GeneAndRepeat_metaanalysis.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import os\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt \n",
      "import pandas as pd\n",
      "import re\n",
      "import cmath\n",
      "import math\n",
      "import glob\n",
      "import subprocess\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "matplotlib.rcParams['savefig.dpi'] = 2 * matplotlib.rcParams['savefig.dpi']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Gene metaanalysis - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getGeneList(name,geneType):\n",
      "    geneListpath=os.getcwd() + '/results/'+name+'/PlotData_ReadsPerGene_%s'%geneType\n",
      "    \n",
      "    path=os.getcwd() + '/results/'+name+'/PlotData_ReadsPerPipeFile'\n",
      "    data=pd.read_csv(path)\n",
      "    data.columns=['Index','FileName','Reads']\n",
      "    data['FileName']=pd.Series(['Raw (R1)','Raw (R2)','3p Trim (R1)','3p Trim (R2)','Filter (R1)','Filter (R2)','No dupes (R1)','No dupes (R2)','RepeatMapped (R1)','RepeatMaped (R2)','Hg19Mapped (R1)','Hg19Mapped (R2)','Blacklist (R1)','Blacklist (R2)','RepeatMask (R1)','RepeatMask (R2)','ClipperIn','ClipperOut'])\n",
      "    data=data.set_index('FileName')\n",
      "    valueForNorm=data.loc['ClipperOut','Reads']\n",
      "    \n",
      "    geneListDF=pd.DataFrame(pd.read_table(geneListpath,header=None,delimiter=','))\n",
      "    geneListDF.columns=['GeneName','Count']\n",
      "    geneListDF.index=geneListDF['GeneName']\n",
      "    geneListDF['Normalized']=geneListDF['Count']/valueForNorm\n",
      "    return (geneListDF,readCounts)\n",
      " \n",
      "def mergeGeneLists(df1,df2):\n",
      "    allGenes=list(df1['GeneName'])+list(df2['GeneName'])\n",
      "    alUniqGenes=list(set(allGenes))\n",
      "    mergedDF=pd.DataFrame(index=alUniqGenes)\n",
      "    mergedDF['Counts_1']=df1['Count']\n",
      "    mergedDF['Counts_1_Norm']=df1['Normalized']\n",
      "    mergedDF['Counts_2']=df2['Count']\n",
      "    mergedDF['Counts_2_Norm']=df2['Normalized']\n",
      "    mergedDF.fillna(0,inplace=True)\n",
      "    mergedDF['Sum']=mergedDF['Counts_1']+mergedDF['Counts_2']\n",
      "    mergedDF.sort(['Sum'],ascending=True,inplace=True)\n",
      "    return mergedDF\n",
      "\n",
      "def makeScatter(target,toCompare,geneType,colors,axisLimit):\n",
      "    target_geneList,target_pipeStats=getGeneList(target,geneType)\n",
      "    for i in range(0,len(toCompare)):   \n",
      "        if i == len(toCompare)-1:\n",
      "            alpha=1\n",
      "        else:\n",
      "            alpha=0.25\n",
      "        background=toCompare[i]\n",
      "        background_geneList,background_pipeStats=getGeneList(background,geneType)\n",
      "        DF1=mergeGeneLists(target_geneList,background_geneList)\n",
      "        plt.scatter(np.array(DF1['Counts_1_Norm']),np.array(DF1['Counts_2_Norm']),label=toCompare[i],color=colors[i],alpha=0.25)\n",
      "    plt.xlim(-0.1*plt.xlim()[1],plt.xlim()[1])\n",
      "    plt.ylim(-0.1*plt.xlim()[1],plt.xlim()[1])\n",
      "    plt.title('Tag count per %s gene across datasets.'%geneType,fontsize=5)\n",
      "    plt.xlabel(\"Normalized tag count per gene for %s\"%target,fontsize=5)\n",
      "    plt.ylabel(\"Normalized tag count per gene for other datasets.\",fontsize=5)\n",
      "    plt.tick_params(axis='xticks',labelsize=5,fontsize=5) \n",
      "    plt.tick_params(axis='yticks',labelsize=5,fontsize=5)\n",
      "    plt.yticks(fontsize=5)\n",
      "    plt.xticks(rotation=90,fontsize=5)\n",
      "    plt.legend(loc='upper left', scatterpoints = 1,prop={'size':5})\n",
      "    plt.grid()\n",
      "    if axisLimit: # Plot limit\n",
      "        plt.xlim(-0.1*axisLimit,axisLimit)\n",
      "        plt.ylim(-0.1*axisLimit,axisLimit)    \n",
      "    \n",
      "    # Label DF for output.\n",
      "    DF1.columns=['Counts_%s'%target,'Norm_Counts_%s'%target,'Counts_%s'%background,'Norm_Counts_%s'%background,'Sum']\n",
      "    return DF1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.subplot(2,2,1)\n",
      "# *** INPUT : Dataset (x-axis) against which others will be compared.\n",
      "target='ddx21_dev' \n",
      "# *** INPUT : Datasets (y-axis) for comparison.\n",
      "toCompare=['ddx21_sat','ddx21_flavo','ddx21_wt'] \n",
      "# *** INPUT : Colors. Must match length of toCompare.\n",
      "colors=['black','red','blue'] \n",
      "# *** INPUT : Specify the gene type to compare.\n",
      "geneType='proteinCoding'\n",
      "# Specify the axis limit (in case one wants to zoom in).\n",
      "axisLimit=None \n",
      "DF1=makeScatter(target,toCompare,geneType,colors,axisLimit) \n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sXbrUYsJ1GUdZn4yVM5NcWJJ8aOxmma3+/ftz8OBBq4OwN1ZNOKDXw+HDUFAA\nJSXarDzOztpwkfbtISxMK57iusl6lPXJmoNmkgtLkg+N3RTKadOmMWPGDIYNG2Z1IPbE6pl5lNJ6\nthYWmmfmad9ea2HewPU/oZFxlEKIG2U34yhTU1MZMWIE7du3JzAwkMDAQHr06NHk423atInQ0FCC\ng4OZN29eve16vZ7ExERCQ0MZPHgwWVlZpjiioqIIDw+nb9++fPXVV6bn7N+/n4iICEJCQnjuueea\nHNsN0em0ohgSAv36Qd++2tCQFlokIyIi6t1krKMQ4nZhVYvSlvR6PT179mTHjh106tSJ6Oholi9f\nbrFo8Pz58zl58iQLFy5k9erVrFq1irVr13L06FH8/Pxo3749GRkZDBw4kIKCAlxcXAgLC+PDDz9k\nwIABjB07lqeeeorx48dfMxZZj9K+SO6FEDfKblqUer2et956i9///vcAnDx50qI1dyN2795Nr169\n6NatG87OzkyYMIH169db7LNhwwYmT54MwMSJE0lNTUUpRd++fWnfvj0AQUFBODk5UVpaSnZ2NuXl\n5abOKJMmTap3TCGEEOJarCqUU6dOpba2lo0bNwJaj8m6now3KicnB39/f9P9Ll26kJOT0+g+zs7O\neHt7U1hYaLHPZ599RnBwML6+vvWO6e/vX++YQgghxLVYVSjT09OZNWsWLi4uALi5ueFYN7/pDbqR\nQe6NOX78OC+//LLNJubW6XSmW1OUlWkrbv08MY2w0pU9+ZKTk9HpdI327rvVt8fFxdl1fDdze0P7\n2FN8N3t7XU7sNT5bbq/7/6Chm00pK/Tr10+VlZWp/v37K6WUOnHihAoPD2/Ssb7//ns1cuRI0/3k\n5GQ1d+5ci33uvPNOtXXrVqWUUtXV1crT01MZDAallFLnzp1TwcHBaseOHab9s7KyVGBgoOn+8uXL\n1RNPPPGLsQDq6tQ09Fhj8vOV2rNHqU2blPr6a6U2blRq926l8vKu6+niKnW5t/LP9ZYiuTCTXFiS\nfGhs+Z1hVYtyzpw5DBs2jOzsbCZMmMCwYcN44403mnSsmJgYTpw4QVZWFtXV1XzxxReMHTvWYp/4\n+HhWrlwJQEpKCnFxcTg4OFBaWkp8fDyvvvoqgwcPNu3frVs3WrVqxa5du1BKsWbNGuLj45v+hq9D\nVpa2FOWPP8JPP2nDKU+d0u4fPKj921aSm3k9yl/SUM9la8g6e2aSCzPJhSXJh+1Z3eu1oKCAbdu2\nAdpUZB3gvEaSAAAgAElEQVQ6dGjysTZu3MisWbMwGAxMnTqVl19+maSkJKKjo0lISECv1zNlyhSO\nHTuGl5cXK1euJCAggNdee43XX3+d3r17WxyrY8eO7N+/nxkzZqDX6xk1ahTvvvvuL8bR1F6vpaWw\ne7e2QEiHDtpsdjqdNqzywgVtTedevSA2Ftq2bWqWzJp7PcpfEhgYyJkzZ6w6BkivVyHEjbObCQcA\nsrKyyM7Oxmg0mgIbPny41YE1p6YWyvR0rTWpFFzRh8gkP1+bfyAiAn6e5vSGzZ49mzVr1tChQwe6\ndOnCmDFjaNOmDS+88ALe3t4MGjSI0tJSli1bxv3338/EiROZMmUKixcvZtu2bfzzn/+0ON66detY\ntGiRRW/g5cuXs3Xr1msWys8++4xdu3Yxf/583nnnHd59911OnTrF6dOnmTp1KlOmTOHZZ58lJCQE\nf39//vWvfzXtDSOFUghx42z5vWHV6iHPP/88//73v+nfv79FJ56WXiib6sIFbYrXxuZcaNdOa21e\nuNC049vTepTDhw/nr3/9KwDbtm3D19eXvLw8tm3bxogRI3j66ad54403SEtLa9qbFUIIO2FVoVy7\ndi3Hjx/Hycnq1bpuCUqB0dj4fOdOPy9H2dQfOPa0HmWHDh0oKyujrKyMnJwcJk+ezPfff8/27dub\nVHiFEMJeWdWZp0ePHhiauAjxrcjdHTw8tGuVDbl8GVq1avqc6L/GepRXLst15TGvx+DBg1m2bBlB\nQUEMHTqU77//np07dzJkyJDrfUtCCGH3mlQo58yZw5w5c+jYsSMxMTG8/PLLpsdeeeUVW8fYYnTu\nrE3pWnct8kq1tVpnHj8/bb+msLf1KIcNG8Zf//pXRowYQUREBKmpqbi5ueHp6Qloi3qXl5c37c02\noLExVbcjyYWZ5MKS5MP2mtSZZ/ny5RYXSq9ugVzZC7MlampnHqNR68xz6pS2cEjbtublKIuLtSJ5\nxx0QGQlNnJfBrtajPHXqFL179yYjI4OePXsyZswYgoODefvttwFtbt4lS5bQq1cvm3TmAenQU0fW\nHDSTXFiSfGjsptfr4sWLefrpp3/xsZbGmknRa2rg2DGtVXn1cpQdOmgLifw8kZG4TrIeZX2NzUhz\nO5JcWJJ8aOymUEZERNTr1divXz9+/PFHqwNrTrZYPaS0tP5ylN7eto/1diDDQ4QQN6rZh4ekpKSw\nfv16srOzLWZwKSsrw1uqAQBeXtrtVnDlUmd1PvroIyIjI5shGiGEuLmaVCijoqLw9fXl1KlTTJ8+\n3fS4u7s7/fv3t1Vswk7IWEghxO3M6pl5cnNz2blzJzqdjkGDBtG5qV067Ygs3GxfJPdCiBtlN9co\n//GPf/D888+bZuLZtm0b8+fPZ8qUKVYH1pyuVShF85FCKYS4XrYslFZNOPCXv/yFo0eP8vnnn/P5\n559z9OhRXn/9dauDske/9qoj4tri4+OlJ98VJBdmkgtLkg/bs6pFGRwczLFjxywqd0hICOnp6TYL\nsDnYy6k+GQ9lSfJhJrkwk1xYknxomr3Xa51Ro0YxYsQIHnroIZRSfPnll4waNcrqoIRG1pWzJPkw\nk1yYSS4sST5sz6oWpdFoZPXq1ezYsQPQpjRLTExs8dfz7KVFKYQQomnspjPPrUoKpRBCtGx205lH\nCCGEuNVJoRRCCCGuocmF0mAwyAK9QgghbnlNLpSOjo4UFhai1+ttGY+4goyHsiT5MJNcmEkuLEk+\nbM+qzjwPPPAA6enpjBs3jlatWmkH1OmYPXu2zQJsDvbSmUfGQ1mSfJhJLswkF5YkHxq7GUd5zz33\ncM8991xzEWfRdDIeypLkw0xyYSa5sCT5sD2rh4dUVVVx5MgRdDodoaGhuLq62iq2ZmMvLUohhBBN\nYzctym+++YYpU6bQtWtXAM6ePcs///lPRo8ebXVgQgghmkgpKC8HoxHc3cHZubkjatGsalGGhoby\n6aefEhISAkB6ejoPPvggR44csVmAzUFalEKIFslohOxsyMuD0lIwGLRC2aEDBATAz31Jbgd206I0\nGo2mIgnaJOlSXIQQohkYjXD4MGRmwrlz2n1HR6iu1grn+fPQvz94ezd3pC2OVYWyX79+PProo0ya\nNAmlFJ9//jnh4eG2ik0IIcT1ys7WimRuLnTtCp6e2uM1NdpjJ09qp2AHDgQHmWvmRliVrWXLlhES\nEsLChQtZtGgRffr0YdmyZbaK7bYn46EsST7MJBdmkgu01mNeHuTlkbx9u7lIglYcu3fXCmZxMRQV\nNV+cLVSTrlEmJCSwbt06kpOTb8k/Unu5RinjoSxJPswkF2aSC7TrkTt2QGYmut/+FpWaWn+foiKo\nqoKYGOjT5+bHeJM1+zXKs2fPkpGRwWeffcbjjz9eb3u3bt2sDkzIeKirST7MJBdmkgu0FqXRCE5O\nJE2b1vA+Tk5a5x6j8ebGdgtoUoty3bp1/N///R/bt28nOjq63vbUhn7NtCD20qIUQojrotfDtm2Q\nng7BwVpRvFp2Nri5aS3KwMCbH+NN1uzLbCUkJLBx40b+3//7f6Smpta7WWPTpk2EhoYSHBzMvHnz\n6m3X6/UkJiYSGhrK4MGDycrKAqCkpISRI0fi6enJY489ZvGcgIAAIiIiiIiIYNCgQVbFJ4QQdsfV\nFdq3hzZttGuVVysr007PtmsHHTve/PhaOLtauFmv19OzZ0927NhBp06diI6OZvny5URERJj2mT9/\nPidPnmThwoWsXr2aVatWsXbtWioqKkhLS+PIkSPs2rXLolNRYGAgZ86cue44pEUphGhxLl+Gffu0\n3q1GI/j4aC3L0lLt1r07BAVB797NHelN0ewtyl/L7t276dWrF926dcPZ2ZkJEyawfv16i302bNjA\n5MmTAZg4cSKpqakopfDw8GDIkCG3xBR6Qghxwzw9tXGSwcHQqZM2M8+FC1prs08frUj26tXcUbZI\ndlUoc3Jy8Pf3N93v0qULOTk5je7j7OyMt7c3hYWF1zyuTqdjwIAB9OvXj4ULF153PDqdrtFbXFwc\nOp2u0V6/ycnJsl22y3bZfnO3t20LgwaR/P336KZPJ3nvXu2a5ODBWkvy51aW3cZ/g9vrvocbutmU\nstIXX3yh3n77baWUUllZWeqHH35o8rFWrlypHn30UdP9Dz/8UD399NMW+/Tu3VudOnXKdL9Lly4q\nPz/fdH/ZsmVq+vTpFs8pLCxUSil17tw51adPH/Wf//znmnEAygapsVpSUlJzh2BXJB9mkgszyYUl\nyYfGlt/jVrUoZ86cyebNm1m0aBEA3t7e/Pa3v23y8bp06UJubq7pfk5OjmnC9Sv3qWtl1tTUcOnS\nJfz8/EzbG/olUbe9Y8eOJCQksGfPnibHeDPNmTOnuUOwK5IPM8mFmeTCkuTD9qwqlFu3buX999/H\nzc0N0AqlsuLCaUxMDCdOnCArK4vq6mq++OILxo4da7FPfHw8K1euBCAlJYW4uDgcrpiO6erXr66u\npry8HICysjI2b95sMT+tPZPxYZYkH2aSCzPJhSXJh+1ZvXrIwYMHiYmJIS0tjaKiIuLi4jh69GiT\nA9q4cSOzZs3CYDAwdepUXn75ZZKSkoiOjiYhIQG9Xs+UKVM4duwYXl5erFy5koCAAEBrbVZWVqLX\n62nbti0pKSn06NGDu+++G4PBwOXLl3n44YeZO3fuNWOQXq9CCNGy2fJ73KpCuWTJEtasWcOxY8d4\n+OGH+fLLL0lOTmZaYzNDtBBSKIUQomWzm0IJ8OOPP/Lvf/8bgLvuuot+/fpZHVRzk0IphBAtm10V\nyurqagoKCjAajabAWvpcr1IohRCiZWv2SdHrvPXWWyxYsIDAwEAcHR1Nj7f0uV6FEEKIOlb1en33\n3XdJT0/n+++/t9lcr8KsscG2tyvJh5nkwkxyYUnyYXtWnXodOnQo27Zts/0sCM3MXk69yjp7liQf\nZpILM8mFJcmHxm5OvYaEhBAfH8/48eNNc6zqdDqmTp1qdWBCxkNdTfJhJrkwk1xYknzYnlUtyrom\n/tUtypb+QdlLi1IIIUTT2FWvV4PBQGFhIZ06dbI6GHshhVIIIVo2u1lma8OGDfTv39+0GPKhQ4cY\nM2aM1UEJIYQQ9sKqQvmHP/yBHTt20LZtWwDCw8PJa2h1bSGEEKKFsqpQGo1GvLy8LB6rra21KiAh\nhBDCnlhVKIODg1m6dCnV1dUcPnyY3/72twwYMMBWsd32ZDyUJcmHmeTCTHJhSfJhe1Z15ikrK2P2\n7Nl8++23AIwZM4ZXX30VDw8PmwXYHOylM4+Mh7Ik+TCTXJhJLixJPjR2M46ydevWLFiwwOogRMNa\n+jAbW5N8mEkuzCQXliQftmdVi3LkyJEWv150Oh2urq5ERUUxa9YsvL29bRbozWQvLUohhBBNYzct\nytjYWHQ6HZMnTwZg1apVVFZW4uHhwSOPPMK//vUvqwMUQgghmpNVLcrY2Fj27Nlj8VhMTAx79+4l\nKCiIjIwMqwNsDtKiFEKIls1uJhwoLy/n+PHjpvsZGRmUl5cDtPgOPUIIIQRYeep1yZIlTJgwAXd3\ndwAqKipYunQplZWVPPvsszYJUAghhGhOVrUohwwZwrFjx9i4cSMbN24kPT2doUOH4u7uzmOPPWar\nGG9bMh7KkuTDTHJhJrmwJPmwPasnRb8V2cs1ShkPZUnyYSa5MJNcWJJ8aOzmGqX4dcl4KEuSDzPJ\nhZnkwpLkw/aa3KI0Go189913jBo1ytYxNTt7aVEKIYRoGrtZjzI6Opp9+/ZZHYS9kUIphBAtm92c\neh05ciQff/wxlZWVVgcihBBC2COrWpQBAQGmqm06oE7H6dOnrQ6sOUmLUgghWja7OfV6q5JCKYQQ\nLZvdnHotLi7mueeeIzExEYDjx4+zcOFCq4MSGhkPZUnyYSa5MJNcWJJ82J5VLcq7776bJ554gldf\nfZVDhw5RW1tL//79OXLkiC1jvOnspUUp46EsST7MJBdmkgtLkg+N3bQo8/LyePDBB3F0dATAycnJ\n9G9hPRkPZUnyYSa5MJNcWJJ82J5VhdLNzY2CggLT/R9++AE3N7cmH2/Tpk2EhoYSHBzMvHnz6m3X\n6/UkJiYSGhrK4MGDycrKAqCkpISRI0fi6elZb+q8/fv3ExERQUhICM8991yTY2sOcgrFkuTDTHJh\nJrmwJPmwPasK5dtvv81vfvMbTp48SWRkJA899BDvv/9+k46l1+t58skn2bBhA4cOHWLlypWkpaVZ\n7PP+++/Trl07jhw5wrPPPmuaeN3NzY25c+fyt7/9rd5xp0+fzqJFizh27BgnTpzgyy+/bFJ8Qggh\nbk9WFcrBgweTlpbGli1bWLJkCadPnyYmJqZJx9q9eze9evWiW7duODs7M2HCBNavX2+xz4YNG0yL\nRE+cOJHU1FSUUnh4eDBkyBBcXV0t9s/Ozqa8vJwBAwYAMGnSpHrHFEIIIa7FqmW2qqqqePvtt9m+\nfTsAw4cP59lnn23S6decnBz8/f1N97t06cLevXsb3cfZ2Rlvb28KCwvp0KHDdR3T39+fnJycG45N\nCCHE7cuqFuWkSZMoKSnh9ddf57XXXqO4uJiHH364Sce6euICe6DT6Rq9xcXFodPpGr0ekJycLNtl\nu2yX7bL9V9xe9z3c0M2mlBVCQ0Ov67Hr8f3336uRI0ea7icnJ6u5c+da7HPnnXeqrVu3KqWUqq6u\nVp6enspgMJi2L1++XE2fPt10PysrSwUGBlpsf+KJJ34xFkBZmRqbSEpKau4Q7Irkw0xyYSa5sCT5\n0Njye9yqFuUdd9xBenq66f7x48cJCgpq0rFiYmI4ceIEWVlZVFdX88UXXzB27FiLfeLj41m5ciUA\nKSkpxMXF4eBgfgvqqvEy3bp1o1WrVuzatQulFGvWrCE+Pr5J8TWHOXPmNHcIdkXyYSa5MJNcWKrL\nh8EANTUgQyqtZ9U1yry8PMLDwwkPDwfg0KFDREVFMXLkSHQ6Hd999911H8vNzY2lS5cybtw4DAYD\nU6dOJTIykqSkJKKjo0lISGDmzJlMmTKF0NBQvLy8TEUTtGualZWV6PV6/vOf/5CSksKAAQNYvnw5\nM2bMQK/XM2rUKCZMmGDNW76pZDyUJcmHmeTCTHJh6fe/T+LAAbh4EYxGcHeHjh2ha1dwcWnu6Fom\nq2bmyczMvOb2gICAph66WdnLzDxCCHEjjh+H06chPx/KykCnAycn8PWFLl2gXz/w8GjuKG8OmRT9\nVyaFUgjR0uTkwI8/wpkzWguyXTtwcICKCsjNBTc3CAqCmBitgN7q7GYKOyGEEM1PKa1Q5uZqLUdf\nX61IgtaC7NEDLl+G8+ehpKR5Y22JpFAKIUQLV16uXZOsrYU2bepvd3QEHx+tSJ4/f/Pja+msKpQN\njXFpbNyLEEKIX0dtrXa7VmcdFxfzfuLGWFUo165dW++xlJQUaw4priA/OixJPswkF2aSC3B11W5V\nVbBsWXKD+1RUaNcpr5rpU1yHJnXmWbp0KStXrmTfvn1ER0ebHi8rK6Nbt258/vnnNg3yZrOXzjyy\nrpwlyYeZ5MJMcqFJS9M68zz2mI7UVMt86PVw4gT06QODBkHr1s0U5E1ky+/xJo2jfPDBB7nrrruY\nPXs2r776qikQd3f3RuddFTdOxodZknyYSS7MJBea7t2huBjGjUsiM1O7JunkBKWlUFQE/v7QqdPt\nUSRtzerhIdXV1RQWFmI0Gk2PdevWzerAmpO9tCiFEOJG5OfDsWNawbxwQesN6+Gh9YL194e+fbWO\nPbcDuxlH+dZbb7FgwQICAwNxvCL7qampVgfWnKRQCiFaqqoqyMvTCqXRqBXKTp20cZW3E7splIGB\ngRw+fJjWt1hbXgqlEEK0bHYz4YC/vz+tWrWyOgghhBDCXlk1KXpISAjx8fGMHz8e15/7HOt0OqZO\nnWqT4IQQQojmZlWLsnPnzgwYMIBz586RmZlJZmYmZ86csVVstz0ZH2ZJ8mEmuTCTXFiSfNieTIre\nAHu5RinjwyxJPswkF2aSC0uSD02zj6OsM3LkyHqP3eg6lKJxMj7MkuTDTHJhJrmwJPmwPZutR1lV\nVcVnn31GeXk5b7zxhi1iazb20qIUQgjRNHYzPKQhMTEx7N2715aHvOmkUAohRMtmN6der6SUYu/e\nvVy8eNFWhxRCCCGanVWFMiAgwFS1HRwc6NatGx9++KFNAhNCCCHsgfR6bYCcehVCiJbNbmbm0ev1\nzJs3j3vuuYeEhAT++te/Ul1dbXVQQiPjoSxJPswkF2aSC0uSD9uzqkX5yCOPoJTi4YcfRinFmjVr\nAPjkk09sFmBzsJcWpYyHsiT5MJNcmEkuLEk+NHbTmSctLY1jx46Z7t97772EhIRYHZTQyHgoS5IP\nM8mFmeTCkuTD9qxqUYaGhpKSkkKfPn0A+Omnnxg/fjxHjhyxWYDNwV5alEIIIZrGblqUb775JsOG\nDTMt1Hz27FmWL19udVBCCCGEvbC612tFRYWpBRkWFoa7u7tNAmtO0qIUQoiWzW56vc6bN4+amhpi\nY2OJjY2lurqaN9980+qghBBCCHthVYuyf//+HDx40OKxiIgI0tLSrA6sOUmLUgghWja7aVFWVVVh\nNBpN9w0GA+Xl5VYHJTQyHsqS5MNMcmEmubAk+bA9q1qUM2fOJCsrixkzZqCU4qOPPiIgIID33nvP\nljHedPbSopTxUJYkH2aSCzPJhSXJh8Zuer2+/fbbvPfeeyxduhSdTsddd93F7373O6uDEhoZD2VJ\n8mEmuTCTXFiSfNie3c31umnTJn7/+99jMBiYPn06L774osV2vV7P1KlTOXr0KF5eXqxatYru3bsD\n8Je//IV//OMfODo6smDBAkaPHg2Ai4sLffv2BaBHjx58/vnn14zBXlqUQgghmsZuWpS2ptfrefLJ\nJ9mxYwedOnUiOjqa0aNHExERYdrn/fffp127dhw5coTVq1fz7LPPsnbtWvbv38/q1as5fPgwubm5\nDB06lNOnT+Ps7Iy/v3+L72AkhBCieVjVmcfWdu/eTa9evejWrRvOzs5MmDCB9evXW+yzYcMGJk+e\nDMDEiRNJTU3FaDSyfv16HnjgAZycnOjevTs9e/Zkz549zfE2hBBC3ELsqlDm5OTg7+9vut+lSxdy\ncnIa3cfZ2Rlvb28KCwvJzc2lc+fODT63oKCAqKgooqOjf/G065V0Ol2jt7i4OHQ6XaM9zJKTk2W7\nbJftsl22/4rb676HG7rZklXXKEeOHFmvh5WbmxtRUVHMmjULb2/vGzreqlWr2LBhAx9//DEAH330\nEXv37mXRokWmfYKCgti4cSM9evQAoGvXruzdu5ekpCRiY2N54oknAJgyZQr33HMPiYmJFBUV4efn\nx08//cTw4cPZunUrvXv3bjQOuUYphBAtm92Mo6ybkeedd97hnXfeYeDAgQQFBeHh4cEjjzxyw8fr\n0qULubm5pvs5OTl07dq13j51LcWamhouXbqEn59fvefm5ubSpUsXAPz8/ADo1asXw4YNY//+/Tcc\nW3No7NfU7UryYSa5MJNcWJJ8/AqUFWJiYuo9Fh0drZRSqnfv3jd8vMrKSuXv768yMzOVXq9X4eHh\nav/+/Rb7/O1vf1NPP/20UkqplStXqoSEBKWUUvv27VPh4eGqurpanTlzRvn7+6vq6mp1+fJlpdfr\nlVJK5efnq65du6qDBw9eMw5AWZkam7CHGOyJ5MNMcmEmubAk+dDY8nvcql6v5eXlHD9+3LTMVkZG\nhmlmHg8Pjxs+npubG0uXLmXcuHEYDAamTp1KZGQkSUlJREdHk5CQwMyZM5kyZQqhoaF4eXmxcuVK\nAKKiokhMTCQ8PBxHR0c++ugjnJ2dSU9PZ9q0aRiNRiorK5k1axb9+vWz5m3fNLf7eKiamhqqq6tx\ncnLC1dX1ts/HlSQXZpILS5IP27PqGuWOHTt48sknTSuGVFRUsHTpUqKioli9ejWPPfaYzQK9meQa\nZfO6ePEi2dnZFBUVUVNTg5OTE+3ataNr166m0+hCCHEttvwet8mEA0VFRQC3zJeYFMrmU1BQwJEj\nR8jNzeXSpUs4OztTU1ND69at6dy5M3369DFNMCGEEI2xmwkHjEYjX375JRkZGdTU1JgCmz17ttWB\niVuP0WikoKCAkpISDAYDbm5udOrUCU9PTwAqKys5evQoJ06cwMvLi5CQEBwdHTEYDJSUlHDy5El0\nOh3e3t60adOmmd+NEOJ2YVWhfPzxx/H29uarr75i1qxZfPzxxxaz6AhR59KlSxw+fJji4mIuXLiA\nwWDA3d0dX19fOnfuTHBwMHl5eRQVFeHh4WExntbR0RE/Pz8MBgMFBQXk5ORIoRRC3DRWFco9e/Zw\n7Ngxtm7dysyZM3nyyScZPny4rWITt4jy8nIOHjzIyZMnqa2txdfXF2dnZy5fvsyJEydMHcAqKiq4\nePGiRZG8ko+PD+np6RQXF9/M8IUQtzmrxlG2atUKACcnJ4qLi3F0dOT8+fM2CUzcOuOhsrKyyM3N\nxdHRkd69e+Pj44OXlxf+/v707NmT3NxccnNzKS0txWAw4OLi0uBxPvnkE4xGo+l2O7tV/jZsQXJh\nSfJhe1YVyjvvvJNLly7xv//7v0RHRxMSEsJDDz1kq9hue3PmzGnuEKxWW1tLYWEhJSUldO7c+eeZ\nnMBg0La7ubnRrl07iouLKSsrw83NjbKysgaPtWLFClxdXXFzc8PBwa5mX7zpboW/DVuRXFiSfNie\nVade582bB8DkyZO599570ev1+Pj42CQwcWuMh9Lr9VRVVeHo6EhVlQc5Oa6UljphNOpwcTHSrl01\nbm5eXLhQQIcOHXBwcCAnJwcvLy+cnMx/nkajkYSEBPz8/OjYsWMzviP7cCv8bdiK5MKS5MP2rBoe\nEhkZyYEDB37xsZZGhofYTlVVFdu2bWPHjjw8PPpx4YIrZWVaAXRyMuLtXYuLSxE+PjkMHBhGTU0N\nJ0+epLi4GF9fXzw8PKiqquL8+fN4eHjQu3dvoqKiGj09K4QQYAfDQy5evMilS5fQ6/VkZ2ejlEKn\n03H58uVGT5sJ2ygpgYsXQSnw8ID27cHRsbmjapybmxtGow8XLtSQmwudOhkJCCjH0RGqqhwoLnYl\nKwscHbvi4+NH+/a+gA7w4cyZSiora3F1daZr1x7ccYc34eFhUiSFEDdVkwrlZ599xsqVK8nOzmba\ntGmmx93d3eX8+K/k8mVIT4fiYnOhbNUK2rWDHj2gkY6idkGpLuh0jhgMeTg7t8bBoTWgw8mpGkfH\nc9TUeKBUOxwdOwCOQCQ63WXgMmBAu5TeGvDCzlaGE0LcBqw69fr1119z77332jIeu2Bvp17Ly2H/\nfjh9GioqoG1bcHCA0lKorYXAQAgLs89iqdfDtm2QmnqOVq0yKSkppqamBkdHR2pra/H09MTZuSPt\n2wcybFhrqqrg1CntB4Gvr9Zq1uuhqEj7d69eEB0N0qgUQlxLs596rXMrFkl7dOoUZGVprcjgYKhb\nk7RDB62gnD6tFZEOHcDJqk/U9mprtVuXLh3x83OiqKgNZWVlGI1GnJ2dadeuLR4eHbl82Z3CQu1H\nQUkJBAWZ34unp9ZyPnUK8vIgN1f7cSCEEDeDnMeyY8nJyVRVQWGhdrq1a1dzkazj46O1roqLoaCg\neeK8FhcXcHaG2lodbdr4ERwcTHh4uOkWEBCI0eiOiwtcugTnz0PHjvULvoMDpKYmU1QE+fnN817s\niYyVM5NcWJJ82J5NJkW/1djLqVedTsf584odO7RTj3fc0fB+RUVQVQVRUVrLq7paK051RbS5HToE\nBw9qnY6uPj1cWwsZGVoL0dFRazX27Nlw3CNH6njrLUW/fjBihFY8b1faeFT5XxckF1eTfGia/dTr\nihUrLIKpC6QusKlTp1odmNDGQ+l0WivyWhPRGI1asTx4UDsFW1co27aFTp2063rNWVS6d9fi++kn\nyMDZf8UAACAASURBVMzUrj06O0NZmdYK9vXVWpG1tVqxrK5uuFBOmZKEgwOm2+1MxsqZSS4sST5s\nr0ktyuTkZHQ6HRkZGezevZv77rsPpRTr1q0jOjqa1atX/xqx3jT20qIEqKmBHTvgyBGt4Lm61t8n\nNVUrlh4e4OcH7u5aB5iKCq0F16MHhIfXP217NYPBQGFhoWmIj7e3N76+vjaZBaegAI4d0wrmxYva\nzDxublqR7NRJi+/sWdi3T2sdN7SSVn6+lo+ICAgNtTokIcQtzG7Woxw+fDgbNmygdevWAJSVlTF6\n9Gh++OEHqwNrTvZUKAGOH4cff9QKTGCgubWl12tDRk6e1B6LiwMvL/PzKiu1jj7du2s9Ra81oc25\nc+dMA/3LysrQ6XR4eXnh4+NDUFCQTWZcqqrSOuMUF5sLZadOWnF3cNDi3blTOxXr5WXunGQwaM8p\nKtJ+LMTGgiweIoS4lmY/9VqnoKAADw8P030PDw+ZFP1X0KOH1tElMxNOnNB6v5aUaIWjtFQ7Vdmz\np3n+1Dru7lohKijQeoo2Vijz8/M5fPgwp06dwsnJiTZt2mA0GsnLy+PcuXNUVlYSGRlJ27ZtrXof\nbm7ae+nRo+Ht7u7Qt6/2/nJztR8BTk7aKdnWrbX3GBQkRVIIcXNZVSgTExOJi4vjoYceQilFSkoK\nkyZNslVs4mcuLtrpRk9P7fTlsWNa71CjUTudWlWlDav46Sfo0gU6dzY/t00b7ZRmYxMmGY1GTp06\nxenTp/H19cXPz8+0rUOHDuTm5pKVlYWXlxcxMTG/8jvVWpGurtqMQ0VF2o8AJyetY1LXrtqpWiGE\nuJmsKpSvvPIKO3fuZPv27eh0OubNm8fAgQNtFdttSSmtCJaUwOXL5VRWltC2rR4vLxc6d+7A2bOu\nuLlpLa8uXSA7WztV2aqV1gozGg24uBho184RB4dfntvu/PnzFBcXo9PpLIpknc6dO3P06FGKi4sp\nLS3F68pzu7+SNm20W02NuWOSPfTeFULcnqzupZGfn4+Liwu///3v6dy5Mzt37rRFXLel0lLYvRu2\nb68mJSWT556bxeefZ/HJJ2dYu/Yo69YdID09Dz8/I507X6Ko6Aznzp2itDSPoqJ8KivzOHToLDt3\n/sTBgz9y6tQpcnLKaN1aa402pLy8nIqKCry9vRvcXnetsqKiwrTA8k1RVYVzXhat8n7CJS8Tyspk\nfNgVJBdmkgtLkg/bs6ozz8yZMwHYvHkz6enpXLp0ibi4ONLS0mwWYHNojs48FRVaj8+MjFpOnsym\nsjKX994bztNP76CqyhkXlzwuX67Gza0LnToZ8fJypLSkhNKSGrLyPDlX6Iqb2yVqanzp2lVPp06F\nuLh4U13tT1SUH3ff7dvgNcqsrCx27dpFbW0t/v7+GI1QU6P9fnJxMaLTwZkzZ2jTpg2DBw+mQ4cO\nv24ijEatiXzunNaDp+7ca7t26EaPRtU1MW9zMlbOTHJhSfKhsZvOPFu3buXw4cNEREQA2nAC+YCa\n5swZ7VrixYsX8PA4S23tJe66awZt25ZSUFDF2bNu1NToOH8+naIzBfT0dKGLpyetyssozKrBeN6N\ns8oP3L3x8nKhU6eeFBdX4+qaiV5/EXd3F7RJxS15e3vj7e1NRsZpjMYeXLzoRlWVVig9PAx4eZVz\n+XIF3bt3b7TVaVNHj2rdeHNztWZw3WSvGRkkPfCA1v03IsK+l0y5CWSsnJnkwpLkw/asKpRKKWpr\na033i4qKqKmpsTqo201NjdYztaRE4excyMWLF2nTpj1+fo+ye3ct5eVGlHKjqsoLfUkl5fnnoV0l\nF4HCkhJcLpfR3uCHUdedgvJaftJV4+npSlRUIK6uNbi7nyI31xtv75B6r92mTRs8PX05f76WjIxK\nnJza4ODgiFKgVC21tefx9+9Bu3Z+uLm5/bqJKC7+/+3de1RU9fr48feeAQFFUQm1BLySgICggHm/\nnDzej3kpU7NQf9XXUrvp8ueppXg6pa5j1ul6SjvZjTJvlQfzstT6ph0xL/1MrfCGCqh4FJDrDDPz\n+f2xDzOOwKgMMBjPa61p4d579n7m2bt55rP357O3/mshK0u/DZGfn2Ne69YkG42Qmal33w0Ort1Y\n6jk5veYguXAm+ah5bhXKOXPmMHToUC5cuMCzzz7Lxo0bZSdVQ0mJ/jIarZSUlHLqVCCXLrXh3Dko\nLrbi7W3Ey8uKshTR1ORLQUknjuX/m2LtP1hsCmW1EmC4SBvljxf/Ib80m/PnITe3C4MH9+PkyVyX\nw3Y0LRKDwZ/S0jwaNTpOs2Z6oSwoUJSVtQGaYjDUwF3IldK7sl654hhI2aaN3hMJ9EGWly7pXV6v\nLZKgn2696y79xrfZ2Q2+UAoh6o5bhfKxxx6jZ8+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SklLpMXOjH0Pl8VR2/Lna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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aab29d76e50>"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# *** OPTION TO SAVE DATA : Sort the data by total hits. \n",
      "# Note that this will apply to the last dataset in toCompare if it contain multiple.\n",
      "DF1.sort('Sum',inplace=True,ascending=False)\n",
      "pathToSave=os.getcwd() + '/results/%s/'%target + '%s_metaanalysis'%geneType \n",
      "DF1.to_csv(pathToSave)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 114
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Repeat RNA metaanalysis - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makeRepeatAnnotation():\n",
      "    # Repeat index sequence \n",
      "    repeatGenomeBuild=os.getcwd() + '/docs/repeat/repeatRNA.fa'\n",
      "    repeat_genome=np.genfromtxt(repeatGenomeBuild,dtype='string')\n",
      "    repeat_genome_bases=repeat_genome[1]\n",
      "    repeat_genome_size=len(repeat_genome[1])\n",
      "    # Repeat index positions\n",
      "    repeatAnnotation=os.getcwd() + '/docs/repeat/Hs_repeatIndex_positions.txt'\n",
      "    repeatAnnotDF=pd.DataFrame(pd.read_table(repeatAnnotation,header=None))\n",
      "    repeatAnnotDF.columns=['Name','Length','IndexStart','IndexEnd']\n",
      "    repeatAnnotDF['End_for_extraction']=repeatAnnotDF['IndexEnd']+1 \n",
      "    return (repeat_genome_bases,repeatAnnotDF)\n",
      "\n",
      "def getRepeatRNAData(repeatName,filePaths,binSize,flag):\n",
      "        \n",
      "    # Create data frame for storage \n",
      "    storageDF=pd.DataFrame()\n",
      "    \n",
      "    # Fill it with histogram data from samples\n",
      "    colNamesForExtraction=[]\n",
      "    for i in range(0,len(filePaths)):\n",
      "        \n",
      "        # Get RT file\n",
      "        path=filePaths[i]\n",
      "        \n",
      "        # Get repeat data\n",
      "        path_repReads=path+'PlotData_RepeatRNAreads_%s'%repeatName\n",
      "        hits_per_rep=pd.read_csv(path_repReads)\n",
      "        RTpositions=hits_per_rep['RT_stop']\n",
      "        start=hits_per_rep.loc[0,'Repeat_Start']\n",
      "        end=hits_per_rep.loc[0,'Repeat_End']\n",
      "             \n",
      "        # Histogram of RT stops across gene body\n",
      "        bins=range(start,end+2,1)\n",
      "        hist,bins=np.histogram(RTpositions,bins=bins)\n",
      "        width=0.7*(bins[1]-bins[0])\n",
      "        center=(bins[:-1] + bins[1:])/2\n",
      "        histPlot=np.array(hist,dtype=float)\n",
      "            \n",
      "        # Read accounting\n",
      "        data=pd.read_csv(path+'/PlotData_ReadsPerPipeFile')\n",
      "        data.columns=['Index','FileName','Reads']\n",
      "        data['FileName']=pd.Series(['Raw (R1)','Raw (R2)','3p Trim (R1)','3p Trim (R2)','Filter (R1)','Filter (R2)','No dupes (R1)','No dupes (R2)','RepeatMapped (R1)','RepeatMapped (R2)','Hg19Mapped (R1)','Hg19Mapped (R2)','Blacklist (R1)','Blacklist (R2)','RepeatMask (R1)','RepeatMask (R2)','ClipperIn','ClipperOut'])\n",
      "        data=data.set_index('FileName')\n",
      "        m1=np.mean([data.loc['RepeatMapped (R1)','Reads'],data.loc['RepeatMapped (R2)','Reads']])\n",
      "        m2=np.mean([data.loc['Hg19Mapped (R1)','Reads'],data.loc['Hg19Mapped (R2)','Reads']])\n",
      "        totalMapped=m1+m2\n",
      "\n",
      "        # Normalize data\n",
      "        histNorm=np.array(histPlot/float(totalMapped),dtype=float)\n",
      "        \n",
      "        # Extract sequence and record counts\n",
      "        sampleName=path.split('/')[-2]\n",
      "        readsPerBase=np.array(list(hist))\n",
      "        colName=sampleName+'RT_stops'\n",
      "        storageDF[colName]=readsPerBase\n",
      "        colName=sampleName+'Total_mapped'\n",
      "        storageDF[colName]=totalMapped\n",
      "        readsPerBaseNorm=np.array(list(histNorm))\n",
      "        colNameNorm=sampleName+'RT_stops_norm'\n",
      "        storageDF[colNameNorm]=readsPerBaseNorm\n",
      "        \n",
      "        # List of names for normalized data to extract\n",
      "        colNamesForExtraction=colNamesForExtraction+[colNameNorm]\n",
      "        # If no flag (= use sequence index), then record bin centers\n",
      "        if not flag:\n",
      "            storageDF['BinCenter']=center\n",
      "    \n",
      "    if not flag:\n",
      "        storageDF['BinCenter']=center\n",
      "    else:\n",
      "        try:\n",
      "            sequence=repeat_genome_bases[start:end+1]\n",
      "            storageDF['Sequence']=pd.Series(list(sequence))\n",
      "        except:\n",
      "            print \"Error setting sequence. Ensure bin size is = 1 so that data shape and sequence size are consistent.\"\n",
      "    \n",
      "    # Dataframe, column names for normalized data, start index postion\n",
      "    return (storageDF,colNamesForExtraction,start)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# *** INPUT : SPECIFY repeat RNA ***\n",
      "repeatName='U1'\n",
      "# *** INPUT : SPECIFY DATASET ***\n",
      "target='ddx21_dev'\n",
      "# *** INPUT : SPECIFY DATASETS TO COMPARE TO TARGET ***\n",
      "toCompare=['ddx21_sat']\n",
      "# *** INPUT : Choose bin size for repeat RNA histograms\n",
      "binsize=1\n",
      "# *** INPUT : Colors\n",
      "colors=['blue','green','red']\n",
      "\n",
      "# - Histogram for target dataset -\n",
      "filePaths=[os.getcwd() + '/results/%s/'%sampleName for sampleName in [target]+toCompare]\n",
      "repeat_genome_bases,repeatAnnotDF=makeRepeatAnnotation()\n",
      "plt.subplot2grid((4,4),(0,0),colspan=2,rowspan=2)\n",
      "if binsize != 1:\n",
      "    recordSequence=0 # Cannot record sequence in the output dataframe.\n",
      "storageDF,colNamesForExtraction,startIndexPosition=getRepeatRNAData(repeatName,filePaths,binsize,recordSequence)\n",
      "plt.bar(np.array(storageDF.index),storageDF[colNamesForExtraction[0]],width=1,align='center',color='blue',alpha=0.45)\n",
      "plt.xlim(0,storageDF.index[-1])\n",
      "if repeatName=='rDNA':\n",
      "    cutoffBase=13314 # Plot a limited windowsize for rDNA.\n",
      "    plt.xlim([0,cutoffBase])\n",
      "plt.tick_params(axis='x',labelbottom='off') \n",
      "plt.xlabel(\"Base position\",fontsize=5)\n",
      "plt.tick_params(axis='y',labelsize=5)  \n",
      "plt.ylabel(\"Counts per bin \\n Bin size = %s\"%binsize,fontsize=5)\n",
      "plt.title('%s coverage for %s'%(repeatName,target),fontsize=5)\n",
      "yMax=plt.ylim()[1]\n",
      "plt.grid()\n",
      "\n",
      "# - Scatterplot -\n",
      "plt.subplot2grid((4,4),(0,2),colspan=2,rowspan=2)\n",
      "for i in range(0,len(toCompare)):\n",
      "    if i == len(toCompare)-1:\n",
      "        alpha=1\n",
      "    else:\n",
      "        alpha=0.25\n",
      "    plt.scatter(np.array(storageDF[colNamesForExtraction[0]]),np.array(storageDF[colNamesForExtraction[i+1]]),color=colors[i],alpha=alpha,label=toCompare[i])\n",
      "plt.xlabel(\"%s\"%target,fontsize=5)\n",
      "plt.ylabel(\"Counts per base\",fontsize=5)\n",
      "plt.title('Compare datasets',fontsize=5)\n",
      "plt.tick_params(axis='xticks',labelsize=5) \n",
      "plt.tick_params(axis='yticks',labelsize=5)\n",
      "plt.xticks(rotation=45,fontsize=5)\n",
      "plt.yticks(fontsize=5)\n",
      "plt.grid()\n",
      "plt.legend(loc='upper left', scatterpoints = 1,prop={'size':5})\n",
      "maxVal=yMax\n",
      "plt.xlim(-0.1*maxVal,maxVal)\n",
      "plt.ylim(-0.1*maxVal,maxVal)\n",
      "\n",
      "plt.tight_layout()\n",
      "\n",
      "pathToSave=os.getcwd() + '/results/%s/'%target + '%s_metaanalysis'%repeatName\n",
      "storageDF.to_csv(pathToSave)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zLCwMKSkpAIADBw7U+l4qIiKyECbvE5PI0i/FEEIel2OoVCpzR6gW80nDfNIw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\nAQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aaaef8bf210>"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Heatmap - \n",
      "plt.subplot2grid((4,4),(2,0),colspan=4,rowspan=1)\n",
      "recordSequence=0\n",
      "storageDF_rawData,colNamesForExtraction,startIndexPosition=getRepeatRNAData(repeatName,filePaths,binsize,recordSequence)\n",
      "absoluteBases=np.array(storageDF_rawData['BinCenter'])-startIndexPosition\n",
      "storageDF_rawData=storageDF_rawData.set_index(absoluteBases)\n",
      "storageDF=storageDF_rawData[colNamesForExtraction]\n",
      "\n",
      "# Settings\n",
      "bottomLabels=1\n",
      "\n",
      "# Display limited data for rDNA.\n",
      "if repeatName=='rDNA':\n",
      "    cutoffBase=13314\n",
      "    indexer=np.array(absoluteBases)<cutoffBase\n",
      "    storageDF=storageDF.loc[indexer,:]\n",
      "   \n",
      "# - Toplogy per position -\n",
      "DF=storageDF.div(storageDF.sum(axis=1),axis=0)\n",
      "DF.fillna(0,inplace=True) # Any bases positions with no reads get corrected\n",
      "DF=DF.transpose()\n",
      "clipStorage=np.array(DF)\n",
      "sampleLabels=np.array(DF.index)\n",
      "baseLabels=np.array(DF.columns)\n",
      "p=plt.pcolormesh(clipStorage,cmap='Reds')\n",
      "locs,labels=plt.xticks(range(baseLabels.size),baseLabels)\n",
      "plt.setp(labels, rotation=45, fontsize=3)\n",
      "plt.xlabel('Base position',fontsize=3)\n",
      "plt.xlim([0,len(baseLabels)])\n",
      "sampleLabels=np.array([samLabel.split('RT_stops_norm')[0] for samLabel in sampleLabels]) # Shorten the labels\n",
      "locs,labels=plt.yticks(range(sampleLabels.size),sampleLabels)\n",
      "plt.setp(labels, fontsize=4)\n",
      "if not bottomLabels:\n",
      "    plt.tick_params(\\\n",
      "    axis='x',          # changes apply to the x-axis\n",
      "    which='off',       # both major and minor ticks are affected\n",
      "    bottom='off',      # ticks along the bottom edge are off\n",
      "    top='off',         # ticks along the top edge are off\n",
      "    labelbottom='off') # labels along the bottom edge are off \n",
      "plt.ylim(0,len(sampleLabels))\n",
      "plt.title('%s tag count per base across datasets'%repeatName,fontsize=5)  \n",
      "plt.tight_layout()\t\n",
      "\n",
      "# - Topology heatmap - \n",
      "plt.subplot2grid((4,4),(3,0),colspan=4,rowspan=1)\n",
      "if cutoffBase: # Remove all positions that are less than a cutoff\n",
      "    indexer=np.array(absoluteBases)<cutoffBase\n",
      "    storageDF=storageDF.loc[indexer,:]\n",
      "DF=storageDF.div(storageDF.sum(axis=0),axis=1) # Normalize across each dataset\n",
      "DF.fillna(0,inplace=True) # Any bases positions with no reads get corrected\n",
      "DF=DF.transpose()\n",
      "clipStorage=np.array(DF)\n",
      "sampleLabels=np.array(DF.index)\n",
      "baseLabels=np.array(DF.columns)\n",
      "p=plt.pcolormesh(clipStorage,cmap='Reds')\n",
      "locs,labels=plt.xticks(range(baseLabels.size),baseLabels)\n",
      "plt.setp(labels, rotation=45, fontsize=3)\n",
      "if not bottomLabels:\n",
      "    plt.tick_params(\\\n",
      "    axis='x',          # changes apply to the x-axis\n",
      "    which='both',      # both major and minor ticks are affected\n",
      "    bottom='off',      # ticks along the bottom edge are off\n",
      "    top='off',         # ticks along the top edge are off\n",
      "    labelbottom='off') # labels along the bottom edge are off \n",
      "plt.xlabel('Base position',fontsize=5)\n",
      "plt.xlim([0,len(baseLabels)])\n",
      "sampleLabels=np.array([samLabel.split('RT_stops_norm')[0] for samLabel in sampleLabels]) # Shorten the labels\n",
      "locs,labels=plt.yticks(range(sampleLabels.size),sampleLabels)\n",
      "plt.setp(labels, fontsize=4)\n",
      "plt.ylim(0,len(sampleLabels))\n",
      "plt.title('%s binding topology per base within each dataset'%repeatName,fontsize=5)  \n",
      "\n",
      "plt.tight_layout()\t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G8X3Vcsqmg7gfPUeyn5cac/Yug/WyneT0baXGrJftRP9ZB7Wck3Qb+40HyXi7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\nSZJqSQ6gJEmSJEmSJEmSaun/AQwx8uJFhLYQAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aab8d30da50>"
       ]
      }
     ],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}