{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1b0424b-f206-4e52-9b07-6b6baae905cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76597777-d1c1-469f-833f-e5ec41211013",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>shAllels</th>\n",
       "      <th>s1short</th>\n",
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       "  </thead>\n",
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       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>2</td>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-2-P142</td>\n",
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       "      <th>1293199</th>\n",
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       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_b...</td>\n",
       "      <td>1</td>\n",
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       "      <td>241768689</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1293201</th>\n",
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       "      <td>241768689</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>1</td>\n",
       "      <td>RNAseq_6-1</td>\n",
       "      <td>RNAseq_5-2-P181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293202</th>\n",
       "      <td>1293202</td>\n",
       "      <td>chr2</td>\n",
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       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>1</td>\n",
       "      <td>RNAseq_6-1</td>\n",
       "      <td>RNAseq_6-2</td>\n",
       "    </tr>\n",
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       "      <th>1293203</th>\n",
       "      <td>1293203</td>\n",
       "      <td>chr2</td>\n",
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       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>1</td>\n",
       "      <td>RNAseq_6-2</td>\n",
       "      <td>RNAseq_6-1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1293204 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0 chrom        pos  \\\n",
       "0                 0  chr2      46145   \n",
       "1                 1  chr2      46145   \n",
       "2                 2  chr2     218386   \n",
       "3                 3  chr2     218386   \n",
       "4                 4  chr2     218386   \n",
       "...             ...   ...        ...   \n",
       "1293199     1293199  chr2  241768689   \n",
       "1293200     1293200  chr2  241768689   \n",
       "1293201     1293201  chr2  241768689   \n",
       "1293202     1293202  chr2  241768689   \n",
       "1293203     1293203  chr2  241768689   \n",
       "\n",
       "                                                        s1  \\\n",
       "0                                 F008254-4-I-ExC93-xgenV2   \n",
       "1        /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "2        /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "3        /sf/storage/gmo/hic/out/by_User/Galya/rnaseq_b...   \n",
       "4        /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "...                                                    ...   \n",
       "1293199  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "1293200  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "1293201  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "1293202  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "1293203  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "\n",
       "                                                        s2  shAllels  \\\n",
       "0        /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...         2   \n",
       "1                                 F008254-4-I-ExC93-xgenV2         2   \n",
       "2        /sf/storage/gmo/hic/out/by_User/Galya/rnaseq_b...         2   \n",
       "3        /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...         2   \n",
       "4        /sf/storage/gmo/hic/out/by_User/Galya/rnaseq_b...         2   \n",
       "...                                                    ...       ...   \n",
       "1293199  /sf/storage/gmo/hic/out/by_User/Galya/rnaseq_b...         1   \n",
       "1293200  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...         1   \n",
       "1293201  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...         1   \n",
       "1293202  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...         1   \n",
       "1293203  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...         1   \n",
       "\n",
       "                 s1short           s2short  \n",
       "0             exome_P142   RNAseq_4-2-P142  \n",
       "1        RNAseq_4-2-P142        exome_P142  \n",
       "2             RNAseq_1-1       RNAseq_P142  \n",
       "3            RNAseq_P142        RNAseq_1-1  \n",
       "4             RNAseq_1-1      RNAseq_181-1  \n",
       "...                  ...               ...  \n",
       "1293199       RNAseq_6-1  RNAseq_5-2g-P181  \n",
       "1293200  RNAseq_5-2-P181        RNAseq_6-1  \n",
       "1293201       RNAseq_6-1   RNAseq_5-2-P181  \n",
       "1293202       RNAseq_6-1        RNAseq_6-2  \n",
       "1293203       RNAseq_6-2        RNAseq_6-1  \n",
       "\n",
       "[1293204 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"allBams.DP10.calls.vcf\"\n",
    "results = pd.read_csv(filename+\".results_DP5.csv.gz\")\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c452b277-5b4e-40f4-8f9b-0f463a10e43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>s1short</th>\n",
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       "      <td>RNAseq</td>\n",
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       "      <th>1</th>\n",
       "      <td>RNAseq_1-1</td>\n",
       "      <td>RNAseq_1-2</td>\n",
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       "      <td>656</td>\n",
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       "      <td>RNAseq</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RNAseq_1-1</td>\n",
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       "      <td>0.846647</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>RNAseq</td>\n",
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       "      <th>1435</th>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>enrich_P143</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>exome</td>\n",
       "      <td>enrich</td>\n",
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       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>enrich_P181</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>exome</td>\n",
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       "    <tr>\n",
       "      <th>1438</th>\n",
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       "      <td>9</td>\n",
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       "      <td>exome</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>exome</td>\n",
       "      <td>enrich</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         s1short       s2short  n_alleles  count  proportion experiment1  \\\n",
       "0     RNAseq_1-1    RNAseq_1-2          0     37    0.008188      RNAseq   \n",
       "1     RNAseq_1-1    RNAseq_1-2          1    656    0.145165      RNAseq   \n",
       "2     RNAseq_1-1    RNAseq_1-2          2   3826    0.846647      RNAseq   \n",
       "3     RNAseq_1-1  RNAseq_181-1          0     94    0.042171      RNAseq   \n",
       "4     RNAseq_1-1  RNAseq_181-1          1    607    0.272319      RNAseq   \n",
       "...          ...           ...        ...    ...         ...         ...   \n",
       "1435  exome_P142   enrich_P143          1      5    0.416667       exome   \n",
       "1436  exome_P142   enrich_P143          2      2    0.166667       exome   \n",
       "1437  exome_P142   enrich_P181          0      6    0.375000       exome   \n",
       "1438  exome_P142   enrich_P181          1      9    0.562500       exome   \n",
       "1439  exome_P142   enrich_P181          2      1    0.062500       exome   \n",
       "\n",
       "     experiment2  \n",
       "0         RNAseq  \n",
       "1         RNAseq  \n",
       "2         RNAseq  \n",
       "3         RNAseq  \n",
       "4         RNAseq  \n",
       "...          ...  \n",
       "1435      enrich  \n",
       "1436      enrich  \n",
       "1437      enrich  \n",
       "1438      enrich  \n",
       "1439      enrich  \n",
       "\n",
       "[1440 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = results.groupby([\"s1short\",\"s2short\"])[\"shAllels\"].apply(lambda x: pd.concat([x.value_counts(),\n",
    "                                                                   x.value_counts(normalize=True)],\n",
    "                                                                   axis=1\n",
    "                                                                  ).sort_index())\n",
    "summary = _.reset_index().rename(columns={\"level_2\":\"n_alleles\"})\n",
    "summary[\"experiment1\"] = summary[\"s1short\"].apply(lambda x: x.split(\"_\")[0])\n",
    "summary[\"experiment2\"] = summary[\"s2short\"].apply(lambda x: x.split(\"_\")[0])\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c53f76c-3dcc-429f-a4e0-c339b84149e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>s1short</th>\n",
       "      <th>s2short</th>\n",
       "      <th>n_alleles</th>\n",
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       "      <th>proportion</th>\n",
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       "      <th>1374</th>\n",
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       "      <th>1376</th>\n",
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       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_1-2</td>\n",
       "      <td>0</td>\n",
       "      <td>1915</td>\n",
       "      <td>0.546674</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_1-2</td>\n",
       "      <td>1</td>\n",
       "      <td>1246</td>\n",
       "      <td>0.355695</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_1-2</td>\n",
       "      <td>2</td>\n",
       "      <td>342</td>\n",
       "      <td>0.097631</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_181-1</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>0.110526</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_181-1</td>\n",
       "      <td>1</td>\n",
       "      <td>851</td>\n",
       "      <td>0.639850</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_181-1</td>\n",
       "      <td>2</td>\n",
       "      <td>332</td>\n",
       "      <td>0.249624</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_181-2</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>0.108275</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_181-2</td>\n",
       "      <td>1</td>\n",
       "      <td>834</td>\n",
       "      <td>0.645012</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_181-2</td>\n",
       "      <td>2</td>\n",
       "      <td>319</td>\n",
       "      <td>0.246713</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_2-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1898</td>\n",
       "      <td>0.526491</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_2-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1366</td>\n",
       "      <td>0.378918</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_2-1</td>\n",
       "      <td>2</td>\n",
       "      <td>341</td>\n",
       "      <td>0.094591</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_2-2</td>\n",
       "      <td>0</td>\n",
       "      <td>1906</td>\n",
       "      <td>0.527393</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1390</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_2-2</td>\n",
       "      <td>1</td>\n",
       "      <td>1356</td>\n",
       "      <td>0.375208</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_2-2</td>\n",
       "      <td>2</td>\n",
       "      <td>352</td>\n",
       "      <td>0.097399</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_3-1-P143</td>\n",
       "      <td>0</td>\n",
       "      <td>1788</td>\n",
       "      <td>0.514977</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_3-1-P143</td>\n",
       "      <td>1</td>\n",
       "      <td>1195</td>\n",
       "      <td>0.344182</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_3-1-P143</td>\n",
       "      <td>2</td>\n",
       "      <td>489</td>\n",
       "      <td>0.140841</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_3-2-P143</td>\n",
       "      <td>0</td>\n",
       "      <td>1789</td>\n",
       "      <td>0.512314</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_3-2-P143</td>\n",
       "      <td>1</td>\n",
       "      <td>1214</td>\n",
       "      <td>0.347652</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_3-2-P143</td>\n",
       "      <td>2</td>\n",
       "      <td>489</td>\n",
       "      <td>0.140034</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>0</td>\n",
       "      <td>1840</td>\n",
       "      <td>0.563208</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>1</td>\n",
       "      <td>559</td>\n",
       "      <td>0.171105</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>2</td>\n",
       "      <td>868</td>\n",
       "      <td>0.265687</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-1g-P142</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0.024978</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-1g-P142</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>0.106155</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-1g-P142</td>\n",
       "      <td>2</td>\n",
       "      <td>974</td>\n",
       "      <td>0.868867</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-2-P142</td>\n",
       "      <td>0</td>\n",
       "      <td>1815</td>\n",
       "      <td>0.563490</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1405</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-2-P142</td>\n",
       "      <td>1</td>\n",
       "      <td>545</td>\n",
       "      <td>0.169202</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_4-2-P142</td>\n",
       "      <td>2</td>\n",
       "      <td>861</td>\n",
       "      <td>0.267308</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-1-P181</td>\n",
       "      <td>0</td>\n",
       "      <td>1744</td>\n",
       "      <td>0.544150</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-1-P181</td>\n",
       "      <td>1</td>\n",
       "      <td>693</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1409</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-1-P181</td>\n",
       "      <td>2</td>\n",
       "      <td>768</td>\n",
       "      <td>0.239626</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-1g-P181</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0.026496</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-1g-P181</td>\n",
       "      <td>1</td>\n",
       "      <td>281</td>\n",
       "      <td>0.240171</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-1g-P181</td>\n",
       "      <td>2</td>\n",
       "      <td>858</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-2-P181</td>\n",
       "      <td>0</td>\n",
       "      <td>1701</td>\n",
       "      <td>0.528918</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-2-P181</td>\n",
       "      <td>1</td>\n",
       "      <td>735</td>\n",
       "      <td>0.228545</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-2-P181</td>\n",
       "      <td>2</td>\n",
       "      <td>780</td>\n",
       "      <td>0.242537</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-2g-P181</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0.031046</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-2g-P181</td>\n",
       "      <td>1</td>\n",
       "      <td>320</td>\n",
       "      <td>0.261438</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_5-2g-P181</td>\n",
       "      <td>2</td>\n",
       "      <td>866</td>\n",
       "      <td>0.707516</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_6-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.536030</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_6-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.364568</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1421</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_6-1</td>\n",
       "      <td>2</td>\n",
       "      <td>349</td>\n",
       "      <td>0.099402</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_6-2</td>\n",
       "      <td>0</td>\n",
       "      <td>1901</td>\n",
       "      <td>0.530265</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_6-2</td>\n",
       "      <td>1</td>\n",
       "      <td>1312</td>\n",
       "      <td>0.365969</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_6-2</td>\n",
       "      <td>2</td>\n",
       "      <td>372</td>\n",
       "      <td>0.103766</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_P142</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_P142</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0.123494</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_P142</td>\n",
       "      <td>2</td>\n",
       "      <td>848</td>\n",
       "      <td>0.851406</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         s1short           s2short  n_alleles  count  proportion experiment1  \\\n",
       "1374  exome_P142        RNAseq_1-1          0   1881    0.534072       exome   \n",
       "1375  exome_P142        RNAseq_1-1          1   1293    0.367121       exome   \n",
       "1376  exome_P142        RNAseq_1-1          2    348    0.098807       exome   \n",
       "1377  exome_P142        RNAseq_1-2          0   1915    0.546674       exome   \n",
       "1378  exome_P142        RNAseq_1-2          1   1246    0.355695       exome   \n",
       "1379  exome_P142        RNAseq_1-2          2    342    0.097631       exome   \n",
       "1380  exome_P142      RNAseq_181-1          0    147    0.110526       exome   \n",
       "1381  exome_P142      RNAseq_181-1          1    851    0.639850       exome   \n",
       "1382  exome_P142      RNAseq_181-1          2    332    0.249624       exome   \n",
       "1383  exome_P142      RNAseq_181-2          0    140    0.108275       exome   \n",
       "1384  exome_P142      RNAseq_181-2          1    834    0.645012       exome   \n",
       "1385  exome_P142      RNAseq_181-2          2    319    0.246713       exome   \n",
       "1386  exome_P142        RNAseq_2-1          0   1898    0.526491       exome   \n",
       "1387  exome_P142        RNAseq_2-1          1   1366    0.378918       exome   \n",
       "1388  exome_P142        RNAseq_2-1          2    341    0.094591       exome   \n",
       "1389  exome_P142        RNAseq_2-2          0   1906    0.527393       exome   \n",
       "1390  exome_P142        RNAseq_2-2          1   1356    0.375208       exome   \n",
       "1391  exome_P142        RNAseq_2-2          2    352    0.097399       exome   \n",
       "1392  exome_P142   RNAseq_3-1-P143          0   1788    0.514977       exome   \n",
       "1393  exome_P142   RNAseq_3-1-P143          1   1195    0.344182       exome   \n",
       "1394  exome_P142   RNAseq_3-1-P143          2    489    0.140841       exome   \n",
       "1395  exome_P142   RNAseq_3-2-P143          0   1789    0.512314       exome   \n",
       "1396  exome_P142   RNAseq_3-2-P143          1   1214    0.347652       exome   \n",
       "1397  exome_P142   RNAseq_3-2-P143          2    489    0.140034       exome   \n",
       "1398  exome_P142   RNAseq_4-1-P142          0   1840    0.563208       exome   \n",
       "1399  exome_P142   RNAseq_4-1-P142          1    559    0.171105       exome   \n",
       "1400  exome_P142   RNAseq_4-1-P142          2    868    0.265687       exome   \n",
       "1401  exome_P142  RNAseq_4-1g-P142          0     28    0.024978       exome   \n",
       "1402  exome_P142  RNAseq_4-1g-P142          1    119    0.106155       exome   \n",
       "1403  exome_P142  RNAseq_4-1g-P142          2    974    0.868867       exome   \n",
       "1404  exome_P142   RNAseq_4-2-P142          0   1815    0.563490       exome   \n",
       "1405  exome_P142   RNAseq_4-2-P142          1    545    0.169202       exome   \n",
       "1406  exome_P142   RNAseq_4-2-P142          2    861    0.267308       exome   \n",
       "1407  exome_P142   RNAseq_5-1-P181          0   1744    0.544150       exome   \n",
       "1408  exome_P142   RNAseq_5-1-P181          1    693    0.216225       exome   \n",
       "1409  exome_P142   RNAseq_5-1-P181          2    768    0.239626       exome   \n",
       "1410  exome_P142  RNAseq_5-1g-P181          0     31    0.026496       exome   \n",
       "1411  exome_P142  RNAseq_5-1g-P181          1    281    0.240171       exome   \n",
       "1412  exome_P142  RNAseq_5-1g-P181          2    858    0.733333       exome   \n",
       "1413  exome_P142   RNAseq_5-2-P181          0   1701    0.528918       exome   \n",
       "1414  exome_P142   RNAseq_5-2-P181          1    735    0.228545       exome   \n",
       "1415  exome_P142   RNAseq_5-2-P181          2    780    0.242537       exome   \n",
       "1416  exome_P142  RNAseq_5-2g-P181          0     38    0.031046       exome   \n",
       "1417  exome_P142  RNAseq_5-2g-P181          1    320    0.261438       exome   \n",
       "1418  exome_P142  RNAseq_5-2g-P181          2    866    0.707516       exome   \n",
       "1419  exome_P142        RNAseq_6-1          0   1882    0.536030       exome   \n",
       "1420  exome_P142        RNAseq_6-1          1   1280    0.364568       exome   \n",
       "1421  exome_P142        RNAseq_6-1          2    349    0.099402       exome   \n",
       "1422  exome_P142        RNAseq_6-2          0   1901    0.530265       exome   \n",
       "1423  exome_P142        RNAseq_6-2          1   1312    0.365969       exome   \n",
       "1424  exome_P142        RNAseq_6-2          2    372    0.103766       exome   \n",
       "1425  exome_P142       RNAseq_P142          0     25    0.025100       exome   \n",
       "1426  exome_P142       RNAseq_P142          1    123    0.123494       exome   \n",
       "1427  exome_P142       RNAseq_P142          2    848    0.851406       exome   \n",
       "\n",
       "     experiment2  \n",
       "1374      RNAseq  \n",
       "1375      RNAseq  \n",
       "1376      RNAseq  \n",
       "1377      RNAseq  \n",
       "1378      RNAseq  \n",
       "1379      RNAseq  \n",
       "1380      RNAseq  \n",
       "1381      RNAseq  \n",
       "1382      RNAseq  \n",
       "1383      RNAseq  \n",
       "1384      RNAseq  \n",
       "1385      RNAseq  \n",
       "1386      RNAseq  \n",
       "1387      RNAseq  \n",
       "1388      RNAseq  \n",
       "1389      RNAseq  \n",
       "1390      RNAseq  \n",
       "1391      RNAseq  \n",
       "1392      RNAseq  \n",
       "1393      RNAseq  \n",
       "1394      RNAseq  \n",
       "1395      RNAseq  \n",
       "1396      RNAseq  \n",
       "1397      RNAseq  \n",
       "1398      RNAseq  \n",
       "1399      RNAseq  \n",
       "1400      RNAseq  \n",
       "1401      RNAseq  \n",
       "1402      RNAseq  \n",
       "1403      RNAseq  \n",
       "1404      RNAseq  \n",
       "1405      RNAseq  \n",
       "1406      RNAseq  \n",
       "1407      RNAseq  \n",
       "1408      RNAseq  \n",
       "1409      RNAseq  \n",
       "1410      RNAseq  \n",
       "1411      RNAseq  \n",
       "1412      RNAseq  \n",
       "1413      RNAseq  \n",
       "1414      RNAseq  \n",
       "1415      RNAseq  \n",
       "1416      RNAseq  \n",
       "1417      RNAseq  \n",
       "1418      RNAseq  \n",
       "1419      RNAseq  \n",
       "1420      RNAseq  \n",
       "1421      RNAseq  \n",
       "1422      RNAseq  \n",
       "1423      RNAseq  \n",
       "1424      RNAseq  \n",
       "1425      RNAseq  \n",
       "1426      RNAseq  \n",
       "1427      RNAseq  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.query(\"experiment1=='exome' and experiment2=='RNAseq'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "667adc9f-2a53-49a1-b2b4-226a4853c0dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4115986/94289248.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  summary_concordant[\"concordant_freq\"] = 1 - summary_concordant[\"proportion\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s1short</th>\n",
       "      <th>s2short</th>\n",
       "      <th>n_alleles</th>\n",
       "      <th>count</th>\n",
       "      <th>proportion</th>\n",
       "      <th>experiment1</th>\n",
       "      <th>experiment2</th>\n",
       "      <th>concordant_freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RNAseq_1-1</td>\n",
       "      <td>RNAseq_1-2</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.008188</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>0.991812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RNAseq_1-1</td>\n",
       "      <td>RNAseq_181-1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>0.042171</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>0.957829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RNAseq_1-1</td>\n",
       "      <td>RNAseq_181-2</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0.045601</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>0.954399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RNAseq_1-1</td>\n",
       "      <td>RNAseq_2-1</td>\n",
       "      <td>0</td>\n",
       "      <td>218</td>\n",
       "      <td>0.041643</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>0.958357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RNAseq_1-1</td>\n",
       "      <td>RNAseq_2-2</td>\n",
       "      <td>0</td>\n",
       "      <td>225</td>\n",
       "      <td>0.042638</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>0.957362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>RNAseq_P142</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>exome</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>0.974900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>enrich_P10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>exome</td>\n",
       "      <td>enrich</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>enrich_P142</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>exome</td>\n",
       "      <td>enrich</td>\n",
       "      <td>0.951613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>enrich_P143</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>exome</td>\n",
       "      <td>enrich</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>exome_P142</td>\n",
       "      <td>enrich_P181</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>exome</td>\n",
       "      <td>enrich</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         s1short       s2short  n_alleles  count  proportion experiment1  \\\n",
       "0     RNAseq_1-1    RNAseq_1-2          0     37    0.008188      RNAseq   \n",
       "3     RNAseq_1-1  RNAseq_181-1          0     94    0.042171      RNAseq   \n",
       "6     RNAseq_1-1  RNAseq_181-2          0     99    0.045601      RNAseq   \n",
       "9     RNAseq_1-1    RNAseq_2-1          0    218    0.041643      RNAseq   \n",
       "12    RNAseq_1-1    RNAseq_2-2          0    225    0.042638      RNAseq   \n",
       "...          ...           ...        ...    ...         ...         ...   \n",
       "1425  exome_P142   RNAseq_P142          0     25    0.025100       exome   \n",
       "1428  exome_P142    enrich_P10          0      4    0.250000       exome   \n",
       "1431  exome_P142   enrich_P142          0      3    0.048387       exome   \n",
       "1434  exome_P142   enrich_P143          0      5    0.416667       exome   \n",
       "1437  exome_P142   enrich_P181          0      6    0.375000       exome   \n",
       "\n",
       "     experiment2  concordant_freq  \n",
       "0         RNAseq         0.991812  \n",
       "3         RNAseq         0.957829  \n",
       "6         RNAseq         0.954399  \n",
       "9         RNAseq         0.958357  \n",
       "12        RNAseq         0.957362  \n",
       "...          ...              ...  \n",
       "1425      RNAseq         0.974900  \n",
       "1428      enrich         0.750000  \n",
       "1431      enrich         0.951613  \n",
       "1434      enrich         0.583333  \n",
       "1437      enrich         0.625000  \n",
       "\n",
       "[500 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_concordant = summary.query(\"n_alleles==0\")\n",
    "summary_concordant[\"concordant_freq\"] = 1 - summary_concordant[\"proportion\"]\n",
    "summary_concordant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "18fedd98-49ba-421f-8521-77b521191010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fd9303b9bd0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = \"n_alleles==0 and (experiment1=='RNAseq') and (experiment2=='RNAseq')\"\n",
    "# q = \"n_alleles==0 and (experiment1=='RNAseq' or experiment1=='exome') and (experiment2=='RNAseq' or experiment2=='exome')\"\n",
    "plot_data = summary_concordant.query(q)\\\n",
    "                    [[\"s1short\",\"s2short\",\"concordant_freq\"]]\\\n",
    "                    .pivot(index=\"s1short\", columns=\"s2short\", values=\"concordant_freq\")\n",
    "plot_data.fillna(1, inplace=True)\n",
    "import seaborn as sns\n",
    "sns.clustermap(plot_data, method=\"average\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfc2dc8a-2c79-4aa7-a4f1-47e2b763442e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shAllels\n",
       "2    1988\n",
       "1     790\n",
       "0      97\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query(\"s1short=='RNAseq_4-1-P142' and s2short=='RNAseq_4-1g-P142'\")[\"shAllels\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccd511dd-41b8-432b-a9f0-2a87710feabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s1short</th>\n",
       "      <th>s2short</th>\n",
       "      <th>n_alleles</th>\n",
       "      <th>count</th>\n",
       "      <th>proportion</th>\n",
       "      <th>experiment1</th>\n",
       "      <th>experiment2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>RNAseq_4-1g-P142</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0.033739</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>RNAseq_4-1g-P142</td>\n",
       "      <td>1</td>\n",
       "      <td>790</td>\n",
       "      <td>0.274783</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>RNAseq_4-1g-P142</td>\n",
       "      <td>2</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.691478</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>RNAseq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             s1short           s2short  n_alleles  count  proportion  \\\n",
       "535  RNAseq_4-1-P142  RNAseq_4-1g-P142          0     97    0.033739   \n",
       "536  RNAseq_4-1-P142  RNAseq_4-1g-P142          1    790    0.274783   \n",
       "537  RNAseq_4-1-P142  RNAseq_4-1g-P142          2   1988    0.691478   \n",
       "\n",
       "    experiment1 experiment2  \n",
       "535      RNAseq      RNAseq  \n",
       "536      RNAseq      RNAseq  \n",
       "537      RNAseq      RNAseq  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.query(\"s1short=='RNAseq_4-1-P142' and s2short=='RNAseq_4-1g-P142'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c212ed83-761d-49af-802b-cc1ee4bc8c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s1short</th>\n",
       "      <th>s2short</th>\n",
       "      <th>n_alleles</th>\n",
       "      <th>count</th>\n",
       "      <th>proportion</th>\n",
       "      <th>experiment1</th>\n",
       "      <th>experiment2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "      <td>0</td>\n",
       "      <td>1840</td>\n",
       "      <td>0.563208</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>exome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "      <td>1</td>\n",
       "      <td>559</td>\n",
       "      <td>0.171105</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>exome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "      <td>2</td>\n",
       "      <td>868</td>\n",
       "      <td>0.265687</td>\n",
       "      <td>RNAseq</td>\n",
       "      <td>exome</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             s1short     s2short  n_alleles  count  proportion experiment1  \\\n",
       "571  RNAseq_4-1-P142  exome_P142          0   1840    0.563208      RNAseq   \n",
       "572  RNAseq_4-1-P142  exome_P142          1    559    0.171105      RNAseq   \n",
       "573  RNAseq_4-1-P142  exome_P142          2    868    0.265687      RNAseq   \n",
       "\n",
       "    experiment2  \n",
       "571       exome  \n",
       "572       exome  \n",
       "573       exome  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.query(\"s1short=='RNAseq_4-1-P142' and s2short=='exome_P142'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2d58e084-e581-4de7-bf8a-94a313c9824f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>s1short</th>\n",
       "      <th>s2short</th>\n",
       "      <th>n_alleles</th>\n",
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      "text/plain": [
       "              s1short     s2short  n_alleles  count  proportion experiment1  \\\n",
       "631  RNAseq_4-1g-P142  exome_P142          0     28    0.024978      RNAseq   \n",
       "632  RNAseq_4-1g-P142  exome_P142          1    119    0.106155      RNAseq   \n",
       "633  RNAseq_4-1g-P142  exome_P142          2    974    0.868867      RNAseq   \n",
       "\n",
       "    experiment2  \n",
       "631       exome  \n",
       "632       exome  \n",
       "633       exome  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.query(\"s1short=='RNAseq_4-1g-P142' and s2short=='exome_P142'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "de84845e-11c6-4f11-8e38-98c12c3472ca",
   "metadata": {},
   "outputs": [
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       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76259</th>\n",
       "      <td>76259</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240686</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76415</th>\n",
       "      <td>76415</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240713</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76571</th>\n",
       "      <td>76571</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240750</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76883</th>\n",
       "      <td>76883</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240828</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77039</th>\n",
       "      <td>77039</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240853</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77195</th>\n",
       "      <td>77195</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240868</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77351</th>\n",
       "      <td>77351</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240888</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77507</th>\n",
       "      <td>77507</td>\n",
       "      <td>chr2</td>\n",
       "      <td>20240892</td>\n",
       "      <td>/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...</td>\n",
       "      <td>F008254-4-I-ExC93-xgenV2</td>\n",
       "      <td>0</td>\n",
       "      <td>RNAseq_4-1-P142</td>\n",
       "      <td>exome_P142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0 chrom       pos  \\\n",
       "26739       26739  chr2   9271464   \n",
       "26895       26895  chr2   9271465   \n",
       "27027       27027  chr2   9271466   \n",
       "35295       35295  chr2   9587413   \n",
       "40497       40497  chr2  10129038   \n",
       "42235       42235  chr2  10443229   \n",
       "42649       42649  chr2  10444092   \n",
       "47741       47741  chr2  10789748   \n",
       "48103       48103  chr2  10791794   \n",
       "48257       48257  chr2  10793094   \n",
       "59057       59057  chr2  12718272   \n",
       "62851       62851  chr2  15629698   \n",
       "63901       63901  chr2  17514448   \n",
       "71353       71353  chr2  20034316   \n",
       "73391       73391  chr2  20239949   \n",
       "73547       73547  chr2  20240034   \n",
       "73703       73703  chr2  20240062   \n",
       "73859       73859  chr2  20240070   \n",
       "74015       74015  chr2  20240081   \n",
       "74171       74171  chr2  20240088   \n",
       "74327       74327  chr2  20240090   \n",
       "74481       74481  chr2  20240107   \n",
       "74637       74637  chr2  20240167   \n",
       "74793       74793  chr2  20240192   \n",
       "74949       74949  chr2  20240307   \n",
       "75105       75105  chr2  20240457   \n",
       "75261       75261  chr2  20240477   \n",
       "75417       75417  chr2  20240509   \n",
       "75655       75655  chr2  20240614   \n",
       "75811       75811  chr2  20240646   \n",
       "75961       75961  chr2  20240648   \n",
       "76103       76103  chr2  20240672   \n",
       "76259       76259  chr2  20240686   \n",
       "76415       76415  chr2  20240713   \n",
       "76571       76571  chr2  20240750   \n",
       "76883       76883  chr2  20240828   \n",
       "77039       77039  chr2  20240853   \n",
       "77195       77195  chr2  20240868   \n",
       "77351       77351  chr2  20240888   \n",
       "77507       77507  chr2  20240892   \n",
       "\n",
       "                                                      s1  \\\n",
       "26739  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "26895  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "27027  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "35295  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "40497  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "42235  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "42649  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "47741  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "48103  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "48257  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "59057  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "62851  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "63901  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "71353  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "73391  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "73547  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "73703  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "73859  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74015  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74171  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74327  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74481  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74637  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74793  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "74949  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "75105  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "75261  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "75417  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "75655  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "75811  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "75961  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "76103  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "76259  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "76415  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "76571  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "76883  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "77039  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "77195  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "77351  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "77507  /sf/storage/gmo/hic/out/by_User/sykozyreva/SIX...   \n",
       "\n",
       "                             s2  shAllels          s1short     s2short  \n",
       "26739  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "26895  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "27027  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "35295  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "40497  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "42235  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "42649  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "47741  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "48103  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "48257  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "59057  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "62851  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "63901  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "71353  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "73391  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "73547  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "73703  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "73859  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74015  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74171  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74327  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74481  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74637  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74793  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "74949  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "75105  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "75261  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "75417  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "75655  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "75811  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "75961  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "76103  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "76259  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "76415  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "76571  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "76883  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "77039  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "77195  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "77351  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  \n",
       "77507  F008254-4-I-ExC93-xgenV2         0  RNAseq_4-1-P142  exome_P142  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query(\"s1short=='RNAseq_4-1-P142' and s2short=='exome_P142' and shAllels==0\").iloc[10:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "36e64950-6ec5-4167-b9d4-61d08ac5edee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'allBams.DP10.calls.vcf'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "678a751b-099f-4914-a64d-9521272d4a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "343098it [01:25, 4021.38it/s]\n"
     ]
    }
   ],
   "source": [
    "import vcfpy\n",
    "import tqdm\n",
    "reader = vcfpy.Reader.from_path(filename)\n",
    "for record in tqdm.tqdm(reader):\n",
    "    if record.POS==10789748:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ac09ee8e-a03c-4b7f-b7d4-eaf6290c756a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Record('chr2', 10789748, [], 'C', [Substitution(type_='SNV', value='T')], 228.405, ['PASS'], {'VDB': 0.28, 'SGB': -0.453602, 'RPBZ': 1.77356, 'MQBZ': 0.136717, 'MQSBZ': 0.990867, 'BQBZ': -2.52544, 'SCBZ': -0.19424, 'MQ0F': 0.0, 'MQ': 59, 'DP': 459, 'DP4': [72, 66, 58, 207], 'AN': 26, 'AC': [20]}, ['GT', 'DP', 'AD', 'PL'], [Call('/sf/storage/gmo/hic/out/by_Project/Six2/BAC_enrichment/July24_bam/trimmed/P10.dedups.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_Project/Six2/BAC_enrichment/July24_bam/trimmed/P142.dedups.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_Project/Six2/BAC_enrichment/July24_bam/trimmed/P143.dedups.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_Project/Six2/BAC_enrichment/July24_bam/trimmed/P181.dedups.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('F008254-4-I-ExC93-xgenV2', {'GT': '0/0', 'DP': 109, 'AD': [107, None], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/1-1.bam', {'GT': '1/1', 'DP': 30, 'AD': [3, 27], 'PL': [255, 0, 0]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/1-2.bam', {'GT': '1/1', 'DP': 25, 'AD': [2, 23], 'PL': [255, 25, 0]}), Call('/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_bams/six/142Aligned.out.sorted.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_bams/six/181-1Aligned.out.sorted.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_bams/six/181-2Aligned.out.sorted.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/2-1.bam', {'GT': '1/1', 'DP': 23, 'AD': [1, 22], 'PL': [255, 9, 0]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/2-2.bam', {'GT': '0/1', 'DP': 22, 'AD': [3, 19], 'PL': [171, 0, 25]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/3-1.bam', {'GT': '0/1', 'DP': 19, 'AD': [6, 13], 'PL': [233, 0, 87]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/3-2.bam', {'GT': '1/1', 'DP': 29, 'AD': [3, 26], 'PL': [239, 0, 0]}), Call('/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_bams/six/4-1Aligned.out.sorted.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/4-1.bam', {'GT': '1/1', 'DP': 29, 'AD': [3, 26], 'PL': [255, 4, 0]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/4-2.bam', {'GT': '1/1', 'DP': 31, 'AD': [1, 30], 'PL': [233, 54, 0]}), Call('/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_bams/six/5-1Aligned.out.sorted.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/5-1.bam', {'GT': '0/1', 'DP': 11, 'AD': [3, 8], 'PL': [174, 0, 61]}), Call('/sf/storage/gmo/hic/out/by_User/Galya/rnaseq_bams/six/5-2Aligned.out.sorted.bam', {'GT': './.', 'DP': None, 'AD': [], 'PL': []}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/5-2.bam', {'GT': '0/1', 'DP': 10, 'AD': [1, 9], 'PL': [184, 0, 7]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/6-1.bam', {'GT': '1/1', 'DP': 27, 'AD': [0, 27], 'PL': [255, 81, 0]}), Call('/sf/storage/gmo/hic/out/by_User/sykozyreva/SIX/bam/6-2.bam', {'GT': '1/1', 'DP': 38, 'AD': [5, 33], 'PL': [255, 17, 0]})])"
      ]
     },
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