CopyNumber_Distance_Runs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ----------------------------------------------------------------------\n",
    "# Mutex Runs\n",
    "# ----------------------------------------------------------------------\n",
    "\n",
    "\n",
    "Mutex is a software package for finding mutual exclusivity and co-occurrence in cancer genes.\n",
    "\n",
    "__To disregard the FDR and filtering method__, set:\n",
    "1. leastPatientThreshold to 100\n",
    "2. test_gene_threshold to 0\n",
    "3. mpairPercntile to 100\n",
    "4. cpairPercentile to 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mutationmatrix = 'data/OV_broad/OV_broad-cna-gl.m2'\n",
    "# Set to name of tab-delimited matrix file. First column of each row should be Patient ID. Remaining row entries are names of present mutations\n",
    "\n",
    "patientFile = '/Users/jlu96/maf/new/OV_broad/shared_patients.plst'\n",
    "# only load patients listed in this file. Set to None to use all the patients.\n",
    "\n",
    "geneFile = 'data/Chrom13-CopyNumber.glst'\n",
    "# Only load genes listed in this file. Set to None to use all the patients\n",
    "\n",
    "\n",
    "geneList1 = None\n",
    "geneList2 = None\n",
    "# If want to specifically look for pairs between certain genes, set geneList1 and geneList2. All\n",
    "# pairs between one gene in geneList1 and one gene in geneList2 will be tested\n",
    "\n",
    "output_prefix =  \"output/OV_broad-cna-gl-distance\" # Prefix for \"OUTPUT FILES\", below:\n",
    "\n",
    "\n",
    "\n",
    "#OUTPUT FILES-------\n",
    "pairfile = output_prefix + 'pairs-min_cohort.txt' #Pair statistics within the least mutated patients\n",
    "new_pairfile = output_prefix + \"-pairs-min_cohort_filtered.txt\" #Pair statistics over all patients\n",
    "new_cpairfile = output_prefix + \"-cpairs-min_cohort_filtered.txt\" #Co-occurringPair statistics over all patients\n",
    "new_mpairfile = output_prefix + \"-mpairs-min_cohort_filtered.txt\"#Mutually exclusive Pair statistics over all patients\n",
    "triplet_output_prefix = output_prefix + '-triplets-'\n",
    "segment_info_file = output_prefix + \"segment_info.txt\" # Name of file to write segmentation information to\n",
    "network_c_file = new_cpairfile[:-4] + \"_network.txt\"\n",
    "network_m_file = new_mpairfile[:-4] + \"_network.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If you want to test all pairs without first using the least mutated patients, run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "minFreq = 0\n",
    "leastPatientThreshold = 100\n",
    "test_gene_threshold = 3\n",
    "closer_than_distance = False\n",
    "only_filter_copy_distance = True\n",
    "cFDRthreshold = 0.05\n",
    "mFDRthreshold = 0.05\n",
    "pairs_have_dependencies = True\n",
    "remove_missing_genes = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1032\n"
     ]
    }
   ],
   "source": [
    "# Let's get all the genes that are on chromosomes 8 and 13\n",
    "import mutex as mex\n",
    "import bingenesbypairs as bgbp\n",
    "import csv\n",
    "numGenes, numCases, genes, patients, geneToCases, patientToGenes = mex.load_mutation_data(mutationmatrix, patientFile, geneFile, minFreq)\n",
    "\n",
    "reload(bgbp)\n",
    "gene_positions_dict = bgbp.load_gene_positions()\n",
    "\n",
    "new_genes = [gene for gene in set(genes).intersection(set(gene_positions_dict.keys())) if gene_positions_dict[gene]['Chromosome'] in {\"13\"}]\n",
    "\n",
    "print len(new_genes)\n",
    "\n",
    "with open(\"data/Chrom13-CopyNumber.glst\", 'w') as csvfile:\n",
    "    writer = csv.writer(csvfile, delimiter='\\t')\n",
    "    for gene in new_genes:\n",
    "        writer.writerow([gene])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Plot the distribution of mutations across genes and across patients\n",
    "# 2) Plot distribution over the least mutated patients\n",
    "# 3) Generate the gene pairs to be tested\n",
    "\n",
    "A good rule of thumb is to "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1032 genes x 140 patients\n",
      "*******************************************************************\n",
      "*                   PLOT MUTATION DISTRIBUTIONS                   *\n",
      "*******************************************************************\n",
      "GENE MUTATION DISTRIBUTION\n"
     ]
    },
    {
     "data": {
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99df3T3/6037hhRf65ptv3jT81ltv9WHDhvmAAQP8wgsvTJ3G+eef71tttZWv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\nYCciIjVPwU5ERGqegp2IiNS8/w++3HIzGPIdHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b1e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zoomed into bottom 95 percent region\n"
     ]
    },
    {
     "data": {
      "image/png": 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cjzzyCEuWLOHBBx9kxRVXbFR+gNtvv51dd901Z/PEpEmTGjTZdOnShY033pg3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1279f1c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PATIENT MUTATION DISTRIBUTION\n"
     ]
    },
    {
     "data": {
      "image/png": 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nnmPbbbdtVRm7GgVMkS4us6N19/qzzbFjx9K9e3emTJnCj370I9asWcPrr7/O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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1308441d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zoomed into bottom 95 percent region\n"
     ]
    },
    {
     "data": {
      "image/png": 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1115j5syZDB06lGnTprH33nu36zew3377sfXWW7Pmmmuy7777MmLECA4++GAk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\nVvV3yvXlyep40rp5pg9/sxZn5uVcjU/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f2cab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*******************************************************************\n",
      "*         PLOT DISTRIBUTIONS AMONG LEAST MUTATED PATIENTS         *\n",
      "*******************************************************************\n",
      "GENE MUTATION DISTRIBUTION IN LEAST MUTATED PATIENTS\n"
     ]
    },
    {
     "data": {
      "image/png": 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33OPdu3f3+fPnZ6avqqryc845J3Pc/Pnzfa211vJbbrnFly5d6j//+c995MiR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\n5svd5wJvm9mQOGh3YDqhTVRu6yo6nLo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f844e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PATIENT MUTATION DISTRIBUTION IN LEAST MUTATED PATIENTS\n"
     ]
    },
    {
     "data": {
      "image/png": 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+9NFH+5AhQ3zEiBH+ox/9yBsbG33u3Lm+9tpre8+ePb1///5eV1fnf/zjH713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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13062a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*******************************************************************\n",
      "*                   GETTING TEST GENE PAIRS                       *\n",
      "*******************************************************************\n",
      "The least mutated patients are the bottom  100  % of patients\n",
      "Least mutated patients: 1032 genes x 140 patients\n",
      "The test gene threshold is  3 % of the least mutated patients\n",
      "Test genes are mutated at least  4  in th least mutated patients\n",
      "\n",
      "\n",
      "Number of test genes:  1032\n",
      "All genes that weren't found in gene_positions file will not be considered\n",
      "Number of pairs to test:  531996\n",
      "Number of pairs is  531996  retrieved in time :  3.99019098282\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mutex as mex\n",
    "import csv\n",
    "import mutex_triangles as met\n",
    "import chisquared as chi\n",
    "import bingenesbypairs as bgbp\n",
    "import time\n",
    "import os\n",
    "import scipy.stats as stats\n",
    "import partition as par\n",
    "import numpy as np\n",
    "import multi_testing as mt\n",
    "%matplotlib inline\n",
    "\n",
    "COSMIC_pair_insertion_index = 8\n",
    "COSMIC_triplet_insertion_index = 5\n",
    "compute_mutex = True\n",
    "\n",
    "numGenes, numCases, genes, patients, geneToCases, patientToGenes = mex.load_mutation_data(mutationmatrix, patientFile, geneFile, minFreq)\n",
    "\n",
    "print '%s genes x %s patients' % (numGenes, numCases)\n",
    "\n",
    "\n",
    "print \"*******************************************************************\"\n",
    "print \"*                   PLOT MUTATION DISTRIBUTIONS                   *\"\n",
    "print \"*******************************************************************\"\n",
    "\n",
    "print \"GENE MUTATION DISTRIBUTION\"\n",
    "mex.graph_mutation_distribution(numCases, genes, geneToCases, filename=mutationmatrix + \" Genes\", bins=100, zoomin_percentile=95)\n",
    "\n",
    "\n",
    "print \"\\nPATIENT MUTATION DISTRIBUTION\"\n",
    "mex.graph_patient_distribution(len(genes), patients, patientToGenes, mutationmatrix + \" Patients\", bottom_percentile=leastPatientThreshold, zoomin_percentile=95)\n",
    "\n",
    "\n",
    "print \"*******************************************************************\"\n",
    "print \"*         PLOT DISTRIBUTIONS AMONG LEAST MUTATED PATIENTS         *\"\n",
    "print \"*******************************************************************\"\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "D = [len(patientToGenes[p]) for p in patientToGenes]\n",
    "minThreshold = stats.scoreatpercentile(D, leastPatientThreshold)\n",
    "\n",
    "c0patients = [p for p in patientToGenes if len(patientToGenes[p]) <= minThreshold]\n",
    "\n",
    "c0cohort_dict = {0: c0patients}\n",
    "\n",
    "c0genes, c0geneToCases, c0patientToGenes = par.get_cluster_gTC_pTG(geneToCases, patientToGenes, c0patients)\n",
    "\n",
    "print \"GENE MUTATION DISTRIBUTION IN LEAST MUTATED PATIENTS\"\n",
    "mex.graph_mutation_distribution(len(c0patients), c0genes, c0geneToCases, filename=mutationmatrix + \" genes in least mutated\", bins=100)\n",
    "\n",
    "print \"\\nPATIENT MUTATION DISTRIBUTION IN LEAST MUTATED PATIENTS\"\n",
    "mex.graph_patient_distribution(len(genes), c0patients, c0patientToGenes, filename=mutationmatrix + \" patients in least mutated\")\n",
    "\n",
    "\n",
    "\n",
    "print \"*******************************************************************\"\n",
    "print \"*                   GETTING TEST GENE PAIRS                       *\"\n",
    "print \"*******************************************************************\"\n",
    "print \"The least mutated patients are the bottom \", leastPatientThreshold, \" % of patients\"\n",
    "print \"Least mutated patients: %s genes x %s patients\" % (len(c0genes), len(c0patients) )\n",
    "\n",
    "\n",
    "test_minFreq = int( test_gene_threshold * 0.01 * len(c0patients))\n",
    "print \"The test gene threshold is \", test_gene_threshold, \"% of the least mutated patients\"\n",
    "print \"Test genes are mutated at least \", test_minFreq, \" in th least mutated patients\"\n",
    "\n",
    "reload(bgbp)\n",
    "print \n",
    "print\n",
    "print \"Number of test genes: \", len([g for g in c0genes if len(c0geneToCases[g]) >= test_minFreq and (not remove_missing_genes or\n",
    "                                                                                                     bgbp.get_segment_gene_info(g)['Chromosome'] != bgbp.missing_chromosome)])\n",
    "\n",
    "t = time.time()\n",
    "if geneList1 != None and geneList2 !=None:\n",
    "    with open(geneList1) as f:\n",
    "            genes1 = set(l.rstrip().split()[0] for l in f if not l.startswith(\"#\"))\n",
    "    with open(geneList2) as f:\n",
    "            genes2 = set(l.rstrip().split()[0] for l in f if not l.startswith(\"#\"))\n",
    "    \n",
    "    genepairs = bgbp.getgenepairs(c0geneToCases, genes1, genes2=genes2, test_minFreq=test_minFreq, closer_than_distance=closer_than_distance, only_filter_copy_distance=only_filter_copy_distance,\n",
    "                                 remove_missing_genes=remove_missing_genes)\n",
    "    \n",
    "            \n",
    "else:\n",
    "    genepairs = bgbp.getgenepairs(c0geneToCases, c0genes, test_minFreq=test_minFreq, closer_than_distance=closer_than_distance, only_filter_copy_distance=only_filter_copy_distance,\n",
    "                                 remove_missing_genes=remove_missing_genes)\n",
    "print \"Number of pairs is \", len(genepairs), \" retrieved in time : \", time.time() - t\n",
    "\n",
    "os.system('say \"exploration finished\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform statistical tests of each of the pairs\n",
    "### This cell's runtime is usually about (number of pairs)/ 900 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting normal statistics...\n",
      "Finished in  195.297889948\n",
      "Testing among least mutated patients...\n",
      "Finished in  1065.97046494\n",
      "All pairs tested in least mutated patients written to  output/OV_broad-cna-gl-distancepairs-min_cohort.txt\n",
      "Pairs in least mutated patients finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print \"Getting normal statistics...\"\n",
    "t = time.time()\n",
    "pairsdict, genedict = met.cooccurpairs(numCases, geneToCases, patientToGenes, genepairs, compute_mutex=compute_mutex)\n",
    "print \"Finished in \", time.time() - t\n",
    "\n",
    "print \"Testing among least mutated patients...\"\n",
    "t = time.time()\n",
    "pairsdict = chi.add_BinomP_cohorts_all_pairs(pairsdict, geneToCases, patientToGenes, c0cohort_dict, c0patients)\n",
    "print \"Finished in \", time.time() - t\n",
    "\n",
    "for pair in pairsdict:\n",
    "    pairsdict[pair][\"Distance\"] = bgbp.get_gene_distance(*tuple(pair))\n",
    "\n",
    "\n",
    "print \"All pairs tested in least mutated patients written to \",  pairfile\n",
    "\n",
    "fieldnames = (pairsdict.values()[0]).keys()\n",
    "fieldnames.remove('MutationFrequencies')\n",
    "fieldnames.remove('MutationFrequencyDifference')\n",
    "fieldnames.remove('MutationFrequencyDifferenceRatio')\n",
    "fieldnames.remove('CooccurrenceRatio')\n",
    "fieldnames.remove('Coverage')\n",
    "fieldnames.remove('SetScore')\n",
    "fieldnames.remove('AverageOverlapPMN')\n",
    "fieldnames.remove('CombinedScore')\n",
    "fieldnames.remove('Concordance')\n",
    "fieldnames.remove('Somatic')\n",
    "fieldnames.remove('RoundedLogPCov')\n",
    "fieldnames.remove('GeneSet')\n",
    "fieldnames.remove('Type')\n",
    "\n",
    "\n",
    "fieldnames.insert(2, 'Type')\n",
    "fieldnames.insert(3, '1CBinomProb0')\n",
    "fieldnames.insert(3, '1MBinomProb0')\n",
    "\n",
    "met.writeanydict(pairsdict, pairfile, fieldnames=fieldnames)\n",
    "print \"Pairs in least mutated patients finished\"\n",
    "os.system('say \"Pairs in least mutated patients finished\"')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the distribution of p-values against distribution of gene distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "cpvalues = np.array([pairsdict[c]['1CBinomProb0'] for c in pairsdict])\n",
    "logcp = np.log10(cpvalues)\n",
    "mpvalues = np.array([pairsdict[c]['1MBinomProb0'] for c in pairsdict])\n",
    "logmp = np.log10(mpvalues)\n",
    "distances = [pairsdict[c]['Distance'] for c in pairsdict]\n",
    "\n",
    "\n",
    "diff_chrom_cpvalues = [cpvalues[i] for i in range(len(cpvalues)) if distances[i] == None]\n",
    "same_chrom_cpvalues = [cpvalues[i] for i in range(len(cpvalues)) if distances[i] != None]\n",
    "same_chrom_distance = [distances[i] for i in range(len(cpvalues)) if distances[i] != None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JexJxGAu+ng3Ui8Fau8da+7a1dgIycIGLGNgHnBJSdhJyvcOiiP6MPIt+C/zG1asNbcFV\niLPoeiQSqkokuFYX+KqUufcw7d+hyHPzVWvtTv8GY0wHt72mfIYM+McaYxJNSv11tyGJsGpN45+U\ngoGVpDITkQfQP4wxx4fVM8acTWwo2F/c+3hjTAtfvVTkz26QkMCq8jyi6h1kjPlZYNtIZPb3T8/u\naq39HlEjtgOmBh9SxpiGxpjG1a3r8CTs4YF6pyPe3/ua2cgNPMEYE5dZ0kj62VNqeIwXEdPNQGPM\nZSHH8D+UX0Ay2d3gfvc4jDF9jTH1a9inveHvyEzMs2NCVFjws8a9xwiaxphewM3VON5n7ngXGGNa\n+tqpj+SDCONu5P/0F2NMk+BGY0wz1w/v+8/DZvpEtWc7Q7YF8f6Pt/t/FycI3+G+Vuf/mIjpwIeI\noP5YgvNr6HxT8iprzPn83ALUJ+qD4m/rRCRs8xNr7RXW2muCLyRUdS1wihPWMMacaIzJDDmkd01j\n7Pk+DUR1ucWZrbx2MomaCYL+EDi7/ULgPEQ42IrY+PcJxpg0Y8xwJPdLBBhlw8NG/ftUdq38958n\ngHcKqe/95072a1uNMQ0RZ/XKTImVYq2NIOdWH5gVFD6NMRneeGQlHPheRNs8M+wcjTFtq6iRq7BT\nB/yF2ID+jSSbKUY8tccRH7bxB8RJrwhxkDm2knZvQew5EST+9B7E0esRJEFMBJ8ntY31ev0WSYRz\nJ6JGirhjViuJCCLF70YcQh5HZkwFrr1vgM6B+k2RlKARxLt5huvD3xCp8ud7WbcloqKMIB7vdyGD\n5W5XP1FUwhcVnFtl209z/ShDQhpnIA/hZ925FwXqzyGBRzkJPOmRkKnNbtvr7veb4a7xnkDdnshs\nw7sf7nfX4WlEaIgQSCBVwbmFhivW4D/gRcTsRvw24u4z5GHwvbun5wF/QoSK3URDV/9SlWuKxPxH\nEKfO+4BZSDjnG65sTcjx73P7fI8kqboDmW0vRO7vB3x1P3Dn8ZzvvvTCiL/AFw5XyXV52rfPdERA\n+cKVPRlSP+E9VMlxWiMmyAgi0P7NXd873TX27uPrfPtchS8qIdCeQYQNL3rl8ZA+jqykT7e6ene6\n78+7frzkfos7EOF4j/tNuvj2TXH7llTjGixy+zyH/D/vQfwHVrny+RXs60X9RIAZe3H/z/Ede6J7\n3YX4MHj/2S2ER0x4v4M/KqE616oH8p/a4M53PL7oHqL/rY/c9kcQoe0zRGMVCfTnFKof9ZNONArn\nS+TZdAfyP/sucG5p7vwiyHj4GCK4PYo45ZYCY2r0PKrJzvvqhTir/NHdXP2RGP8i4F5fnZtd2fXI\ngPMPZKBrXUnbPZBZ0HJ3o+x2N/0/EMeRsPjUS5GHxA+IoLLcHX9vMx+egDxcvnPH9374ijIf/gF5\nsOx0/fgYeSi2rEHdI915exkS/4XMkoYiD72gYLAmeANXZ7ur0wmRcFe6a7kNEWLyCcTaIrORMqoh\nGLhtHd31/MJd302I6eCmkLot3Z9oubteO5Csc39FnMZCsxCGtLOvBYN+XpvAPRXUOwIR6DYi4U3v\nIc59nQgXDCq6pmORh753T96BzFoS/q7IWgQvuuPvRh7a7yD/38N99X6FPFBXumu8HXmwTgKaV+O6\nGCQhzXvu9/LOOVEYbMLzreKxLkaErq/d/brT3a8PAX0C9b3/TZxg4Laf6ftNH3NlTdw5FFNJPgfE\nDFGKTFLSkbj5vyBmHS+87TNE8OoY2PdYKsifkOB4r7u+prvf6QuimQ9vIeRZ6ds3xf3vyvCFIFfj\n2LN91yrizns74n3/d+S53zTBvnHPr+pcK1d/MGImKSLw33b/icmuL8WIUHAvkrDrdQLPASoXDEL/\nX4j24QZEu+tl+VyBCO2HhtS/Aln/ZzPyX/waEQxuIiQjY3Vexh2gzmGMmQzcYK1t5tQlG5FkL5Pd\n9ixcWkpr7S0HrqeKoih1C2NMDqJhOdpaGwwHro3jdUUGzrestdXxd1HqIHXZx2ALIrmCrHjWCJ/n\ntZX42BepmhOToijKj4mfAy/sD6HA4fle3LefjqfUInUqj4FzVqqHJPf4PaJCATEHlCESqZ//Imp/\nRVEUxWGtvbi2j+FCEwcjMfVXAR9Ya/9W28dVap86JRgg9jzPI/NJxNcAJNa30MbbPbYCWcaYNBuS\nBEVRFEWpNboiztQ7Eefi6w5sd5R9RV0TDPogznQ/Qzxy/4zLmKYoiqLUHay1i6jb5mhlL6lTgoG1\n9gP38d8u/Wa+MeZORDPQ0BhjAlqDZkjIW5y2wBhTN70qFUVRFKUWsdZWmKirMuqytOdl2OqEhJmk\nEs385tGD+AV4yqlJuIa+qvaaMGHCAe9Dsr/0Gus1TpaXXufaf+0L6rJg0M+9r0Fi0n8ALvE2unDF\n86neSniKoiiKolRAnTAlGGMWIFmfPkWiD/oBucDT1to1rs4dSKrOrUjSh1y3+737v8eKoiiKkpzU\nCcEAWRDoKmTtgFIkPe1NRMMVsdbe4XJV34ysOvYecIa1tsLlN5Xa5ZRTTjnQXUh69BrXPnqN9w96\nnQ8O6mzmw5oS76eoKIqiKMmNMQabxM6HiqIoiqLsZ1QwUBRFURSlHBUMFEVRFEUpRwUDRVEURVHK\nqStRCYqiHGQYUyP/JkVRqsn+cqhXwUBRlL1GI38UZf+wPwVxNSUoiqIoilKOCgaKoiiKopSjgoGi\nKIqiKOWoYKAoiqIoSjkqGCiKoiiKUo4KBoqiKIqilKOCgaIoirJXdO7cmS5duhzoblSJiRMnkpKS\nwptvvlmnjnHKKaeQklK3huK61RtFUZSDkHXr1vHrX/+adu3akZmZSZcuXRg1ahTbtm2r1ba+/PJL\nUlJSEr4GDRpU6fEqayPs5R/4NNFVzalr11ATHCmKotSA1atXc+KJJ7Jp0yYuvPBCevTowbvvvss9\n99zDggULWLJkCdnZ2bXa1nHHHceFF14YV3700UdXesxmzZoxYcKEmMHJWsttt92GMYYJEybE7dO5\nc+cqnY9ykGKtTcqXnJqiKLWF/seEM8880xpj7H333RdTnpuba40x9tprr621ttasWWONMXbYsGF7\nfwIJMMbYlJSUCut06tTJdunSZZ8fuzaYMGGCNcbYN954o04do3///pVeZ2ur/n9z9Wo0fqopQVEU\nZS9ZvXo1CxcupEuXLtxwww0x22677TaysrKYO3cuRUVF+7Wt/U1RURE33ngjhxxyCJmZmRx22GHc\neeedcfU8s8WwYcNYuXIll156Ka1atSI1NZU33nijvF5BQQHnnHMOLVq0IDMzk27dujFmzBi2b98e\n1+ZHH33EoEGD6Ny5M5mZmbRq1Yrjjz+eUaNGUVpaGlffWsuzzz7LT3/6Uxo0aEDz5s0ZNGgQ69ev\nDz23zz//nCuvvJL27dtTr1492rdvz9ChQ1m1alW1rtHTTz/N8ccfT1ZWFq1bt+bKK69MeMwDjZoS\nFEVR9pLXX38dgDPPPDNuW8OGDenXrx8LFy7knXfe4bTTTqu1tr755hsefPBBNm/eTPPmzTnxxBPp\n2bPn3p5WtSgpKeHMM89kw4YNnHvuuaSlpfHcc89x0003sWvXLm699da4fVavXk2fPn3o3r07Q4YM\nobi4mCZNmgAiBN122200b96c888/n1atWvHhhx8ydepUXn75Zd5++20aNWoEiFDws5/9jNTUVAYO\nHEiXLl344Ycf+Pzzz/nzn//MlClTSEuLHeYeeOAB5s+fzwUXXMCpp57KO++8wzPPPMOHH37IBx98\nQEZGRnnd9957j1/84hcUFhZywQUXcOSRR/LZZ58xd+5cXnjhBf75z39ywgknVHqNpk+fzujRo2nW\nrBlDhw6ladOmLFiwgH79+pWfd52ipiqHuvpC1ZyKUqvof8zavLw8a4yxd999d+j2G264wRpj7KxZ\ns2qlLc+UEPY69dRT7VdffbV3J2arbkowxthzzz3X7tq1q7z8u+++s02bNrVNmza1e/bsCe3vuHHj\n4tr717/+ZY0xtl+/fnb79u0x2+bMmWONMXbUqFHlZZ6JZf78+XFtbdu2zUYikfLvnpq/SZMm9uOP\nP46pe/nll1tjjP3rX/9aXhaJRGyPHj1sSkqKffLJJ2PqP/PMM9YYY3v06BF6DL8pYc2aNTY9Pd02\nb97crl27Nqb9iy66qErX2Vo1JSiKksxMnAjGxL8mTqyd+onq7QM81XaiWZ9XXpXohL1pq0GDBtx6\n660sXbqUbdu2sW3bNt544w1OPfVUFi1axOmnn17rpgdjDDNnzqRevXrlZS1btmTgwIFs376dlStX\nxu3Tpk2bUKfGmTNnAvDwww/TuHHjmG1Dhw7l2GOP5YknnojbLzMzM66sSZMmod7+OTk5HHXUUTFl\nw4cPB0RD4PHvf/+bFStW0Ldv37jojksuuYSTTjqJFStWsHjx4rhj+HniiScoLS3l97//PYccckh5\nuTGGu+66q85FJICaEhRFUQ5aWrZsycSA4HPyySfz6quvctJJJ/Huu+/yyCOPkJOTU2t9aNKkCYce\nemhceceOHQHYunVr3LZjjz2W9PT0uPK3336b9PR0/vrXv3qa3xhKSkrYtGkTW7dupVmzZlx22WXM\nnDmTCy+8kIsvvpjTTz+dfv360bVr14T9DVP9d+jQIa6vS5cuBUhoAjr11FNZvHgxH3zwASeffHLC\n43nt9O/fP25bly5d6NixI19//XXC/Q8EKhgoiqLsJd4sPswpzl/etGnT/dpWamoqV199Ne+++y5v\nvfVWrQoGifrj2fbLysritrVp0yZ0n82bN1NWVsZtt92W8HjGGAoLC2nWrBk/+clPeOutt5gyZQrP\nPvssjz/+OADdu3d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Rapd9YUpIcsGgKbKAkrdgUiGS6hjEt8Cb8Xspk2XBJElklA5spVGj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VaQJ9zR2o43rNK4UPIsealdZ7xznp3oJ2yTYtSbIeppiJkb7d620w+9EMZwaGMaFM/mGO/4w3LU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\nEREpKDAQERGRwv8D3Yig0wP3pQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1274fe810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,10))\n",
    "plot = fig.add_subplot(111)\n",
    "plt.scatter(same_chrom_distance, -np.log(same_chrom_cpvalues))\n",
    "plt.title(\"Co-occurrence P-values of CNAs, by Distance\", fontsize=20)\n",
    "plt.xlim([0,1.0e8])\n",
    "plt.xlabel(\"Distance (bp)\", fontsize=20)\n",
    "plt.ylabel(\"-log 10 P-values\", fontsize=20)\n",
    "plt.axhline(y=-1 * np.log10(0.05), color='r', linestyle = '--',linewidth=3, label=\"0.05 Threshold\")\n",
    "plt.legend(loc='best', fontsize=20)\n",
    "plot.tick_params(axis='both', which='major', labelsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set the cutoff of significant pairs to test over the whole distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cpairPercentile = 100\n",
    "# Percentile of most significant co-occurring pairs to test\n",
    "\n",
    "mpairPercentile = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the cutoff\n",
    "## Go back and adjust if you'd like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top  100 percent of cooccurring pairs:  1.0\n",
      "Top  100 percent of mutually exclusive pairs :  0.999990784855\n",
      "Now number of mutex pairs to test  31626\n",
      "Now number of coocurr pairs to test  31626\n",
      "Number of genes  252\n"
     ]
    },
    {
     "data": {
      "image/png": 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5pJt9bsrp25EOEKuSOs4fldNXIQV67yPtFAdHxFNLZQHNOpAvTz4CXBoRn2x1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dae2910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QdIN6LqlU5IW8jR4gBRprVeT9k6Rr6Ct0c21q5hhoZp+gwX0Gbb7W5H2r8jzY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\nEwgAAMAEAgAAMIEAAABMIAAAAAuObzWY0LXNhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1254e9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "cpvalues = np.array([pairsdict[c]['1CBinomProb0'] for c in pairsdict])\n",
    "logcp = np.log10(cpvalues)\n",
    "mpvalues = np.array([pairsdict[c]['1MBinomProb0'] for c in pairsdict])\n",
    "logmp = np.log10(mpvalues)\n",
    "\n",
    "\n",
    "cthreshold = stats.scoreatpercentile(cpvalues, cpairPercentile)\n",
    "mthreshold = stats.scoreatpercentile(mpvalues, mpairPercentile)\n",
    "print \"Top \", cpairPercentile, \"percent of cooccurring pairs: \", cthreshold\n",
    "print \"Top \", mpairPercentile, \"percent of mutually exclusive pairs : \", mthreshold\n",
    "\n",
    "# Let's get the top 10 percent of pairs\n",
    "\n",
    "goodcpairs = [c for c in pairsdict if (pairsdict[c]['1CBinomProb0'] <= cthreshold)]\n",
    "goodmpairs = [c for c in pairsdict if (pairsdict[c]['1MBinomProb0'] <= mthreshold)]\n",
    "goodpairs = goodcpairs + goodmpairs\n",
    "goodgenes = set.union(*(set(goodpair) for goodpair in goodpairs))\n",
    "print \"Now number of mutex pairs to test \", len(goodmpairs)\n",
    "print \"Now number of coocurr pairs to test \", len(goodcpairs)\n",
    "print \"Number of genes \", len(goodgenes)\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(logcp, bins=50)\n",
    "plt.axvline(x= np.log10(cthreshold), label=\"Top \" + str(cpairPercentile) + \"%\")\n",
    "plt.title(\"Distribution of \" + str(len(logcp)) + \" Co-occurring Pairs over \" + str(leastPatientThreshold) + \"% Least mut. patients\", fontsize=20)\n",
    "plt.xlabel(\"Log-10 P-values\", fontsize=20)\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(logmp, bins=50)\n",
    "plt.axvline(x= np.log10(mthreshold), label=\"Top \" + str(mpairPercentile) + \"%\")\n",
    "plt.title(\"Distribution of \" + str(len(logmp))  + \" Mutually Exclusive Pairs over \" + str(leastPatientThreshold) + \"% Least mut. patients\", fontsize=20)\n",
    "plt.xlabel(\"Log-10 P-values\", fontsize=20)\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test new pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting normal statistics across whole distribution\n",
      "Testing across the whole distribution\n",
      "All tested pairs have been written to  output/BRCA_wustl-som--pairs-min_cohort_filtered.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print \"Getting normal statistics across whole distribution\"\n",
    "\n",
    "new_mpairsdict, new_mgenedict = met.cooccurpairs(numCases, geneToCases, patientToGenes, goodmpairs, compute_mutex=compute_mutex)\n",
    "new_cpairsdict, new_cgenedict = met.cooccurpairs(numCases, geneToCases, patientToGenes, goodcpairs, compute_mutex=compute_mutex)\n",
    "\n",
    "print \"Testing across the whole distribution\"\n",
    "\n",
    "new_mpairsdict = chi.add_BinomP_cohorts_all_pairs(new_mpairsdict, geneToCases, patientToGenes, c0cohort_dict, c0patients)\n",
    "new_cpairsdict = chi.add_BinomP_cohorts_all_pairs(new_cpairsdict, geneToCases, patientToGenes, c0cohort_dict, c0patients)\n",
    "\n",
    "new_pairsdict = new_mpairsdict.copy()\n",
    "new_pairsdict.update(new_mpairsdict)\n",
    "\n",
    "\n",
    "print \"All tested pairs have been written to \", new_pairfile\n",
    "\n",
    "fieldnames = (new_pairsdict.values()[0]).keys()\n",
    "fieldnames.remove('MutationFrequencies')\n",
    "fieldnames.remove('MutationFrequencyDifference')\n",
    "fieldnames.remove('MutationFrequencyDifferenceRatio')\n",
    "fieldnames.remove('CooccurrenceRatio')\n",
    "fieldnames.remove('Coverage')\n",
    "fieldnames.remove('SetScore')\n",
    "fieldnames.remove('AverageOverlapPMN')\n",
    "fieldnames.remove('CombinedScore')\n",
    "fieldnames.remove('Concordance')\n",
    "fieldnames.remove('Somatic')\n",
    "fieldnames.remove('RoundedLogPCov')\n",
    "fieldnames.remove('GeneSet')\n",
    "fieldnames.remove('Type')\n",
    "\n",
    "fieldnames.insert(2, 'Type')\n",
    "fieldnames.insert(3, 'AllMBinomProb')\n",
    "fieldnames.insert(3, 'AllCBinomProb')\n",
    "\n",
    "met.writeanydict(new_pairsdict, new_pairfile, fieldnames=fieldnames)\n",
    "\n",
    "os.system('say \"whole distribution pairs finished\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zhjfeeANIndu22WYb+vfvz0UXXcSPfvSjhtMt+t73vscGG2zAtttuyz777MNH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10993ec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b9u5ampmZNZ8DlMYsAb4UETsB7wG+KGkHYBwwNSK2Be7I35G0I3AksCNwAHCB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bd85410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number significant mutex 1\n",
      "Number significant coocurr 0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "cpairs = [c for c in new_cpairsdict]\n",
    "cpvalues = np.array([new_cpairsdict[c]['AllCBinomProb'] for c in new_cpairsdict])\n",
    "sig_cpairs, sig_cpvalues, cthreshold = mt.BH(cpairs, cpvalues, Q=cFDRthreshold, use_dependencies=pairs_have_dependencies)\n",
    "\n",
    "not_sig_cpairs = set(new_cpairsdict.keys()).difference(set(sig_cpairs))\n",
    "\n",
    "new_sig_c_dict = new_cpairsdict.copy()\n",
    "for not_sig_cpair in not_sig_cpairs:\n",
    "    del new_sig_c_dict[not_sig_cpair]\n",
    "\n",
    "logcp = np.log10(cpvalues)\n",
    "\n",
    "if len(logcp) > 1:\n",
    "    plt.figure()\n",
    "    plt.hist(logcp, bins=50)\n",
    "    plt.axvline(x=np.log10(cthreshold), label=\"FDR Threshold\", c='r')\n",
    "    plt.title(\"Distribution of \" + str(len(logcp)) + \" Co-occurring Pairs over all\", fontsize=20)\n",
    "    plt.xlabel(\"Log-10 P-values\", fontsize=20)\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()\n",
    "elif len(logcp) == 1:\n",
    "    print \"One tested co-occurring pair with p=\", cpvalues[0]\n",
    "\n",
    "\n",
    "mpairs = [c for c in new_mpairsdict]\n",
    "mpvalues = np.array([new_mpairsdict[c]['AllMBinomProb'] for c in new_mpairsdict])\n",
    "sig_mpairs, sig_mpvalues, mthreshold = mt.BH(mpairs, mpvalues, Q=mFDRthreshold, use_dependencies=pairs_have_dependencies)\n",
    "\n",
    "not_sig_mpairs = set(new_mpairsdict.keys()).difference(set(sig_mpairs))\n",
    "\n",
    "new_sig_m_dict = new_mpairsdict.copy()\n",
    "for not_sig_mpair in not_sig_mpairs:\n",
    "    del new_sig_m_dict[not_sig_mpair]\n",
    "\n",
    "logmp = np.log10(mpvalues)\n",
    "\n",
    "if len(logmp) > 1:\n",
    "    plt.figure()\n",
    "    plt.hist(logmp, bins=50)\n",
    "    plt.axvline(x=np.log10(mthreshold), label=\"FDR Threshold\", c='r')\n",
    "    plt.title(\"Distribution of \" + str(len(logmp)) + \" Mutually Exclusive Pairs over all\", fontsize=20)\n",
    "    plt.xlabel(\"Log-10 P-values\", fontsize=20)\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()\n",
    "elif len(logmp) == 1:\n",
    "    print \"One tested mutex pair with p=\", mpvalues[0]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print \"Number significant mutex\", len(new_sig_m_dict)\n",
    "print \"Number significant coocurr\", len(new_sig_c_dict)\n",
    "\n",
    "\n",
    "new_sig_dict = new_sig_m_dict.copy()\n",
    "new_sig_dict.update(new_sig_c_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutual exclusivity info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene  CYP2D7P1  was not found in gene_positions.txt. We pretend it is on Chromosome Z.\n",
      "Gene  AKR1CL1  was not found in gene_positions.txt. We pretend it is on Chromosome Z.\n",
      "Gene  NPIPL2  was not found in gene_positions.txt. We pretend it is on Chromosome Z.\n",
      "Gene  GPR98  was not found in gene_positions.txt. We pretend it is on Chromosome Z.\n",
      "Gene  KIAA0182  was not found in gene_positions.txt. We pretend it is on Chromosome Z.\n",
      "*********MUTUAL EXCLUSIVITY INFO**********\n",
      "\n",
      "1  Mutually exclusive pairs\n",
      "updated with segment info  output/BRCA_wustl-som--mpairs-min_cohort_filtered.txt\n",
      "updated with COSMIC info  output/BRCA_wustl-som--mpairs-min_cohort_filtered.txt\n",
      "\n",
      "**********SINGLE GENES**********\n",
      "CDH1 has 1\n",
      "TP53 has 1\n",
      "\n",
      "**********SINGLE CYTOBANDS**********\n",
      "['16q22.1'] has 1\n",
      "['17p13.1'] has 1\n",
      "\n",
      "**********PAIRED CYTOBANDS**********\n",
      "frozenset([\"['16q22.1']\", \"['17p13.1']\"]) has 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "bgbp.write_segment_infos(goodgenes, segment_info_file)\n",
    "\n",
    "\n",
    "\n",
    "print \"*********MUTUAL EXCLUSIVITY INFO**********\\n\"\n",
    "\n",
    "print len(new_sig_m_dict), \" Mutually exclusive pairs\"\n",
    "\n",
    "mfieldnames = ['Gene0', 'Gene1', 'Gene0Cytobands', 'Gene1Cytobands', 'AllMBinomProb', \n",
    "                'AllFreqs', 'AllOverlap', 'AllSize','1MBinomProb0', '1Freqs0', '1Overlap0',\n",
    "               '1Size0']\n",
    "\n",
    "\n",
    "gene_m_dict = {}\n",
    "cyto_m_dict = {}\n",
    "cyto_pair_m_dict = {}\n",
    "\n",
    "for pair in new_sig_m_dict:\n",
    "    if new_sig_m_dict[pair]['Gene0'][-4:] in {'loss', 'gain'}:\n",
    "        suffix0 = new_sig_m_dict[pair]['Gene0'][-4:]\n",
    "    else:\n",
    "        suffix0 = \"\"\n",
    "\n",
    "    if new_sig_m_dict[pair]['Gene1'][-4:] in {'loss', 'gain'}:\n",
    "        suffix1 = new_sig_m_dict[pair]['Gene1'][-4:]\n",
    "    else:\n",
    "        suffix1 = \"\"\n",
    "    \n",
    "    \n",
    "    info0 = bgbp.get_segment_gene_info(new_sig_m_dict[pair]['Gene0'])\n",
    "    new_sig_m_dict[pair]['Gene0Loc'] = str(info0['Chromosome']) + ':' + str(info0['Start'])\n",
    "    info1 = bgbp.get_segment_gene_info(new_sig_m_dict[pair]['Gene1'])\n",
    "    new_sig_m_dict[pair]['Gene1Loc'] = str(info1['Chromosome']) + ':' + str(info1['Start'])\n",
    "    new_sig_m_dict[pair]['Gene0Cytobands'] = str(info0['Cytobands'])\n",
    "    new_sig_m_dict[pair]['Gene1Cytobands'] = str(info1['Cytobands'])\n",
    "    \n",
    "    \n",
    "    if new_sig_m_dict[pair]['Gene0'] not in gene_m_dict:\n",
    "        gene_m_dict[new_sig_m_dict[pair]['Gene0']] = set()\n",
    "    gene_m_dict[new_sig_m_dict[pair]['Gene0']].add(pair)\n",
    "    if new_sig_m_dict[pair]['Gene1'] not in gene_m_dict:\n",
    "        gene_m_dict[new_sig_m_dict[pair]['Gene1']] = set()\n",
    "    gene_m_dict[new_sig_m_dict[pair]['Gene1']].add(pair)\n",
    "    \n",
    "    \n",
    "    \n",
    "    if str(info0['Cytobands']) + suffix0 not in cyto_m_dict:\n",
    "        cyto_m_dict[str(info0['Cytobands']) + suffix0] = set()\n",
    "    cyto_m_dict[str(info0['Cytobands'])].add(pair)\n",
    "    if str(info1['Cytobands']) + suffix1 not in cyto_m_dict:\n",
    "        cyto_m_dict[str(info1['Cytobands']) + suffix1] = set()\n",
    "    cyto_m_dict[str(info1['Cytobands'])].add(pair)\n",
    "    \n",
    "    \n",
    "    \n",
    "    cyto_pair = frozenset([str(info0['Cytobands']) + suffix0, str(info1['Cytobands']) + suffix1])\n",
    "    \n",
    "    if cyto_pair not in cyto_pair_m_dict:\n",
    "        cyto_pair_m_dict[cyto_pair] = set()\n",
    "        \n",
    "    cyto_pair_m_dict[cyto_pair].add(pair)\n",
    "\n",
    "\n",
    "if new_sig_m_dict:\n",
    "    for key in new_sig_m_dict[pair].keys():\n",
    "        if key not in mfieldnames:\n",
    "            mfieldnames.append(key)\n",
    "    \n",
    "\n",
    "orderedmpairs = sorted(new_sig_m_dict.keys(), key=lambda entry: (new_sig_m_dict[entry]['AllMBinomProb'],))\n",
    "    \n",
    "met.writeanydict(new_sig_m_dict, new_mpairfile, fieldnames=mfieldnames, orderedkeys=orderedmpairs)\n",
    "print \"updated with segment info \", new_mpairfile\n",
    "!python add_COSMIC.py -ic Gene0 Gene1 -ip $COSMIC_pair_insertion_index -i $new_mpairfile -o $new_mpairfile\n",
    "print \"updated with COSMIC info \", new_mpairfile\n",
    "\n",
    "\n",
    "# Writing gene infos\n",
    "    \n",
    "\n",
    "sorted_genes = sorted(gene_m_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "sorted_cytobands = sorted(cyto_m_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "sorted_cytotuples = sorted(cyto_pair_m_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "\n",
    "print \"\\n**********SINGLE GENES**********\"\n",
    "print_length = 15\n",
    "for gene in sorted_genes:\n",
    "    if len(gene[0]) <= print_length + 4:\n",
    "        print gene[0], \"has\", len(gene[1])\n",
    "    else:\n",
    "        print gene[0][0:print_length] + gene[0][-4:], \"has\\t\", len(gene[1])\n",
    "\n",
    "print \"\\n**********SINGLE CYTOBANDS**********\"\n",
    "for cytoband in sorted_cytobands:\n",
    "    print cytoband[0], \"has\", len(cytoband[1])\n",
    "\n",
    "print \"\\n**********PAIRED CYTOBANDS**********\"\n",
    "for cytotuple in sorted_cytotuples:\n",
    "    print  cytotuple[0], \"has\", len(cytotuple[1])\n",
    "\n",
    "\n",
    "os.system('say \"finished\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw mutual exclusivity network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sy9ITTzwhh8OhM2fOqLKyUrfeeiuzNYALYsoGeklpaamcTqcaGxv1zDPPKDk52e4nAQgC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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12db199d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "***********************\n",
      "COMPONENT 0\n",
      "***********************\n",
      "Number of nodes  2\n",
      "Number of edges  1\n",
      "---\n",
      "Top 3 nodes by degree centraliy in component 0\n",
      "Gene: Degree Centrality   Degree\n",
      "CDH1 : 1.0 1\n",
      "TP53 : 1.0 1\n",
      "---\n",
      "Writing nodes to:  output/BRCA_wustl-som--mpairs-min_cohort_filtered_network_comp0.txt\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import csv\n",
    "\n",
    "G=nx.Graph()\n",
    "for pair in new_sig_m_dict:\n",
    "    pair_type = new_sig_m_dict[pair]['Type']\n",
    "    \n",
    "    G.add_edge(tuple(pair)[0], tuple(pair)[1], {\"Type\": pair_type})\n",
    "\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw(G, pos)\n",
    "\n",
    "# show graph\n",
    "plt.show()\n",
    "\n",
    "graphs = list(nx.connected_component_subgraphs(G))\n",
    "graphs = sorted(graphs, key = lambda entry: entry.number_of_nodes(), reverse=True)\n",
    "\n",
    "top_number = 3\n",
    "\n",
    "for i in range(len(graphs)):\n",
    "    print \"\\n***********************\"\n",
    "    print \"COMPONENT \" + str(i) \n",
    "    print \"***********************\"\n",
    "    graph = graphs[i]\n",
    "    print \"Number of nodes \", graph.number_of_nodes()\n",
    "    print \"Number of edges \", graph.number_of_edges()\n",
    "    \n",
    "    node_items = [g + (graph.degree(g[0]),) for g in nx.degree_centrality(graph).items()]\n",
    "    node_items = sorted(node_items, key=lambda entry: entry[1], reverse = True)\n",
    "    \n",
    "\n",
    "    print \"---\"\n",
    "    print \"Top\", top_number, \"nodes by degree centraliy in component\", i\n",
    "    print \"Gene: Degree Centrality   Degree\"\n",
    "    for j in range(min(top_number, len(node_items))):\n",
    "        print node_items[j][0], \":\", np.round(node_items[j][1], 2), node_items[j][2]\n",
    "    \n",
    "    component_file = network_m_file[:-4] + \"_comp\" + str(i) + \".txt\"\n",
    "    \n",
    "    print \"---\"\n",
    "    print \"Writing nodes to: \", component_file\n",
    "    with open(component_file, 'w') as csvfile:\n",
    "        writer = csv.writer(csvfile, delimiter='\\t')\n",
    "        writer.writerow([\"Node\", \"Degreee Centrality\", \"Degree\"])\n",
    "        for node_item in node_items:\n",
    "            writer.writerow(node_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Co-occurrence info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********COOCCURRENCE INFO**********\n",
      "\n",
      "0  Co-occurring pairs\n",
      "updated with segment info  output/BRCA_wustl-som--cpairs-min_cohort_filtered.txt\n",
      "updated with COSMIC info  output/BRCA_wustl-som--cpairs-min_cohort_filtered.txt\n",
      "\n",
      "**********SINGLE GENES**********\n",
      "\n",
      "**********SINGLE CYTOBANDS**********\n",
      "\n",
      "**********PAIRED CYTOBANDS**********\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgbp.write_segment_infos(goodgenes, segment_info_file)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print \"*********COOCCURRENCE INFO**********\\n\"\n",
    "\n",
    "print len(new_sig_c_dict), \" Co-occurring pairs\"\n",
    "\n",
    "cfieldnames = ['Gene0', 'Gene1', 'Gene0Cytobands', 'Gene1Cytobands', 'AllCBinomProb', \n",
    "               'AllFreqs', 'AllOverlap', 'AllSize','1CBinomProb0', '1Freqs0', '1Overlap0',\n",
    "               '1Size0']\n",
    "\n",
    "\n",
    "gene_c_dict = {}\n",
    "cyto_c_dict = {}\n",
    "cyto_pair_c_dict = {}\n",
    "\n",
    "for pair in new_sig_c_dict:\n",
    "    if new_sig_c_dict[pair]['Gene0'][-4:] in {'loss', 'gain'}:\n",
    "        suffix0 = new_sig_c_dict[pair]['Gene0'][-4:]\n",
    "    else:\n",
    "        suffix0 = \"\"\n",
    "\n",
    "    if new_sig_c_dict[pair]['Gene1'][-4:] in {'loss', 'gain'}:\n",
    "        suffix1 = new_sig_c_dict[pair]['Gene1'][-4:]\n",
    "    else:\n",
    "        suffix1 = \"\"\n",
    "    \n",
    "    \n",
    "    info0 = bgbp.get_segment_gene_info(new_sig_c_dict[pair]['Gene0'])\n",
    "    new_sig_c_dict[pair]['Gene0Loc'] = str(info0['Chromosome']) + ':' + str(info0['Start'])\n",
    "    info1 = bgbp.get_segment_gene_info(new_sig_c_dict[pair]['Gene1'])\n",
    "    new_sig_c_dict[pair]['Gene1Loc'] = str(info1['Chromosome']) + ':' + str(info1['Start'])\n",
    "    new_sig_c_dict[pair]['Gene0Cytobands'] = str(info0['Cytobands'])\n",
    "    new_sig_c_dict[pair]['Gene1Cytobands'] = str(info1['Cytobands'])\n",
    "    \n",
    "    \n",
    "    if new_sig_c_dict[pair]['Gene0'] not in gene_c_dict:\n",
    "        gene_c_dict[new_sig_c_dict[pair]['Gene0']] = set()\n",
    "    gene_c_dict[new_sig_c_dict[pair]['Gene0']].add(pair)\n",
    "    if new_sig_c_dict[pair]['Gene1'] not in gene_c_dict:\n",
    "        gene_c_dict[new_sig_c_dict[pair]['Gene1']] = set()\n",
    "    gene_c_dict[new_sig_c_dict[pair]['Gene1']].add(pair)\n",
    "    \n",
    "    \n",
    "    \n",
    "    if str(info0['Cytobands']) + suffix0 not in cyto_c_dict:\n",
    "        cyto_c_dict[str(info0['Cytobands']) + suffix0] = set()\n",
    "    cyto_c_dict[str(info0['Cytobands']) + suffix0].add(pair)\n",
    "    if str(info1['Cytobands']) + suffix1 not in cyto_c_dict:\n",
    "        cyto_c_dict[str(info1['Cytobands']) + suffix1] = set()\n",
    "    cyto_c_dict[str(info1['Cytobands']) + suffix1].add(pair)\n",
    "    \n",
    "    \n",
    "    \n",
    "    cyto_pair = frozenset([str(info0['Cytobands']) + suffix0, str(info1['Cytobands']) + suffix1])\n",
    "    \n",
    "    if cyto_pair not in cyto_pair_c_dict:\n",
    "        cyto_pair_c_dict[cyto_pair] = set()\n",
    "        \n",
    "    cyto_pair_c_dict[cyto_pair].add(pair)\n",
    "    \n",
    "# Writing to file\n",
    "if new_sig_c_dict:\n",
    "    for key in new_sig_c_dict[pair].keys():\n",
    "        if key not in cfieldnames:\n",
    "            cfieldnames.append(key)\n",
    "    \n",
    "orderedcpairs = sorted(new_sig_c_dict.keys(), key=lambda entry: (new_sig_c_dict[entry]['AllCBinomProb'],))\n",
    "    \n",
    "met.writeanydict(new_sig_c_dict, new_cpairfile, fieldnames=cfieldnames, orderedkeys=orderedcpairs)\n",
    "print \"updated with segment info \", new_cpairfile\n",
    "!python add_COSMIC.py -ic Gene0 Gene1 -ip $COSMIC_pair_insertion_index -i $new_cpairfile -o $new_cpairfile\n",
    "print \"updated with COSMIC info \", new_cpairfile\n",
    "\n",
    "\n",
    "# Writing gene infos\n",
    "    \n",
    "\n",
    "sorted_genes = sorted(gene_c_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "sorted_cytobands = sorted(cyto_c_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "sorted_cytotuples = sorted(cyto_pair_c_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "\n",
    "print \"\\n**********SINGLE GENES**********\"\n",
    "print_length = 15\n",
    "for gene in sorted_genes:\n",
    "    if len(gene[0]) <= print_length + 4:\n",
    "        print gene[0], \"has\", len(gene[1])\n",
    "    else:\n",
    "        print gene[0][0:print_length] + gene[0][-4:], \"has\\t\", len(gene[1])\n",
    "\n",
    "print \"\\n**********SINGLE CYTOBANDS**********\"\n",
    "for cytoband in sorted_cytobands:\n",
    "    print cytoband[0], \"has\", len(cytoband[1])\n",
    "\n",
    "print \"\\n**********PAIRED CYTOBANDS**********\"\n",
    "for cytotuple in sorted_cytotuples:\n",
    "    print  cytotuple[0], \"has\", len(cytotuple[1])\n",
    "\n",
    "\n",
    "os.system('say \"finished\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw co-occurrence network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAFBCAYAAADUsL4SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f01cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import csv\n",
    "\n",
    "G=nx.Graph()\n",
    "for pair in new_sig_c_dict:\n",
    "    pair_type = new_sig_c_dict[pair]['Type']\n",
    "    \n",
    "    G.add_edge(tuple(pair)[0], tuple(pair)[1], {\"Type\": pair_type})\n",
    "\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw(G, pos)\n",
    "\n",
    "# show graph\n",
    "plt.show()\n",
    "\n",
    "graphs = list(nx.connected_component_subgraphs(G))\n",
    "graphs = sorted(graphs, key = lambda entry: entry.number_of_nodes(), reverse=True)\n",
    "\n",
    "top_number = 3\n",
    "\n",
    "for i in range(len(graphs)):\n",
    "    print \"\\n***********************\"\n",
    "    print \"COMPONENT \" + str(i) \n",
    "    print \"***********************\"\n",
    "    graph = graphs[i]\n",
    "    print \"Number of nodes \", graph.number_of_nodes()\n",
    "    print \"Number of edges \", graph.number_of_edges()\n",
    "    \n",
    "    node_items = [g + (graph.degree(g[0]),) for g in nx.degree_centrality(graph).items()]\n",
    "    node_items = sorted(node_items, key=lambda entry: entry[1], reverse = True)\n",
    "    \n",
    "\n",
    "    print \"---\"\n",
    "    print \"Top\", top_number, \"nodes by degree centraliy in component\", i\n",
    "    print \"Gene: Degree Centrality   Degree\"\n",
    "    for j in range(min(top_number, len(node_items))):\n",
    "        print node_items[j][0], \":\", np.round(node_items[j][1], 2), node_items[j][2]\n",
    "    \n",
    "    component_file = network_c_file[:-4] + \"_comp\" + str(i) + \".txt\"\n",
    "    \n",
    "    print \"---\"\n",
    "    print \"Writing nodes to: \", component_file\n",
    "    with open(component_file, 'w') as csvfile:\n",
    "        writer = csv.writer(csvfile, delimiter='\\t')\n",
    "        writer.writerow([\"Node\", \"Degreee Centrality\", \"Degree\"])\n",
    "        for node_item in node_items:\n",
    "            writer.writerow(node_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search for triplets from among the pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of pairs  1\n",
      "Pair info of triplets initialized\n",
      "0  triplets calculated in  0.000514030456543\n",
      "Including sorting time  0.000821828842163\n"
     ]
    }
   ],
   "source": [
    "# Search for triplets\n",
    "import mutex_triangles as met\n",
    "import edgereader as edg\n",
    "\n",
    "\n",
    "        \n",
    "    \n",
    "    \n",
    "# Search for triplets\n",
    "import mutex_triangles as met\n",
    "import edgereader as edg\n",
    "\n",
    "genedict = edg.get_gene_dict(new_sig_dict)\n",
    "Triplets, pairsdict_Triplets, sorted_pairs, genesdict_Triplets, sorted_genes = met.getTriplets(new_sig_dict, genedict, new_sig_dict, numCases, geneToCases=geneToCases, patientToGenes=patientToGenes)\n",
    "Triplet_dict = met.sort_triplets_by_type(Triplets)\n",
    "\n",
    "for t_type in Triplet_dict:\n",
    "    print len(Triplet_dict[t_type]), \" of type \",  t_type\n",
    "    Triplet_dict[t_type] = sorted(Triplet_dict[t_type], key=lambda entry: entry.stats['Count'], reverse=True)\n",
    "    triplet_file_name = triplet_output_prefix + t_type + '.txt'\n",
    "    met.writeTriplets2(Triplet_dict[t_type], triplet_file_name)\n",
    "    !python add_COSMIC.py -ic Gene0 Gene1 Gene2 -ip $COSMIC_triplet_insertion_index -i $triplet_file_name -o $triplet_file_name\n",
    "    print \"updated with COSMIC info \", triplet_file_name\n",
    "    \n",
    "    \n",
    "    \n",
    "    print \"*********\" + str(t_type) + \"INFO**********\\n\"\n",
    "    gene_t_dict = {}\n",
    "    cyto_t_dict = {}\n",
    "    cyto_trip_t_dict = {}\n",
    "    \n",
    "    \n",
    "    # LEFT OFF HERE-JLU\n",
    "    \n",
    "    for triplet in Triplet_dict[t_type]:\n",
    "        genes = triplet.genes\n",
    "        suffixes = [gene[-4:] if gene[-4:] in {'loss', 'gain'} else \"\" for gene in genes]\n",
    "        \n",
    "        cytobands = set([str(bgbp.get_segment_gene_info(gene)['Cytobands']) for gene, suffix in zip(genes, suffixes)])\n",
    "        \n",
    "        for gene in genes:\n",
    "            if gene not in gene_t_dict:\n",
    "                gene_t_dict[gene] = set()\n",
    "            gene_t_dict[gene].add(frozenset(genes.difference(set([gene]))))\n",
    "    \n",
    "        \n",
    "        for cytoband in cytobands:\n",
    "            if cytoband not in cyto_t_dict:\n",
    "                cyto_t_dict[cytoband] = set()\n",
    "            cyto_t_dict[cytoband].add(frozenset(cytobands.difference(set([cytoband]))))\n",
    "    \n",
    "        if frozenset(cytobands) not in cyto_trip_t_dict:\n",
    "            cyto_trip_t_dict[frozenset(cytobands)] = set()\n",
    "            \n",
    "        cyto_trip_t_dict[frozenset(cytobands)].add(frozenset(genes))\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    sorted_genes = sorted(gene_t_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "    sorted_cytobands = sorted(cyto_t_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "    sorted_cytotuples = sorted(cyto_trip_t_dict.items(), key = lambda entry: len(entry[1]), reverse=True)\n",
    "\n",
    "    print \"\\n**********SINGLE GENES**********\"\n",
    "    print_length = 15\n",
    "    for gene in sorted_genes:\n",
    "        if len(gene[0]) <= print_length + 4:\n",
    "            print gene[0], \"has\", len(gene[1])\n",
    "        else:\n",
    "            print gene[0][0:print_length] + gene[0][-4:], \"has\\t\", len(gene[1])\n",
    "\n",
    "    print \"\\n**********SINGLE CYTOBANDS**********\"\n",
    "    for cytoband in sorted_cytobands:\n",
    "        print cytoband[0], \"has\", len(cytoband[1])\n",
    "\n",
    "    print \"\\n**********TRIPLET CYTOBANDS**********\"\n",
    "    for cytotuple in sorted_cytotuples:\n",
    "        print  cytotuple[0], \"has\", len(cytotuple[1])\n",
    "\n",
    "\n",
    "    os.system('say \"finished\"')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# IGNORE THE BELOW\n",
    "# Run weSME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_samples = 1000\n",
    "pthresh = 0.05\n",
    "num_permute = 100\n",
    "\n",
    "bin_threshold = test_minFreq\n",
    "\n",
    "cancer_name = mutationmatrix.split(\"/\")[-1][:-3]\n",
    "\n",
    "weSME_folder = '/Users/jlu96/conte/wsampling/'\n",
    "weSME_data_folder = '/Users/jlu96/conte/wsampling/data/' + cancer_name + '/'\n",
    "weSME_file = weSME_data_folder + cancer_name + \"-list.txt\"\n",
    "weSME_freq_file = weSME_data_folder + cancer_name + \"-freqs.txt\"\n",
    "weSME_sampling_folder = weSME_data_folder + 'sampling/'\n",
    "weSME_permute_folder = weSME_data_folder + 'permute/'\n",
    "weSME_permute_prefix = weSME_permute_folder + \"permute\"\n",
    "me_output = weSME_data_folder + \"ME_\" + str(pthresh) + \".txt\"\n",
    "co_output = weSME_data_folder + \"CO_\" + str(pthresh) + \".txt\"\n",
    "permute_me_output_prefix = weSME_permute_folder + \"permute_output_ME_\" + str(pthresh)\n",
    "permute_co_output_prefix = weSME_permute_folder + \"permute_output_CO_\" + str(pthresh)\n",
    "fdr_me_output = weSME_data_folder + \"ME_FDR.txt\"\n",
    "fdr_co_output = weSME_data_folder + \"CO_FDR.txt\"\n",
    "pv_fdr_me_output = weSME_data_folder + \"pv_ME_FDR.txt\"\n",
    "pv_fdr_co_output = weSME_data_folder + \"pv_CO_FDR.txt\"\n",
    "\n",
    "\n",
    "comp_sample_weights = weSME_folder + 'comp_sample_weights.py'\n",
    "comp_me_for_all_pairs = weSME_folder + 'comp_me_for_all_pairs.py'\n",
    "comp_co_for_all_pairs = weSME_folder + 'comp_co_for_all_pairs.py'\n",
    "comp_fdr = weSME_folder + 'comp_fdr.py'\n",
    "run_permute_data = weSME_folder + 'run_permute_data.py'\n",
    "run_weighted_sampling = weSME_folder + 'run_weighted_sampling.py'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/: File exists\n",
      "WeSME mut_list written to  /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/BRCA_wustl-som-list.txt\n"
     ]
    }
   ],
   "source": [
    "# convert the mutationmatirx to weSME format\n",
    "\n",
    "!mkdir $weSME_data_folder\n",
    "!python preprocessing/matrixToMut_list.py -i $mutationmatrix -o $weSME_file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:BRCA_wustl-som\n",
      "mkdir: /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/sampling/: File exists\n",
      "Making samples\n",
      "INFO:root:BRCA_wustl-som\n",
      "INFO:root:for all observed k's\n",
      "\n",
      "real\t0m29.327s\n",
      "user\t0m26.609s\n",
      "sys\t0m0.629s\n",
      "Sampling written to  /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/sampling/\n"
     ]
    }
   ],
   "source": [
    "# sample\n",
    "\n",
    "!python $comp_sample_weights $cancer_name $cancer_name -i $weSME_file -o $weSME_freq_file\n",
    "!mkdir $weSME_sampling_folder\n",
    "\n",
    "print \"Making samples\"\n",
    "!time python $run_weighted_sampling $cancer_name $cancer_name $num_samples -m $weSME_file -f $weSME_freq_file -o $weSME_sampling_folder\n",
    "!echo \"Sampling written to \" $weSME_sampling_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_me_for_all_pairs.py\", line 17, in <module>\n",
      "    from mut_ex import ws_mut_ex, misc_mut_ex\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/misc_mut_ex.py\", line 5, in <module>\n",
      "    import scipy.stats as ss\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/stats/__init__.py\", line 338, in <module>\n",
      "    from .stats import *\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/stats/stats.py\", line 183, in <module>\n",
      "    from . import distributions\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/stats/distributions.py\", line 13, in <module>\n",
      "    from . import _continuous_distns\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/stats/_continuous_distns.py\", line 1703, in <module>\n",
      "    genexpon = genexpon_gen(a=0.0, name='genexpon')\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py\", line 1478, in __init__\n",
      "    self._construct_doc(docdict, dct.get(self.name))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py\", line 732, in _construct_doc\n",
      "    self.__doc__ = doccer.docformat(self.__doc__, tempdict)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/misc/doccer.py\", line 64, in docformat\n",
      "    newlines.append(indent+line)\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m1.450s\n",
      "user\t0m0.690s\n",
      "sys\t0m0.424s\n",
      "Mutex written to  /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/ME_0.05.txt\n",
      "DEBUG:root:comp CO for BRCA_wustl-som, BRCA_wustl-som\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_co_for_all_pairs.py\", line 81, in <module>\n",
      "    (covers, ws_k_cover_dic, max_pair_num=10**5, me_co=\"co\", ws_ex_cover_sizes_dic=ws_ex_cover_sizes_dic)\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/ws_mut_ex.py\", line 142, in compute_me_co_pv_ws\n",
      "    compute_ws_cover_sizes(cover_sizes, ws_k_cover_dic, pair_num*10, ws_ex_cover_sizes_dic)\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/ws_mut_ex.py\", line 102, in compute_ws_cover_sizes\n",
      "    [len(compute_ex_cover(random_covers[i])) for i in range(len(random_covers))] # NULL ex_cover_sizes\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/ws_mut_ex.py\", line 23, in compute_ex_cover\n",
      "    union_cov = union_cov.union(cov)\n",
      "KeyboardInterrupt\n",
      "\n",
      "\n",
      "real\t0m12.708s\n",
      "user\t0m11.622s\n",
      "sys\t0m0.553s\n",
      "Cooccur written to  /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/CO_0.05.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "512"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get mutex + co-occurrence\n",
    "!time python $comp_me_for_all_pairs $cancer_name $cancer_name $pthresh -m $weSME_file -s $weSME_sampling_folder -o $me_output\n",
    "!echo \"Mutex written to \" $me_output\n",
    "!time python $comp_co_for_all_pairs $cancer_name $cancer_name $pthresh -m $weSME_file -s $weSME_sampling_folder -o $co_output\n",
    "!echo \"Cooccur written to \" $co_output\n",
    "\n",
    "os.system('Pairs completed\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/permute/: File exists\n",
      "FILE  0\n",
      "Permuting\n",
      "INFO:root:0-th permutation.. (permute 100 times)\n",
      "INFO:root:\n",
      "****** reading mutation data files (BRCA_wustl-som)...\n",
      "\n",
      "DEBUG:root:10000 swaps..\n",
      "\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/run_permute_data.py\", line 74, in <module>\n",
      "    permuted_graphs[cantype] = permute_mut_data.permute_mut_graph(mut_graphs[cantype], genes, range(nsamples), pnum)\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/permute_mut_data.py\", line 87, in permute_mut_graph\n",
      "    bipartite_double_edge_swap(H, genes, samples, nswap=Q * len(G.edges()))\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/permute_mut_data.py\", line 54, in bipartite_double_edge_swap\n",
      "    gene2 = random.choice(list(G[sample1]))\n",
      "KeyboardInterrupt\n",
      "\n",
      "\n",
      "real\t0m5.185s\n",
      "user\t0m3.896s\n",
      "sys\t0m0.636s\n",
      "ME\n",
      "^C\n",
      "CO\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_co_for_all_pairs.py\", line 11, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 7, in <module>\n",
      "    from pandas import hashtable, tslib, lib\n",
      "  File \"pandas/tslib.pyx\", line 46, in init pandas.tslib (pandas/tslib.c:73538)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py\", line 29, in <module>\n",
      "    from pkg_resources import resource_stream\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3084, in <module>\n",
      "    @_call_aside\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3070, in _call_aside\n",
      "    f(*args, **kwargs)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3112, in _initialize_master_working_set\n",
      "    list(map(working_set.add_entry, sys.path))\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 691, in add_entry\n",
      "    for dist in find_distributions(entry, True):\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2114, in find_on_path\n",
      "    if lower.endswith('.egg-info') or lower.endswith('.dist-info'):\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.352s\n",
      "user\t0m0.192s\n",
      "sys\t0m0.104s\n",
      "FILE  1\n",
      "Permuting\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/run_permute_data.py\", line 23, in <module>\n",
      "    import data_proc.io_utils as io\n",
      "  File \"/Users/jlu96/conte/wsampling/data_proc/io_utils.py\", line 7, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 7, in <module>\n",
      "    from pandas import hashtable, tslib, lib\n",
      "  File \"pandas/tslib.pyx\", line 46, in init pandas.tslib (pandas/tslib.c:73538)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py\", line 29, in <module>\n",
      "    from pkg_resources import resource_stream\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 89, in <module>\n",
      "    import pkg_resources._vendor.packaging.specifiers\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/specifiers.py\", line 14, in <module>\n",
      "    from __future__ import absolute_import, division, print_function\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.536s\n",
      "user\t0m0.302s\n",
      "sys\t0m0.182s\n",
      "ME\n",
      "DEBUG:root:comp ME for BRCA_wustl-som, BRCA_wustl-som\n",
      "INFO:root:0 \n",
      "CO\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_co_for_all_pairs.py\", line 11, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 7, in <module>\n",
      "    from pandas import hashtable, tslib, lib\n",
      "  File \"pandas/tslib.pyx\", line 46, in init pandas.tslib (pandas/tslib.c:73538)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py\", line 29, in <module>\n",
      "    from pkg_resources import resource_stream\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 37, in <module>\n",
      "    import email.parser\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/parser.py\", line 12, in <module>\n",
      "    from email.feedparser import FeedParser\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/feedparser.py\", line 27, in <module>\n",
      "    from email import message\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/message.py\", line 16, in <module>\n",
      "    import email.charset\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/charset.py\", line 13, in <module>\n",
      "    import email.base64mime\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/base64mime.py\", line 40, in <module>\n",
      "    from email.utils import fix_eols\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/utils.py\", line 26, in <module>\n",
      "    import base64\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/base64.py\", line 3, in <module>\n",
      "    \"\"\"RFC 3548: Base16, Base32, Base64 Data Encodings\"\"\"\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.281s\n",
      "user\t0m0.123s\n",
      "sys\t0m0.098s\n",
      "FILE  2\n",
      "Permuting\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/run_permute_data.py\", line 22, in <module>\n",
      "    from mut_ex import permute_mut_data\n",
      "  File \"/Users/jlu96/conte/wsampling/mut_ex/permute_mut_data.py\", line 5, in <module>\n",
      "    import networkx as nx\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/networkx/__init__.py\", line 82, in <module>\n",
      "    import networkx.linalg\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/networkx/linalg/__init__.py\", line 9, in <module>\n",
      "    from networkx.linalg.algebraicconnectivity import *\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/networkx/linalg/algebraicconnectivity.py\", line 18, in <module>\n",
      "    from numpy import (array, asmatrix, asarray, dot, matrix, ndarray, ones,\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/__init__.py\", line 180, in <module>\n",
      "    from . import add_newdocs\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/add_newdocs.py\", line 13, in <module>\n",
      "    from numpy.lib import add_newdoc\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/__init__.py\", line 18, in <module>\n",
      "    from .polynomial import *\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/polynomial.py\", line 4, in <module>\n",
      "    \"\"\"\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.314s\n",
      "user\t0m0.166s\n",
      "sys\t0m0.087s\n",
      "ME\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_me_for_all_pairs.py\", line 12, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 7, in <module>\n",
      "    from pandas import hashtable, tslib, lib\n",
      "  File \"pandas/tslib.pyx\", line 46, in init pandas.tslib (pandas/tslib.c:73538)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py\", line 29, in <module>\n",
      "    from pkg_resources import resource_stream\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 37, in <module>\n",
      "    import email.parser\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/parser.py\", line 12, in <module>\n",
      "    from email.feedparser import FeedParser\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/feedparser.py\", line 27, in <module>\n",
      "    from email import message\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/message.py\", line 16, in <module>\n",
      "    import email.charset\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/charset.py\", line 13, in <module>\n",
      "    import email.base64mime\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/base64mime.py\", line 40, in <module>\n",
      "    from email.utils import fix_eols\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/email/utils.py\", line 28, in <module>\n",
      "    import socket\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/socket.py\", line 53, in <module>\n",
      "    import _ssl\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.265s\n",
      "user\t0m0.119s\n",
      "sys\t0m0.090s\n",
      "CO\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_co_for_all_pairs.py\", line 11, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 43, in <module>\n",
      "    import pandas.core.config_init\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/config_init.py\", line 239, in <module>\n",
      "    cf.register_option('precision', 7, pc_precision_doc, validator=is_int)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/config.py\", line 723, in inner\n",
      "    return func(pkey, *args, **kwds)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/config.py\", line 433, in register_option\n",
      "    import tokenize\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/tokenize.py\", line 103, in <module>\n",
      "    re.compile, (Token, PseudoToken, Single3, Double3))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/re.py\", line 194, in compile\n",
      "    return _compile(pattern, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/re.py\", line 249, in _compile\n",
      "    p = sre_compile.compile(pattern, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 576, in compile\n",
      "    code = _code(p, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 561, in _code\n",
      "    _compile(code, p.data, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 151, in _compile\n",
      "    _compile(code, av[1], flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 192, in _compile\n",
      "    _compile(code, av, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 151, in _compile\n",
      "    _compile(code, av[1], flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 192, in _compile\n",
      "    _compile(code, av, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 151, in _compile\n",
      "    _compile(code, av[1], flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 192, in _compile\n",
      "    _compile(code, av, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 151, in _compile\n",
      "    _compile(code, av[1], flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 192, in _compile\n",
      "    _compile(code, av, flags)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/sre_compile.py\", line 126, in _compile\n",
      "    emit(OPCODES[REPEAT_ONE])\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.452s\n",
      "user\t0m0.251s\n",
      "sys\t0m0.137s\n",
      "FILE  3\n",
      "Permuting\n",
      "ME\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_me_for_all_pairs.py\", line 12, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 7, in <module>\n",
      "    from pandas import hashtable, tslib, lib\n",
      "  File \"pandas/tslib.pyx\", line 46, in init pandas.tslib (pandas/tslib.c:73538)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py\", line 29, in <module>\n",
      "    from pkg_resources import resource_stream\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3084, in <module>\n",
      "    @_call_aside\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3070, in _call_aside\n",
      "    f(*args, **kwargs)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3097, in _initialize_master_working_set\n",
      "    working_set = WorkingSet._build_master()\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 642, in _build_master\n",
      "    ws = cls()\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 635, in __init__\n",
      "    self.add_entry(entry)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 691, in add_entry\n",
      "    for dist in find_distributions(entry, True):\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2122, in find_on_path\n",
      "    path_item, entry, metadata, precedence=DEVELOP_DIST\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2484, in from_location\n",
      "    py_version=py_version, platform=platform, **kw\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2463, in __init__\n",
      "    self._version = safe_version(version)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 1375, in safe_version\n",
      "    return str(packaging.version.Version(version))\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/version.py\", line 254, in __str__\n",
      "    parts.append(\".\".join(str(x) for x in self._version.release))\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.306s\n",
      "user\t0m0.161s\n",
      "sys\t0m0.091s\n",
      "CO\n",
      "^CTraceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_co_for_all_pairs.py\", line 11, in <module>\n",
      "    import pandas\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/__init__.py\", line 7, in <module>\n",
      "    from pandas import hashtable, tslib, lib\n",
      "  File \"pandas/tslib.pyx\", line 46, in init pandas.tslib (pandas/tslib.c:73538)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pytz/__init__.py\", line 29, in <module>\n",
      "    from pkg_resources import resource_stream\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3084, in <module>\n",
      "    @_call_aside\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3070, in _call_aside\n",
      "    f(*args, **kwargs)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 3097, in _initialize_master_working_set\n",
      "    working_set = WorkingSet._build_master()\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 642, in _build_master\n",
      "    ws = cls()\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 635, in __init__\n",
      "    self.add_entry(entry)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 691, in add_entry\n",
      "    for dist in find_distributions(entry, True):\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2122, in find_on_path\n",
      "    path_item, entry, metadata, precedence=DEVELOP_DIST\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2484, in from_location\n",
      "    py_version=py_version, platform=platform, **kw\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2463, in __init__\n",
      "    self._version = safe_version(version)\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 1375, in safe_version\n",
      "    return str(packaging.version.Version(version))\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/version.py\", line 217, in __init__\n",
      "    release=tuple(int(i) for i in match.group(\"release\").split(\".\")),\n",
      "  File \"/Users/jlu96/.virtualenvs/djmac/lib/python2.7/site-packages/pkg_resources/_vendor/packaging/version.py\", line 217, in <genexpr>\n",
      "    release=tuple(int(i) for i in match.group(\"release\").split(\".\")),\n",
      "KeyboardInterrupt\n",
      "\n",
      "real\t0m0.289s\n",
      "user\t0m0.157s\n",
      "sys\t0m0.078s\n",
      "FILE  4\n",
      "Permuting\n",
      "^C\n",
      "ME\n",
      "DEBUG:root:comp ME for BRCA_wustl-som, BRCA_wustl-som\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/jlu96/conte/wsampling/comp_me_for_all_pairs.py\", line 53, in <module>\n",
      "    alt_list_dic, samples = io_utils.read_mut_list(mut_file, samples=True)\n",
      "  File \"/Users/jlu96/conte/wsampling/data_proc/io_utils.py\", line 53, in read_mut_list\n",
      "    temp_dic = read_dic(filename)\n",
      "  File \"/Users/jlu96/conte/wsampling/data_proc/io_utils.py\", line 17, in read_dic\n",
      "    lines = open(filename).readlines()\n",
      "IOError: [Errno 2] No such file or directory: '/Users/jlu96/conte/wsampling/data/BRCA_wustl-som/permute/permute_4.txt'\n",
      "\n",
      "real\t0m0.911s\n",
      "user\t0m0.651s\n",
      "sys\t0m0.248s\n",
      "CO\n"
     ]
    }
   ],
   "source": [
    "# compute FDR\n",
    "# CHECK TO MAKE SURE ME AND CO ARE WRITTEN TO THE RIGHT FILES ANDL OADED RIGHT BY FDR\n",
    "import time \n",
    "!mkdir $weSME_permute_folder\n",
    "\n",
    "t = time.time()\n",
    "for i in range(num_permute):\n",
    "    permute_file = weSME_permute_prefix  + \"_\" + str(i) + \".txt\"\n",
    "    me_output_file = permute_me_output_prefix + str(i) + \".txt\"\n",
    "    co_output_file = permute_co_output_prefix + str(i) + \".txt\"\n",
    "    \n",
    "    print \"FILE \", i\n",
    "    print \"Permuting\"\n",
    "    !time python $run_permute_data $cancer_name $cancer_name $i $num_permute -i $weSME_file -p $weSME_permute_prefix\n",
    "    print \"ME\"\n",
    "    !time python $comp_me_for_all_pairs $cancer_name $cancer_name $pthresh -m $permute_file -s $weSME_sampling_folder -o $me_output_file\n",
    "    print \"CO\"\n",
    "    !time python $comp_co_for_all_pairs $cancer_name $cancer_name $pthresh -m $permute_file -s $weSME_sampling_folder -o $co_output_file\n",
    "print \"Time used for running over permutes: \", time.time() - t    \n",
    "\n",
    "os.system('Permutes completed\"')\n",
    "\n",
    "print \"FDR ME\"\n",
    "!time python $comp_fdr $cancer_name $cancer_name me $num_permute $bin_threshold -m $weSME_file -p $me_output -n $permute_me_output_prefix -f $fdr_me_output -pf $pv_fdr_me_output\n",
    "\n",
    "print \"FDR CO\"\n",
    "!time python $comp_fdr $cancer_name $cancer_name co $num_permute $bin_threshold -m $weSME_file -p $co_output -n $permute_co_output_prefix -f $fdr_co_output -pf $pv_fdr_co_output\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in  output/BRCA_wustl-som--pairs-min_cohort_filtered.txt  ....\n",
      "number of repeats  0\n",
      "Repeats were not included.\n",
      "Reading in  /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/ME_0.05.txt  ....\n",
      "number of repeats  0\n",
      "Repeats were not included.\n",
      "Combined file written to output output/BRCA_wustl-som--pairs-min_cohort_filtered_weSME_integrated_me.txt\n",
      "\n",
      "real\t0m9.682s\n",
      "user\t0m9.186s\n",
      "sys\t0m0.239s\n",
      "Reading in  output/BRCA_wustl-som--pairs-min_cohort_filtered.txt  ....\n",
      "number of repeats  0\n",
      "Repeats were not included.\n",
      "Reading in  /Users/jlu96/conte/wsampling/data/BRCA_wustl-som/CO_0.05.txt  ....\n",
      "number of repeats  0\n",
      "Repeats were not included.\n",
      "Combined file written to output output/BRCA_wustl-som--pairs-min_cohort_filtered_weSME_integrated_co.txt\n",
      "\n",
      "real\t20m57.644s\n",
      "user\t19m39.593s\n",
      "sys\t0m49.816s\n"
     ]
    }
   ],
   "source": [
    "# Use Edgereader to integrate cpairs and mpairs\n",
    "me_integrated = new_pairfile[:-4] + \"_weSME_integrated_me.txt\"\n",
    "co_integrated = new_pairfile[:-4] + \"_weSME_integrated_co.txt\"\n",
    "!time python postprocessing/edgereader.py -i $new_pairfile $me_output -o $me_integrated\n",
    "!time python postprocessing/edgereader.py -i $new_pairfile $co_output -o $co_integrated\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10a2c8e90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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FRPKAivHbHhXji4iISMoU7EVERFo5BXsREZFWTiPoiYjkiVyeL11ym4K9iEge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\nIhmnZC8iIpJx/z93cVGWBZcGjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1090a1910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the p-values of mutex in each\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.read_csv(me_integrated, sep='\\t')\n",
    "df_me = df[df['Type'] == \"MutuallyExclusive\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "plt.scatter(-np.log10(df_me['AllMBinomProb']), -np.log10(df_me['pv (ws)']))\n",
    "plt.xlabel(\"COFFDROP Log-10 Pvalues\", fontsize=20)\n",
    "plt.ylabel(\"WeSME Log-10 Pvalues\", fontsize=20)\n",
    "\n",
    "line_xs = np.linspace((-np.log10(df_me['AllMBinomProb'])).min(), (-np.log10(df_me['AllMBinomProb'])).max(), 100)\n",
    "ax.plot(line_xs, line_xs, ls=\"--\", c=\".3\", linewidth=2,label=\"Same p-value\")\n",
    "plt.legend()\n",
    "plt.title(\"Mutual Exclusivity P-values, OV_broad SNVs\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10bc21410>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MbP+UZecSSnJ/9eheprsvI1zJjgKutZQbpGa2mZltXui6kX9HP6emrHcYof9zJUt0i2uM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\nZhXz/397tNbvq/URAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a50bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(co_integrated, sep='\\t')\n",
    "df_co = df[df['Type'] == \"Cooccurring\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "plt.scatter(-np.log10(df_co['AllCBinomProb']), -np.log10(df_co['pv (ws)']))\n",
    "plt.xlabel(\"COFFDROP Log-10 Pvalues\", fontsize=20)\n",
    "plt.ylabel(\"WeSME Log-10 Pvalues\", fontsize=20)\n",
    "line_xs = np.linspace((-np.log10(df_co['AllCBinomProb'])).min(), (-np.log10(df_co['AllCBinomProb'])).max(), 100)\n",
    "ax.plot(line_xs, line_xs, ls=\"--\", c=\".3\", linewidth=2,label=\"Same p-value\")\n",
    "plt.legend()\n",
    "plt.title(\"Co-occurring P-values, OV_broad SNVs\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene0\tGene1\tType\tAllCBinomProb\tAllMBinomProb\tMutationFrequency0\tMutationFrequency1\tProbability\tOverlap\tMutexProb\tAllSize\tAllFreqs\tAllOverlap\tAllCBinomProb\tAllMBinomProb\t1Size0\t1Freqs0\t1Overlap0\t1CBinomProb0\t1MBinomProb0\t1CAllSig\t1MAllSig\tjaccard index\tpv (fisher)\tpv (ws)\r",
      "\r\n",
      "MAP3K1\tEPG5\tCooccurring\t0.04767972325455884\t0.9878460873152505\t72\t18\t0.039420319076154925\t4\t0.9919208122067659\t963\t[72, 18]\t4\t0.04767972325455884\t0.9878460873152505\t155\t[12, 2]\t1\t0.14351296628166846\t0.989236992696382\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "OTOGL\tTTN\tCooccurring\t0.0010913253872335245\t0.9996685642701596\t21\t188\t0.0001252439899519038\t12\t0.9999807219773625\t963\t[21, 188]\t12\t0.0010913253872335245\t0.9996685642701596\t155\t[2, 7]\t1\t0.08638763659298487\t0.9961803042921608\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "CDK10\tHS6ST1\tMutuallyExclusive\t1.0\t0.9279575807096055\t4\t18\t1.0\t0\t0.9271935855680765\t963\t[4, 18]\t0\t1.0\t0.9279575807096055\t155\t[2, 2]\t0\t1.0\t0.9745215950455555\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "MUC5B\tCOASY\tMutuallyExclusive\t1.0\t0.6331715200812317\t55\t8\t1.0\t0\t0.6236019945772437\t963\t[55, 8]\t0\t1.0\t0.6331715200812317\t155\t[4, 2]\t0\t1.0\t0.9496882574120252\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "PPM1F\tDNM1P46\tMutuallyExclusive\t1.0\t0.8901990026693144\t7\t16\t1.0\t0\t0.8890068417143173\t963\t[7, 16]\t0\t1.0\t0.8901990026693144\t155\t[2, 2]\t0\t1.0\t0.9745215950455555\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "DCAF6\tGBP6\tMutuallyExclusive\t1.0\t0.9298869536225539\t10\t7\t1.0\t0\t0.9293225300852619\t963\t[10, 7]\t0\t1.0\t0.9298869536225539\t155\t[2, 2]\t0\t1.0\t0.9745215950455555\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "DMD\tFBN3\tCooccurring\t0.2917367189382025\t0.9055469306661005\t45\t23\t0.29243328556088866\t2\t0.9122009950754261\t963\t[45, 23]\t2\t0.2917367189382025\t0.9055469306661005\t155\t[2, 2]\t0\t1.0\t0.9745215950455555\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "PLCG1\tCOL6A5\tCooccurring\t0.04684934078113671\t0.9948139043118924\t11\t30\t0.043346205313197196\t2\t0.9961917037699551\t963\t[11, 30]\t2\t0.04684934078113671\t0.9948139043118924\t155\t[2, 2]\t0\t1.0\t0.9745215950455555\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n",
      "KIAA0182\tUHRF1BP1L\tMutuallyExclusive\t1.0\t0.933757729668957\t6\t11\t1.0\t0\t0.9332231493081344\t963\t[6, 11]\t0\t1.0\t0.933757729668957\t155\t[2, 2]\t0\t1.0\t0.9745215950455555\tTrue\tTrue\tNA\tNA\tNA\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head $co_integrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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