04B.analyze_pvalues_simultaneous.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from copy import deepcopy\n",
    "import time\n",
    "import random\n",
    "from scipy import stats\n",
    "from scipy.stats import mode\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from collections import defaultdict\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from scipy.stats import chisquare\n",
    "\n",
    "def dist(p1,p2):\n",
    "    \n",
    "    total_nucleotides = 4411532    \n",
    "\n",
    "    max_dist = int(total_nucleotides / 2)\n",
    "    # accounts for circularization of genome\n",
    "    d1 = np.abs(p1 - p2)\n",
    "    \n",
    "    #print(i,j,d1)\n",
    "    if d1 > max_dist:\n",
    "        i,j=sorted([p1,p2])\n",
    "        #print(\"too_far!\", d1)\n",
    "        i = i + total_nucleotides\n",
    "        d1 = i-j\n",
    "        #print(\"new distance\", d1)\n",
    "        \n",
    "    return d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘output/04B.pair_annotation’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir output/04B.pair_annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total pairs 150518\n",
      "total non-lineage pairs 132012\n",
      "total_significant 62804\n"
     ]
    }
   ],
   "source": [
    "snps = pd.read_csv(\"output/03.annotation/snps_with_all_annotation.csv\", index_col=0)\n",
    "snps_columns_to_keep = [\n",
    "    \"pos\", \n",
    "    \"gene_name\", \n",
    "    \"gene_id\", \n",
    "    \"is_lineage\",\n",
    "    \"in_antigen_epitope\",\n",
    "    \"in_antigen_protein\", \n",
    "    \"in_known_ABR_position\",\n",
    "    \"known_drug\", \n",
    "    \"in_possible_ABR_gene\", \n",
    "    \"possible_drug\"\n",
    "\n",
    "]\n",
    "\n",
    "# Add gene and lineage  annotation to the results\n",
    "results = pd.read_csv(\"output/02B.comutation_pvalues/results_file_allpairs.csv\")\n",
    "results = results.merge(snps[snps_columns_to_keep], left_on=\"position_i\", right_on=\"pos\")\n",
    "results = results.merge(snps[snps_columns_to_keep], left_on=\"position_j\", right_on=\"pos\", suffixes=[\"_i\", \"_j\"])\n",
    "results.columns\n",
    "\n",
    "print(\"total pairs\", len(results))\n",
    "print(\"total non-lineage pairs\", len(results.query(\"not is_lineage_i and not is_lineage_j\")))\n",
    "\n",
    "results = results.query(\"not is_lineage_i and not is_lineage_j\")\n",
    "\n",
    "results = results[[\n",
    "   'position_i', 'gene_name_i','position_j', 'gene_name_j',\n",
    "    'n_mutations_i',\n",
    "   'n_mutations_j', 'n_branches_with_i', 'n_branches_with_j',\n",
    "   'n_muts_i_on_branch_j', 'n_muts_j_on_branch_i', 'n_muts_same_branch',\n",
    "   'n_branch_with_i_no_j', 'n_branch_with_j_no_i', 'n_branch_i_and_j',\n",
    "   'pval_beta_i_on_j', 'BH_critical_value', 'pval_beta_i_on_j_BH_sig',\n",
    "    'is_lineage_i','is_lineage_j',\n",
    "    'gene_id_i', 'gene_id_j', \n",
    "    \"in_antigen_epitope_i\",\n",
    "    \"in_antigen_protein_i\", \n",
    "    \"in_known_ABR_position_i\",\n",
    "    \"known_drug_i\", \n",
    "    \"in_possible_ABR_gene_i\", \n",
    "    \"possible_drug_i\",\n",
    "    \"in_antigen_epitope_j\",\n",
    "    \"in_antigen_protein_j\", \n",
    "    \"in_known_ABR_position_j\",\n",
    "    \"known_drug_j\", \n",
    "    \"in_possible_ABR_gene_j\", \n",
    "    \"possible_drug_j\"\n",
    "]]\n",
    "results.to_csv(\"output/04B.pair_annotation/results_allpairs_annotated.csv\")\n",
    "\n",
    "print(\"total_significant\", len(results.query(\"pval_beta_i_on_j_BH_sig\")))\n",
    "results.query(\"pval_beta_i_on_j_BH_sig\").to_csv(\"output/04B.pair_annotation/results_significant_annotated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47574462927612643"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "62804/132012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall analysis of hits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annotate(top_hits):\n",
    "    def _separate_hits(logic_columns, current_df):\n",
    "        is_hit = np.sum(current_df[logic_columns].values, axis=1) > 0\n",
    "\n",
    "        hit_df = current_df.iloc[is_hit, :]\n",
    "        current_df = current_df.iloc[~is_hit, :]\n",
    "        return hit_df, current_df\n",
    "\n",
    "    # start with the df of top hits. Get just the known antibiotic hits\n",
    "    top_hits[\"annotation\"] = 'other'\n",
    "    logic_columns = [\"in_known_ABR_position_i\", \"in_known_ABR_position_j\"]\n",
    "\n",
    "    antibiotic_hits, current_df = _separate_hits(logic_columns, top_hits)\n",
    "\n",
    "    # if both are antibiotic, annotate that\n",
    "    antibiotic_hits.loc[\n",
    "        np.logical_and(\n",
    "            antibiotic_hits.in_known_ABR_position_i.values, \n",
    "            antibiotic_hits.in_known_ABR_position_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"between_known_AB\"\n",
    "\n",
    "    # if just one, annotate that\n",
    "    antibiotic_hits.loc[\n",
    "        np.logical_and(\n",
    "            antibiotic_hits.in_known_ABR_position_i.values, \n",
    "            ~antibiotic_hits.in_known_ABR_position_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"known_AB_second\"\n",
    "\n",
    "    antibiotic_hits.loc[\n",
    "        np.logical_and(\n",
    "            ~antibiotic_hits.in_known_ABR_position_i.values, \n",
    "            antibiotic_hits.in_known_ABR_position_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"known_AB_first\"\n",
    "\n",
    "    # Then get the antigen proteins\n",
    "    logic_columns = ['in_antigen_protein_i','in_antigen_protein_j',]\n",
    "\n",
    "    antigen_hits, current_df = _separate_hits(logic_columns, current_df)\n",
    "\n",
    "    # if just one, annotate that\n",
    "    antigen_hits.loc[\n",
    "        np.logical_or(\n",
    "            antigen_hits.in_antigen_protein_i.values, \n",
    "            antigen_hits.in_antigen_protein_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"one_antigen\"\n",
    "\n",
    "    antigen_hits.loc[\n",
    "        np.logical_and(\n",
    "            antigen_hits.in_antigen_protein_i.values, \n",
    "            antigen_hits.in_antigen_protein_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"both_antigen\"\n",
    "\n",
    "    # then get the maybe antibiotic hits\n",
    "    logic_columns = [\"in_possible_ABR_gene_i\", \"in_possible_ABR_gene_j\"]\n",
    "\n",
    "    maybe_antibiotic_hits, current_df = _separate_hits(logic_columns, current_df)\n",
    "\n",
    "    # if both are antibiotic, annotate that\n",
    "    maybe_antibiotic_hits.loc[\n",
    "        np.logical_and(\n",
    "            maybe_antibiotic_hits.in_possible_ABR_gene_i.values, \n",
    "            maybe_antibiotic_hits.in_possible_ABR_gene_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"between_maybe_AB\"\n",
    "\n",
    "    # if just one, annotate that\n",
    "    maybe_antibiotic_hits.loc[\n",
    "        np.logical_and(\n",
    "            maybe_antibiotic_hits.in_possible_ABR_gene_i.values, \n",
    "            ~maybe_antibiotic_hits.in_possible_ABR_gene_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"maybe_AB_second\"\n",
    "\n",
    "    maybe_antibiotic_hits.loc[\n",
    "        np.logical_and(\n",
    "            ~maybe_antibiotic_hits.in_possible_ABR_gene_i.values, \n",
    "            maybe_antibiotic_hits.in_possible_ABR_gene_j.values\n",
    "        ), \n",
    "        \"annotation\"\n",
    "    ] = \"maybe_AB_first\"\n",
    "\n",
    "    return pd.concat([antigen_hits, antibiotic_hits, maybe_antibiotic_hits, current_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_hits = pd.read_csv(\"output/04B.pair_annotation/results_significant_annotated.csv\")\n",
    "top_hits = annotate(top_hits)\n",
    "top_hits.to_csv(\"output/04B.pair_annotation/results_significant_annotated_with_categories.csv\")\n",
    "top_hits = pd.read_csv(\"output/04B.pair_annotation/results_significant_annotated_with_categories.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotation\n",
      "between_known_AB     1196\n",
      "between_maybe_AB     1607\n",
      "both_antigen         3721\n",
      "known_AB_first       3349\n",
      "known_AB_second      2970\n",
      "maybe_AB_first       3280\n",
      "maybe_AB_second      1524\n",
      "one_antigen         15700\n",
      "other               29457\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(top_hits.groupby(\"annotation\").size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position_i</th>\n",
       "      <th>gene_name_i</th>\n",
       "      <th>position_j</th>\n",
       "      <th>gene_name_j</th>\n",
       "      <th>n_mutations_i</th>\n",
       "      <th>n_mutations_j</th>\n",
       "      <th>n_muts_same_branch</th>\n",
       "      <th>pval_beta_i_on_j</th>\n",
       "      <th>annotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105060</td>\n",
       "      <td>Rv0095c</td>\n",
       "      <td>105063</td>\n",
       "      <td>Rv0095c</td>\n",
       "      <td>376</td>\n",
       "      <td>361</td>\n",
       "      <td>352</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3588</th>\n",
       "      <td>1341114</td>\n",
       "      <td>esxL</td>\n",
       "      <td>1341120</td>\n",
       "      <td>esxL</td>\n",
       "      <td>184</td>\n",
       "      <td>222</td>\n",
       "      <td>167</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>both_antigen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>761155</td>\n",
       "      <td>rpoB</td>\n",
       "      <td>2155168</td>\n",
       "      <td>katG</td>\n",
       "      <td>1687</td>\n",
       "      <td>2098</td>\n",
       "      <td>687</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>between_known_AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>3847215</td>\n",
       "      <td>PPE59</td>\n",
       "      <td>3847221</td>\n",
       "      <td>PPE59</td>\n",
       "      <td>161</td>\n",
       "      <td>145</td>\n",
       "      <td>136</td>\n",
       "      <td>5.497857e-288</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>2197331</td>\n",
       "      <td>Rv1945</td>\n",
       "      <td>2197332</td>\n",
       "      <td>Rv1945</td>\n",
       "      <td>124</td>\n",
       "      <td>126</td>\n",
       "      <td>122</td>\n",
       "      <td>6.988375e-277</td>\n",
       "      <td>both_antigen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11443</th>\n",
       "      <td>1341099</td>\n",
       "      <td>esxL</td>\n",
       "      <td>1341114</td>\n",
       "      <td>esxL</td>\n",
       "      <td>199</td>\n",
       "      <td>184</td>\n",
       "      <td>137</td>\n",
       "      <td>4.252785e-264</td>\n",
       "      <td>both_antigen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12476</th>\n",
       "      <td>3841652</td>\n",
       "      <td>None</td>\n",
       "      <td>3841654</td>\n",
       "      <td>None</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>6.451416e-262</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3589</th>\n",
       "      <td>1341099</td>\n",
       "      <td>esxL</td>\n",
       "      <td>1341120</td>\n",
       "      <td>esxL</td>\n",
       "      <td>199</td>\n",
       "      <td>222</td>\n",
       "      <td>137</td>\n",
       "      <td>1.318227e-252</td>\n",
       "      <td>both_antigen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781687</td>\n",
       "      <td>rpsL</td>\n",
       "      <td>2155168</td>\n",
       "      <td>katG</td>\n",
       "      <td>918</td>\n",
       "      <td>2098</td>\n",
       "      <td>405</td>\n",
       "      <td>4.842913e-245</td>\n",
       "      <td>between_known_AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8156</th>\n",
       "      <td>1339435</td>\n",
       "      <td>PPE18</td>\n",
       "      <td>1339436</td>\n",
       "      <td>PPE18</td>\n",
       "      <td>109</td>\n",
       "      <td>119</td>\n",
       "      <td>107</td>\n",
       "      <td>5.308867e-243</td>\n",
       "      <td>both_antigen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       position_i gene_name_i  position_j gene_name_j  n_mutations_i  \\\n",
       "0          105060     Rv0095c      105063     Rv0095c            376   \n",
       "3588      1341114        esxL     1341120        esxL            184   \n",
       "2          761155        rpoB     2155168        katG           1687   \n",
       "1089      3847215       PPE59     3847221       PPE59            161   \n",
       "2431      2197331      Rv1945     2197332      Rv1945            124   \n",
       "11443     1341099        esxL     1341114        esxL            199   \n",
       "12476     3841652        None     3841654        None            114   \n",
       "3589      1341099        esxL     1341120        esxL            199   \n",
       "4          781687        rpsL     2155168        katG            918   \n",
       "8156      1339435       PPE18     1339436       PPE18            109   \n",
       "\n",
       "       n_mutations_j  n_muts_same_branch  pval_beta_i_on_j        annotation  \n",
       "0                361                 352      0.000000e+00             other  \n",
       "3588             222                 167      0.000000e+00      both_antigen  \n",
       "2               2098                 687      0.000000e+00  between_known_AB  \n",
       "1089             145                 136     5.497857e-288             other  \n",
       "2431             126                 122     6.988375e-277      both_antigen  \n",
       "11443            184                 137     4.252785e-264      both_antigen  \n",
       "12476            114                 113     6.451416e-262             other  \n",
       "3589             222                 137     1.318227e-252      both_antigen  \n",
       "4               2098                 405     4.842913e-245  between_known_AB  \n",
       "8156             119                 107     5.308867e-243      both_antigen  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits.sort_values(\"pval_beta_i_on_j\")[[\n",
    "    \"position_i\",  \"gene_name_i\", \"position_j\", \"gene_name_j\", \n",
    "    \"n_mutations_i\", \"n_mutations_j\", \"n_muts_same_branch\", \"pval_beta_i_on_j\", \"annotation\"\n",
    "]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What fraction of the top hits in terms of p value are within 100 base pairs? \n",
    "top_hits = top_hits.sort_values(\"pval_beta_i_on_j\")\n",
    "top_hits[\"mutation_distance\"]=[dist(x,y) for x,y in zip(top_hits.position_i, top_hits.position_j)]\n",
    "len(top_hits.iloc[0:100].query(\"mutation_distance <= 100\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pal = sns.color_palette(\"Blues\",4)\n",
    "\n",
    "for_display = top_hits\n",
    "for_display[\"mutation_distance\"] = [dist(x,y) for x,y in zip(for_display.position_i, for_display.position_j)]\n",
    "figure = plt.figure()\n",
    "ax = figure.gca()\n",
    "\n",
    "ax.hist(for_display.mutation_distance, 50, color=pal[3])\n",
    "plt.xlabel(\"Distance on genome (Mb)\")\n",
    "plt.ylabel(\"Number of pairs\")\n",
    "sns.despine()\n",
    "#plt.show()\n",
    "plt.savefig(\"figures/simultaneous_mutations_distance.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Characterization of simultaneous, proximal pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_hits = pd.read_csv(\"output/04B.pair_annotation/results_significant_annotated.csv\")\n",
    "top_hits = annotate(top_hits)\n",
    "top_hits[\"mutation_distance\"] = [dist(x,y) for x,y in zip(top_hits.position_i, top_hits.position_j)]\n",
    "\n",
    "top_hits.to_csv(\"output/04B.pair_annotation/results_significant_annotated_with_categories.csv\")\n",
    "\n",
    "top_hits = pd.read_csv(\"output/04B.pair_annotation/results_significant_annotated_with_categories.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of close pairs 2361 number of far pairs 60443\n",
      "fraction intergenic 0.15586615840745446 \n",
      " fraction genic 0.8242270224481152 \n",
      " fraction not same gene/one genic one intergenic 0.019906819144430325 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Are the near distance hits more likely to be genic or intergenic? \n",
    "\n",
    "all_hits = pd.read_csv(\"output/04B.pair_annotation/results_significant_annotated.csv\", index_col=0)\n",
    "all_hits[\"mutation_distance\"] = [dist(x,y) for x,y in zip(all_hits.position_i, all_hits.position_j)]\n",
    "\n",
    "near_hits = all_hits.query('mutation_distance <= 100')\n",
    "far_hits = all_hits.query('mutation_distance > 100')\n",
    "\n",
    "print(\"number of close pairs\", len(near_hits), \"number of far pairs\", len(far_hits))\n",
    "N = len(near_hits)\n",
    "print(\"fraction intergenic\", len(near_hits.query(\"gene_name_i == gene_name_j == 'None'\"))/N, \"\\n\",\n",
    "      \"fraction genic\", len(near_hits.query(\"gene_name_i == gene_name_j != 'None'\"))/N,\"\\n\",\n",
    "      \"fraction not same gene/one genic one intergenic\", len(near_hits.query(\"gene_name_i != gene_name_j\"))/N,\"\\n\"\n",
    "     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VPdTmP80gRMZtPH4ReKqqvlVV3wc+C/ws4zcOU83n/v+wTZLVwIXAd6Z78eUaGmPxdSPtDoaPAo9X1YeGFu0Htrfp7QyudZyqb2t3PFwObAIeboeoLyS5tm3z1qE2y15V7aqq9VW1kcHP+l+q6h2M2TicUlX/Azyb5PWtdAPwGOM3Hs8A1yY5v/X/BuBxxm8cpprP/R/e1lsZ/Nub/ihsqS/yTHPx5yYGdxN9A3j/Uvdngfbx5xgcCn4deLQ9bmJwTvEA8GR7vniozfvbmBxh6A4QYAuDr5D/BvAXzHAxa7k+gOs5fSF8nMfhKmCi/W78I3DROI4H8AfAE20f/o7BnUFjMw7AvQyu53yfwVHBbfO5/8ArgH8AjjK4w+p1M/XJrxGRJHVbrqenJEnLkKEhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkrr9P7B2DgWdtjO/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# At what distance are the pairs actually enriched? \n",
    "# Bin size of 100\n",
    "plt.xlim([0,10000])\n",
    "plt.hist(top_hits.mutation_distance, bins = np.arange(0,10000,100))\n",
    "plt.savefig(\"figures/enrichment_by_distance.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62804\n",
      "2361\n",
      "number of pairs in the same codon 115\n",
      "92\n"
     ]
    }
   ],
   "source": [
    "## What fraction are in the same codon? \n",
    "\n",
    "# annotate with codon position\n",
    "snps = pd.read_csv(\"output/03.annotation/snps_with_all_annotation.csv\",index_col=0)\n",
    "snps_aa = snps.query(\"AA_change != 'None'\")\n",
    "snps_aa[\"AA\"] = [\"\".join(list(x)[1:-1]) for x in snps_aa.AA_change]\n",
    "\n",
    "print(len(all_hits))\n",
    "\n",
    "within_3 = all_hits.query(\"mutation_distance <= 100\")\n",
    "print(len(within_3))\n",
    "\n",
    "within_3 = within_3.merge(snps_aa[[\"pos\", \"AA\"]], left_on=\"position_i\", right_on=\"pos\", how=\"left\")\n",
    "within_3 = within_3.merge(snps_aa[[\"pos\", \"AA\"]], left_on=\"position_j\", \n",
    "                          right_on=\"pos\", how=\"left\", suffixes=[\"_i\", \"_j\"])\n",
    "print(\"number of pairs in the same codon\", len(within_3.query(\"AA_i == AA_j\")))\n",
    "\n",
    "within_3[\"fraction\"] = within_3.n_muts_same_branch / pd.concat(\n",
    "    [within_3.n_mutations_i,within_3.n_mutations_j], axis=1\n",
    ").min(axis=1)\n",
    "\n",
    "print(len(within_3.query(\"AA_i == AA_j and fraction > .8\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04870817450232952"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "115/2361"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fraction of times a mutation happens simultaneously compared to the # of times\n",
    "# the less frequent mutation happens\n",
    "top_hits[\"fraction\"] = top_hits.n_muts_same_branch / pd.concat([top_hits.n_mutations_i,top_hits.n_mutations_j], axis=1).min(axis=1)\n",
    "\n",
    "sns.jointplot(data = top_hits.query('mutation_distance<1000'),\n",
    "                                       x=\"mutation_distance\", \n",
    "              y=\"fraction\", linewidth=0, alpha=0.2, kind='kde')\n",
    "plt.savefig(\"figures/fraction_simultaneous_vs_distance_density.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.jointplot(data = top_hits.query('mutation_distance<1000'),\n",
    "                                       x=\"mutation_distance\", \n",
    "              y=\"fraction\", linewidth=0, alpha=0.2)\n",
    "plt.savefig(\"figures/fraction_simultaneous_vs_distance.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrichment statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotation\n",
      "between_known_AB     3369\n",
      "between_maybe_AB     2298\n",
      "both_antigen         8872\n",
      "known_AB_first      13102\n",
      "known_AB_second     14790\n",
      "maybe_AB_first       5948\n",
      "maybe_AB_second      2874\n",
      "one_antigen         36310\n",
      "other               44449\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "all_hits = pd.read_csv(\"output/04B.pair_annotation/results_allpairs_annotated.csv\")\n",
    "\n",
    "all_hits = annotate(all_hits)\n",
    "all_hits.to_csv(\"output/04B.pair_annotation/results_allpairs_annotation_withcategories.csv\")\n",
    "print(all_hits.groupby(\"annotation\").size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 1: are the overlapping hits an enrichment in some way based on our expectation from the distribution of SNPs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotation\n",
      "between_known_AB     3369\n",
      "between_maybe_AB     2298\n",
      "both_antigen         8872\n",
      "known_AB_first      13102\n",
      "known_AB_second     14790\n",
      "maybe_AB_first       5948\n",
      "maybe_AB_second      2874\n",
      "one_antigen         36310\n",
      "other               44449\n",
      "dtype: int64\n",
      "Power_divergenceResult(statistic=60410.674337234035, pvalue=0.0)\n",
      "                  actual_pair_freq  expected_pair_freq\n",
      "annotation                                            \n",
      "between_known_AB              3369          366.764404\n",
      "between_maybe_AB              2298         1866.660637\n",
      "both_antigen                  8872         3515.516032\n",
      "known_AB_first               13102         6591.489866\n",
      "known_AB_second              14790         6591.489866\n",
      "maybe_AB_first                5948        10442.046647\n",
      "maybe_AB_second               2874        10442.046647\n",
      "one_antigen                  36310        33783.473184\n",
      "other                        44449        58412.512717\n"
     ]
    }
   ],
   "source": [
    "\n",
    "top_hits = pd.read_csv(\"output/04B.pair_annotation/results_significant_annotated_with_categories.csv\", index_col=0)\n",
    "\n",
    "all_hits = pd.read_csv(\"output/04B.pair_annotation/results_allpairs_annotated.csv\")\n",
    "\n",
    "all_hits = annotate(all_hits)\n",
    "all_hits.to_csv(\"output/04B.pair_annotation/results_allpairs_annotation_withcategories.csv\")\n",
    "print(all_hits.groupby(\"annotation\").size())\n",
    "\n",
    "## Actual numbers: 159 epitopes, 774 antigens, 250 known resistance, 814 possible resistance, 2747 no category\n",
    "total_snps = len(snps)\n",
    "\n",
    "p_epitope = 159/total_snps\n",
    "p_antigen = 774/total_snps\n",
    "p_known_R = 250/total_snps\n",
    "p_possible_R = (814-250)/total_snps\n",
    "p_other = (total_snps - 814 -774)/total_snps\n",
    "\n",
    "# bang this out\n",
    "e_between_known_AB = p_known_R * p_known_R\n",
    "e_between_maybe_AB = p_possible_R * p_possible_R\n",
    "e_both_antigen = p_antigen * p_antigen\n",
    "\n",
    "e_known_AB_first = p_known_R * (1-p_known_R)\n",
    "e_known_AB_second = p_known_R * (1-p_known_R)\n",
    "\n",
    "e_maybe_AB_first = p_possible_R * (p_other)\n",
    "e_maybe_AB_second = p_possible_R * (p_other)\n",
    "\n",
    "e_one_antigen = 2 * p_antigen * (p_other + p_possible_R)\n",
    "e_other = p_other * p_other\n",
    "\n",
    "\n",
    "top_hits_values = all_hits.groupby(\"annotation\").size()\n",
    "labels = all_hits.groupby(\"annotation\").size().index\n",
    "\n",
    "all_hits_expectation = np.array([e_between_known_AB, e_between_maybe_AB, e_both_antigen, e_known_AB_first, \n",
    "                        e_known_AB_second, e_maybe_AB_first, e_maybe_AB_second, e_one_antigen, e_other]) * len(all_hits)\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "result_df = pd.DataFrame({\n",
    "                          \"actual_pair_freq\": top_hits_values, \n",
    "                          \"expected_pair_freq\": all_hits_expectation})\n",
    "print(chisquare(top_hits_values,  f_exp=all_hits_expectation))\n",
    "\n",
    "print(result_df)\n",
    "result_df1 = result_df\n",
    "result_df1[\"chi_square\"] = chisquare(top_hits_values,  f_exp=all_hits_expectation)[0]\n",
    "result_df1[\"p_value\"] = chisquare(top_hits_values,  f_exp=all_hits_expectation)[1]\n",
    "result_df1[\"category\"] = result_df1.index\n",
    "result_df1.to_csv(\"output/04B.chisquare_all_pairs_vs_expected.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power_divergenceResult(statistic=7587.502894880856, pvalue=0.0)\n",
      "           category  actual_hit_freq  expected_hit_freq\n",
      "0  between_known_AB             1196        1602.783656\n",
      "1  between_maybe_AB             1607        1093.261158\n",
      "2      both_antigen             3721        4220.806351\n",
      "3    known_AB_first             3349        6233.206133\n",
      "4   known_AB_second             2970        7036.263067\n",
      "5    maybe_AB_first             3280        2829.729055\n",
      "6   maybe_AB_second             1524        1367.290065\n",
      "7       one_antigen            15700       17274.287489\n",
      "8             other            29457       21146.373027\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chisquare\n",
    "\n",
    "top_hits_values = top_hits.groupby(\"annotation\").size().values\n",
    "all_hits_expectation = all_hits.groupby(\"annotation\").size().values / len(all_hits) * len(top_hits)\n",
    "\n",
    "print(chisquare(top_hits_values,  f_exp=all_hits_expectation))\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "result_df = pd.DataFrame({ \n",
    "    \"category\": top_hits.groupby(\"annotation\").size().index,\n",
    "    \"actual_hit_freq\": top_hits_values, \n",
    "    \"expected_hit_freq\": all_hits_expectation})\n",
    "\n",
    "print(result_df)\n",
    "result_df2 = result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_hits = top_hits.sort_values(\"pval_beta_i_on_j\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "between_known_AB      36\n",
       "between_maybe_AB     194\n",
       "both_antigen        1202\n",
       "known_AB_first         6\n",
       "known_AB_second        9\n",
       "one_antigen           14\n",
       "other                884\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits.query(\"mutation_distance < 100\").groupby(\"annotation\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "between_known_AB     8\n",
       "both_antigen        46\n",
       "known_AB_second      1\n",
       "other               45\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits.iloc[0:100].groupby(\"annotation\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "between_known_AB     1196\n",
       "between_maybe_AB     1607\n",
       "both_antigen         3721\n",
       "known_AB_first       3349\n",
       "known_AB_second      2970\n",
       "maybe_AB_first       3280\n",
       "maybe_AB_second      1524\n",
       "one_antigen         15700\n",
       "other               29457\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits.groupby(\"annotation\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "between_known_AB     8\n",
       "both_antigen        46\n",
       "known_AB_second      1\n",
       "other               45\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits_values = top_hits.iloc[0:100].groupby(\"annotation\").size()\n",
    "top_hits_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power_divergenceResult(statistic=334.1370556231716, pvalue=2.1948988662393545e-67)\n",
      "                  actual_top_hit_freq  expected_top_hit_freq\n",
      "annotation                                                  \n",
      "between_known_AB                  8.0               1.904337\n",
      "between_maybe_AB                  NaN               2.558754\n",
      "both_antigen                     46.0               5.924782\n",
      "known_AB_first                    NaN               5.332463\n",
      "known_AB_second                   1.0               4.728998\n",
      "maybe_AB_first                    NaN               5.222597\n",
      "maybe_AB_second                   NaN               2.426597\n",
      "one_antigen                       NaN              24.998408\n",
      "other                            45.0              46.903063\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chisquare\n",
    "\n",
    "top_hits=top_hits.sort_values(\"pval_beta_i_on_j\")\n",
    "\n",
    "top_hits_values = top_hits.iloc[0:100].groupby(\"annotation\").size()\n",
    "all_hits_expectation = top_hits.groupby(\"annotation\").size() / len(top_hits) * 100\n",
    "\n",
    "print(chisquare([  8, 0, 46,  0, 1, 0, 0,  0, 45],  f_exp=all_hits_expectation))\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "\n",
    "result_df = pd.DataFrame({ \n",
    "    #\"category\": top_hits.groupby(\"annotation\").size().index,\n",
    "    \"actual_top_hit_freq\": top_hits_values, \n",
    "    \"expected_top_hit_freq\": all_hits_expectation})\n",
    "\n",
    "print(result_df)\n",
    "result_df3 = result_df\n",
    "result_df3[\"category\"] = result_df3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Combine all three into bar plot\n",
    "def _process_result_df(result_df):\n",
    "    cats_to_remove = [\"between_maybe_AB\", \"maybe_AB_first\", \"maybe_AB_second\"]\n",
    "    result_df = result_df.query(\"category not in @cats_to_remove\")\n",
    "\n",
    "    desired_order = [\"known_AB_first\", \"known_AB_second\", \"between_known_AB\", \"one_antigen\",\"both_antigen\",  \"other\"]\n",
    "\n",
    "\n",
    "    to_plot = result_df.melt([\"category\"])\n",
    "    to_plot[\"category_sorter\"] = [desired_order.index(x) for x in to_plot.category]\n",
    "    #to_plot[\"value\"] = [np.log10(x) for x in to_plot.value]\n",
    "\n",
    "    # normalize the result dataframe\n",
    "    to_plot[\"value_norm\"] = to_plot.value / (to_plot.value.sum()/2)\n",
    "    \n",
    "    return to_plot\n",
    "\n",
    "pal = sns.color_palette(\"Blues\",4)\n",
    "\n",
    "toplot1 = _process_result_df(result_df1)\n",
    "toplot2 = _process_result_df(result_df2)\n",
    "toplot3 = _process_result_df(result_df3)\n",
    "\n",
    "to_plot = pd.concat([toplot1, toplot2, toplot3])\n",
    "\n",
    "categories_to_keep = [\"expected_pair_freq\", \"actual_top_hit_freq\"]\n",
    "to_plot = to_plot.query(\"variable in @categories_to_keep\")\n",
    "\n",
    "desired_order = [\"expected_pair_freq\", \"actual_pair_freq\", \"actual_hit_freq\", \"actual_top_hit_freq\"]\n",
    "to_plot[\"sorter\"] = [desired_order.index(x) for x in to_plot.variable]\n",
    "to_plot = to_plot.sort_values([\"sorter\", \"category_sorter\"])\n",
    "\n",
    "\n",
    "fig= plt.figure(figsize=(10,5))\n",
    "ax = fig.gca()\n",
    "\n",
    "sns.barplot(data = to_plot, x='category', y = 'value_norm', hue='variable',\n",
    "            palette=[pal[0], pal[3]]\n",
    "            \n",
    "           )\n",
    "plt.xticks(rotation=90)\n",
    "#cs = chisquare(top_hits_values,  f_exp=all_hits_expectation)\n",
    "\n",
    "sns.despine()\n",
    "plt.savefig(\"figures/simultaneous_mutations_category.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repeat enrichment analysis treating the proximal mutations as separate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2578 129434\n"
     ]
    }
   ],
   "source": [
    "all_hits[\"mutation_distance\"]=[dist(x,y) for x,y in zip(all_hits.position_i, all_hits.position_j)]\n",
    "\n",
    "all_hits_close = all_hits.query(\"mutation_distance <= 100\")\n",
    "all_hits_far = all_hits.query(\"mutation_distance > 100\")\n",
    "print(len(all_hits_close), len(all_hits_far))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "between_known_AB      70\n",
       "between_maybe_AB     199\n",
       "both_antigen        1312\n",
       "known_AB_first        15\n",
       "known_AB_second       20\n",
       "one_antigen           33\n",
       "other                929\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits_values = all_hits_close.groupby(\"annotation\").size()\n",
    "top_hits_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power_divergenceResult(statistic=25028.7338581076, pvalue=0.0)\n",
      "   actual_pair_freq  expected_pair_freq\n",
      "0                70            7.162369\n",
      "1               199           36.453134\n",
      "2              1312           68.652852\n",
      "3                15          128.722092\n",
      "4                20          128.722092\n",
      "5                 0          203.917797\n",
      "6                 0          203.917797\n",
      "7                33          659.741492\n",
      "8               929         1140.710373\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# need to manually define to ensure that the 0's are in place\n",
    "top_hits_values = [  70, 199, 1312,   15,   20,  0, 0, 33, 929]\n",
    "\n",
    "labels = all_hits_close.groupby(\"annotation\").size().index\n",
    "\n",
    "all_hits_close_expectation = np.array([\n",
    "    e_between_known_AB, e_between_maybe_AB, e_both_antigen, e_known_AB_first, \n",
    "    e_known_AB_second, e_maybe_AB_first, e_maybe_AB_second, e_one_antigen, e_other\n",
    "]) * np.sum(top_hits_values)\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "result_df = pd.DataFrame({\n",
    "                          \"actual_pair_freq\": top_hits_values, \n",
    "                          \"expected_pair_freq\": all_hits_close_expectation})\n",
    "print(chisquare(top_hits_values,  f_exp=all_hits_close_expectation))\n",
    "\n",
    "print(result_df)\n",
    "results_df1_close = result_df\n",
    "#result_df1_close[\"category\"] = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power_divergenceResult(statistic=57139.046666225535, pvalue=0.0)\n",
      "                  actual_pair_freq  expected_pair_freq\n",
      "annotation                                            \n",
      "between_known_AB              3299          359.602035\n",
      "between_maybe_AB              2099         1830.207503\n",
      "both_antigen                  7560         3446.863180\n",
      "known_AB_first               13087         6462.767773\n",
      "known_AB_second              14770         6462.767773\n",
      "maybe_AB_first                5948        10238.128850\n",
      "maybe_AB_second               2874        10238.128850\n",
      "one_antigen                  36277        33123.731692\n",
      "other                        43520        57271.802344\n"
     ]
    }
   ],
   "source": [
    "top_hits_values = all_hits_far.groupby(\"annotation\").size()\n",
    "\n",
    "labels = all_hits_far.groupby(\"annotation\").size().index\n",
    "\n",
    "all_hits_far_expectation = np.array([e_between_known_AB, e_between_maybe_AB, e_both_antigen, e_known_AB_first, \n",
    "                        e_known_AB_second, e_maybe_AB_first, e_maybe_AB_second, e_one_antigen, e_other]) * len(all_hits_far)\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "result_df = pd.DataFrame({\n",
    "                          \"actual_pair_freq\": top_hits_values, \n",
    "                          \"expected_pair_freq\": all_hits_far_expectation})\n",
    "print(chisquare(top_hits_values,  f_exp=all_hits_far_expectation))\n",
    "\n",
    "print(result_df)\n",
    "results_df1_far = result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "both_antigen    52\n",
       "other           48\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "top_hits=all_hits_close.sort_values(\"pval_beta_i_on_j\")\n",
    "\n",
    "top_hits_values = top_hits.iloc[0:100].groupby(\"annotation\").size()\n",
    "top_hits_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power_divergenceResult(statistic=17.068571241040722, pvalue=0.009034511214282233)\n",
      "                  actual_top_hit_freq_close  expected_top_hit_freq_close\n",
      "annotation                                                              \n",
      "between_known_AB                        NaN                     2.715283\n",
      "between_maybe_AB                        NaN                     7.719162\n",
      "both_antigen                           52.0                    50.892164\n",
      "known_AB_first                          NaN                     0.581846\n",
      "known_AB_second                         NaN                     0.775795\n",
      "one_antigen                             NaN                     1.280062\n",
      "other                                  48.0                    36.035687\n"
     ]
    }
   ],
   "source": [
    "\n",
    "top_hits=all_hits_close.sort_values(\"pval_beta_i_on_j\")\n",
    "\n",
    "top_hits_values = top_hits.iloc[0:100].groupby(\"annotation\").size()\n",
    "\n",
    "all_hits_expectation = all_hits_close.groupby(\"annotation\").size() / len(top_hits) * 100\n",
    "\n",
    "print(chisquare([  0, 0, 52,  0, 0,  0, 48],  f_exp=all_hits_expectation))\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "\n",
    "result_df = pd.DataFrame({ \n",
    "    #\"category\": top_hits.groupby(\"annotation\").size().index,\n",
    "    \"actual_top_hit_freq_close\": top_hits_values, \n",
    "    \"expected_top_hit_freq_close\": all_hits_expectation})\n",
    "\n",
    "print(result_df)\n",
    "results_df3_close = result_df\n",
    "results_df3_close[\"category\"] = results_df3_close.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annotation\n",
       "between_known_AB    29\n",
       "between_maybe_AB     2\n",
       "both_antigen        10\n",
       "known_AB_first       2\n",
       "known_AB_second      1\n",
       "maybe_AB_first       5\n",
       "one_antigen         20\n",
       "other               31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hits=all_hits_far.sort_values(\"pval_beta_i_on_j\")\n",
    "top_hits_values = top_hits.iloc[0:100].groupby(\"annotation\").size()\n",
    "top_hits_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power_divergenceResult(statistic=298.3246367947302, pvalue=9.358750940408153e-60)\n",
      "                  actual_top_hit_freq_far  expected_top_hit_freq_far\n",
      "annotation                                                          \n",
      "between_known_AB                     29.0                   2.548789\n",
      "between_maybe_AB                      2.0                   1.621676\n",
      "both_antigen                         10.0                   5.840815\n",
      "known_AB_first                        2.0                  10.110945\n",
      "known_AB_second                       1.0                  11.411221\n",
      "maybe_AB_first                        5.0                   4.595392\n",
      "maybe_AB_second                       NaN                   2.220437\n",
      "one_antigen                          20.0                  28.027412\n",
      "other                                31.0                  33.623314\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_top_hit_freq_far</th>\n",
       "      <th>expected_top_hit_freq_far</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annotation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>between_known_AB</th>\n",
       "      <td>29.0</td>\n",
       "      <td>2.548789</td>\n",
       "      <td>between_known_AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>between_maybe_AB</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.621676</td>\n",
       "      <td>between_maybe_AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>both_antigen</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.840815</td>\n",
       "      <td>both_antigen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>known_AB_first</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.110945</td>\n",
       "      <td>known_AB_first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>known_AB_second</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.411221</td>\n",
       "      <td>known_AB_second</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maybe_AB_first</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.595392</td>\n",
       "      <td>maybe_AB_first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maybe_AB_second</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.220437</td>\n",
       "      <td>maybe_AB_second</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_antigen</th>\n",
       "      <td>20.0</td>\n",
       "      <td>28.027412</td>\n",
       "      <td>one_antigen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>31.0</td>\n",
       "      <td>33.623314</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  actual_top_hit_freq_far  expected_top_hit_freq_far  \\\n",
       "annotation                                                             \n",
       "between_known_AB                     29.0                   2.548789   \n",
       "between_maybe_AB                      2.0                   1.621676   \n",
       "both_antigen                         10.0                   5.840815   \n",
       "known_AB_first                        2.0                  10.110945   \n",
       "known_AB_second                       1.0                  11.411221   \n",
       "maybe_AB_first                        5.0                   4.595392   \n",
       "maybe_AB_second                       NaN                   2.220437   \n",
       "one_antigen                          20.0                  28.027412   \n",
       "other                                31.0                  33.623314   \n",
       "\n",
       "                          category  \n",
       "annotation                          \n",
       "between_known_AB  between_known_AB  \n",
       "between_maybe_AB  between_maybe_AB  \n",
       "both_antigen          both_antigen  \n",
       "known_AB_first      known_AB_first  \n",
       "known_AB_second    known_AB_second  \n",
       "maybe_AB_first      maybe_AB_first  \n",
       "maybe_AB_second    maybe_AB_second  \n",
       "one_antigen            one_antigen  \n",
       "other                        other  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "top_hits=all_hits_far.sort_values(\"pval_beta_i_on_j\")\n",
    "\n",
    "top_hits_values = top_hits.iloc[0:100].groupby(\"annotation\").size()\n",
    "\n",
    "all_hits_expectation = all_hits_far.groupby(\"annotation\").size() / len(top_hits) * 100\n",
    "\n",
    "#top_hits_values\n",
    "print(chisquare([29,  2, 10,  2,  1,  5, 0,20, 31], f_exp=all_hits_expectation))\n",
    "\n",
    "## Table of nice results\n",
    "\n",
    "\n",
    "result_df = pd.DataFrame({ \n",
    "    #\"category\": top_hits.groupby(\"annotation\").size().index,\n",
    "    \"actual_top_hit_freq_far\": top_hits_values, \n",
    "    \"expected_top_hit_freq_far\": all_hits_expectation})\n",
    "\n",
    "print(result_df)\n",
    "results_df3_far = result_df\n",
    "results_df3_far[\"category\"] = results_df3_far.index\n",
    "results_df3_far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Combine all three into bar plot\n",
    "def _process_result_df(result_df):\n",
    "    cats_to_remove = [\"between_maybe_AB\", \"maybe_AB_first\", \"maybe_AB_second\"]\n",
    "    result_df = result_df.query(\"category not in @cats_to_remove\")\n",
    "\n",
    "    desired_order = [\"known_AB_first\", \"known_AB_second\", \"between_known_AB\", \"one_antigen\",\"both_antigen\",  \"other\"]\n",
    "\n",
    "\n",
    "    to_plot = result_df.melt([\"category\"])\n",
    "    to_plot[\"category_sorter\"] = [desired_order.index(x) for x in to_plot.category]\n",
    "    #to_plot[\"value\"] = [np.log10(x) for x in to_plot.value]\n",
    "\n",
    "    # normalize the result dataframe\n",
    "    to_plot[\"value_norm\"] = to_plot.value / (to_plot.value.sum()/2)\n",
    "    \n",
    "    return to_plot\n",
    "\n",
    "pal = sns.color_palette(\"Blues\",4)\n",
    "\n",
    "results_df1_far[\"category\"] = results_df3_far.index\n",
    "\n",
    "toplot1 = _process_result_df(results_df1_far).fillna(0)\n",
    "toplot2 = _process_result_df(results_df3_close).fillna(0)\n",
    "toplot3 = _process_result_df(results_df3_far).fillna(0)\n",
    "\n",
    "to_plot = pd.concat([toplot1, toplot2, toplot3])\n",
    "\n",
    "categories_to_keep = [\"expected_pair_freq\", \"actual_top_hit_freq_far\", \"actual_top_hit_freq_close\"]\n",
    "to_plot = to_plot.query(\"variable in @categories_to_keep\")\n",
    "\n",
    "desired_order = [\"expected_pair_freq\", \"actual_top_hit_freq_far\",  \"actual_top_hit_freq_close\"]\n",
    "to_plot[\"sorter\"] = [desired_order.index(x) for x in to_plot.variable]\n",
    "to_plot = to_plot.sort_values([\"sorter\", \"category_sorter\"])\n",
    "\n",
    "\n",
    "fig= plt.figure(figsize=(10,5))\n",
    "ax = fig.gca()\n",
    "\n",
    "sns.barplot(data = to_plot, x='category', y = 'value_norm', hue='variable',\n",
    "            palette=[pal[0], pal[3],\"mediumorchid\"]\n",
    "            \n",
    "           )\n",
    "plt.xticks(rotation=90)\n",
    "#cs = chisquare(top_hits_values,  f_exp=all_hits_expectation)\n",
    "\n",
    "sns.despine()\n",
    "plt.savefig(\"figures/simultaneous_mutations_far_category.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_hits = pd.read_csv(\"output/04B.pair_annotation/results_allpairs_annotated.csv\")\n",
    "all_hits[\"mutation_distance\"] = [dist(x,y) for x,y in zip(all_hits.position_i, all_hits.position_j)]\n",
    "\n",
    "all_hits.to_csv(\"_max_all_simultaneous_pairs.csv\")\n",
    "all_hits.query(\"mutation_distance < 100 and pval_beta_i_on_j_BH_sig\").to_csv(\"_max_significant_proximal_pairs.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>is_lineage</th>\n",
       "      <th>in_antigen_protein</th>\n",
       "      <th>AA_change</th>\n",
       "      <th>aa_position</th>\n",
       "      <th>in_antigen_epitope</th>\n",
       "      <th>in_known_ABR_position</th>\n",
       "      <th>known_drug</th>\n",
       "      <th>in_possible_ABR_gene</th>\n",
       "      <th>possible_drug</th>\n",
       "      <th>in_essential_gene_minato</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>290</td>\n",
       "      <td>G</td>\n",
       "      <td>['C', 'T']</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>289.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>G97A</td>\n",
       "      <td>97</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>490</td>\n",
       "      <td>G</td>\n",
       "      <td>['A']</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>489.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>G164S</td>\n",
       "      <td>164</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>573</td>\n",
       "      <td>G</td>\n",
       "      <td>['C', 'T']</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>572.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>L191F</td>\n",
       "      <td>191</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>696</td>\n",
       "      <td>G</td>\n",
       "      <td>['A', 'C', 'T']</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>695.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>M232I</td>\n",
       "      <td>232</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>697</td>\n",
       "      <td>C</td>\n",
       "      <td>['T']</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>696.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>R233W</td>\n",
       "      <td>233</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4738</th>\n",
       "      <td>7274</td>\n",
       "      <td>4408156</td>\n",
       "      <td>A</td>\n",
       "      <td>['C', 'G']</td>\n",
       "      <td>Rv3919c</td>\n",
       "      <td>628.0</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gid</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>L16R</td>\n",
       "      <td>16</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>STM</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4739</th>\n",
       "      <td>7276</td>\n",
       "      <td>4408168</td>\n",
       "      <td>A</td>\n",
       "      <td>['C', 'G']</td>\n",
       "      <td>Rv3919c</td>\n",
       "      <td>640.0</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gid</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>F12C</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>STM</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4740</th>\n",
       "      <td>7278</td>\n",
       "      <td>4408185</td>\n",
       "      <td>G</td>\n",
       "      <td>['A', 'C']</td>\n",
       "      <td>Rv3919c</td>\n",
       "      <td>657.0</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gid</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>P6P</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>STM</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4741</th>\n",
       "      <td>7280</td>\n",
       "      <td>4408806</td>\n",
       "      <td>G</td>\n",
       "      <td>['A']</td>\n",
       "      <td>Rv3920c</td>\n",
       "      <td>472.0</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>Rv3920c</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>A31V</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4742</th>\n",
       "      <td>7281</td>\n",
       "      <td>4411475</td>\n",
       "      <td>G</td>\n",
       "      <td>['A']</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4743 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0      pos ref              alt     gene_id  gene_pos  \\\n",
       "0              0      290   G       ['C', 'T']      Rv0001     289.0   \n",
       "1              2      490   G            ['A']      Rv0001     489.0   \n",
       "2              3      573   G       ['C', 'T']      Rv0001     572.0   \n",
       "3              5      696   G  ['A', 'C', 'T']      Rv0001     695.0   \n",
       "4              8      697   C            ['T']      Rv0001     696.0   \n",
       "...          ...      ...  ..              ...         ...       ...   \n",
       "4738        7274  4408156   A       ['C', 'G']     Rv3919c     628.0   \n",
       "4739        7276  4408168   A       ['C', 'G']     Rv3919c     640.0   \n",
       "4740        7278  4408185   G       ['A', 'C']     Rv3919c     657.0   \n",
       "4741        7280  4408806   G            ['A']     Rv3920c     472.0   \n",
       "4742        7281  4411475   G            ['A']  intergenic       NaN   \n",
       "\n",
       "              gene_category gene_name  is_lineage  in_antigen_protein  \\\n",
       "0                 Essential      dnaA       False               False   \n",
       "1                 Essential      dnaA       False               False   \n",
       "2                 Essential      dnaA       False               False   \n",
       "3                 Essential      dnaA       False               False   \n",
       "4                 Essential      dnaA       False               False   \n",
       "...                     ...       ...         ...                 ...   \n",
       "4738  Antibiotic Resistance       gid        True               False   \n",
       "4739  Antibiotic Resistance       gid       False               False   \n",
       "4740  Antibiotic Resistance       gid       False               False   \n",
       "4741          Non-Essential   Rv3920c       False               False   \n",
       "4742                   None      None       False               False   \n",
       "\n",
       "     AA_change  aa_position  in_antigen_epitope  in_known_ABR_position  \\\n",
       "0         G97A           97               False                  False   \n",
       "1        G164S          164               False                  False   \n",
       "2        L191F          191               False                  False   \n",
       "3        M232I          232               False                  False   \n",
       "4        R233W          233               False                  False   \n",
       "...        ...          ...                 ...                    ...   \n",
       "4738      L16R           16               False                  False   \n",
       "4739      F12C           12               False                  False   \n",
       "4740       P6P            6               False                  False   \n",
       "4741      A31V           31               False                  False   \n",
       "4742      None           -1               False                  False   \n",
       "\n",
       "     known_drug  in_possible_ABR_gene possible_drug  in_essential_gene_minato  \n",
       "0           NaN                 False           NaN                      True  \n",
       "1           NaN                 False           NaN                      True  \n",
       "2           NaN                 False           NaN                      True  \n",
       "3           NaN                 False           NaN                      True  \n",
       "4           NaN                 False           NaN                      True  \n",
       "...         ...                   ...           ...                       ...  \n",
       "4738        NaN                  True           STM                     False  \n",
       "4739        NaN                  True           STM                     False  \n",
       "4740        NaN                  True           STM                     False  \n",
       "4741        NaN                 False           NaN                     False  \n",
       "4742        NaN                 False           NaN                     False  \n",
       "\n",
       "[4743 rows x 18 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"output/03.annotation/snps_with_all_annotation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>pos</th>\n",
       "      <th>n_mutations</th>\n",
       "      <th>n_reversions</th>\n",
       "      <th>n_branches_with</th>\n",
       "      <th>mat_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>290</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>490</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>696</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>697</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4738</th>\n",
       "      <td>4738</td>\n",
       "      <td>4408156</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>11371</td>\n",
       "      <td>4738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4739</th>\n",
       "      <td>4739</td>\n",
       "      <td>4408168</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4740</th>\n",
       "      <td>4740</td>\n",
       "      <td>4408185</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>4740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4741</th>\n",
       "      <td>4741</td>\n",
       "      <td>4408806</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4742</th>\n",
       "      <td>4742</td>\n",
       "      <td>4411475</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>484</td>\n",
       "      <td>4742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4743 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0      pos  n_mutations  n_reversions  n_branches_with  \\\n",
       "0              0      290            5             0                4   \n",
       "1              1      490            5             0               10   \n",
       "2              2      573            7             0                2   \n",
       "3              3      696            8             0                4   \n",
       "4              4      697            8             0                0   \n",
       "...          ...      ...          ...           ...              ...   \n",
       "4738        4738  4408156            7             2            11371   \n",
       "4739        4739  4408168            5             0                0   \n",
       "4740        4740  4408185            6             0               46   \n",
       "4741        4741  4408806            5             0                8   \n",
       "4742        4742  4411475            5             0              484   \n",
       "\n",
       "      mat_index  \n",
       "0             0  \n",
       "1             1  \n",
       "2             2  \n",
       "3             3  \n",
       "4             4  \n",
       "...         ...  \n",
       "4738       4738  \n",
       "4739       4739  \n",
       "4740       4740  \n",
       "4741       4741  \n",
       "4742       4742  \n",
       "\n",
       "[4743 rows x 6 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"output/01.mutation_and_comutation_data/first_order_statistics.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_hits = all_hits.query(\"mutation_distance < 100 and pval_beta_i_on_j_BH_sig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(close_hits.gene_name_i.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['None',\n",
       " 'PE_PGRS17',\n",
       " 'PE_PGRS18',\n",
       " 'PE_PGRS20',\n",
       " 'PE_PGRS21',\n",
       " 'PPE18',\n",
       " 'PPE19',\n",
       " 'PPE24',\n",
       " 'PPE27',\n",
       " 'PPE34',\n",
       " 'PPE35',\n",
       " 'PPE40',\n",
       " 'PPE5',\n",
       " 'PPE54',\n",
       " 'PPE55',\n",
       " 'PPE56',\n",
       " 'PPE59',\n",
       " 'PPE6',\n",
       " 'PPE60',\n",
       " 'PPE8',\n",
       " 'Rv0095c',\n",
       " 'Rv0331',\n",
       " 'Rv0397',\n",
       " 'Rv0740',\n",
       " 'Rv0750',\n",
       " 'Rv0813c',\n",
       " 'Rv1129c',\n",
       " 'Rv1148c',\n",
       " 'Rv1288',\n",
       " 'Rv1318c',\n",
       " 'Rv1319c',\n",
       " 'Rv1435c',\n",
       " 'Rv1566c',\n",
       " 'Rv1944c',\n",
       " 'Rv1945',\n",
       " 'Rv2021c',\n",
       " 'Rv2082',\n",
       " 'Rv2390c',\n",
       " 'Rv2824c',\n",
       " 'Rv2825c',\n",
       " 'Rv2980',\n",
       " 'Rv3405c',\n",
       " 'Rv3424c',\n",
       " 'Rv3466',\n",
       " 'Rv3900c',\n",
       " 'Rv3901c',\n",
       " 'ald',\n",
       " 'cyp130',\n",
       " 'dnaA',\n",
       " 'eccD1',\n",
       " 'embB',\n",
       " 'espA',\n",
       " 'esxJ',\n",
       " 'esxL',\n",
       " 'esxM',\n",
       " 'esxN',\n",
       " 'esxO',\n",
       " 'esxW',\n",
       " 'ethA',\n",
       " 'fadD18',\n",
       " 'fadE35',\n",
       " 'folC',\n",
       " 'folP2',\n",
       " 'gid',\n",
       " 'gyrA',\n",
       " 'katG',\n",
       " 'lipN',\n",
       " 'lppA',\n",
       " 'lppB',\n",
       " 'mycP1',\n",
       " 'pks12',\n",
       " 'pncA',\n",
       " 'ppsA',\n",
       " 'ppsB',\n",
       " 'prpD',\n",
       " 'rpoB',\n",
       " 'rpoC',\n",
       " 'ubiA',\n",
       " 'vapB31',\n",
       " 'vapC25',\n",
       " 'vapC31',\n",
       " 'whiB6']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(close_hits.gene_name_i.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['None',\n",
       " 'PE_PGRS17',\n",
       " 'PE_PGRS18',\n",
       " 'PE_PGRS20',\n",
       " 'PE_PGRS21',\n",
       " 'PPE18',\n",
       " 'PPE19',\n",
       " 'PPE24',\n",
       " 'PPE27',\n",
       " 'PPE34',\n",
       " 'PPE40',\n",
       " 'PPE5',\n",
       " 'PPE54',\n",
       " 'PPE55',\n",
       " 'PPE56',\n",
       " 'PPE59',\n",
       " 'PPE6',\n",
       " 'PPE60',\n",
       " 'PPE8',\n",
       " 'Rv0095c',\n",
       " 'Rv0331',\n",
       " 'Rv0397',\n",
       " 'Rv0740',\n",
       " 'Rv0750',\n",
       " 'Rv0813c',\n",
       " 'Rv1129c',\n",
       " 'Rv1148c',\n",
       " 'Rv1288',\n",
       " 'Rv1318c',\n",
       " 'Rv1319c',\n",
       " 'Rv1435c',\n",
       " 'Rv1566c',\n",
       " 'Rv1944c',\n",
       " 'Rv1945',\n",
       " 'Rv2021c',\n",
       " 'Rv2082',\n",
       " 'Rv2390c',\n",
       " 'Rv2824c',\n",
       " 'Rv2825c',\n",
       " 'Rv2980',\n",
       " 'Rv3405c',\n",
       " 'Rv3424c',\n",
       " 'Rv3466',\n",
       " 'Rv3900c',\n",
       " 'Rv3901c',\n",
       " 'ald',\n",
       " 'cyp130',\n",
       " 'dnaA',\n",
       " 'eccD1',\n",
       " 'espA',\n",
       " 'esxJ',\n",
       " 'esxL',\n",
       " 'esxM',\n",
       " 'esxN',\n",
       " 'esxO',\n",
       " 'esxW',\n",
       " 'fadD18',\n",
       " 'fadE35',\n",
       " 'folC',\n",
       " 'folP2',\n",
       " 'lipN',\n",
       " 'lppA',\n",
       " 'lppB',\n",
       " 'mycP1',\n",
       " 'pks12',\n",
       " 'ppsA',\n",
       " 'ppsB',\n",
       " 'prpD',\n",
       " 'vapB31',\n",
       " 'vapC25',\n",
       " 'vapC31']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(close_hits.query(\"not in_possible_ABR_gene_i\").gene_name_i.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'position_i', 'gene_name_i', 'position_j', 'gene_name_j',\n",
       "       'n_mutations_i', 'n_mutations_j', 'n_branches_with_i',\n",
       "       'n_branches_with_j', 'n_muts_i_on_branch_j', 'n_muts_j_on_branch_i',\n",
       "       'n_muts_same_branch', 'n_branch_with_i_no_j', 'n_branch_with_j_no_i',\n",
       "       'n_branch_i_and_j', 'pval_beta_i_on_j', 'BH_critical_value',\n",
       "       'pval_beta_i_on_j_BH_sig', 'is_lineage_i', 'is_lineage_j', 'gene_id_i',\n",
       "       'gene_id_j', 'in_antigen_epitope_i', 'in_antigen_protein_i',\n",
       "       'in_known_ABR_position_i', 'known_drug_i', 'in_possible_ABR_gene_i',\n",
       "       'possible_drug_i', 'in_antigen_epitope_j', 'in_antigen_protein_j',\n",
       "       'in_known_ABR_position_j', 'known_drug_j', 'in_possible_ABR_gene_j',\n",
       "       'possible_drug_j', 'mutation_distance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_hits.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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