5_7_meta_analysis2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\njupyter nbconvert 5_association.ipynb --to script\\npython 5_association.py albumin 2 0\\n\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "\n",
    "import logging\n",
    "import re\n",
    "import pathlib\n",
    "\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pyplink import PyPlink\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib\n",
    "\n",
    "from basic_tools import *\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "jupyter nbconvert 5_association.ipynb --to script\n",
    "python 5_association.py albumin 2 0\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plink_KCHIP_HLA_AA_SNP_1000G=PyPlink(plink_KCHIP_HLA_AA_SNP_1000G_path)\n",
    "plink_KCHIP_HLA_AA_SNP_1000G_fam=plink_KCHIP_HLA_AA_SNP_1000G.get_fam().astype({'fid':str,'iid':str}).rename(columns={'fid':'FID','iid':'IID'})\n",
    "plink_KCHIP_HLA_AA_SNP_1000G_bim=plink_KCHIP_HLA_AA_SNP_1000G.get_bim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype_info=pd.read_csv('data/phenotype_pubname.tsv',sep='\\t',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97\n"
     ]
    }
   ],
   "source": [
    "phenotypes=pd.read_csv(pheno_all_file_path,sep='\\t')\n",
    "phenotypes=phenotypes.set_index('ID').loc[plink_KCHIP_HLA_AA_SNP_1000G_fam['IID']]\n",
    "\n",
    "binary_continuous_traits=phenotypes.columns.difference(['age','sex','cohort','diabetes'])\n",
    "print(len(binary_continuous_traits))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_out_assoc_phenotype_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-6095fe261948>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlog_file_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y%m%d_%H%M%S'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.log'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mlog_file_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_out_assoc_phenotype_path\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlog_file_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mfileHandler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFileHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mstreamHandler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStreamHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data_out_assoc_phenotype_path' is not defined"
     ]
    }
   ],
   "source": [
    "log = logging.getLogger('logger')\n",
    "log.setLevel(logging.DEBUG)\n",
    "\n",
    "log_file_name=datetime.datetime.now().strftime('%Y%m%d_%H%M%S')+'.log'\n",
    "log_file_path=data_out_assoc_phenotype_path+log_file_name\n",
    "fileHandler = logging.FileHandler(log_file_path)\n",
    "streamHandler = logging.StreamHandler()\n",
    "\n",
    "formatter = logging.Formatter('%(message)s')\n",
    "fileHandler.setFormatter(formatter)\n",
    "streamHandler.setFormatter(formatter)\n",
    "\n",
    "log.addHandler(fileHandler)\n",
    "log.addHandler(streamHandler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "log.info_head=lambda x: log.info('-'*int((100-len(x))/2)+x+'-'*int((100-len(x))/2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Table generating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_DB=pd.read_csv('data/mart_export_SNP.txt',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_DB['Variant alleles set']=SNP_DB['Variant alleles'].apply(lambda x: set(x.split('/')).difference(['-']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsid_mapper=plink_KCHIP_HLA_AA_SNP_1000G_bim.copy()\n",
    "rsid_mapper=rsid_mapper[(~rsid_mapper.index.str.contains('HLA'))\n",
    "                        |~(rsid_mapper.index.str.contains('AA_'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pos_a1_a2_to_rsid(row):\n",
    "    \n",
    "    SNP_DB_match=SNP_DB[(SNP_DB['Chromosome/scaffold position start (bp)']<=row['pos'])&\n",
    "                        (SNP_DB['Chromosome/scaffold position end (bp)']>=row['pos'])]\n",
    "    if len(SNP_DB_match)==0:\n",
    "        return row.name\n",
    "    \n",
    "    SNP_DB_match_match=SNP_DB_match[SNP_DB_match['Variant alleles set'].apply(lambda x: set([row['a1'],row['a2']]).issubset(x))]\n",
    "    if len(SNP_DB_match_match)==0:\n",
    "        return row.name\n",
    "    elif len(SNP_DB_match_match)==1:\n",
    "        return SNP_DB_match_match.iloc[0]['Variant name']\n",
    "\n",
    "    SNP_DB_match_match_match=SNP_DB_match_match[(SNP_DB_match_match['Chromosome/scaffold position start (bp)']<=row['pos'])&\n",
    "                            (SNP_DB_match_match['Chromosome/scaffold position end (bp)']>=row['pos'])]        \n",
    "    if len(SNP_DB_match_match_match)==0:\n",
    "        return SNP_DB_match_match.iloc[0]['Variant name']\n",
    "    elif len(SNP_DB_match_match_match)==1:\n",
    "        return SNP_DB_match_match_match.iloc[0]['Variant name']\n",
    "    else:\n",
    "        SNP_DB_match_match_match.iloc[0]['Variant name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsid_mapper['marker_name_pub']=rsid_mapper.apply(pos_a1_a2_to_rsid,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def marker_name_to_marker_name_pub(marker_name):\n",
    "    if marker_name[:4]=='HLA_':\n",
    "        return marker_name.replace('_','-')\n",
    "    elif marker_name[:3]=='AA_':\n",
    "        marker_name_split=marker_name.split('_')\n",
    "        return 'HLA-{} position {}'.format(marker_name_split[1],marker_name_split[2])\n",
    "    else:\n",
    "        return rsid_mapper.loc[marker_name]['marker_name_pub']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype_info=pd.read_csv('data/phenotype_pubname.tsv',sep='\\t',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for phenotype_name in binary_continuous_traits:\n",
    "    data_out_assoc_phenotype_path=data_out_assoc_path+phenotype_name+'/'\n",
    "\n",
    "    pheotype_name_publication=phenotype_info.loc[phenotype_name]['phenotype_name_publication'].replace(' ','_').replace('/','_')\n",
    "    data_out_pub_phenotype_path=data_out_pub_path+pheotype_name_publication+'/'\n",
    "    pathlib.Path(data_out_pub_phenotype_path).mkdir(parents=True, exist_ok=True)\n",
    "    \n",
    "    pheno=pd.read_csv(data_out_pheno_path+phenotype_name+'.phe',sep='\\t',names=['FID','IID','pheno'])\n",
    "    phenotype_type='binary' if len(pheno['pheno'][pheno['pheno']!=-9].value_counts())<3 else 'continuous'\n",
    "    phenotype_type    \n",
    "    \n",
    "    for step_idx_sub in range(1,100):\n",
    "        log.info_head(\"phenotype_name: {}, phenotype_type:{} , Step : {} \".format(phenotype_name,phenotype_type,step_idx_sub))\n",
    "        #print(step_idx_sub)\n",
    "        if os.path.exists(data_out_assoc_phenotype_path+'step_{:02d}.cond.stop'.format(step_idx_sub)):\n",
    "            print('meets end',step_idx_sub)\n",
    "            break\n",
    "            \n",
    "        result_merge=pd.read_csv(data_out_assoc_phenotype_path+'step_{:02d}.merge.result.tsv'.format(step_idx_sub),sep='\\t',index_col=0)\n",
    "\n",
    "        result_merge=result_merge.drop(columns=['phenotype_name'])        \n",
    "        result_merge['marker_name_pub']=result_merge['marker_name'].apply(marker_name_to_marker_name_pub)\n",
    "        \n",
    "        result_merge[['marker_name', 'marker_name_pub','samples(case/control)', 'step', 'condition', \n",
    "                      'note', 'term', 'POS', 'A1', 'A2', 'A1_freq_case', 'A1_freq_control',\n",
    "                       'multi_allele', 'nobs', 'Z', 'coef', 'std', 'chisq', 'df', 'P']].to_csv(data_out_pub_phenotype_path+'step_{:02d}.result.tsv'.format(step_idx_sub),sep='\\t',index=None)\n",
    "\n",
    "        shutil.copy(data_out_assoc_phenotype_path+'step_{:02d}.merge.manhattan.png'.format(step_idx_sub),\n",
    "                    data_out_pub_phenotype_path+'step_{:02d}.manhattan.png'.format(step_idx_sub))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all=pd.read_csv('data/result_concat_3_top_gene_annot.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub=result_all.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub['No. signals']=result_all_pub['phenotype_name'].map(lambda x: result_all[(result_all['P_significant']==1) & (result_all['phenotype_name']==x)].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub=result_all_pub[(result_all_pub['P_significant']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all_pub['phenotype_category']=phenotype_info.loc[result_all_pub['phenotype_name']]['phenotype_category'].values\n",
    "result_all_pub['phenotype_type']=phenotype_info.loc[result_all_pub['phenotype_name']]['phenotype_type'].values\n",
    "result_all_pub['phenotype_name_publication']=phenotype_info.loc[result_all_pub['phenotype_name']]['phenotype_name_publication'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>T</td>\n",
       "      <td>TAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>p</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>a</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>AT</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>a</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>G</td>\n",
       "      <td>GGAGCCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A1       A2\n",
       "1      C        A\n",
       "3      G  GGAGCCC\n",
       "7    NaN      NaN\n",
       "8      C        T\n",
       "10     C        T\n",
       "12     C        T\n",
       "13     A        C\n",
       "17   NaN      NaN\n",
       "18     p        a\n",
       "24     A        G\n",
       "26     .        A\n",
       "27     A        G\n",
       "32     C        G\n",
       "45   NaN      NaN\n",
       "46     T        C\n",
       "47     A        G\n",
       "49     C        T\n",
       "58     T        C\n",
       "61     T        C\n",
       "63   NaN      NaN\n",
       "64     C        T\n",
       "65     G        T\n",
       "68     T      TAA\n",
       "69   NaN      NaN\n",
       "70     G        A\n",
       "71     A        G\n",
       "72     C        G\n",
       "74     T        C\n",
       "75     G        T\n",
       "76     G        A\n",
       "..   ...      ...\n",
       "88     p        a\n",
       "94     T        C\n",
       "97   NaN      NaN\n",
       "100    a        p\n",
       "109    C        T\n",
       "110  NaN      NaN\n",
       "111    A        G\n",
       "112  NaN      NaN\n",
       "113    C        T\n",
       "116   AT        A\n",
       "119    G        A\n",
       "122  NaN      NaN\n",
       "123    C        T\n",
       "124    G        A\n",
       "125    A        G\n",
       "134    C        T\n",
       "136    C        T\n",
       "139  NaN      NaN\n",
       "140  NaN      NaN\n",
       "143  NaN      NaN\n",
       "146  NaN      NaN\n",
       "147    C        T\n",
       "153  NaN      NaN\n",
       "154    a        p\n",
       "155    A        G\n",
       "156    G        A\n",
       "158    G  GGAGCCC\n",
       "159    A        G\n",
       "160    A        G\n",
       "161    G        A\n",
       "\n",
       "[66 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all_pub[['A1','A2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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