsFsivGML6/UhQJouUyJp9zNxygHnvvZub71jO9Xu6mfWPD/BBgBsf5696MtR+7SLuPtR93zGbdV29NGzuoHb8CNpHJdh+2nj2xWPk5zSxdOsBpgBUVdB++QxeALh2PksPpJlYvmfoD0P+s0zQzmyWEgEWsBnIs5daRtLOaNYyli5q6WUcK9jJFFYwCYDjaSUCnMwSWpnBJhpJ0cAP+DI3czO9jOZWvsiOAZ+1wsl/mKtD+CEFaA44F/5mIuy6Gx7pu74FTr4XLnsU/mMcth8MYBv8e9/nV8BbqyH1C3j8cD8H4GxY9ijM+mdY81toykHsFAY8Ingo2gItctNF3AfcB/CrVZzw4HredMsV3PZfizhnUwcnf+dyvtXXdwmweCeNp4+nNSLwyEam5PPEptTRNWEE3b9YTtWmdmqnj6Zr7X5mTxppu1qOG82yp7cy69Tx/PnXqzmhtlIP3/vLDXV2yBSWsZFZnM0a1tCEIUYTXSxkJSu4jC4qiZOllROYyyM00UGS8eymlnF0sZ4TGcku5rCDOXz6lZ97MzfzIW4+hlF4DdCB+i7MfBHOaoAdTfBlgI/Bff8FV+Ugdin8P4DpsHER/OJYf04LrLgFnjkPrm2A5ihkvwQ/GcpEM8nrNKaB+NNGrqmqYP/f/4HPARxfzwvNF3D/Q+s57T+f5Q0RIReN0PuBefw4IlAZxbxtFvd86TE+iYGGGrZ+bAFPAfz9Qh688XE+dM29XBKLkv7gfP7X7bPznmPqlvo+H6adE8hSy9f4V07ht1zBM/yEa/lXmomQ5Tx+QgRoIMlJPML3+AJgGMsKLmI5ACfxe37GZxByJNjPVfy0BE/FSQNFjHHwuCINwF+X/4HdefIyUgfrXz9YopRLtzWSvK8+EDujvWyazTPlflBXfaCHHMwJsmhOD+GV93QFZ26Ik0N4Vy9f79G/JVjiqaN/j1Ll1hELTNdSxsWDaoCWSTwVmE0bVIB0RAMzPznp4kHdvHi249XJO4YriWRg3ulVgHTEAhOgQ5khM2guX7xQtUITPUf/HqXK7WA0MPvydh/9W0rPZYCGaiAp0ROYd3oVEBnIpyOB+bsMXYA6ecKuxDVAlccko4Hpl8+ZZuNkmNblP3WodsaJpwJzqKQCojsSmAB11hhzGaBOOn1diaeJkte5oMo7uqKBOSOnBmgYxLKB+YNVAbCtMjBv6BqgYVDdVdh0VikP2BqcM5OFMkBD1QcKMLIjMH1OKgA2xwMzsBnKAE1CuFpkdcd6lhelhtGWOBWuaygRZ0ez7gLUrkZqd/b4Doxq8/8pVFQw9Ar5/RWBmRnirGniugkfqjbZiA5iHMtJvJQaJq2xwBz9pUyzOejqwV0H6H7Hj19WsRyRRE9g/nCVj+2sDMyMEKdnGXAdoKFqgQLUHgzXJirKm7YGZwrTHpcP7jpAQ9UCBRjZHpg/XOVja6oCsztYiFugxqQJ2XzQujbnb1pK8WI1QZgFagh1gFqhOlPimNbATB1RPrUnRm8yGNvYtZtm47RLzAsButt1AeWU6CFW1RWuvVCVt6yuCsxApvPGlxcCdKfrAsqtYY8OJCl3XgzCOTgtpwNI4IUANaaNkG2uPG67B153FVrLagLTjaQBWhCqw/gxe4hLXtfFq/I7ECXTWhGIAO01zabDdRFeCdBdrgsop1iOyMh27QdV5bcuEZjuI09khlcCNHT9oI27tQWqym9ZcPo/N7ouALwSoMbsI2TnSBq3PRDTSJTPPDUyEPM/c8Bm10WAVwLU2uK6gHIa1UY8mgnMemTlA1srSbXFArEj2DbX8z/7eClAN7suoNwa9oRr9oFy688jAvOG7YnDd/BWgO6EwHRwD8i0dZ56/VXAPRqcw3fPHK165x/YmDywzXUZ5dS4m0RlKlxvGsqNPTF6d1UGYvrSVq8cvoOXAtTa7LqAcpuwVaczqeG3qDYwb9SeOXwH7wXoFkJ2nqSp6wLRKlAe92hdIAaPsnjo8B28FqDGZIBNrssopxEHqaw9oINJavh0RMlsSBB3XUcJbDXNxlMNLG8FqLXWdQHlNmlTYEZHlQc9OjIw3USeOnwHLwaoMTsI2SbLkzcSJ6871avSy4H5TX0gWp+9wFbXRfTnvQC1QtUKjaeJ1reScl2HCp6VVYGZPL/Ka4fvoAHqGTNWu65ABdF99a4rKIk88LLrIg7FmwFqzEFCtsHI2J1UVXfqYJIqnY4omcW1VLmuowQ2m2bjyW49bwaotdx1AeV2wgodTFKl81BdYAaPXnJdwOF4N0CN2QIccF1GOU3cTFU8GZg/euVQDszvRgdi8GivaTbOz310ON4NUCtUrVABmbkqXAsJ1PB4qZqejmAMHi11XcCReD1A1xKy8yVNXUdVrFdDVA3N7Q2B2G+2zTQbT6086s/bAWpMFljluoxyihhk+ppwvWmo0lqZoGddVSAO3z3d+gSvB6i1AsI1uDJjNVXRbLiesyqd25p88X99NB14cOVRf95/oY1JAqGaJRnLEpm8QSfWq2O3MkHPmmC0PpeZZuP51XneD1BrKSFrhc5+SftC1bH7aaNv/qePpA1Y57qIgfDHi21boStdl1FOsSyR2S/qlCY1cKsT9KyqDkTr82k/tD7BLwFqLSVsp/xYT7VudacG6rZgtD7Xmmaz23URA+WfF9yYFCGbFwow93nXFSg/CEjrsxdY5LqIY+GfALVeBHpcF1FO9fuIj90eruesjk0OzHfGBWLe5xLTbHz1t+6vALU71vvqHaoU5i6mIqLTmtRh/KmO5La478+4uR+P7rh0JP4KUABj1gJ7XJdRTvEUsRmrdFqTer2uCNkfNwVix6Vn/DJwVMx/AWo9A+Hawf34l6lO6EYjqp/bGulNRXz7f9zHVwNHxfz5whuzjxAu8Zz/LHlMuN441OFtjJN6eBTVrusYIt8NHBXzZ4BaiyFch7UNe0lMW0vSdR3KvRyYb4/z9f9vn8V+Gzgq5t9fgDFp4DnXZZTbSUt1bqiCx0fSsynh+4Gjnfh8gYx/AxT6BpQ8vd1VqUUMsvBJIpEcede1KDcORMn8qMn3cz6TwJ/8OHBUzN8Baj1JyA7la7qoOHFZuJ6zetU3x5NLRn0979Ngw9O3h+59/B+gxvQAT7suo9ymr6W6fo9OsA+bh0fSvbSGhOs6hmixaTa7XBdRCv4PUABjNgIbXJdRbqc/Q6Xu2BQe+2L0/vdY38/53GqazTLXRZRKMALUehrCNUIdTxM99VkyOrUp+LKQv2kiJuPvOZ9dwGOuiyglP/8yXsuOyj9KyCbYj91J1cyV4XrjCKM7GkhtSPh64CgPPGKaTaBmkAQnQAGM2YmdHxoqs1+ipnGnhmhQrUzQc/cY30+Yf87LpycerGAFKIAxy4DNrssotwVPkdD5ocHTESXztYm+n++5yTSbFa6LGA7BC1DrceCg6yLKKZonctZjRCtT4dp0Osh6hfy/TMIcjPl6ylIb8ITrIoZLMAPUmF7gYQjXCHWih9iZj2F06zv/y4P51njSPl9tdBD4g2k2gd0EJ5gBCmDMfuwk+1Cp66By4VNkyIdrMC1o7qkn+cwIX09Z6gJ+b5pNoPvmgxugAMasB5a4LqPcGneTmLNEJ9n71fM1JG9vpMZ1HUPQA9xvmk2X60KGW7ADFMCYFwjZeeUBpm6getaLOjLvN9sqSX9jgq9XGqWx4XnAdSHlEPwAtZ4GtrsuotyOX0n1iUs1RP2iM0L2S5OJ+niyfAZ4wDSbNteFlItff1HHxpg8di8EYWMAAA7CSURBVFApNL/YPjNWU33SCxqiXpcUcp+fQr4tRsx1LYOUA/4YxLmeRxKOAIW+E9I9AHS6LqXcjltD9clLSOqST29KCbkvTCG7xb8nhssDD5tms9N1IeUWngAFMKYb+B12hDBUpq+j+pQl9GiIektayH1pMlkfL9M0wGOm2Wx1XYgL4QpQAGO6gN8D3a5LKbdp66me+7yGqFdkIN8yicyaKt+GZxbb8gzdTmh9xPh7Q+jBExkJvA18v8b4mG2bTvLFM6gigriuJawykP/qJHp9vLdnCngwbH2e/YU3QAFE6oC3EsIQbR1Hask5VOQqfL1M0JeykL95IunFtb6dKH8QO9oeiqlKRxLuAAUQGQW8Gah1XUq5ddeSefYiTKrGt4MXvpOB/L9OIL3Iv6uM9mJbnnpKGTRALZFqbIjWuy6l3LIx8s+fT7qtybf/0L7RHSHbPImcj/s81wNPmGajey0UaID2EakELgPGuy6l3AyYFQtIbjne18sHPW1/lN7PT0F2VVLhupZBMNjzGAXmVBylogFaTCQKXAxMc1yJE1uPI7l8IQnj35UwnrStkvTnJhPz6bZ0vcCjYZ2mdDQaoP2JCHA2cJLrUlxoH0P6+fOJZuK+XRHjKS9X0dM8iXjan29K7djTcLS7LsSrNEAPR+Qk4I2EcK5sbyW5pW8k3To+fLMTSumJESS/OZ4qI76bLmaAl4Al2t95ZBqgRyIyDriEEE5zAjtfdMUC4jl/Hno6k4X8/zaSuq/el383B4DHTbPZ47oQP9AAPRqRGuBSoMl1KS6kqsguOYdMR4OO0g9ER5TMVyaSX+fPkfYVwPOm2YTqTA5DoQE6EHZw6WxgtutSXNk4i+SqeSRMNHxdGgO1vIqemyZSmYz6rsXehW11hm4zkKHSAD0WIidgg9SPU1GGrLuWzJJzyXWO8u3yw2GRA/PLMfT8ssGXh+yrgWdNs9GTEQ6CBuixsmvoLwYaXZfiggGzcTY9a08hrstA4UCUzNcnkHu52ndvKkngSZ2eNDQaoIMhEgFOA+YTwlF6gEwFuZWnkto2neqwbkryQjXJb44n7rP5nVlsX+eyIJ8ts1w0QIdCpAm4EKhzXYor3bVkXlpIdv+48AwydUbI3jKOzLP+Ws+exx6uvxD0M2WWkwboUNkBplMJcWsUYF8TqeULke6Rvhx9HrAnRpD87jgSKX9NjF+PndN50HUhQaMBWip2V6dzgAmuS3FpywySq+dRGbSVTPti9H5rPPnl/urr3IadlrTfdSFBpQFaanak/izw1T9aSeUFs3UGPRtOJNZT6++t8nJgHhhF8rZGqnx0tsw92ODc5bqQoNMAHQ4icWAhdt6oX/7phsXuiaTWnQwHxvjvDWVZNT0/aCK6wz8ne9uDHRza4rqQsNAAHU52x/szgOmuS3Gto570mjnkWsd5/1QiG+OkftCErKr2RX9uL7AWWB2m87F7hQZoOdjR+jMJ4V6j/SWryaw7hd4dU0nkPTb9Z0+M3lubyPlkdH0PsArYqEsv3dEALSeRKcACoMF1Ka7lBbNrMqltMzD7G90uET0QJfOLMWQeHOX5nZO0tekxGqAuiEzCTnsK9Yh9n2yM/I5ppLdNRzrGEKdMIbY3Ru+v68k+WEfC4wNE2tr0KA1Ql0QagXnYPlIvt3zKJp0gu206vdunE+mqG56Bp7UJev5vDPJ8rWcHtrLATmALsNU0m27H9ajD0AD1AjvYNAeYCb4Z8R126QTZ1nH07h0PbU1UpKoHv4lLFvKLa0ndOYbYpoQnX+ODwA5saO7UlqY/aIB6iUgMmIGd/jTWcTWek6oiu2cCmdbxmLZGKnoTRw/UzgjZx0aSvnsM8Y6Ypyb3J7GBuQMbmF2O61GDoAHqVSKjgROB48EX02nKrruGzL5xZA7Uw8HRSNcIKrKVxDKQX1pD6oFRRP5SQ9zxwFAG6MCeX+iVi2k2nQ5rUiWiAep1duenScBxwFQ0TA8nD+xoH8O6Mz9M57oqRmE3eRlZ+FgHw9oC7eXQQaktywDTAPUTG6YTsGE6jRAvFy3oBbZj13xvwZjUkb5ZWqQCuxl2ZeHj4T6vxIZtFtuC7C1cDvu5nnwtnDRA/cqefnksNlAnFj738lScUmnDBuZWYA/G5B3Xo0JMAzQo7ADUOGyYTgTq8X+g5rGBuadw2Y3RQ2LlHRqgQWX3Ka3HnnqkofBxNN4NVYOdytMOtGIDcy9Gp/Mo79IADRMbqqN5dVClb5BlBJTlhGgG6MFO4TnIawddDmC0H1H5iwaosmy4VmMHpvou8aLPY9jWqxQ+Fn+e5dUBl+KPvdiwTALdQI/2Waog0QBVSqlB8mp/mFJKeZ4GqFJKDZIGqFJKDZIGqFJKDZIGqFJKDZIGqFI+JSIXiMj2Idz/MhH5dSlrOszjfFNEPjbcj+OCBqhS4fU14BsD+UYR+bmI7BKRgyKyVkQ+XHRbpYjcIyKbRcSIyAX97v4fwBdExIsbWQ+JBqhSISQiC4E6Y8xzA7zL14FpxpiRwNuAm0Tk9KLbnwbeD+zuf0djzC5gdeF+gaIBqtRhiMhpIrJURDpF5G4R+T8Ruano9r8SkWUi0iEifxaRuUW3bRaRT4vISyJyoHDfRNHtby/c96CIbBCRywvXTxCR34pIm4isF5Hri+5TJSI/FZF2EVkJLOxX7wQRuVdEWkVkk4h8/AhP7wrgiaL7/reI/Ee/n/cbEfkkgDHmZWNMunCTKVxmFG7rNcZ82xjzNHC45biPA285Qj2+pAGq1CEUDjfvA36K3ZTlTuCdRbefCtwG3ACMAX4I/FZEije8fi9wOfakgXOBvy3c9wzgf4HPAKOA84DNhfv8ErvH6QTg3cDNInJR4bZmbGjNAC4Dri2qJwL8DngRuxvXxcA/ichlh3mKc4A1RV/fCVwpdptExJ4R4U2Fevoe4/siksS2JncBfzjMzz6UVdgTKAaKBqhSh3YWdv3/d4wxGWPMr4Dni27/CPBDY8wiY0zOGPMzIF24X5/vGGN2GmPasOE2v3D9h4DbjDEPG2PyxpgdxpjVIjIZOBv4rDEmZYxZBvwP8DeF+70X+Joxps0Ysw34TtFjLQQajTFfKbQINwI/Bq46zPMbBRSfVuQpbKvy3MLX7waeNcbs7PsGY8zfYTeeORf4VeH5DlRn4TEDRQM0IETkThF5xzA/RlxEVos9HXPQTQB2mNduFrGt6POpwKcKh+8dItIBTC7cr09xf2ASqC18PhnYcJjHbDPmNedL2oJtUfbdvq3fbcX1TOhXzxc4/MkJ27FhCEDhef4SeF/hqquBX/S/U+HN4mnsaWaOZWR9BHb3rUDRAA2AQt/bPOA3A/jes0Tk4UIfW2uhb2980e2jRORnIrK3cLmx77ZCH9htwOeG4Wl4zS5gYt8hbcHkos+3YVuDo4ou1caYOwfws7dR6D/sZydQLyIjiq6bgj1zZ19Nk/vdVvwzN/WrZ4Qx5s2HqeEl4IR+190JvFtEpgJnAvce4Tn0nUF2oE7Edi8EigZoMNwA/KJfa+lwRgM/wp5TaSr20OonRbf/J3Zbu2nAGcAHROS6otvvAK7t19cXRM9iB0T+QURiIvJ27OvR58fAR0XkTLFqROQt/cLvcG4FrhORi0UkIiITRWR24bD8z8DXRSRReGP8EPDzwv3uAj4vIqNFZBLwj0U/83mgU0Q+WxhsiorIKYXR9kP5A3B+8RXGmKXAPmy3wR+NMR0AItIkIleJSG3h516Gban+qe++haOTvkGyykL9xW8+5wMPDOC18RdjjF5KcMH2H80s+vqnwE1FX78dWIbdSHgDcHnh+r8FNmKDbBNwTdF9rsd2vncCK4HTDvPYG4FzCp/HsYdKpxTd3ojdyLjpEPc9Degs+nofsLDo6y8AT/W7zzrgfNeveRl+pwsKv7Mu4G5sv9+Xi26/HFhceL13Fb5nROG2zcAlRd97I/Dzoq/fiW0FdgLrgcsK108Cfo89lckG4KNF96nGDj51FP4ePgNsL7p9ArYVuRt7iP5ccQ2HeH6LgTP7Xfflwt/ye/r9/TxReNyDwHLg+n7328yro/N9l2mF28ZjB8YqXf9OS/434rqAoFyOFKDYlssB4FJsq38iMBuoKfxBzip833jg5MLn78Eeui3Eblw8E5h6iMetKTx2Y9F1t2EPL/u+/nvgwcPU/U/Ac0Vf7wPOKPr6i0B7v/v8Fvi469fcwe94EXCd6zpK+HzeBPy6DI/zTeDvXD/f4bgM53my1ateGXUtfL0DQERqsCdOO0VEtho74XhX4Xs+DPybMWZx4ev1h/nZfSObxQMPd2Cn1Xyx8PXVha9fo3CI+C/Y1nGfB4HPici12AGID/L6030EckS1PxE5HzvVZx9wDXYq0oNOiyohY8xDwENleJxPDfdjuKJ9oOVxyFFXY0w3cCXwUWCXiNwvIrOPdJ9D6BvZLO57ewyoLvTPTcNOn7mv+E4iMhPbJ/UJY8xTRTd9HHu4vw47KHUn9vCrWCBHVA9hFnbgowP4FPDuwpucUoAGaCkleW1LbVzR54cbdcUY80djzKXYw/fV2MGJI96n3/27sUF7QtF1OeyAw/sKl9+boqkxhVHWR4CvGmNu7/fz2owx1xhjxhljTsb+jRTPf4SAjqj2Z4z5kTFmrDGm1hgz1xhzv+ualLdogJbOMuDqwijl5bx2hPOQo64iMrawpK8GOym5C3tID3Yk9NMicnphlHdmIfgO5XUjqtjD+Cuxh5539F0pIhOBR4HvGmN+0P8HicgMERlTeB5XYCeM39Tv/vXYAQqlQk0DtHQ+AbwVe7h3DfDKNmHGmOeB67BThA5gRzSnYl//T2Ln/7VhQ/Bjhfvcjd0t5w5sn+OvscF1KD8CrimeNmKMWYQ9E+YEXjt95MPAccCNItLVdym6/XTsKGsndgOJa4wxLxfdfjXwM/PqumilQkvPyhkQInIHcJcxZtj2dyzM/XwROM8Ys3e4Hkcpv9AAVUqpQdJDeKWUGiQNUKWUGiQNUKWUGiQNUKWUGiQNUKWUGiQNUKWUGiQNUKWUGqT/D10/t/qGQHBjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (10, 5)\n",
    "venn2((set(ucsc_canonical.index), set(my_canonical.index)), ('ucsc (v29)', 'gencode (v31)'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting to just the shared gene_id's, how many transcripts are mutually canonical?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "shared_gene_ids = set(ucsc_canonical.index) & set(my_canonical.index)\n",
    "\n",
    "ucsc_canonical = ucsc_canonical.loc[shared_gene_ids].rename('ucsc_canonical')\n",
    "my_canonical = my_canonical.loc[shared_gene_ids].rename('my_canonical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib_venn._common.VennDiagram at 0x137a32518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (10, 5)\n",
    "venn2((set(ucsc_canonical.values), set(my_canonical.values)), ('ucsc (v29)', 'gencode (v31)'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ENST00000425588.1</td>\n",
       "      <td>ENST00000438267.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000274333.4</th>\n",
       "      <td>ENST00000624444.1</td>\n",
       "      <td>ENST00000624627.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000188558.6</th>\n",
       "      <td>ENST00000343414.6</td>\n",
       "      <td>ENST00000641804.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000183935.5</th>\n",
       "      <td>ENST00000538670.1</td>\n",
       "      <td>ENST00000624664.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000240654.6</th>\n",
       "      <td>ENST00000332018.4</td>\n",
       "      <td>ENST00000382071.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000173209.23</th>\n",
       "      <td>ENST00000357022.6</td>\n",
       "      <td>ENST00000394457.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000185662.9</th>\n",
       "      <td>ENST00000523047.3</td>\n",
       "      <td>ENST00000330910.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000221888.4</th>\n",
       "      <td>ENST00000408896.4</td>\n",
       "      <td>ENST00000641256.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000248115.1</th>\n",
       "      <td>ENST00000510351.1</td>\n",
       "      <td>ENST00000508813.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000047579.19</th>\n",
       "      <td>ENST00000344537.9</td>\n",
       "      <td>ENST00000622898.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000204648.11</th>\n",
       "      <td>ENST00000608568.1</td>\n",
       "      <td>ENST00000376909.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000283554.1</th>\n",
       "      <td>ENST00000637043.1</td>\n",
       "      <td>ENST00000637462.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000240356.6</th>\n",
       "      <td>ENST00000391616.3</td>\n",
       "      <td>ENST00000416673.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000258940.2</th>\n",
       "      <td>ENST00000553520.1</td>\n",
       "      <td>ENST00000605298.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000262468.6</th>\n",
       "      <td>ENST00000573042.2</td>\n",
       "      <td>ENST00000649264.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000158716.8</th>\n",
       "      <td>ENST00000368107.1</td>\n",
       "      <td>ENST00000368109.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000266952.2</th>\n",
       "      <td>ENST00000588074.1</td>\n",
       "      <td>ENST00000649058.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000147274.14</th>\n",
       "      <td>ENST00000320676.11</td>\n",
       "      <td>ENST00000431446.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000114779.19</th>\n",
       "      <td>ENST00000361143.9</td>\n",
       "      <td>ENST00000483233.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000255319.5</th>\n",
       "      <td>ENST00000529093.1</td>\n",
       "      <td>ENST00000527856.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000155749.12</th>\n",
       "      <td>ENST00000286190.9</td>\n",
       "      <td>ENST00000405148.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000147789.15</th>\n",
       "      <td>ENST00000528372.5</td>\n",
       "      <td>ENST00000525266.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000173715.16</th>\n",
       "      <td>ENST00000360962.9</td>\n",
       "      <td>ENST00000525908.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000184698.5</th>\n",
       "      <td>ENST00000328611.5</td>\n",
       "      <td>ENST00000642046.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2468 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          my_canonical     ucsc_canonical\n",
       "gene_id                                                  \n",
       "ENSG00000147381.11   ENST00000360243.6  ENST00000276344.6\n",
       "ENSG00000133316.15   ENST00000278856.8  ENST00000525239.5\n",
       "ENSG00000108479.11   ENST00000588479.5  ENST00000225614.6\n",
       "ENSG00000232490.6    ENST00000455961.1  ENST00000428872.6\n",
       "ENSG00000100242.15   ENST00000405510.5  ENST00000406622.5\n",
       "ENSG00000204136.10   ENST00000481799.5  ENST00000495328.5\n",
       "ENSG00000164638.10   ENST00000297195.8  ENST00000396872.7\n",
       "ENSG00000254369.6    ENST00000518947.6  ENST00000524304.1\n",
       "ENSG00000251018.2    ENST00000514724.2  ENST00000521666.1\n",
       "ENSG00000148985.19   ENST00000278243.8  ENST00000464906.6\n",
       "ENSG00000214944.9    ENST00000437974.5  ENST00000545377.5\n",
       "ENSG00000186715.11   ENST00000455405.6  ENST00000545160.1\n",
       "ENSG00000221994.10   ENST00000409324.7  ENST00000442455.7\n",
       "ENSG00000270433.1    ENST00000604278.1  ENST00000604456.1\n",
       "ENSG00000182634.8    ENST00000330487.6  ENST00000641585.1\n",
       "ENSG00000214182.5    ENST00000393073.4  ENST00000607242.1\n",
       "ENSG00000184887.13   ENST00000392554.7  ENST00000536364.5\n",
       "ENSG00000177200.17   ENST00000564845.5  ENST00000566029.5\n",
       "ENSG00000182670.13   ENST00000354749.6  ENST00000355666.5\n",
       "ENSG00000120693.13   ENST00000350148.9  ENST00000379826.4\n",
       "ENSG00000176826.15   ENST00000455909.5  ENST00000441699.1\n",
       "ENSG00000156531.16   ENST00000332070.7  ENST00000370803.7\n",
       "ENSG00000261760.8    ENST00000561715.1  ENST00000636339.2\n",
       "ENSG00000261188.1    ENST00000565764.1  ENST00000566814.1\n",
       "ENSG00000087884.14   ENST00000393427.6  ENST00000526415.5\n",
       "ENSG00000250379.1    ENST00000512295.1  ENST00000510176.1\n",
       "ENSG00000265758.1    ENST00000585185.1  ENST00000584898.1\n",
       "ENSG00000205882.8    ENST00000382205.4  ENST00000526438.5\n",
       "ENSG00000251637.6    ENST00000511677.1  ENST00000529069.5\n",
       "ENSG00000256274.1    ENST00000422992.2  ENST00000534866.1\n",
       "...                                ...                ...\n",
       "ENSG00000168661.14  ENST00000303586.11  ENST00000439785.5\n",
       "ENSG00000237438.7    ENST00000441006.5  ENST00000609932.5\n",
       "ENSG00000227888.4    ENST00000525829.1  ENST00000602658.1\n",
       "ENSG00000090097.21  ENST00000322099.11  ENST00000355852.6\n",
       "ENSG00000163870.15   ENST00000355552.7  ENST00000648957.1\n",
       "ENSG00000144713.12   ENST00000396953.6  ENST00000429711.6\n",
       "ENSG00000237027.1    ENST00000425588.1  ENST00000438267.1\n",
       "ENSG00000274333.4    ENST00000624444.1  ENST00000624627.3\n",
       "ENSG00000188558.6    ENST00000343414.6  ENST00000641804.1\n",
       "ENSG00000183935.5    ENST00000538670.1  ENST00000624664.1\n",
       "ENSG00000240654.6    ENST00000332018.4  ENST00000382071.6\n",
       "ENSG00000173209.23   ENST00000357022.6  ENST00000394457.7\n",
       "ENSG00000185662.9    ENST00000523047.3  ENST00000330910.7\n",
       "ENSG00000221888.4    ENST00000408896.4  ENST00000641256.1\n",
       "ENSG00000248115.1    ENST00000510351.1  ENST00000508813.1\n",
       "ENSG00000047579.19   ENST00000344537.9  ENST00000622898.4\n",
       "ENSG00000204648.11   ENST00000608568.1  ENST00000376909.6\n",
       "ENSG00000283554.1    ENST00000637043.1  ENST00000637462.1\n",
       "ENSG00000240356.6    ENST00000391616.3  ENST00000416673.6\n",
       "ENSG00000258940.2    ENST00000553520.1  ENST00000605298.1\n",
       "ENSG00000262468.6    ENST00000573042.2  ENST00000649264.1\n",
       "ENSG00000158716.8    ENST00000368107.1  ENST00000368109.5\n",
       "ENSG00000266952.2    ENST00000588074.1  ENST00000649058.1\n",
       "ENSG00000147274.14  ENST00000320676.11  ENST00000431446.7\n",
       "ENSG00000114779.19   ENST00000361143.9  ENST00000483233.5\n",
       "ENSG00000255319.5    ENST00000529093.1  ENST00000527856.5\n",
       "ENSG00000155749.12   ENST00000286190.9  ENST00000405148.6\n",
       "ENSG00000147789.15   ENST00000528372.5  ENST00000525266.5\n",
       "ENSG00000173715.16   ENST00000360962.9  ENST00000525908.6\n",
       "ENSG00000184698.5    ENST00000328611.5  ENST00000642046.1\n",
       "\n",
       "[2468 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canonicals = pd.concat((my_canonical, ucsc_canonical), axis=1)\n",
    "canonicals[canonicals.my_canonical != canonicals.ucsc_canonical]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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