mrna_distribution_cbioportal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check range mrna v2 continous in pancan\n",
    "\n",
    "Data file generated on cgds_gdac db pipelines with:\n",
    "```\n",
    "echo \"select * from genetic_alteration where genetic_profile_id in (select genetic_profile_id from genetic_profile where stable_id like '%pan_can%%v2%mrna%' and datatype='CONTINUOUS')\" | mysql -h localhost -u username -ppassword database_name  > genetic_alteration_pancan.tsv\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first_1k = pd.read_csv(\"1000_values.txt\",header=None)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_values = pd.read_csv(\"all_values.txt\",header=None)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "780000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(first_1k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206143299"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.99121057347755-7092440.0 (mean: 1004.0774317541006, min >0 : 4.823772759010581e-07)\n"
     ]
    }
   ],
   "source": [
    "def print_stats(x):\n",
    "    print(\"{} - {} (mean: {}, min >0 : {})\".format(\n",
    "        x.min(),\n",
    "        x.max(),\n",
    "        x.mean(),\n",
    "        x[x>0].min()\n",
    "    ))\n",
    "print_stats(all_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist(all_values[~pd.isnull(all_values)], log=True)\n",
    "plt.title('All values mrna v2 TCGA pancan values')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of values below 0: 1190144 (0.58%) of 206143299 total\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of values below 0: {} ({:.2f}%) of {} total\".format(\n",
    "    len(all_values[all_values < 0]),\n",
    "    len(all_values[all_values < 0]) * 100.0 / len(all_values),\n",
    "    len(all_values)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution values < 1 (17.17% of total)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c481b5048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_values[(all_values < 1)], log=True)\n",
    "plt.title(\"Distribution values < 1 ({:.2f}% of total)\".format(len(all_values[(all_values < 1)]) * 100.0 / len(all_values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of values that are 0: 25329688 (12.29%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of values that are 0: {} ({:.2f}%)\".format(\n",
    "      len(all_values[all_values == 0]),\n",
    "      len(all_values[all_values == 0]) * 100.0 / len(all_values)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of profiles:\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of profiles:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      33\r\n"
     ]
    }
   ],
   "source": [
    "!cut -f1 genetic_alteration_pancan.tsv | tail -n+2 | cut -f1 | sort -u | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of profiles with negative values:\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of profiles with negative values:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       8\r\n"
     ]
    }
   ],
   "source": [
    "!grep  '-' genetic_alteration_pancan.tsv | cut -f1 | sort -u | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Es3MbKzq/CemA4FNlelh4JiGNkuBGyW9G3gOcHxXqjMzs3Gp03NXk3nRML0YWB4RM4ETgSslbbduSUskrZG0ZmRkZOzVmplZLXXCfRiYVZmeyfbDLmcC1wBExPeA3YEZjSuKiGURMS8i5g0NDY2vYjMz66hOuN8KzJF0sKRdSQdMVza0+SlwHICkl5DC3V1zM7Me6RjuEbEVOBu4AVhHOitmraQLJZ2Sm50LnCXpDuBq4IyIaBy6MTOzKVLngCoRsQpY1TDvgsr1u4Df725pZmY2XrXC3WzQzD7vG70uwayn/PMDZmYFcribmRXIwzJmfazV8NLGi0+a4kps0LjnbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgVyuJuZFcjhbmZWIIe7mVmBHO5mZgXy77nbQPO/0zNrzj13M7MCOdzNzArkcDczK1CtcJe0QNLdktZLOq9FmzdIukvSWklXdbdMMzMbi44HVCVNAy4B/hAYBm6VtDIi7qq0mQOcD/x+RGyR9LzJKtjMzDqr03M/GlgfERsi4klgBbCwoc1ZwCURsQUgIh7obplmZjYWdcL9QGBTZXo4z6s6FDhU0i2SVkta0GxFkpZIWiNpzcjIyPgqNjOzjuqEu5rMi4bpnYE5wHxgMXC5pL23u1HEsoiYFxHzhoaGxlqrmZnVVCfch4FZlemZwOYmbb4WEU9FxL3A3aSwNzOzHqgT7rcCcyQdLGlXYBGwsqHNV4HXAEiaQRqm2dDNQs3MrL6O4R4RW4GzgRuAdcA1EbFW0oWSTsnNbgAeknQX8G3gfRHx0GQVbWZm7dX6bZmIWAWsaph3QeV6AO/JFzMz6zH/cJgNBP9AmNnY+OcHzMwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQT4U0G0CtTg3dePFJU1yJ9Sv33M3MCuRwNzMrkIdlrG/4W6hm3eOeu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYF8qmQNuV8yqPZ5HPP3cysQA53M7MCOdzNzArkcDczK5DD3cysQLXOlpG0APgEMA24PCIubtHudcC1wH+IiDVdq9IGks+KMeudjj13SdOAS4ATgLnAYklzm7TbA/gz4F+6XaSZmY1NnWGZo4H1EbEhIp4EVgALm7T7MPAx4FddrM/MzMahTrgfCGyqTA/nec+QdCQwKyKub7ciSUskrZG0ZmRkZMzFmplZPXXCXU3mxTMLpZ2AvwLO7bSiiFgWEfMiYt7Q0FD9Ks3MbEzqHFAdBmZVpmcCmyvTewCHATdJAtgfWCnpFB9U3TH4wKlZ/6nTc78VmCPpYEm7AouAlaMLI+LRiJgREbMjYjawGnCwm5n1UMdwj4itwNnADcA64JqIWCvpQkmnTHaBZmY2drXOc4+IVcCqhnkXtGg7f+JlWT/y8IvZ4PA3VM3MCuRwNzMrkP9Zh23DQy9mZXC4mxWk1ZvzxotPmuJKrNc8LGNmViD33HdQHn4xK5t77mZmBXLPvXDuoZvtmNxzNzMrkMPdzKxAHpYphIdfzKzKPXczswK55z5g3EM3szrcczczK5DD3cysQB6W6UMeejGziXLP3cysQO6595B76GY2WdxzNzMrkHvuU8A9dDObau65m5kVyD33FvwfbcxskDnczXYA7YYG3WEpU61wl7QA+AQwDbg8Ii5uWP4e4G3AVmAE+JOI+EmXa+17Hls3s37Rccxd0jTgEuAEYC6wWNLchmY/BOZFxOHAdcDHul2omZnVV+eA6tHA+ojYEBFPAiuAhdUGEfHtiHgiT64GZna3TDMzG4s6wzIHApsq08PAMW3anwl8s9kCSUuAJQAHHXRQzRL7i4dezGwQ1Om5q8m8aNpQejMwD/h4s+URsSwi5kXEvKGhofpVmpnZmNTpuQ8DsyrTM4HNjY0kHQ98AHh1RPy6O+WZmdl41Om53wrMkXSwpF2BRcDKagNJRwKfBU6JiAe6X6aZmY1Fx3CPiK3A2cANwDrgmohYK+lCSafkZh8HngtcK+l2SStbrM7MzKZArfPcI2IVsKph3gWV68d3uS4zM5sA/7aMmVmBHO5mZgVyuJuZFWiH/+EwfynJdnT+BdQyueduZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBdrhz3M3s+Z8/vtgc8/dzKxADnczswI53M3MCuRwNzMrkMPdzKxAPlvGzMbEZ9EMBvfczcwK5HA3MyuQh2XMrCs8XNNfdphw939cMrMdyQ4T7mbWG+06Vu7VT55aY+6SFki6W9J6Sec1Wb6bpC/l5f8iaXa3CzUzs/o69twlTQMuAf4QGAZulbQyIu6qNDsT2BIRL5K0CPgo8MbJKBg8xGJWCo/TT546wzJHA+sjYgOApBXAQqAa7guBpfn6dcCnJSkioou1mtkOYqwdOL8ZbK9OuB8IbKpMDwPHtGoTEVslPQpMBx6sNpK0BFiSJx+XdDewF/BopVl1evR6498ZjeuuqXFbdZbVqa9T3eOpt12trZa3q7VTjVNda936OtXtfet9+6g+2rNaW9XXqe6J1PuCWq0jou0FeD1weWX6dOBTDW3WAjMr0z8Gpndad267rNX06PUmf9fUWXenbdVZVqe+GnWPud52tbZa3q7Wydy346nV+9b7dhD3bad5vdy3jZc6B1SHgVmV6ZnA5lZtJO1Meod5uMa6Ab7eZvrrLf6OV7vbt1pWp75W1ydSb6fbNlvertbG6W7u2/HU2my+9229Wuos977trBu1Ns7r5b7dhvI7QusGKazvAY4DfgbcCrwpItZW2rwLeGlEvCMfUD01It4w1sprFy2tiYh5k7X+bhukegepVhisegepVhisegepVpiaejuOuUcaQz8buAGYBlwREWslXUj6aLES+DxwpaT1pB77osksGlg2yevvtkGqd5BqhcGqd5BqhcGqd5BqhSmot2PP3czMBo9/OMzMrEAOdzOzAjnczcwKVFy4SzpE0uclXdfrWpqR9BxJfyPpc5JO63U9nfT7/mwk6b/kffs1Sa/tdT3tSHqJpMskXSfpnb2up5P83L1N0sm9rqUTSfMlfSfv3/m9rqcdSTtJ+oikT0n6426tt6/CXdIVkh6QdGfD/LY/XFYVERsi4szJrXRbY6z7VOC6iDgLOGUq66zUVbveXuzPRmOs96t5357BJP6+UZdqXRcR7wDeAEz5aXzjeL29H7hmaqvcpq6x1BvA48DupO/h9HOtC0nf8n+qq7WO9VtSk3kBXgW8HLizMm8a6RuvhwC7AncAc4GXAtc3XJ5Xud11fVr3+cARuc1V/b6fe7E/u1TvXwIv7/daSW/w3yV9d6RvawWOJ53ifAZwcr8/D4Cd8vL9gC/2ea3nAW/Pbbr2OuurnntE3Mz232x95ofLIuJJYAWwMCL+NSJObrg8MOVFM7a6Se/MM3Obnuz/Mdbbc2OpV8lHgW9GxA/6udbcfmVEvBKY8iG6Mdb6GuAVwJuAsyRN+XN3jPnwm7x8C7DbFJYJjCsTtuQ2T3erhr4K9xaa/XDZga0aS5ou6TLgSEnnT3ZxbbSq+8vAH0n6DBP/OYVualpvH+3PRq3277tJvczXSXpHLwprotW+nS/pk5I+C6zqTWnbaVprRHwgIs4BrgI+VwnPXmu1b0/N+/VK4NM9qWx77TLhP0n6FHBztzY2CP+JSU3mtfzmVUQ8BPTDi7pp3RHxS+CtU11MDa3q7Zf92ahVvZ8EPjnVxXTQqtabgJumtpSO2r7eImL51JVSS6t9+2VSaPaTVrU+QfqfGF01CD33Oj9c1o8GrW7XO3lc6+QZpHqntNZBCPdbgTmSDpa0K+mgzsoe11THoNXteiePa508g1Tv1NY61UeROxxhvhq4j9+eEnRmnn8i6Zcpfwx8oNd1Dnrdrte1Dlqtg1ZvP9TqHw4zMyvQIAzLmJnZGDnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAv1/8vjX3UQb0Q0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be16400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_values[~pd.isnull(all_values)], bins=np.logspace(np.log10(0.1),np.log10(1000000), 50))\n",
    "plt.gca().set_xscale(\"log\")\n",
    "plt.title(\"mrna v2 continuous data in pancan studies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}