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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Cluster neoblast transcripts with CAP3\n",
      "\n",
      "Use CAP3 to cluster neoblast transcripts:\n",
      "    \n",
      "    cap3 aboobaker.graveley.pearson.rajewsky.renamed.fa > cap3.out 2> cap3.err"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import gzip, os, sys\n",
      "#import custom functions for displaying tables, bash commands\n",
      "sys.path.append(os.path.abspath(\"/home/damian/\"))\n",
      "from dk_ipython import *\n",
      "from IPython.display import HTML\n",
      "HTML(addToggle())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<script>\n",
        "code_show=true;\n",
        "function code_toggle() {\n",
        " if (code_show){\n",
        " $('div.input').hide();\n",
        " } else {\n",
        " $('div.input').show();\n",
        " }\n",
        " code_show = !code_show\n",
        "}\n",
        "\n",
        "$( document ).ready(code_toggle);\n",
        "</script>\n",
        "The raw code for this IPython notebook is by default hidden for easier reading.\n",
        "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x221eed0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prefix = '/home/share/projects/smed_neoblast/cap3/'\n",
      "\n",
      "faFile = open(prefix + 'aboobaker.graveley.pearson.rajewsky.renamed.fa')\n",
      "\n",
      "from collections import defaultdict\n",
      "\n",
      "sourceKey = {'a':'Aboobaker','P':'Pearson','R':'Rajewsky','G':'Graveley'}\n",
      "sources = defaultdict(int)\n",
      "for line in faFile:\n",
      "    if line[0] == '>':\n",
      "        source = sourceKey[line[1]]\n",
      "        sources[source] += 1\n",
      "\n",
      "print 'Data sources used:'\n",
      "for source, count in sources.items():\n",
      "    print '  ' + source + ' - ' + commas(count) + ' transcripts'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data sources used:\n",
        "  Pearson - 2,147 transcripts\n",
        "  Graveley - 4,056 transcripts\n",
        "  Rajewsky - 7,696 transcripts\n",
        "  Aboobaker - 823 transcripts\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inFile = open('/home/share/projects/smed_neoblast/cap3/cap3.out')\n",
      "\n",
      "clusters = []\n",
      "block = []\n",
      "used = defaultdict(int)\n",
      "for line in inFile:\n",
      "    if line.find('*******************') != -1:\n",
      "        break\n",
      "        \n",
      "for line in inFile:\n",
      "    if line.strip() == '':\n",
      "        break\n",
      "    if line.find('*******************') != -1:\n",
      "        if len(block) != 0:\n",
      "            block.sort()\n",
      "            aBlock = tuple(set(block))\n",
      "            if len(block) > 1:\n",
      "                for x in block:\n",
      "                    used[x] += 1\n",
      "                clusters.append(tuple(set(block)))\n",
      "        \n",
      "        block = []\n",
      "    else:\n",
      "        name = sourceKey[line.strip().split()[0][0]]\n",
      "        block.append(name)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter\n",
      "\n",
      "clusterCount = Counter(clusters).items()\n",
      "clusterCount.sort(key = lambda x : x[1], reverse = True)\n",
      "\n",
      "comboTable = ListTable()\n",
      "comboHeader = ['Pearson','Graveley','Rajewsky','Aboobaker']\n",
      "comboTable.append(comboHeader + ['Number of clusters'])\n",
      "for combo, count in clusterCount:\n",
      "    row = []\n",
      "    for c in comboHeader:\n",
      "        if c in combo:\n",
      "            row.append('X')\n",
      "        else:\n",
      "            row.append('-')\n",
      "    row.append(count)\n",
      "    comboTable.append(row)\n",
      "    \n",
      "print 'Cluster compositions:'\n",
      "print '*Number of clusters total with members from at least 2 or more sources:', commas(len(clusters))\n",
      "print '*Number of clusters with the composition described in the row'\n",
      "print '*For example, there are 81 clusters containing at least one transcript from all 4 sources'\n",
      "comboTable"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster compositions:\n",
        "*Number of clusters total with members from at least 2 or more sources: 2,577\n",
        "*Number of clusters with the composition described in the row\n",
        "*For example, there are 81 clusters containing at least one transcript from all 4 sources\n"
       ]
      },
      {
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        "<table style='font-size:12px'><tr style='font-weight:bold'><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Pearson</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Graveley</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Rajewsky</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Aboobaker</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Number of clusters</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>698</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>661</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>321</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>212</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>170</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>131</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>105</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 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15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>57</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>34</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>30</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>10</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>X</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>-</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>4</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "[['Pearson', 'Graveley', 'Rajewsky', 'Aboobaker', 'Number of clusters'],\n",
        " ['X', '-', 'X', '-', 698],\n",
        " ['-', 'X', 'X', '-', 661],\n",
        " ['-', '-', 'X', '-', 321],\n",
        " ['X', '-', 'X', 'X', 212],\n",
        " ['X', 'X', 'X', '-', 170],\n",
        " ['-', '-', 'X', 'X', 131],\n",
        " ['-', 'X', '-', '-', 105],\n",
        " ['X', 'X', 'X', 'X', 81],\n",
        " ['X', 'X', '-', '-', 63],\n",
        " ['-', 'X', 'X', 'X', 57],\n",
        " ['-', 'X', '-', 'X', 34],\n",
        " ['X', '-', '-', 'X', 30],\n",
        " ['X', 'X', '-', 'X', 10],\n",
        " ['X', '-', '-', '-', 4]]"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "sourceCluster = defaultdict(int)\n",
      "for combo, count in clusterCount:\n",
      "    if len(combo) > 1:\n",
      "        for item in combo:\n",
      "            sourceCluster[item] += count\n",
      "\n",
      "df = pd.concat([pd.Series(sources),pd.Series(sourceCluster)],axis=1)\n",
      "df.columns = ['Number of neoblast transcripts','Number of neoblast transcripts clustered']\n",
      "df.plot(kind='bar',figsize=[12,6])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x8abc210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XUOsYd+53xa+7Lioq0tdff63nnnvO1RYfH6+JEycqOTlZISEhWrBggSSpU6dO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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8bfc710>"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "usedTable = ListTable()\n",
      "usedTable.append(['Source','Number of neoblast transcripts','Number of transcripts clustered','% clustered'])\n",
      "for c in comboHeader:\n",
      "    usedTable.append([c,commas(sources[c]),commas(used[c]),str(float(used[c]) / sources[c] * 100)[:4]])\n",
      "    \n",
      "usedTable"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table style='font-size:12px'><tr style='font-weight:bold'><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Source</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Number of neoblast transcripts</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>Number of transcripts clustered</td><td style='padding:4px 15px 4px 15px;border-color:white;text-align:center'>% clustered</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>Pearson</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>2,147</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>1,383</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>64.4</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>Graveley</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>4,056</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>1,460</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>35.9</td></tr><tr style='background:#E8E8E8'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>Rajewsky</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>7,696</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>3,172</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>41.2</td></tr><tr style='background:white'><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>Aboobaker</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>823</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>575</td><td style='padding:4px 15px 4px 15px;border-color:#B0B0B0;text-align:center'>69.8</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "[['Source',\n",
        "  'Number of neoblast transcripts',\n",
        "  'Number of transcripts clustered',\n",
        "  '% clustered'],\n",
        " ['Pearson', '2,147', '1,383', '64.4'],\n",
        " ['Graveley', '4,056', '1,460', '35.9'],\n",
        " ['Rajewsky', '7,696', '3,172', '41.2'],\n",
        " ['Aboobaker', '823', '575', '69.8']]"
       ]
      }
     ],
     "prompt_number": 64
    }
   ],
   "metadata": {}
  }
 ]
}