test_for_linked_svs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linked SV analysis for SVclone paper\n",
    "\n",
    "Please install sv_tools before running:\n",
    "\n",
    "    git clone git@github.com:mcmero/sv_tools.git\n",
    "    cd sv_tools\n",
    "    python setup.py install\n",
    "    \n",
    "Run the `analyse_select_pcawg.RmD` R markdown notebook *before* running this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels\n",
    "import statsmodels.stats.multitest as smm\n",
    "import contextlib\n",
    "import io\n",
    "import sys\n",
    "import os\n",
    "import itertools\n",
    "from sv_tools import sv_data, sv_diagram, kc_tests\n",
    "from os.path import expanduser "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_pp = '~/data/pcawg_data/consensus.20170217.purity.ploidy.txt'\n",
    "cc_path = '~/data/svclone_paper_20181219/%s/ccube_out/%s_cluster_certainty.txt'\n",
    "bal_enr_samples = 'pcawg_bal_enr/bal_enr_samples.txt'\n",
    "other_samples = 'pcawg_bal_enr/other_samples.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "@contextlib.contextmanager\n",
    "def nostdout():\n",
    "    save_stdout = sys.stdout\n",
    "    sys.stdout = io.BytesIO()\n",
    "    yield\n",
    "    sys.stdout = save_stdout\n",
    "\n",
    "purities = pd.read_csv(expanduser(consensus_pp), delimiter='\\t')\n",
    "chroms = [str(x) for x in np.array(range(1,23))] + ['X', 'Y']\n",
    "aliquots = pd.read_csv(bal_enr_samples, header=-1)\n",
    "aliquots = aliquots[0].values\n",
    "aliquots_other = pd.read_csv(other_samples, header=-1)\n",
    "aliquots_other = aliquots_other[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate SV files as input for sv_tools\n",
    "for aliquot_id in np.append(aliquots, aliquots_other):\n",
    "    sv_file = expanduser(cc_path % (aliquot_id, aliquot_id))\n",
    "    svs = pd.read_csv(sv_file, delimiter='\\t')\n",
    "\n",
    "    if len(svs) > 0:\n",
    "        svs['average_proportion'] = svs[['average_proportion1','average_proportion2']].apply(np.mean, axis=1)\n",
    "        pur = purities[purities.samplename == aliquot_id].purity.values[0]\n",
    "        ccfs = svs.average_proportion / pur\n",
    "        \n",
    "        svs = svs[['chr1','pos1','dir1','chr2','pos2','dir2']]            \n",
    "        rename_cols =  {'chr1': 'chrom1', 'dir1': 'strand1', 'chr2': 'chrom2', 'dir2': 'strand2'}\n",
    "        svs = svs.rename(columns = rename_cols)\n",
    "\n",
    "        sv_file = 'pcawg_bal_enr/%s_svs.txt' % aliquot_id\n",
    "        svs.to_csv(sv_file, sep='\\t', index=False)\n",
    "                \n",
    "        sc_svs = svs[ccfs < 0.7]\n",
    "        sc_sv_file = 'pcawg_bal_enr/%s_sc_svs.txt' % aliquot_id\n",
    "        sc_svs.to_csv(sc_sv_file, sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_cols = ['aliquot', 'svs', 'cl_svs', 'sc_svs', 'sig_clus', \n",
    "               'clonal_sig_svs', 'subclonal_sig_svs', 'chroms_tested', \n",
    "               'chroms_walked', 'linked_chroms']\n",
    "chrom_cols = ['chrom', 'all_svs_tested', 'cl_svs_tested', 'sc_svs_tested', \n",
    "                 'ks_pval', 'cl_ks_pval', 'sc_ks_pval', 'walk_pval']\n",
    "\n",
    "def test_sv_linkage(aliquots):\n",
    "\n",
    "    test_results = pd.DataFrame(columns=result_cols)\n",
    "    for aliquot_id in aliquots:\n",
    "        sv_file = 'pcawg_bal_enr/%s_svs.txt' % aliquot_id\n",
    "        sc_sv_file = 'pcawg_bal_enr/%s_sc_svs.txt' % aliquot_id\n",
    "\n",
    "        if not os.path.exists(sv_file) or not os.path.exists(sc_sv_file):\n",
    "            continue\n",
    "\n",
    "        svs      = pd.read_csv(sv_file, delimiter='\\t')\n",
    "        svs      = svs[svs.chrom1 == svs.chrom2]\n",
    "        sc_svs   = pd.read_csv(sc_sv_file, delimiter='\\t')\n",
    "        itrx_svs = sc_svs[['chrom1','chrom2']]\n",
    "        itrx_svs = itrx_svs[itrx_svs.chrom1.map(str) != itrx_svs.chrom2.map(str)]\n",
    "        itrx_svs = itrx_svs.drop_duplicates()\n",
    "        if len(svs) == 0 or len(sc_svs) == 0: continue\n",
    "\n",
    "        print(aliquot_id)\n",
    "        chrom_results = pd.DataFrame()\n",
    "        chroms_tested = 0\n",
    "        for chrom in chroms:\n",
    "\n",
    "            bps = sv_data.get_breakpoints(sv_file, chrom)\n",
    "            sc_bps = sv_data.get_breakpoints(sc_sv_file, chrom)\n",
    "            cl_bps = [bp for bp in bps if bp not in sc_bps]\n",
    "\n",
    "            fusions = sv_data.get_fusions(sv_file, chrom)\n",
    "            sc_fusions = sv_data.get_fusions(sc_sv_file, chrom)\n",
    "            cl_fusions = [fus for fus in fusions if fus not in sc_fusions]\n",
    "\n",
    "            if len(sc_bps) < 4 or len(cl_bps) < 4:\n",
    "                continue\n",
    "            chroms_tested += 1\n",
    "\n",
    "            walk=''.join(pd.Series(map(lambda x: x.type(), fusions)).values)\n",
    "            if walk.count('H') > 0 and walk.count('T') > 0:\n",
    "                with nostdout():\n",
    "                    kct = kc_tests.F_walk(walk)\n",
    "            else:\n",
    "                kct = [float('nan'), float('nan'), float('nan')]\n",
    "\n",
    "            with nostdout():\n",
    "                sc_ks_test = kc_tests.test_A(sc_bps)\n",
    "                cl_ks_test = kc_tests.test_A(cl_bps)\n",
    "                ks_test    = kc_tests.test_A(bps)\n",
    "\n",
    "            results = pd.DataFrame([[chrom, len(bps)/2, len(sc_bps)/2, len(cl_bps)/2, ks_test.pvalue, \n",
    "                                     cl_ks_test.pvalue, sc_ks_test.pvalue, kct[2]]], columns = chrom_cols)\n",
    "            chrom_results = chrom_results.append(results)\n",
    "\n",
    "        if len(chrom_results) > 0:            \n",
    "            sig_ks    = smm.multipletests(chrom_results.ks_pval.values, method='fdr_bh')[0]\n",
    "            sc_sig_ks = smm.multipletests(chrom_results.sc_ks_pval.values, method='fdr_bh')[0]\n",
    "            cl_sig_ks = smm.multipletests(chrom_results.cl_ks_pval.values, method='fdr_bh')[0]\n",
    "            sig_walk  = np.sum(smm.multipletests(chrom_results.walk_pval.values, method='fdr_bh')[0])            \n",
    "            chroms_tested = len(chrom_results)\n",
    "            \n",
    "            # check linkage of chroms with clusters\n",
    "            linked_chroms = 0\n",
    "            if len(itrx_svs) > 0 and len(chrom_results) > 0:\n",
    "                sig_scs = chrom_results[np.array(sc_sig_ks)]\n",
    "                perms = np.array([c for c in itertools.permutations(sig_scs.chrom, 2)])\n",
    "                if len(perms) > 0:\n",
    "                    itx_chr1 = itrx_svs.chrom1.map(str).values\n",
    "                    itx_chr2 = itrx_svs.chrom2.map(str).values\n",
    "                    matched = []\n",
    "                    for perm in perms:\n",
    "                        match_chrom1 = np.array(itx_chr1 == str(perm[0]))\n",
    "                        match_chrom2 = np.array(itx_chr2 == str(perm[1]))\n",
    "                        n_matched    = np.sum(np.logical_and(match_chrom1, match_chrom2))\n",
    "                        matched.append(n_matched > 0)\n",
    "                    linked_chroms = np.sum(np.array(matched))\n",
    "\n",
    "            sig_ks    = sum(chrom_results[sig_ks].all_svs_tested)\n",
    "            sc_sig_ks = sum(chrom_results[sc_sig_ks].sc_svs_tested)\n",
    "            cl_sig_ks = sum(chrom_results[cl_sig_ks].cl_svs_tested)\n",
    "            \n",
    "            results   = pd.DataFrame([[aliquot_id, len(svs), len(svs) - len(sc_svs), \n",
    "                                       len(sc_svs), sig_ks, cl_sig_ks, sc_sig_ks, \n",
    "                                       chroms_tested, sig_walk, linked_chroms]], columns=result_cols)\n",
    "            test_results = test_results.append(results)\n",
    "    return test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "feccee20-a62d-4152-b832-b9fdaca87a61\n"
     ]
    }
   ],
   "source": [
    "bal_enr_linkage = test_sv_linkage(aliquots)\n",
    "other_linkage = test_sv_linkage(aliquots_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        bal_enriched     other\n",
      "prop_svs_linked             0.970471  0.871229\n",
      "prop_clonal_svs_linked      0.269430  0.164889\n",
      "prop_subcl_svs_linked       0.391145  0.313271\n",
      "frac_chrs_walkable          0.027340  0.020941\n",
      "chroms_linked               0.503106  0.215909\n"
     ]
    }
   ],
   "source": [
    "n_svs         = float(sum(bal_enr_linkage.svs.values))\n",
    "svs_linked    = sum(bal_enr_linkage.sig_clus.values) / n_svs\n",
    "cl_svs_linked = sum(bal_enr_linkage.clonal_sig_svs.values) / n_svs\n",
    "sc_svs_linked = sum(bal_enr_linkage.subclonal_sig_svs.values) / n_svs\n",
    "perc_chrs_wlk = np.mean(bal_enr_linkage.chroms_walked.values / bal_enr_linkage.chroms_tested.values)\n",
    "perc_chrs_lnk = np.sum(bal_enr_linkage.linked_chroms.values > 0) / float(len(bal_enr_linkage))\n",
    "\n",
    "collated_cols = ['prop_svs_linked', 'prop_clonal_svs_linked', 'prop_subcl_svs_linked', 'frac_chrs_walkable', 'chroms_linked']\n",
    "sv_linkage    = pd.DataFrame([svs_linked, cl_svs_linked, sc_svs_linked, perc_chrs_wlk, perc_chrs_lnk], index = collated_cols)\n",
    "\n",
    "n_svs         = float(sum(other_linkage.svs.values))\n",
    "svs_linked    = sum(other_linkage.sig_clus.values) / n_svs\n",
    "cl_svs_linked = sum(other_linkage.clonal_sig_svs.values) / n_svs\n",
    "sc_svs_linked = sum(other_linkage.subclonal_sig_svs.values) / n_svs\n",
    "perc_chrs_wlk = np.mean(other_linkage.chroms_walked.values / other_linkage.chroms_tested.values)\n",
    "perc_chrs_lnk = np.sum(other_linkage.linked_chroms.values > 0) / float(len(other_linkage))\n",
    "\n",
    "sv_linkage    = pd.concat([sv_linkage, pd.DataFrame([svs_linked, cl_svs_linked, sc_svs_linked, \n",
    "                                                     perc_chrs_wlk, perc_chrs_lnk], index = collated_cols)], axis=1)\n",
    "\n",
    "sv_linkage.columns = ['bal_enriched', 'other']\n",
    "print(sv_linkage)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.1028207134779766, pvalue=0.0020799283555914708)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "fracs_scnr_linked = bal_enr_linkage.subclonal_sig_svs.values / bal_enr_linkage.svs.values\n",
    "fracs_other_linked = other_linkage.subclonal_sig_svs.values / other_linkage.svs.values\n",
    "\n",
    "stats.ttest_ind(fracs_scnr_linked, fracs_other_linked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.8375608993167786, pvalue=0.40287449450568713)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scnr_prop_chroms_walked = bal_enr_linkage.chroms_walked.values / bal_enr_linkage.chroms_tested.values\n",
    "other_prop_chroms_walked = other_linkage.chroms_walked.values / other_linkage.chroms_tested.values\n",
    "\n",
    "stats.ttest_ind(scnr_prop_chroms_walked, other_prop_chroms_walked)"
   ]
  }
 ],
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