utr3_stats.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:c4b01c370a306b74bb90e23a1804940c1a913c3b5c5d4a62032ac90ff2ed5950"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "\n",
      "import pandas as pd \n",
      "\n",
      "from clipper.src import get_genomic_regions\n",
      "import gffutils"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db = gffutils.FeatureDB(\"/nas3/gpratt/gencode/gencode.v17.annotation.gtf.db\")\n",
      "genomic_features = get_genomic_regions.GenomicFeatures(\"hg19\", db)\n",
      "genomic_regions = genomic_features.get_genomic_regions()\n",
      "three_prime_utrs = genomic_regions['three_prime_utrs']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Get combined 3' UTR lengths\n",
      "three_prime_utrs_len = defaultdict(int)\n",
      "for interval in three_prime_utrs:\n",
      "    three_prime_utrs_len[interval.name] += len(interval)\n",
      "    \n",
      "three_prime_utrs_len = pd.Series(three_prime_utrs_len)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = hist(three_prime_utrs_len, range=(0, 10000), bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+ICLSFYMsHEREpCt6HA4nT54kJyeH1NRUkpKSqKuro6mpidmzZ5OamsqcOXNobm4OrV9Y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\nKBxERMRG4SAiIjYKBxERsVE4iIiIzf8HIrNjOy3aOhIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1be841d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature_types = list(db.featuretypes())\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "genomic_features._get_utrs??"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = defaultdict(dict)\n",
      "db = gffutils.FeatureDB(\"/nas3/gpratt/gencode/gencode.v17.annotation.gtf.db\")\n",
      "for feature in db.features_of_type(featuretype=\"transcript\"):\n",
      "    utr5, utr3 = genomic_features._get_utrs(feature, list(db.children(feature, featuretype=\"CDS\")), feature_types)\n",
      "    #if utr5 != [] and utr3 != []:\n",
      "    result[feature.attributes['gene_id'][0]][feature.attributes['transcript_id'][0]] = {'utr3' : utr3, 'utr5' : utr5}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result_lens = defaultdict(dict)\n",
      "\n",
      "for gene in result:\n",
      "    for transcript in result[gene]:\n",
      "        result_lens[gene][transcript] = {key : [len(item) for item in values] for key, values in result[gene][transcript].items()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utr3_lens = defaultdict(dict)\n",
      "\n",
      "for gene in result:\n",
      "    for transcript in result[gene]:\n",
      "        utr3_lens[gene][transcript] = result_lens[gene][transcript]['utr3'] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get total length of all 3' UTRs per transcript\n",
      "utr3_total_lens = defaultdict(dict)\n",
      "\n",
      "for gene in result:\n",
      "    for transcript in result[gene]:\n",
      "        utr3_total_lens[gene][transcript] = sum(utr3_lens[gene][transcript])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#combine results into a list per gene\n",
      "utr3_combined_total_lens = defaultdict(dict)\n",
      "\n",
      "for gene in result:\n",
      "    for transcript in result[gene]:\n",
      "        utr3_combined_total_lens[gene] = utr3_total_lens[gene].values()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = pd.Series(utr3_combined_total_lens)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean([])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/nas/nas0/yeolab/Software/lib/python2.7/site-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.\n",
        "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "nan"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_utr3_length = x.apply(max)\n",
      "min_utr3_length = x.apply(min)\n",
      "mean_utr3_length = x.apply(mean)\n",
      "non_zero_mean = x.apply(lambda lengths: mean([length for length in lengths if length != 0]))\n",
      "three_prime_utrs_len.name = \"combined_len\"\n",
      "max_utr3_length.name = \"max_len\"\n",
      "min_utr3_length.name = \"min_len\"\n",
      "median_utr3_length.name   = \"mean_len\"\n",
      "non_zero_median.name   = \"non_zero_mean_len\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utr3_comparisons = pd.DataFrame({\"max_len\" : max_utr3_length, \n",
      "              \"min_len\" : min_utr3_length, \n",
      "              \"med_len\" : median_utr3_length,\n",
      "              \"non_zero_mean_len\" : non_zero_mean,\n",
      "              \"combined_len\" : three_prime_utrs_len}).dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(utr3_comparisons.combined_len, utr3_comparisons.max_len)\n",
      "xlabel(\"max len\")\n",
      "ylabel(\"combined len\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "<matplotlib.text.Text at 0x2aab79021e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s/nXUsgO63loJ9Lb2b2KN17QRUxrH2Z0WWEYnsIabvV8bMZNMzfiRc9zmYrua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\n3JLBmf+gQQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1c801190>"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(utr3_comparisons.combined_len, utr3_comparisons.med_len)\n",
      "xlabel(\"max len\")\n",
      "ylabel(\"combined len\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "<matplotlib.text.Text at 0x2aab79052350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZVgfBlmvVytgAU6W/HatVBwYOchjTaHpL1yaSv38IGY3GVvmH1OaOjIwhrba3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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1c8eee50>"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(utr3_comparisons.combined_len,utr3_comparisons.non_zero_mean_len)\n",
      "xlabel(\"max len\")\n",
      "ylabel(\"combined len\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "<matplotlib.text.Text at 0x2aab79e3a3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lckl6erqkp6eLy+WSwYMHt/rWK9qcZqpKO8Skw0FMbZ0cCc08fbbmtVCc/ohI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\nhdta7BGTpiY0i/Mk67ocx4K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tQW233HKLLF++XEREfvazn/k/f/rpp2Xq1KkiIvLXv/7V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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2aab78f09850>"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(max_utr3_length, three_prime_utrs_len)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "x and y must be the same size",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-76-423525fbe5b5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_utr3_length\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthree_prime_utrs_len\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/nas3/yeolab/Software/lib/python2.7/site-packages/matplotlib-1.4.x-py2.7-linux-x86_64.egg/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs)\u001b[0m\n\u001b[0;32m   3177\u001b[0m         ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,\n\u001b[0;32m   3178\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3179\u001b[1;33m                          linewidths=linewidths, verts=verts, **kwargs)\n\u001b[0m\u001b[0;32m   3180\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3181\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/nas3/yeolab/Software/lib/python2.7/site-packages/matplotlib-1.4.x-py2.7-linux-x86_64.egg/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)\u001b[0m\n\u001b[0;32m   3460\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3461\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3462\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y must be the same size\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3464\u001b[0m         \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: x and y must be the same size"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HRcnsRWNjo9atW6epU6dKkqZNmzYRo467ZPbC5/Pp4sWLkqSff/5ZhYWFEzFqRsyfP193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\nABhD2AHAmP8Bcx99mnIY1GwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1c3948d0>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.concat[max_utr3_length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "ENSG00000000003.10    1359\n",
        "ENSG00000000005.5      171\n",
        "ENSG00000000419.8      268\n",
        "ENSG00000000457.8     1053\n",
        "ENSG00000000460.12    1096\n",
        "ENSG00000000938.8      853\n",
        "ENSG00000000971.11     237\n",
        "ENSG00000001036.8      980\n",
        "ENSG00000001084.6     1418\n",
        "ENSG00000001167.10    2569\n",
        "ENSG00000001460.13    1547\n",
        "ENSG00000001461.12    3794\n",
        "ENSG00000001497.12    4272\n",
        "ENSG00000001561.6     3062\n",
        "ENSG00000001617.7      963\n",
        "...\n",
        "ENSGR0000124333.10    1821\n",
        "ENSGR0000124334.12     968\n",
        "ENSGR0000167393.12     407\n",
        "ENSGR0000168939.6     7724\n",
        "ENSGR0000169084.8     1528\n",
        "ENSGR0000169093.10     133\n",
        "ENSGR0000169100.8      453\n",
        "ENSGR0000182162.5     2910\n",
        "ENSGR0000182378.8     4024\n",
        "ENSGR0000185291.6      223\n",
        "ENSGR0000185960.7     2190\n",
        "ENSGR0000196433.6       70\n",
        "ENSGR0000197976.6     1911\n",
        "ENSGR0000198223.9      905\n",
        "ENSGR0000214717.4     2224\n",
        "Length: 19307, dtype: int64"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "three_prime_utrs_len"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "ENSG00000000003.10    1357\n",
        "ENSG00000000005.5      170\n",
        "ENSG00000000419.8      267\n",
        "ENSG00000000457.8     1052\n",
        "ENSG00000000460.12    1568\n",
        "ENSG00000000938.8      852\n",
        "ENSG00000000971.11     429\n",
        "ENSG00000001036.8     1210\n",
        "ENSG00000001084.6     3108\n",
        "ENSG00000001167.10    2568\n",
        "ENSG00000001460.13    2192\n",
        "ENSG00000001461.12    6835\n",
        "ENSG00000001497.12    4494\n",
        "ENSG00000001561.6     3061\n",
        "ENSG00000001617.7      962\n",
        "...\n",
        "ENSGR0000167393.12     406\n",
        "ENSGR0000168939.6     7723\n",
        "ENSGR0000169084.8     1836\n",
        "ENSGR0000169093.10     132\n",
        "ENSGR0000169100.8      452\n",
        "ENSGR0000178605.8      664\n",
        "ENSGR0000182162.5     2909\n",
        "ENSGR0000182378.8     4023\n",
        "ENSGR0000185291.6      222\n",
        "ENSGR0000185960.7     4630\n",
        "ENSGR0000196433.6       69\n",
        "ENSGR0000197976.6     2539\n",
        "ENSGR0000198223.9     1319\n",
        "ENSGR0000205755.6        4\n",
        "ENSGR0000214717.4     2223\n",
        "Length: 20169, dtype: int64"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}