Fig3b-Enrichment of CTCF(NestedvsSingle).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import pandas\n",
    "from scipy import stats\n",
    "from StringIO import StringIO\n",
    "from collections import Counter\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "import urllib2\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###Define a function to caculate the ChIP-seq signal in each bin of corresponding Hi-C matrix######\n",
    "def computeMatrix(bedfile,boundarylist,chrn,winsize,res,chrs_l,tabfile):\n",
    "    blist = boundarylist[(boundarylist>winsize)&(boundarylist<(chrs_l-winsize*res)/res)].astype(int)\n",
    "    mm = np.zeros((len(blist),2*winsize+1))\n",
    "    chrinfo = tabfile.loc[bedfile[0]==chrn]\n",
    "    for i in range(0,len(blist)):\n",
    "        mm[i,:]=chrinfo.iloc[blist[i]-winsize:blist[i]+winsize+1,4].values\n",
    "    return mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getlevel(tads):\n",
    "    ftads = tads[(tads[:,1]-tads[:,0]).argsort()[::-1],:]\n",
    "    rtads = tads[(tads[:,1]-tads[:,0]).argsort(),:]\n",
    "    flevel = np.ones(len(tads))\n",
    "    rlevel = np.ones(len(tads))\n",
    "    for i in range(0,len(tads)):\n",
    "        rn = []\n",
    "        fn = []\n",
    "        for j in range(0,i):\n",
    "            if rtads[i,0]<=rtads[j,0] and rtads[i,1]>=rtads[j,1]:\n",
    "                rn.append(rlevel[j])\n",
    "            if ftads[i,0]>=ftads[j,0] and ftads[i,1]<=ftads[j,1]:\n",
    "                fn.append(flevel[j])\n",
    "        if len(rn)>=1:\n",
    "            rlevel[i] = max(rn)+1\n",
    "        if len(fn)>=1:\n",
    "            flevel[i] = max(fn)+1\n",
    "    return (np.column_stack((ftads,flevel,rlevel[::-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def peakatboundary (peak, boundary):#####check the number of peaks at each boundary\n",
    "    peaklist = (peak[:,0]+peak[:,1])/2/res\n",
    "    pinb = np.zeros(len(boundary))\n",
    "    for i in range(0, len(boundary)):\n",
    "        pinb[i] = sum(peaklist == boundary[i])\n",
    "    return pinb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###chr length based on hg19; all ChIP-seq datasets are derived from ENCODE & Roadmap data portal#####\n",
    "###bedfiles and tabfiles are generated by the bigWigAverageOverBed program from ENCODE#####\n",
    "chrs_length = [249250621,243199373,198022430,191154276,180915260,171115067,159138663,146364022,141213431,135534747,135006516,133851895,115169878,107349540,102531392,90354753,81195210,78077248,59128983,63025520,48129895,51304566]\n",
    "res = 10000\n",
    "bedf = pandas.read_table(urllib2.urlopen('http://bx.psu.edu/~lua137/OnTAD/data/E116-Ctcf.fc.signal.bigwig.10kb.bed'),sep='\\t',header=None)\n",
    "tabf = pandas.read_table(urllib2.urlopen('http://bx.psu.edu/~lua137/OnTAD/data/E116-Ctcf.fc.signal.bigwig.10kb.tab'),sep='\\t',header=None)\n",
    "peakf = pandas.read_table(urllib2.urlopen('http://bx.psu.edu/~lua137/OnTAD/data/Gm12878Ctcf.bed.sorted.uniqpeak'),sep='\\t',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cOnTAD_rawoverball = np.empty((0,21))\n",
    "cOnTAD_rawhierball = np.empty((0,21))\n",
    "cOnTAD_rawsoloball = np.empty((0,21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####Done with chr1####\n",
      "####Done with chr2####\n",
      "####Done with chr3####\n",
      "####Done with chr4####\n",
      "####Done with chr5####\n",
      "####Done with chr6####\n",
      "####Done with chr7####\n",
      "####Done with chr8####\n",
      "####Done with chr9####\n",
      "####Done with chr10####\n",
      "####Done with chr11####\n",
      "####Done with chr12####\n",
      "####Done with chr13####\n",
      "####Done with chr14####\n",
      "####Done with chr15####\n",
      "####Done with chr16####\n",
      "####Done with chr17####\n",
      "####Done with chr18####\n",
      "####Done with chr19####\n",
      "####Done with chr20####\n",
      "####Done with chr21####\n",
      "####Done with chr22####\n"
     ]
    }
   ],
   "source": [
    "hierb = 0\n",
    "solob = 0\n",
    "overb = 0\n",
    "hierp = np.empty(1)\n",
    "solop = np.empty(1)\n",
    "overp = np.empty(1)\n",
    "for chrnum in range(1,23):\n",
    "    OnTAD_raw = pandas.read_table(urllib2.urlopen('http://bx.psu.edu/~lua137/OnTAD/output/OnTAD/Gm12878/10kb/OnTAD_KRnorm_pen0.1_max200_hsz5_chr'+str(chrnum)+'.tad'),sep='\\t',header=None)\n",
    "    OnTAD_rawa = OnTAD_raw.loc[(OnTAD_raw[2]>0),:].values[:,0:2]-1\n",
    "    OnTAD_rawb = np.unique(OnTAD_rawa.flatten())  \n",
    "    OnTAD_rawb = np.unique(OnTAD_rawa.flatten())\n",
    "    OnTAD_rawfr = getlevel(OnTAD_rawa)\n",
    "    OnTAD_rawsoloa = OnTAD_rawfr[(OnTAD_rawfr[:,3]==1)&(OnTAD_rawfr[:,2]==1),0:2]\n",
    "    OnTAD_rawhiera = OnTAD_rawfr[np.logical_or((OnTAD_rawfr[:,3]!=1),(OnTAD_rawfr[:,2]!=1)),0:2]\n",
    "    OnTAD_rawb = np.unique(OnTAD_rawa.flatten())\n",
    "    OnTAD_rawsolob1 = np.unique(OnTAD_rawsoloa.flatten())\n",
    "    OnTAD_rawhierb1 = np.unique(OnTAD_rawhiera.flatten())\n",
    "    OnTAD_rawsolob = np.setdiff1d(OnTAD_rawsolob1,OnTAD_rawhierb1)\n",
    "    OnTAD_rawhierb = np.setdiff1d(OnTAD_rawhierb1,OnTAD_rawsolob1)\n",
    "    OnTAD_rawoverb = np.intersect1d(OnTAD_rawsolob1,OnTAD_rawhierb1)\n",
    "    cOnTAD_rawoverball = np.append(cOnTAD_rawoverball,computeMatrix(bedf,OnTAD_rawoverb,'chr'+str(chrnum),10,10000,chrs_length[chrnum-1],tabf), axis=0)\n",
    "    cOnTAD_rawhierball = np.append(cOnTAD_rawhierball,computeMatrix(bedf,OnTAD_rawhierb,'chr'+str(chrnum),10,10000,chrs_length[chrnum-1],tabf), axis=0)\n",
    "    cOnTAD_rawsoloball = np.append(cOnTAD_rawsoloball,computeMatrix(bedf,OnTAD_rawsolob,'chr'+str(chrnum),10,10000,chrs_length[chrnum-1],tabf), axis=0)\n",
    "    peaks = peakf.loc[peakf[0]=='chr'+str(chrnum),1:2].values\n",
    "    hierb += len(OnTAD_rawhierb)\n",
    "    solob += len(OnTAD_rawsolob)\n",
    "    overb += len(OnTAD_rawoverb)\n",
    "    hierp = np.append(hierp, peakatboundary(peaks, OnTAD_rawhierb))\n",
    "    solop = np.append(solop, peakatboundary(peaks, OnTAD_rawsolob))\n",
    "    overp = np.append(overp, peakatboundary(peaks, OnTAD_rawoverb))\n",
    "    print '####Done with chr'+str(chrnum)+'####'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out = np.array([np.mean(cOnTAD_rawsoloball, axis=0),np.mean(cOnTAD_rawhierball, axis=0),np.mean(cOnTAD_rawoverball, axis=0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b76450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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TTz/l97//Penp6QwaNIi2bduSn5/PunXrWLJkCXa7nZdeeonIyNr/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\nkdd4MRhAHnkxuoEpFIrrGE+m6U1AKuACPj/B830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aphC/7iYoY+euISh9BIM+fON4j9WU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\n18AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b76310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(6)\n",
    "fig,ax = plt.subplots(1)\n",
    "ax.plot(out[0,:],c='#66c2a5',label='Singletons',linewidth=2)\n",
    "ax.plot(out[1,:],c='#a6d854',label='Nested TADs',linewidth=2)\n",
    "ax.plot(out[2,:],c='#fc8d62',label='Shared',linewidth=2)\n",
    "ax.legend(loc=\"upper right\",prop={'size': 14})\n",
    "#ax.axes.get_xaxis().set_visible(False)\n",
    "plt.ylabel('CTCF signal', {'color': 'k', 'fontsize': 20})\n",
    "plt.xlabel('Relative position', {'color': 'k', 'fontsize': 20})\n",
    "plt.yticks(color='k',size=14)\n",
    "plt.ylim(1.40,3.60)\n",
    "plt.xticks((0,5,10,15,20),('-100kb','-50kb','0','50kb','100kb'),color='k',size=15)\n",
    "plt.savefig(\"/Users/linan/Desktop/hier_solo.png\", dpi=300, transparent=True, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((790L, 21L), (8638L, 21L), (724L, 21L))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cOnTAD_rawsoloball.shape, cOnTAD_rawhierball.shape, cOnTAD_rawoverball.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10152"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "790+8638+724"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.45074488971012822, 0.1811320754716981, 0.4366391184573003)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(hierp)/hierb, sum(solop)/solob, sum(overp)/overb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-8.8060595218687858, pvalue=1.5252363139858317e-18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(cOnTAD_rawsoloball[:,10],cOnTAD_rawhierball[:,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=11.432628982867882, pvalue=4.5256351149856623e-30)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(hierp,solop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}