fast_iCLIP_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": 1
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Bound gene scatterplot - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getGeneList(name,geneType):\n",
      "    geneListpath=os.getcwd() + '/results/'+name+'/SourceData_geneList_%s'%geneType\n",
      "    path=os.getcwd() + '/results/'+name+'/SourceData_plot_ReadAccounting'\n",
      "    data=pd.DataFrame(pd.read_table(path,header=True,delimiter=','))\n",
      "    data.columns=['Index','FileName','Reads']\n",
      "    readCounts=np.array(data['Reads'],dtype=int)\n",
      "    readLabels=np.array(data['FileName'],dtype=str)\n",
      "    geneListDF=pd.DataFrame(pd.read_table(geneListpath,header=None))\n",
      "    geneListDF.columns=['GeneName','Count']\n",
      "    geneListDF.index=geneListDF['GeneName']\n",
      "    # Normalize by clipper output\n",
      "    geneListDF['Normalized']=geneListDF['Count']/readCounts[-1]\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):\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",
      "        print background\n",
      "        print background_pipeStats\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",
      "    \n",
      "    # Plot limit\n",
      "    maxVal=0.002\n",
      "    plt.xlim(-0.1*maxVal,maxVal)\n",
      "    plt.ylim(-0.1*maxVal,maxVal)\n",
      "        \n",
      "    plt.grid()\n",
      "    return DF1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# *** INPUT ***\n",
      "plt.subplot(2,2,1)\n",
      "target='ddx3wtv2' # Dataset (x-axis) against which others will be compared\n",
      "toCompare=['ddx3mutv2'] # Datasets (y-axis) for comparison\n",
      "colors=['black'] # Colors\n",
      "DF1=makeScatter(target,toCompare,'proteinCoding',colors) # Specify the gene type to compare\n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ddx3av2\n",
        "[25143140 29289059 23534243 27723976 22642396 21201732 15985806  4607689\n",
        "  3247997  5142594  3746210  5142594  3746210  3898639  2976022  2091360\n",
        "  1165428]\n",
        "ddx3mutv2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[33334647 28398351 29859928 26838152 28557594 11568693 11916768  2629503\n",
        "  2821553  3297130  3011331  3297130  3011331  2560587  2359932  1258896\n",
        "   764772]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dnc2qVatYunQpGzZs6FUdBZTk8rm5uWqx69FAfX09paWlGI1GLBYLer2e4OBg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Xs3sVgs9ek7OdNSkhlUVaW7u5uuri68Xi+KomCxWCgpKaGmpgaz2SxGeu3Zk0pGGRwcJBgMEg6H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\nKMv0e0mRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aaaac2a4150>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# *** SAVE DATA ***\n",
      "# Sort the data by total hits\n",
      "DF1.sort('Sum',inplace=True,ascending=False)\n",
      "# Pick output file name and save \n",
      "name='Protein_ddx3wtv2_v_ddx3mutv2'\n",
      "pathToSave=os.getcwd() + '/results/ddx3wtv2/' + name\n",
      "DF1.to_csv(pathToSave)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Counts_1</th>\n",
        "      <th>Counts_1_Norm</th>\n",
        "      <th>Counts_2</th>\n",
        "      <th>Counts_2_Norm</th>\n",
        "      <th>Sum</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>ENSG00000109971</th>\n",
        "      <td> 1454</td>\n",
        "      <td> 0.001205</td>\n",
        "      <td> 966</td>\n",
        "      <td> 0.001263</td>\n",
        "      <td> 2420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ENSG00000253729</th>\n",
        "      <td> 1560</td>\n",
        "      <td> 0.001293</td>\n",
        "      <td> 838</td>\n",
        "      <td> 0.001096</td>\n",
        "      <td> 2398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ENSG00000156508</th>\n",
        "      <td> 1370</td>\n",
        "      <td> 0.001136</td>\n",
        "      <td> 964</td>\n",
        "      <td> 0.001261</td>\n",
        "      <td> 2334</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ENSG00000177606</th>\n",
        "      <td> 1258</td>\n",
        "      <td> 0.001043</td>\n",
        "      <td> 936</td>\n",
        "      <td> 0.001224</td>\n",
        "      <td> 2194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ENSG00000215301</th>\n",
        "      <td> 1136</td>\n",
        "      <td> 0.000942</td>\n",
        "      <td> 764</td>\n",
        "      <td> 0.000999</td>\n",
        "      <td> 1900</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                 Counts_1  Counts_1_Norm  Counts_2  Counts_2_Norm   Sum\n",
        "ENSG00000109971      1454       0.001205       966       0.001263  2420\n",
        "ENSG00000253729      1560       0.001293       838       0.001096  2398\n",
        "ENSG00000156508      1370       0.001136       964       0.001261  2334\n",
        "ENSG00000177606      1258       0.001043       936       0.001224  2194\n",
        "ENSG00000215301      1136       0.000942       764       0.000999  1900\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Repeat RNA metaanalysis - "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makeRepeatAnnotation():\n",
      "    \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",
      "    # Python list extraction is not end index inclusive; to extract sequence, use end + 1.\n",
      "    repeatAnnotDF['End_for_extraction']=repeatAnnotDF['IndexEnd']+1 \n",
      "    return (repeat_genome_bases,repeatAnnotDF)\n",
      "\n",
      "def getRepeatRNAData(repeatName,filePaths,binSize,flag):\n",
      "    \n",
      "    # Get start,end for repeat RNA and extract the sequence\n",
      "    coords=np.array(repeatAnnotDF[repeatAnnotDF['Name']==repeatName][['IndexStart','End_for_extraction']])\n",
      "    sequence=np.array(list(repeat_genome_bases[coords[0][0]:coords[0][1]]))\n",
      "    \n",
      "    # Create data frame for storage \n",
      "    storageDF=pd.DataFrame()\n",
      "    if flag:\n",
      "        try:\n",
      "            storageDF['Sequence']=sequence\n",
      "        except:\n",
      "            \"Setting sequence failed. Ensure bin size is 1.\"\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",
      "        # Extract RT stops\n",
      "        fileName='SourceData_RepeatRNA_%s'%repeatName\n",
      "        RTpositions=np.loadtxt(path+fileName,delimiter='\\t',dtype=int)\n",
      "        \n",
      "        # Read accounting\n",
      "        data=pd.DataFrame(pd.read_table(path+'/SourceData_plot_ReadAccounting',header=True,delimiter=','))\n",
      "        data.columns=['Index','FileName','Reads']\n",
      "        readCounts=np.array(data['Reads'],dtype=int)\n",
      "        meanRepeatMapped=np.mean([readCounts[-9],readCounts[-10]])\n",
      "        meanHG19Mapped=np.mean([readCounts[-7],readCounts[-8]])\n",
      "        totalMapped=meanRepeatMapped+meanHG19Mapped\n",
      "\n",
      "        # Generate histogram and normalize by lowFDR stops\n",
      "        bins=range(coords[0][0],coords[0][1]+1,binSize) # Make sure bins are end coordinate inclusive\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",
      "        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",
      "    # Dataframe, column names for normalized data, start index postion\n",
      "    return (storageDF,colNamesForExtraction,coords[0][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# *** INPUT: SPECIFY repeat RNA ***\n",
      "repeatName='rDNA'\n",
      "repeatName='7SK'\n",
      "\n",
      "# *** INPUT: SPECIFY DATASET ***\n",
      "target='ddx3wtv2'\n",
      "# *** INPUT: SPECIFY DATASETS TO COMPARE TO TARGET ***\n",
      "toCompare=['heximlef','hexim']\n",
      "filePaths=[os.getcwd() + '/results/%s/'%sampleName for sampleName in [target]+toCompare]\n",
      "\n",
      "# - Repeat annotation - \n",
      "repeat_genome_bases,repeatAnnotDF=makeRepeatAnnotation()\n",
      "\n",
      "# - Histogram -\n",
      "plt.subplot2grid((4,4),(0,0),colspan=2,rowspan=2)\n",
      "binsize=1\n",
      "recordSequence=0\n",
      "# *** rDNA *** \n",
      "if repeatName=='rDNA':\n",
      "    cutoffBase=13314\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",
      "# *** rDNA *** \n",
      "if repeatName=='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",
      "# *** SAVE DATA ***\n",
      "# Pick output file name and save \n",
      "name='7sk_comparison_heximVsDDX3'\n",
      "pathToSave=os.getcwd() + '/results/ddx3wtv2/' + name\n",
      "storageDF.to_csv(pathToSave)\n",
      "\n",
      "plt.tight_layout()\t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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onfRQhRBCiB4mDaoQQgjhAtKgCiGEEC4gDaoQQgjhAtKgCpfR+hwyR/SaTa+5QL/Z9JoL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\nAkCAoAJAgKACQICgAkCAoAJAgKACQMATYJBNabvRNBwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1ae77050>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "storageDF.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ddx3wtv2RT_stops</th>\n",
        "      <th>ddx3wtv2Total_mapped</th>\n",
        "      <th>ddx3wtv2RT_stops_norm</th>\n",
        "      <th>BinCenter</th>\n",
        "      <th>ddx3mutv2RT_stops</th>\n",
        "      <th>ddx3mutv2Total_mapped</th>\n",
        "      <th>ddx3mutv2RT_stops_norm</th>\n",
        "      <th>heximRT_stops</th>\n",
        "      <th>heximTotal_mapped</th>\n",
        "      <th>heximRT_stops_norm</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  35</td>\n",
        "      <td> 7939733</td>\n",
        "      <td> 0.000004</td>\n",
        "      <td> 1621</td>\n",
        "      <td> 18</td>\n",
        "      <td> 5879758.5</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 16405</td>\n",
        "      <td> 2725427.5</td>\n",
        "      <td> 0.006019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  21</td>\n",
        "      <td> 7939733</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 1622</td>\n",
        "      <td> 19</td>\n",
        "      <td> 5879758.5</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td>  6663</td>\n",
        "      <td> 2725427.5</td>\n",
        "      <td> 0.002445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 136</td>\n",
        "      <td> 7939733</td>\n",
        "      <td> 0.000017</td>\n",
        "      <td> 1623</td>\n",
        "      <td> 89</td>\n",
        "      <td> 5879758.5</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 11857</td>\n",
        "      <td> 2725427.5</td>\n",
        "      <td> 0.004351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 104</td>\n",
        "      <td> 7939733</td>\n",
        "      <td> 0.000013</td>\n",
        "      <td> 1624</td>\n",
        "      <td> 86</td>\n",
        "      <td> 5879758.5</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td>  2683</td>\n",
        "      <td> 2725427.5</td>\n",
        "      <td> 0.000984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   7</td>\n",
        "      <td> 7939733</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 1625</td>\n",
        "      <td> 13</td>\n",
        "      <td> 5879758.5</td>\n",
        "      <td> 0.000002</td>\n",
        "      <td>   840</td>\n",
        "      <td> 2725427.5</td>\n",
        "      <td> 0.000308</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "   ddx3wtv2RT_stops  ddx3wtv2Total_mapped  ddx3wtv2RT_stops_norm  BinCenter  \\\n",
        "0                35               7939733               0.000004       1621   \n",
        "1                21               7939733               0.000003       1622   \n",
        "2               136               7939733               0.000017       1623   \n",
        "3               104               7939733               0.000013       1624   \n",
        "4                 7               7939733               0.000001       1625   \n",
        "\n",
        "   ddx3mutv2RT_stops  ddx3mutv2Total_mapped  ddx3mutv2RT_stops_norm  \\\n",
        "0                 18              5879758.5                0.000003   \n",
        "1                 19              5879758.5                0.000003   \n",
        "2                 89              5879758.5                0.000015   \n",
        "3                 86              5879758.5                0.000015   \n",
        "4                 13              5879758.5                0.000002   \n",
        "\n",
        "   heximRT_stops  heximTotal_mapped  heximRT_stops_norm  \n",
        "0          16405          2725427.5            0.006019  \n",
        "1           6663          2725427.5            0.002445  \n",
        "2          11857          2725427.5            0.004351  \n",
        "3           2683          2725427.5            0.000984  \n",
        "4            840          2725427.5            0.000308  \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colNamesForExtraction"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "['ddx3wtv2RT_stops_norm', 'ddx3mutv2RT_stops_norm', 'heximRT_stops_norm']"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Scatterplot - \n",
      "plt.subplot2grid((4,4),(0,2),colspan=2,rowspan=2)\n",
      "binsize=1\n",
      "recordSequence=1\n",
      "colors=['blue','green','red']\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)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "(-1.6000000000000003e-05, 0.00016000000000000001)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4OFiFhoaqL774wmmbCxcuVN26dVMGg0Hl5eVVOM5f/vIXFRISoiIjI9W2bduq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\nwZEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1ae77dd0>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - Heatmap - \n",
      "plt.subplot2grid((4,4),(2,0),colspan=4,rowspan=1)\n",
      "# *** rDNA *** \n",
      "if repeatName!='7SK':\n",
      "    binsize=1\n",
      "else:\n",
      "    binsize=1\n",
      "recordSequence=0\n",
      "storageDF_rawData,colNamesForExtraction,startIndexPosition=getRepeatRNAData(repeatName,filePaths,binsize,recordSequence)\n",
      "# Make absolute start base of the RNA the index\n",
      "absoluteBases=np.array(storageDF_rawData['BinCenter'])-startIndexPosition\n",
      "storageDF_rawData=storageDF_rawData.set_index(absoluteBases)\n",
      "# Extract the normalized data and set the index\n",
      "storageDF=storageDF_rawData[colNamesForExtraction]\n",
      "# Settings\n",
      "noBottomLabels=0\n",
      "cutoffBase=0 \n",
      "# *** rDNA *** \n",
      "if repeatName=='7SK':\n",
      "    cutoffBase=13314\n",
      "# Remove all positions that are less than a cutoff\n",
      "if cutoffBase:\n",
      "    indexer=np.array(absoluteBases)<cutoffBase\n",
      "    storageDF=storageDF.loc[indexer,:]\n",
      "# Compute the max of each base position across datasets\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",
      "# Transpose \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 repeatName=='rDNA':\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",
      "# Remove all positions that are less than a cutoff\n",
      "if cutoffBase:\n",
      "    indexer=np.array(absoluteBases)<cutoffBase\n",
      "    storageDF=storageDF.loc[indexer,:]\n",
      "# Normalize across each dataset\n",
      "DF=storageDF.div(storageDF.sum(axis=0),axis=1)\n",
      "DF.fillna(0,inplace=True) # Any bases positions with no reads get corrected\n",
      "# Make plot\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 repeatName=='rDNA':\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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Xc4qWZrR+aGhwd1cfuLkBUPLzeT3N6OFqia3ub75RqKeZ0tRYJXnqa+YCax04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\nCNFLUkAJIYQQQgghRC9JASWEEOIrpVariYiI4Nlnn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       "text": [
        "<matplotlib.figure.Figure at 0x2aaac848a750>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}