8_maf_cal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\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",
    "\n",
    "from basic_tools import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/HLA_A_freq.txt') as f:\n",
    "    data=f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A*01:01:01G</td>\n",
       "      <td>C</td>\n",
       "      <td>913(1.742)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A*02:01:01G</td>\n",
       "      <td>C</td>\n",
       "      <td>8339(15.913)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A*02:01:02a</td>\n",
       "      <td>R</td>\n",
       "      <td>2(0.004)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A*02:01:09a</td>\n",
       "      <td>R</td>\n",
       "      <td>1(0.002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A*02:01:18a</td>\n",
       "      <td>R</td>\n",
       "      <td>1(0.002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>A*68:02:01Ga</td>\n",
       "      <td>C</td>\n",
       "      <td>1(0.002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>A*69:01</td>\n",
       "      <td>C</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>A*02:149VN</td>\n",
       "      <td>1(0.002)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>A*26VN</td>\n",
       "      <td>1(0.002)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>A*31:01VN</td>\n",
       "      <td>1(0.002)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1             2\n",
       "0    A*01:01:01G         C    913(1.742)\n",
       "1    A*02:01:01G         C  8339(15.913)\n",
       "2    A*02:01:02a         R      2(0.004)\n",
       "3    A*02:01:09a         R      1(0.002)\n",
       "4    A*02:01:18a         R      1(0.002)\n",
       "..           ...       ...           ...\n",
       "69  A*68:02:01Ga         C      1(0.002)\n",
       "70       A*69:01         C             -\n",
       "71    A*02:149VN  1(0.002)          None\n",
       "72        A*26VN  1(0.002)          None\n",
       "73     A*31:01VN  1(0.002)          None\n",
       "\n",
       "[74 rows x 3 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([row.replace(' (','(').replace(' V','V').replace(' N','N').split(' ')[:-1] for row in data.split('\\n')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load plink, aa and check integrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# load phenotype and check integrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotypes=pd.read_csv(pheno_all_file_path,sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125673, 101)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phenotypes=phenotypes.set_index('ID').loc[plink_KCHIP_HLA_AA_SNP_1000G_fam['IID']]\n",
    "phenotypes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plink_KCHIP_HLA_AA_SNP_1000G_bim[plink_KCHIP_HLA_AA_SNP_1000G_bim.index.str.contains('HLA')]['a2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "dosage_dict={}\n",
    "for idx_bim,(SNP,row) in enumerate(plink_KCHIP_HLA_AA_SNP_1000G_bim.iterrows()):\n",
    "    if 'HLA' in SNP:\n",
    "        dosage=plink_KCHIP_HLA_AA_SNP_1000G.get_geno_marker(SNP)\n",
    "        dosage_dict[SNP]=dosage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "dosage_df=pd.DataFrame(dosage_dict).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119, 125673)"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dosage_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([125673])"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dosage_df!=-1).sum(axis=1).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df=pd.concat([(dosage_df==2).sum(axis=1)*2+(dosage_df==1).sum(axis=1),\n",
    "                (dosage_df==0).sum(axis=1)*2+(dosage_df==1).sum(axis=1),\n",
    "                (dosage_df!=-1).sum(axis=1)*2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df.columns=['Present','Absent','Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dosage_df==-1).sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Present</th>\n",
       "      <th>Absent</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HLA_A*01:01</th>\n",
       "      <td>4389</td>\n",
       "      <td>246957</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_A*02:01</th>\n",
       "      <td>41413</td>\n",
       "      <td>209933</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_A*02:06</th>\n",
       "      <td>21644</td>\n",
       "      <td>229702</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_A*02:07</th>\n",
       "      <td>8681</td>\n",
       "      <td>242665</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_A*03:01</th>\n",
       "      <td>4018</td>\n",
       "      <td>247328</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_DPB1*05:01</th>\n",
       "      <td>90550</td>\n",
       "      <td>160796</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_DPB1*09:01</th>\n",
       "      <td>7635</td>\n",
       "      <td>243711</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_DPB1*13:01</th>\n",
       "      <td>14350</td>\n",
       "      <td>236996</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_DPB1*14:01</th>\n",
       "      <td>3413</td>\n",
       "      <td>247933</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA_DPB1*17:01</th>\n",
       "      <td>4468</td>\n",
       "      <td>246878</td>\n",
       "      <td>251346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Present  Absent   Total\n",
       "HLA_A*01:01        4389  246957  251346\n",
       "HLA_A*02:01       41413  209933  251346\n",
       "HLA_A*02:06       21644  229702  251346\n",
       "HLA_A*02:07        8681  242665  251346\n",
       "HLA_A*03:01        4018  247328  251346\n",
       "...                 ...     ...     ...\n",
       "HLA_DPB1*05:01    90550  160796  251346\n",
       "HLA_DPB1*09:01     7635  243711  251346\n",
       "HLA_DPB1*13:01    14350  236996  251346\n",
       "HLA_DPB1*14:01     3413  247933  251346\n",
       "HLA_DPB1*17:01     4468  246878  251346\n",
       "\n",
       "[119 rows x 3 columns]"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df_prop=count_df['Present'].astype('str')+' ('+(100*count_df['Present']/count_df['Total']).round(3).astype('str')+')'\n",
    "count_df_prop=count_df_prop.to_frame()\n",
    "count_df_prop.index=count_df_prop.index.str.replace('HLA_','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df_prop['Count']=count_df_prop[0].map(lambda x: x.split(' (')[0]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df_prop['Prop']=100*count_df_prop['Count']/(2*125673)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120846.5"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "241693/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>Count</th>\n",
       "      <th>Prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A*01:01</th>\n",
       "      <td>4389 (1.746)</td>\n",
       "      <td>4389</td>\n",
       "      <td>1.746198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*02:01</th>\n",
       "      <td>41413 (16.476)</td>\n",
       "      <td>41413</td>\n",
       "      <td>16.476491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*02:06</th>\n",
       "      <td>21644 (8.611)</td>\n",
       "      <td>21644</td>\n",
       "      <td>8.611237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*02:07</th>\n",
       "      <td>8681 (3.454)</td>\n",
       "      <td>8681</td>\n",
       "      <td>3.453805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*03:01</th>\n",
       "      <td>4018 (1.599)</td>\n",
       "      <td>4018</td>\n",
       "      <td>1.598593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*11:01</th>\n",
       "      <td>26078 (10.375)</td>\n",
       "      <td>26078</td>\n",
       "      <td>10.375339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*24:02</th>\n",
       "      <td>56255 (22.381)</td>\n",
       "      <td>56255</td>\n",
       "      <td>22.381498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*26:01</th>\n",
       "      <td>8602 (3.422)</td>\n",
       "      <td>8602</td>\n",
       "      <td>3.422374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*26:02</th>\n",
       "      <td>6497 (2.585)</td>\n",
       "      <td>6497</td>\n",
       "      <td>2.584883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*30:01</th>\n",
       "      <td>7428 (2.955)</td>\n",
       "      <td>7428</td>\n",
       "      <td>2.955289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*30:04</th>\n",
       "      <td>3275 (1.303)</td>\n",
       "      <td>3275</td>\n",
       "      <td>1.302985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*31:01</th>\n",
       "      <td>12804 (5.094)</td>\n",
       "      <td>12804</td>\n",
       "      <td>5.094173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A*33:03</th>\n",
       "      <td>40609 (16.157)</td>\n",
       "      <td>40609</td>\n",
       "      <td>16.156613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0  Count       Prop\n",
       "A*01:01    4389 (1.746)   4389   1.746198\n",
       "A*02:01  41413 (16.476)  41413  16.476491\n",
       "A*02:06   21644 (8.611)  21644   8.611237\n",
       "A*02:07    8681 (3.454)   8681   3.453805\n",
       "A*03:01    4018 (1.599)   4018   1.598593\n",
       "A*11:01  26078 (10.375)  26078  10.375339\n",
       "A*24:02  56255 (22.381)  56255  22.381498\n",
       "A*26:01    8602 (3.422)   8602   3.422374\n",
       "A*26:02    6497 (2.585)   6497   2.584883\n",
       "A*30:01    7428 (2.955)   7428   2.955289\n",
       "A*30:04    3275 (1.303)   3275   1.302985\n",
       "A*31:01   12804 (5.094)  12804   5.094173\n",
       "A*33:03  40609 (16.157)  40609  16.156613"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df_prop[count_df_prop.index.str.contains('A\\*')]#['Count'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_freq_ref=pd.read_csv('data/allele_freq_ref.csv',header=None)\n",
    "allele_freq_ref[0]=allele_freq_ref[0].map(lambda x: x.strip().split()[0].replace('a','').replace('b',''))\n",
    "allele_freq_ref[0]=allele_freq_ref[0].map(lambda x: x if x[-1] in [str(i) for i in range(10)] else x[:-1])\n",
    "allele_freq_ref[2]=allele_freq_ref[2].map(lambda x: x.strip().split(' (')[0].strip().replace('#VALUE!','0').replace('-','0').replace(',','')).astype(int)\n",
    "allele_freq_ref.columns=['Allele','type','Count_ref']\n",
    "allele_freq_ref=allele_freq_ref.set_index('Allele')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_freq_ref['four_digit']=allele_freq_ref.index.map(lambda x: ':'.join(x.split(':')[:-1]) if x.count(':')==2 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "#allele_freq_ref.to_csv('data/allele_freq_ref_cleaned.csv',header=None)#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#allele_freq_ref.index[0].count(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_freq_complete=pd.read_csv('data/allele_freq_ref_cleaned_corrected.csv',header=None, names=['Allele','type','Count_ref','four_digit'],index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_freq_complete_four=allele_freq_complete.groupby('four_digit').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_freq_complete_four['Prop_ref']=(100*allele_freq_complete_four['Count_ref']/52403)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>Count</th>\n",
       "      <th>Prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A*01:01</th>\n",
       "      <td>4389 (1.746)</td>\n",
       "      <td>4389</td>\n",
       "      <td>0.017462</td>\n",
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       "    <tr>\n",
       "      <th>A*02:01</th>\n",
       "      <td>41413 (16.476)</td>\n",
       "      <td>41413</td>\n",
       "      <td>0.164765</td>\n",
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       "    <tr>\n",
       "      <th>A*02:06</th>\n",
       "      <td>21644 (8.611)</td>\n",
       "      <td>21644</td>\n",
       "      <td>0.086112</td>\n",
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       "    <tr>\n",
       "      <th>A*02:07</th>\n",
       "      <td>8681 (3.454)</td>\n",
       "      <td>8681</td>\n",
       "      <td>0.034538</td>\n",
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       "    <tr>\n",
       "      <th>A*03:01</th>\n",
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       "      <th>DPB1*05:01</th>\n",
       "      <td>90550 (36.026)</td>\n",
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       "      <td>0.360260</td>\n",
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       "    <tr>\n",
       "      <th>DPB1*09:01</th>\n",
       "      <td>7635 (3.038)</td>\n",
       "      <td>7635</td>\n",
       "      <td>0.030376</td>\n",
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       "    <tr>\n",
       "      <th>DPB1*13:01</th>\n",
       "      <td>14350 (5.709)</td>\n",
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       "      <td>0.057093</td>\n",
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       "      <th>DPB1*14:01</th>\n",
       "      <td>3413 (1.358)</td>\n",
       "      <td>3413</td>\n",
       "      <td>0.013579</td>\n",
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       "    <tr>\n",
       "      <th>DPB1*17:01</th>\n",
       "      <td>4468 (1.778)</td>\n",
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       "      <td>0.017776</td>\n",
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       "  </tbody>\n",
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       "<p>119 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                         0  Count      Prop\n",
       "A*01:01       4389 (1.746)   4389  0.017462\n",
       "A*02:01     41413 (16.476)  41413  0.164765\n",
       "A*02:06      21644 (8.611)  21644  0.086112\n",
       "A*02:07       8681 (3.454)   8681  0.034538\n",
       "A*03:01       4018 (1.599)   4018  0.015986\n",
       "...                    ...    ...       ...\n",
       "DPB1*05:01  90550 (36.026)  90550  0.360260\n",
       "DPB1*09:01    7635 (3.038)   7635  0.030376\n",
       "DPB1*13:01   14350 (5.709)  14350  0.057093\n",
       "DPB1*14:01    3413 (1.358)   3413  0.013579\n",
       "DPB1*17:01    4468 (1.778)   4468  0.017776\n",
       "\n",
       "[119 rows x 3 columns]"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A*01:01</th>\n",
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       "      <th>A*02:07</th>\n",
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      ],
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       "                         0  Count      Prop\n",
       "A*01:01       4389 (1.746)   4389  0.017462\n",
       "A*02:01     41413 (16.476)  41413  0.164765\n",
       "A*02:06      21644 (8.611)  21644  0.086112\n",
       "A*02:07       8681 (3.454)   8681  0.034538\n",
       "A*03:01       4018 (1.599)   4018  0.015986\n",
       "...                    ...    ...       ...\n",
       "DPB1*05:01  90550 (36.026)  90550  0.360260\n",
       "DPB1*09:01    7635 (3.038)   7635  0.030376\n",
       "DPB1*13:01   14350 (5.709)  14350  0.057093\n",
       "DPB1*14:01    3413 (1.358)   3413  0.013579\n",
       "DPB1*17:01    4468 (1.778)   4468  0.017776\n",
       "\n",
       "[119 rows x 3 columns]"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count_df_prop.merge??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df_prop_merged=count_df_prop.merge(right=allele_freq_complete_four,how='outer', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df_prop_merged.iloc[count_df_prop_merged.index.map(lambda x: x.split('*')[0] not in ['A','B','DRB1']),[3,4]].fillna('-',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_df_prop_merged.fillna(0).to_csv('data/out_pub/allele_freq_compare.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>Count_ref</th>\n",
       "      <th>Prop_ref</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>DRB1*14:03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1.02475</td>\n",
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      ],
      "text/plain": [
       "              0  Count  Prop  Count_ref  Prop_ref\n",
       "DRB1*14:03  NaN    NaN   NaN      537.0   1.02475"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df_prop_merged[count_df_prop_merged.index=='DRB1*14:03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: '0#0V0A0L0U0E0!0'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-275-3522282a9fb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mallele_freq_ref\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallele_freq_ref\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' ('\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'#VALUE!'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\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;32m----> 2\u001b[0;31m \u001b[0mallele_freq_ref\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallele_freq_ref\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5870\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5871\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5872\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5873\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 631\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m     def convert(\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    425\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\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[1;32m    429\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\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[1;32m    672\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\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[1;32m    675\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tools/miniconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1072\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\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;32m-> 1074\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1075\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '0#0V0A0L0U0E0!0'"
     ]
    }
   ],
   "source": [
    "allele_freq_ref[2]=allele_freq_ref[2].map(lambda x: x.strip().split(' (')[0].strip().replace('-','0').replace('','0').replace(',','').replace('#VALUE!',''))\n",
    "allele_freq_ref[2]=allele_freq_ref[2].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0901030\n",
       "1      080303090\n",
       "2            020\n",
       "3            010\n",
       "4            010\n",
       "         ...    \n",
       "247          010\n",
       "248          020\n",
       "249      0502060\n",
       "250          010\n",
       "251          010\n",
       "Name: 2, Length: 252, dtype: object"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allele_freq_ref[2].map(lambda x: x.strip().split(' (')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>DRB1*15:06:01</td>\n",
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       "                 0  1         2\n",
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       "2       A*02:01:02  R        20\n",
       "3       A*02:01:09  R        10\n",
       "4       A*02:01:18  R        10\n",
       "..             ... ..       ...\n",
       "247     DRB1*15:04  C        10\n",
       "248  DRB1*15:06:01  C        20\n",
       "249  DRB1*16:02:01  C    502060\n",
       "250     DRB1*04:05  N        10\n",
       "251     DRB1*04:06  N        10\n",
       "\n",
       "[252 rows x 3 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allele_freq_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "913\n",
      "8339\n",
      "2\n",
      "1\n",
      "1\n",
      "1\n",
      "298\n",
      "32\n",
      "4447\n",
      "1764\n",
      "166\n",
      "3\n",
      "2\n",
      "1\n",
      "9\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "2\n",
      "797\n",
      "120\n",
      "1\n",
      "5319\n",
      "11\n",
      "1\n",
      "123\n",
      "1\n",
      "\n",
      "3\n",
      "\n",
      "6\n",
      "11,957\n",
      "18\n",
      "1\n",
      "1\n",
      "38\n",
      "1\n",
      "103\n",
      "\n",
      "1\n",
      "1\n",
      "1\n",
      "1922\n",
      "4\n",
      "1142\n",
      "457\n",
      "5\n",
      "16\n",
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      "3\n",
      "1\n",
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      "1\n",
      "5\n",
      "381\n",
      "3\n",
      "1611\n",
      "2\n",
      "706\n",
      "2693\n",
      "11\n",
      "344\n",
      "1\n",
      "8494\n",
      "1\n",
      "1\n",
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      "90\n",
      "1\n",
      "\n",
      "1\n",
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      "1\n",
      "1930\n",
      "4\n",
      "384\n",
      "4\n",
      "156\n",
      "1016\n",
      "1591\n",
      "680\n",
      "4720\n",
      "1\n",
      "146\n",
      "3\n",
      "610\n",
      "1\n",
      "913\n",
      "2\n",
      "1\n",
      "27\n",
      "655\n",
      "16\n",
      "124\n",
      "2\n",
      "\n",
      "1\n",
      "8\n",
      "92\n",
      "3\n",
      "\n",
      "1\n",
      "1\n",
      "5\n",
      "89\n",
      "1\n",
      "1370\n",
      "2\n",
      "12\n",
      "\n",
      "3077\n",
      "2\n",
      "197\n",
      "1\n",
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      "1\n",
      "2\n",
      "\n",
      "766\n",
      "2\n",
      "49\n",
      "493\n",
      "636\n",
      "#VALUE!\n",
      "16\n",
      "8\n",
      "2\n",
      "2110\n",
      "1\n",
      "2317\n",
      "381\n",
      "1949\n",
      "1\n",
      "3\n",
      "1\n",
      "1\n",
      "784\n",
      "3023\n",
      "1641\n",
      "1\n",
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      "2425\n",
      "1\n",
      "80\n",
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      "1\n",
      "8\n",
      "65\n",
      "4938\n",
      "263\n",
      "9\n",
      "2\n",
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      "1\n",
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      "3168\n",
      "31\n",
      "880\n",
      "47\n",
      "29\n",
      "200\n",
      "1\n",
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      "3\n",
      "4\n",
      "144\n",
      "3255\n",
      "2\n",
      "856\n",
      "373\n",
      "137\n",
      "7\n",
      "1\n",
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      "1\n",
      "1\n",
      "1\n",
      "1\n",
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      "1\n",
      "3221\n",
      "5\n",
      "2\n",
      "1103\n",
      "1\n",
      "300\n",
      "3\n",
      "1581\n",
      "705\n",
      "4257\n",
      "2605\n",
      "261\n",
      "54\n",
      "535\n",
      "1\n",
      "5\n",
      "1\n",
      "4220\n",
      "28\n",
      "1145\n",
      "1\n",
      "3935\n",
      "3\n",
      "2\n",
      "2\n",
      "5180\n",
      "3\n",
      "2\n",
      "835\n",
      "2206\n",
      "15\n",
      "13\n",
      "53\n",
      "2\n",
      "8\n",
      "1\n",
      "1\n",
      "2432\n",
      "1547\n",
      "1\n",
      "7\n",
      "1\n",
      "1\n",
      "864\n",
      "4762\n",
      "22\n",
      "15\n",
      "1\n",
      "25\n",
      "537\n",
      "35\n",
      "1484\n",
      "332\n",
      "142\n",
      "11\n",
      "53\n",
      "1\n",
      "\n",
      "1411\n",
      "1\n",
      "1\n",
      "\n",
      "1\n",
      "4062\n",
      "1821\n",
      "8\n",
      "1\n",
      "2\n",
      "526\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in allele_freq_ref[2]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_save.columns=['2n = 251,346 (%)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_save.to_csv('data/out_pub/allele_freq.tsv',sep='\\t')"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "BHAN000001    Control\n",
       "BHAN000002       Case\n",
       "BHAN000003    Control\n",
       "BHAN000004       Case\n",
       "BHAN000005    Control\n",
       "               ...   \n",
       "BHAN125868    Control\n",
       "BHAN125869    Control\n",
       "BHAN125870    Control\n",
       "BHAN125871    Control\n",
       "BHAN125872    Control\n",
       "Name: hypertension, Length: 125673, dtype: object"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenotype_name='hypertension'\n",
    "data_out_assoc_phenotype_path=data_out_assoc_path+phenotype_name+'/'\n",
    "pheno=pd.read_csv(data_out_pheno_path+phenotype_name+'.phe',sep='\\t',names=['FID','IID','pheno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict={}\n",
    "for idx_bim,(SNP,row) in enumerate(plink_KCHIP_HLA_AA_SNP_1000G_bim.iterrows()):\n",
    "    if SNP in ['AA_B_80_31324497_exon2_I' ,'AA_B_80_31324497_exon2_N', 'AA_B_80_31324497_exon2_T']:\n",
    "        dosage=plink_KCHIP_HLA_AA_SNP_1000G.get_geno_marker(SNP)\n",
    "        data_dict[SNP]=dosage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df=pd.DataFrame(data_dict)\n",
    "data_df['case']=pheno['pheno'].astype('str').replace('2', 'Case').replace('1', 'Control').replace('-9', 'Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>746</td>\n",
       "      <td>412</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>2836</td>\n",
       "      <td>1500</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>1121</td>\n",
       "      <td>605</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T     0     1    2\n",
       "case                                     \n",
       "Case                       746   412   72\n",
       "Control                   2836  1500  250\n",
       "Missing                   1121   605   65"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=data_df['case'][(phenotypes['cohort']==1).values],\\\n",
    "            columns=data_df['AA_B_80_31324497_exon2_T'][(phenotypes['cohort']==1).values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>11651</td>\n",
       "      <td>6610</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>40111</td>\n",
       "      <td>20766</td>\n",
       "      <td>3128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>9799</td>\n",
       "      <td>5213</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T      0      1     2\n",
       "case                                        \n",
       "Case                      11651   6610  1076\n",
       "Control                   40111  20766  3128\n",
       "Missing                    9799   5213   805"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=data_df['case'][(phenotypes['cohort']==2).values],\\\n",
    "            columns=data_df['AA_B_80_31324497_exon2_T'][(phenotypes['cohort']==2).values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>3093</td>\n",
       "      <td>1688</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>7545</td>\n",
       "      <td>3743</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>1298</td>\n",
       "      <td>608</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T     0     1    2\n",
       "case                                     \n",
       "Case                      3093  1688  266\n",
       "Control                   7545  3743  572\n",
       "Missing                   1298   608   94"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=data_df['case'][(phenotypes['cohort']==3).values],\\\n",
    "            columns=data_df['AA_B_80_31324497_exon2_T'][(phenotypes['cohort']==3).values])"
   ]
  },
  {
   "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": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "=(phenotypes['hypertension']==2).astype('str').replace('False', 'Control').replace('True', 'Case').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>15490</td>\n",
       "      <td>8710</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>62710</td>\n",
       "      <td>32435</td>\n",
       "      <td>4914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T      0      1     2\n",
       "case                                        \n",
       "Case                      15490   8710  1414\n",
       "Control                   62710  32435  4914"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=data_df['case'], columns=data_df['AA_B_80_31324497_exon2_T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1230, 7607)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((phenotypes['hypertension']==2)&(phenotypes['cohort']==1)).sum(),(phenotypes['cohort']==1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1616931773366636"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1230/7607"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19337, 99159)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((phenotypes['hypertension']==2)&(phenotypes['cohort']==2)).sum(),(phenotypes['cohort']==2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19501003438921327"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "19337/99159"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5047, 18907)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((phenotypes['hypertension']==2)&(phenotypes['cohort']==3)).sum(),(phenotypes['cohort']==3).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2669381710477601"
      ]
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     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5047/18907"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>746</td>\n",
       "      <td>412</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>3957</td>\n",
       "      <td>2105</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T     0     1    2\n",
       "case                                     \n",
       "Case                       746   412   72\n",
       "Control                   3957  2105  315"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60650407, 0.33495935, 0.05853659])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([746,412,72])/np.array([746,412,72]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>11651</td>\n",
       "      <td>6610</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>49910</td>\n",
       "      <td>25979</td>\n",
       "      <td>3933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T      0      1     2\n",
       "case                                        \n",
       "Case                      11651   6610  1076\n",
       "Control                   49910  25979  3933"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=data_df['case'][(phenotypes['cohort']==2).values],\\\n",
    "            columns=data_df['AA_B_80_31324497_exon2_T'][(phenotypes['cohort']==2).values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60252366, 0.34183172, 0.05564462])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([11651,6610,1076])/np.array([11651,6610,1076]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>AA_B_80_31324497_exon2_T</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>3093</td>\n",
       "      <td>1688</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>8843</td>\n",
       "      <td>4351</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AA_B_80_31324497_exon2_T     0     1    2\n",
       "case                                     \n",
       "Case                      3093  1688  266\n",
       "Control                   8843  4351  666"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=data_df['case'][(phenotypes['cohort']==3).values],\\\n",
    "            columns=data_df['AA_B_80_31324497_exon2_T'][(phenotypes['cohort']==3).values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.61283931, 0.33445611, 0.05270458])"
      ]
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     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "np.array([3093,1688,266])/np.array([3093,1688,266]).sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5047"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3093+1688+266"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99159"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(phenotypes['cohort']==2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18907"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(phenotypes['cohort']==3).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'AS','CT','NC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>cm</th>\n",
       "      <th>a1</th>\n",
       "      <th>a2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA_B_80_31324497_exon2_I</th>\n",
       "      <td>6</td>\n",
       "      <td>31324497</td>\n",
       "      <td>0</td>\n",
       "      <td>p</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA_B_80_31324497_exon2_N</th>\n",
       "      <td>6</td>\n",
       "      <td>31324497</td>\n",
       "      <td>0</td>\n",
       "      <td>p</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA_B_80_31324497_exon2_T</th>\n",
       "      <td>6</td>\n",
       "      <td>31324497</td>\n",
       "      <td>0</td>\n",
       "      <td>p</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          chrom       pos  cm a1 a2\n",
       "snp                                                \n",
       "AA_B_80_31324497_exon2_I      6  31324497   0  p  a\n",
       "AA_B_80_31324497_exon2_N      6  31324497   0  p  a\n",
       "AA_B_80_31324497_exon2_T      6  31324497   0  p  a"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plink_KCHIP_HLA_AA_SNP_1000G_bim[plink_KCHIP_HLA_AA_SNP_1000G_bim.index.str.contains('AA_B_80')]"
   ]
  },
  {
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125673.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "251346/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ch6845/tools/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "def cal_maf(dosage_array):\n",
    "    maf=((dosage_array==1).sum()+2*(dosage_array==2).sum())/\\\n",
    "         (2*(dosage_array!=-1).sum())\n",
    "    return maf\n",
    "\n",
    "maf_list_list=[]\n",
    "for idx_bim,(SNP,row) in enumerate(plink_KCHIP_HLA_AA_SNP_1000G_bim.iterrows()):\n",
    "    maf_list=[]\n",
    "    dosage=plink_KCHIP_HLA_AA_SNP_1000G.get_geno_marker(SNP)\n",
    "    maf_list.append(cal_maf(dosage))\n",
    "    for cohort in range(1,3+1):\n",
    "        cohort_dosage=dosage[phenotypes['cohort']==cohort]\n",
    "        #print('total: {:5d} -1:{:5d} 0:{:5d} 1:{:5d} 2:{:5d}'.format(len(cohort_dosage),(cohort_dosage==-1).sum(),(cohort_dosage==0).sum(),(cohort_dosage==1).sum(),(cohort_dosage==2).sum()))\n",
    "        maf_list.append(cal_maf(cohort_dosage))\n",
    "    maf_list_list.append(maf_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (phenotypes.index!=plink_KCHIP_HLA_AA_SNP_1000G_fam['IID']).sum()==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "maf_df=pd.DataFrame(maf_list_list,index=plink_KCHIP_HLA_AA_SNP_1000G_bim.index,columns=['all','AS','CT','NC'])\n",
    "maf_df['pos_MB']=plink_KCHIP_HLA_AA_SNP_1000G_bim['pos']/1000000\n",
    "maf_df['pos']=plink_KCHIP_HLA_AA_SNP_1000G_bim['pos']\n",
    "maf_df['A1']=plink_KCHIP_HLA_AA_SNP_1000G_bim['a1']\n",
    "maf_df['A2']=plink_KCHIP_HLA_AA_SNP_1000G_bim['a2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "maf_df[['pos','A1','A2','all','AS','CT','NC']].to_csv(\"maf.tsv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#maf_df=maf_df[maf_df.isnull().sum(axis=1)==0]\n",
    "\n",
    "maf_df['all_minor']=np.where(maf_df['all']>0.5,1-maf_df['all'],maf_df['all'])\n",
    "maf_df['AS_minor']=np.where(maf_df['all']>0.5,1-maf_df['AS'],maf_df['AS'])\n",
    "maf_df['CT_minor']=np.where(maf_df['all']>0.5,1-maf_df['CT'],maf_df['CT'])\n",
    "maf_df['NC_minor']=np.where(maf_df['all']>0.5,1-maf_df['NC'],maf_df['NC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "maf_df['all_weighted_minor']=(maf_df['AS_minor']+maf_df['CT_minor']+maf_df['NC_minor'])/3#(maf_df['AS']*(phenotypes['cohort']==1).sum()+maf_df['CT']*(phenotypes['cohort']==2).sum()+maf_df['NC']*(phenotypes['cohort']==3).sum())/(phenotypes['cohort'].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#maf_df['AS_diff_minor']=(maf_df['AS_minor']-maf_df['all_minor'])#.abs()\n",
    "#maf_df['CT_diff_minor']=(maf_df['CT_minor']-maf_df['all_minor'])#.abs()\n",
    "#maf_df['NC_diff_minor']=(maf_df['NC_minor']-maf_df['all_minor'])#.abs()\n",
    "\n",
    "\n",
    "maf_df['AS_diff_weighted_minor']=(maf_df['AS_minor']-maf_df['all_weighted_minor']).abs()\n",
    "maf_df['CT_diff_weighted_minor']=(maf_df['CT_minor']-maf_df['all_weighted_minor']).abs()\n",
    "maf_df['NC_diff_weighted_minor']=(maf_df['NC_minor']-maf_df['all_weighted_minor']).abs()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "      <th>AS</th>\n",
       "      <th>CT</th>\n",
       "      <th>NC</th>\n",
       "      <th>pos_MB</th>\n",
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       "      <th>CT_diff_weighted_minor</th>\n",
       "      <th>NC_diff_weighted_minor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snp</th>\n",
       "      <th></th>\n",
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       "      <th>6:28000361_T/C</th>\n",
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       "      <td>0.095235</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>0.002586</td>\n",
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       "    <tr>\n",
       "      <th>6:28000610_C/A</th>\n",
       "      <td>0.094949</td>\n",
       "      <td>0.093269</td>\n",
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       "      <td>0.094530</td>\n",
       "      <td>0.097821</td>\n",
       "      <td>0.095207</td>\n",
       "      <td>0.001937</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>0.002614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6:28001003_A/C</th>\n",
       "      <td>0.088858</td>\n",
       "      <td>0.086368</td>\n",
       "      <td>0.088550</td>\n",
       "      <td>0.091474</td>\n",
       "      <td>28.001003</td>\n",
       "      <td>0.088858</td>\n",
       "      <td>0.086368</td>\n",
       "      <td>0.088550</td>\n",
       "      <td>0.091474</td>\n",
       "      <td>0.088797</td>\n",
       "      <td>0.002429</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.002677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6:28001033_T/C</th>\n",
       "      <td>0.309947</td>\n",
       "      <td>0.309780</td>\n",
       "      <td>0.309543</td>\n",
       "      <td>0.312133</td>\n",
       "      <td>28.001033</td>\n",
       "      <td>0.309947</td>\n",
       "      <td>0.309780</td>\n",
       "      <td>0.309543</td>\n",
       "      <td>0.312133</td>\n",
       "      <td>0.310486</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.001647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6:28001610_T/C</th>\n",
       "      <td>0.309939</td>\n",
       "      <td>0.309780</td>\n",
       "      <td>0.309533</td>\n",
       "      <td>0.312133</td>\n",
       "      <td>28.001610</td>\n",
       "      <td>0.309939</td>\n",
       "      <td>0.309780</td>\n",
       "      <td>0.309533</td>\n",
       "      <td>0.312133</td>\n",
       "      <td>0.310482</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.001651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     all        AS        CT        NC     pos_MB  all_minor  \\\n",
       "snp                                                                            \n",
       "6:28000361_T/C  0.094969  0.093335  0.094550  0.097821  28.000361   0.094969   \n",
       "6:28000610_C/A  0.094949  0.093269  0.094530  0.097821  28.000610   0.094949   \n",
       "6:28001003_A/C  0.088858  0.086368  0.088550  0.091474  28.001003   0.088858   \n",
       "6:28001033_T/C  0.309947  0.309780  0.309543  0.312133  28.001033   0.309947   \n",
       "6:28001610_T/C  0.309939  0.309780  0.309533  0.312133  28.001610   0.309939   \n",
       "\n",
       "                AS_minor  CT_minor  NC_minor  all_weighted_minor  \\\n",
       "snp                                                                \n",
       "6:28000361_T/C  0.093335  0.094550  0.097821            0.095235   \n",
       "6:28000610_C/A  0.093269  0.094530  0.097821            0.095207   \n",
       "6:28001003_A/C  0.086368  0.088550  0.091474            0.088797   \n",
       "6:28001033_T/C  0.309780  0.309543  0.312133            0.310486   \n",
       "6:28001610_T/C  0.309780  0.309533  0.312133            0.310482   \n",
       "\n",
       "                AS_diff_weighted_minor  CT_diff_weighted_minor  \\\n",
       "snp                                                              \n",
       "6:28000361_T/C                0.001900                0.000685   \n",
       "6:28000610_C/A                0.001937                0.000677   \n",
       "6:28001003_A/C                0.002429                0.000247   \n",
       "6:28001033_T/C                0.000705                0.000942   \n",
       "6:28001610_T/C                0.000702                0.000949   \n",
       "\n",
       "                NC_diff_weighted_minor  \n",
       "snp                                     \n",
       "6:28000361_T/C                0.002586  \n",
       "6:28000610_C/A                0.002614  \n",
       "6:28001003_A/C                0.002677  \n",
       "6:28001033_T/C                0.001647  \n",
       "6:28001610_T/C                0.001651  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (50,10)\n",
    "plt.rcParams[\"font.size\"]=30\n",
    "\n",
    "ax=maf_df.set_index('pos_MB')[['AS_diff_weighted_minor','CT_diff_weighted_minor','NC_diff_weighted_minor']].plot(alpha=0.2)\n",
    "\n",
    "leg = ax.legend()\n",
    "\n",
    "for line in leg.get_lines():\n",
    "    line.set_linewidth(20)\n",
    "\n",
    "plt.title(\"MAF difference between cohorts ( abs(MAF_cohort-avg(MAF_cohort)) )\")\n",
    "plt.savefig('MAF.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.19483053254227672, 0.004732483239121861, 0.5)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df['AS'].mean(),maf_df['AS'].min(),maf_df['AS'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1946544550292758, 0.004921388880484878, 0.4999848727800805)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df['CT'].mean(),maf_df['CT'].min(),maf_df['CT'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.19395682067393166, 0.005103929761464008, 0.5)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df['NC'].mean(),maf_df['NC'].min(),maf_df['NC'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0022412417310651704, 4.579547852046062e-10, 0.019479151573028652)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df['AS_diff_weighted'].mean(),maf_df['AS_diff_weighted'].min(),maf_df['AS_diff_weighted'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.001673300881159929, 1.2356453422956992e-07, 0.018359628370642617)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df['CT_diff_weighted'].mean(),maf_df['CT_diff_weighted'].min(),maf_df['CT_diff_weighted'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.002263989566338635, 6.980417109961334e-08, 0.01458894060723423)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df['NC_diff_weighted'].mean(),maf_df['NC_diff_weighted'].min(),maf_df['NC_diff_weighted'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAF AS>=CT>NC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74904, 11)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maf_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15116, 237, 3)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(maf_df['AS']<0.05).sum(),(maf_df['AS']<0.01).sum(),(maf_df['AS']<0.005).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15187, 210, 2)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(maf_df['CT']<0.05).sum(),(maf_df['CT']<0.01).sum(),(maf_df['CT']<0.005).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15357, 160, 0)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(maf_df['NC']<0.05).sum(),(maf_df['NC']<0.01).sum(),(maf_df['NC']<0.005).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "all                 0\n",
       "AS                  0\n",
       "CT                  0\n",
       "NC                  0\n",
       "all_weighted        0\n",
       "AS_diff             0\n",
       "CT_diff             0\n",
       "NC_diff             0\n",
       "AS_diff_weighted    0\n",
       "CT_diff_weighted    0\n",
       "NC_diff_weighted    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(maf_df==0).sum()"
   ]
  }
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