overlap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "table1 = pd.read_csv('./filtered_table1.csv', index_col=0)\n",
    "table2 = pd.read_csv('./filtered_table2.csv', index_col=0)\n",
    "seg_dup = pd.read_csv('./seg_dup.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs_dup = seg_dup[['chromStart', 'chromEnd']].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qvalue seggest removing 0 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check intersection between table1 in seg_dup\n",
    "segs_table1 = table1[['Chromosome', 'Start', 'End']].to_numpy()\n",
    "seg_sizes = []\n",
    "seg_ids = []\n",
    "for seg in segs_table1:\n",
    "    seg_size = 0\n",
    "    seg_id = []\n",
    "    if seg[0] == 6:\n",
    "        for dup_id in range(len(segs_dup)):\n",
    "            dup = segs_dup[dup_id]\n",
    "            if seg[1] <= dup[1] and seg[2] >= dup[0]:\n",
    "                seg_size += min(seg[2], dup[1]) - max(seg[1], dup[0])\n",
    "                seg_id.append(dup_id)\n",
    "    seg_sizes.append(seg_size)\n",
    "    seg_ids.append(seg_id)\n",
    "\n",
    "table1['seg_size'] = seg_sizes\n",
    "table1['seg_number'] = [len(x) for x in seg_ids]\n",
    "table1['seg_ids'] = seg_ids\n",
    "table1['seg_size_portion'] = table1['seg_size'] / table1['Length']\n",
    "\n",
    "# plot distribution of seg_size, seg_number, seg_size_portion as 1*3 subplots\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
    "ax[0].hist(table1.query('Chromosome==6')['seg_size'], bins=100)\n",
    "ax[0].set_title('Length of overlap with duplication regions')\n",
    "ax[1].hist(table1.query('Chromosome==6')['seg_size_portion'], bins=100)\n",
    "ax[1].set_title('Percentage of overlap with duplication regions')\n",
    "ax[2].hist(table1.query('Chromosome==6')['seg_number'], bins=100)\n",
    "ax[2].set_title('Number of overlapper duplication regions')\n",
    "# add super title\n",
    "fig.suptitle('table 1')\n",
    "\n",
    "# calculate the significance of seg size by individual lengths in dup segs\n",
    "dup_region_sizes = segs_dup[:, 1] - segs_dup[:, 0]\n",
    "# calculate the significance of seg size by individual lengths in dup segs\n",
    "seg_sizes = table1['seg_size'].to_numpy()\n",
    "p_values = []\n",
    "from scipy import stats\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "for seg_size in seg_sizes:\n",
    "    p_values.append(stats.distributions.norm.sf(seg_size, dup_region_sizes.mean(), dup_region_sizes.std()) )\n",
    "_, p_adjusted, _, _ = multipletests(p_values, method='fdr_bh')\n",
    "table1['dup_overlap_pvalue'] = p_values\n",
    "table1['dup_overlap_qvalue'] = p_adjusted\n",
    "print('qvalue seggest removing', table1.query('dup_overlap_qvalue<0.05').shape[0], 'rows')\n",
    "table1.to_csv('./table1_analyzed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qvalue seggest removing 0 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg_table2 = table2[['Chromosome', 'Start', 'End']].to_numpy()\n",
    "seg_sizes = []\n",
    "seg_ids = []\n",
    "for seg in seg_table2:\n",
    "    seg_size = 0\n",
    "    seg_id = []\n",
    "    if seg[0] == 6:\n",
    "        for dup_id in range(len(segs_dup)):\n",
    "            dup = segs_dup[dup_id]\n",
    "            if seg[1] <= dup[1] and seg[2] >= dup[0]:\n",
    "                seg_size += min(seg[2], dup[1]) - max(seg[1], dup[0])\n",
    "                seg_id.append(dup_id)\n",
    "    seg_sizes.append(seg_size)\n",
    "    seg_ids.append(seg_id)\n",
    "table2['seg_size'] = seg_sizes\n",
    "table2['seg_number'] = [len(x) for x in seg_ids]\n",
    "table2['seg_ids'] = seg_ids\n",
    "table2['seg_size_portion'] = table2['seg_size'] / table2['Length']\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
    "ax[0].hist(table2.query('Chromosome==6')['seg_size'], bins=100)\n",
    "ax[0].set_title('Length of overlap with duplication regions')\n",
    "ax[1].hist(table2.query('Chromosome==6')['seg_size_portion'], bins=100)\n",
    "ax[1].set_title('Percentage of overlap with duplication regions')\n",
    "ax[2].hist(table2.query('Chromosome==6')['seg_number'], bins=100)\n",
    "ax[2].set_title('Number of overlapper duplication regions')\n",
    "fig.suptitle('table 2')\n",
    "\n",
    "\n",
    "# calculate the significance of seg size by individual lengths in dup segs\n",
    "seg_sizes = table2['seg_size'].to_numpy()\n",
    "p_values = []\n",
    "for seg_size in seg_sizes:\n",
    "    p_values.append(stats.distributions.norm.sf(seg_size, dup_region_sizes.mean(), dup_region_sizes.std()) )\n",
    "_, p_adjusted, _, _ = multipletests(p_values, method='fdr_bh')\n",
    "table2['dup_overlap_pvalue'] = p_values\n",
    "table2['dup_overlap_qvalue'] = p_adjusted\n",
    "print('qvalue seggest removing', table2.query('dup_overlap_qvalue<0.05').shape[0], 'rows')\n",
    "table2.to_csv('./table2_analyzed.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Chromosome</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Length</th>\n",
       "      <th>MHC region?</th>\n",
       "      <th>Average of Pa</th>\n",
       "      <th>Standard deviation of Pa</th>\n",
       "      <th>p-value</th>\n",
       "      <th>Corrected p-value  (FDR/BH method)</th>\n",
       "      <th>seg_size</th>\n",
       "      <th>seg_number</th>\n",
       "      <th>seg_ids</th>\n",
       "      <th>seg_size_portion</th>\n",
       "      <th>dup_overlap_pvalue</th>\n",
       "      <th>dup_overlap_qvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GIH</th>\n",
       "      <td>3</td>\n",
       "      <td>157950988</td>\n",
       "      <td>158348010</td>\n",
       "      <td>397022</td>\n",
       "      <td>No</td>\n",
       "      <td>0.061075</td>\n",
       "      <td>0.029004</td>\n",
       "      <td>5.368670e-03</td>\n",
       "      <td>1.315324e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LWK</th>\n",
       "      <td>14</td>\n",
       "      <td>105863438</td>\n",
       "      <td>106385732</td>\n",
       "      <td>522294</td>\n",
       "      <td>No</td>\n",
       "      <td>0.064520</td>\n",
       "      <td>0.033310</td>\n",
       "      <td>6.130000e-05</td>\n",
       "      <td>1.201796e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LWK</th>\n",
       "      <td>10</td>\n",
       "      <td>102847077</td>\n",
       "      <td>103283225</td>\n",
       "      <td>436148</td>\n",
       "      <td>No</td>\n",
       "      <td>0.058774</td>\n",
       "      <td>0.018805</td>\n",
       "      <td>9.436036e-03</td>\n",
       "      <td>1.778330e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LWK</th>\n",
       "      <td>9</td>\n",
       "      <td>64439637</td>\n",
       "      <td>65054326</td>\n",
       "      <td>614689</td>\n",
       "      <td>No</td>\n",
       "      <td>0.059009</td>\n",
       "      <td>0.029505</td>\n",
       "      <td>7.998330e-03</td>\n",
       "      <td>1.599666e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LWK</th>\n",
       "      <td>2</td>\n",
       "      <td>93680283</td>\n",
       "      <td>94160185</td>\n",
       "      <td>479902</td>\n",
       "      <td>No</td>\n",
       "      <td>0.057041</td>\n",
       "      <td>0.015056</td>\n",
       "      <td>2.885918e-02</td>\n",
       "      <td>3.213863e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDX</th>\n",
       "      <td>11</td>\n",
       "      <td>50311095</td>\n",
       "      <td>50728899</td>\n",
       "      <td>417804</td>\n",
       "      <td>No</td>\n",
       "      <td>0.063723</td>\n",
       "      <td>0.018791</td>\n",
       "      <td>2.867257e-02</td>\n",
       "      <td>3.213863e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDX</th>\n",
       "      <td>8</td>\n",
       "      <td>70075248</td>\n",
       "      <td>70483016</td>\n",
       "      <td>407768</td>\n",
       "      <td>No</td>\n",
       "      <td>0.064305</td>\n",
       "      <td>0.021759</td>\n",
       "      <td>2.020568e-02</td>\n",
       "      <td>2.591377e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEB</th>\n",
       "      <td>15</td>\n",
       "      <td>77138940</td>\n",
       "      <td>77566277</td>\n",
       "      <td>427337</td>\n",
       "      <td>No</td>\n",
       "      <td>0.049670</td>\n",
       "      <td>0.016189</td>\n",
       "      <td>1.532069e-02</td>\n",
       "      <td>2.314762e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASW</th>\n",
       "      <td>14</td>\n",
       "      <td>105859287</td>\n",
       "      <td>106198900</td>\n",
       "      <td>339613</td>\n",
       "      <td>No</td>\n",
       "      <td>0.070627</td>\n",
       "      <td>0.012596</td>\n",
       "      <td>2.284301e-02</td>\n",
       "      <td>2.763722e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.404370e-01</td>\n",
       "      <td>5.404370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>6</td>\n",
       "      <td>32589647</td>\n",
       "      <td>32751796</td>\n",
       "      <td>162149</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.031347</td>\n",
       "      <td>0.031433</td>\n",
       "      <td>3.010000e-12</td>\n",
       "      <td>1.550000e-10</td>\n",
       "      <td>2042</td>\n",
       "      <td>2</td>\n",
       "      <td>[134, 135]</td>\n",
       "      <td>0.012593</td>\n",
       "      <td>3.466367e-23</td>\n",
       "      <td>3.397039e-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Chromosome      Start        End  Length MHC region?  Average of Pa  \\\n",
       "Race                                                                        \n",
       "GIH            3  157950988  158348010  397022          No       0.061075   \n",
       "LWK           14  105863438  106385732  522294          No       0.064520   \n",
       "LWK           10  102847077  103283225  436148          No       0.058774   \n",
       "LWK            9   64439637   65054326  614689          No       0.059009   \n",
       "LWK            2   93680283   94160185  479902          No       0.057041   \n",
       "...          ...        ...        ...     ...         ...            ...   \n",
       "CDX           11   50311095   50728899  417804          No       0.063723   \n",
       "CDX            8   70075248   70483016  407768          No       0.064305   \n",
       "BEB           15   77138940   77566277  427337          No       0.049670   \n",
       "ASW           14  105859287  106198900  339613          No       0.070627   \n",
       "All            6   32589647   32751796  162149         Yes       0.031347   \n",
       "\n",
       "      Standard deviation of Pa       p-value  \\\n",
       "Race                                           \n",
       "GIH                   0.029004  5.368670e-03   \n",
       "LWK                   0.033310  6.130000e-05   \n",
       "LWK                   0.018805  9.436036e-03   \n",
       "LWK                   0.029505  7.998330e-03   \n",
       "LWK                   0.015056  2.885918e-02   \n",
       "...                        ...           ...   \n",
       "CDX                   0.018791  2.867257e-02   \n",
       "CDX                   0.021759  2.020568e-02   \n",
       "BEB                   0.016189  1.532069e-02   \n",
       "ASW                   0.012596  2.284301e-02   \n",
       "All                   0.031433  3.010000e-12   \n",
       "\n",
       "      Corrected p-value  (FDR/BH method)  seg_size  seg_number     seg_ids  \\\n",
       "Race                                                                         \n",
       "GIH                         1.315324e-02         0           0          []   \n",
       "LWK                         1.201796e-03         0           0          []   \n",
       "LWK                         1.778330e-02         0           0          []   \n",
       "LWK                         1.599666e-02         0           0          []   \n",
       "LWK                         3.213863e-02         0           0          []   \n",
       "...                                  ...       ...         ...         ...   \n",
       "CDX                         3.213863e-02         0           0          []   \n",
       "CDX                         2.591377e-02         0           0          []   \n",
       "BEB                         2.314762e-02         0           0          []   \n",
       "ASW                         2.763722e-02         0           0          []   \n",
       "All                         1.550000e-10      2042           2  [134, 135]   \n",
       "\n",
       "      seg_size_portion  dup_overlap_pvalue  dup_overlap_qvalue  \n",
       "Race                                                            \n",
       "GIH           0.000000        5.404370e-01        5.404370e-01  \n",
       "LWK           0.000000        5.404370e-01        5.404370e-01  \n",
       "LWK           0.000000        5.404370e-01        5.404370e-01  \n",
       "LWK           0.000000        5.404370e-01        5.404370e-01  \n",
       "LWK           0.000000        5.404370e-01        5.404370e-01  \n",
       "...                ...                 ...                 ...  \n",
       "CDX           0.000000        5.404370e-01        5.404370e-01  \n",
       "CDX           0.000000        5.404370e-01        5.404370e-01  \n",
       "BEB           0.000000        5.404370e-01        5.404370e-01  \n",
       "ASW           0.000000        5.404370e-01        5.404370e-01  \n",
       "All           0.012593        3.466367e-23        3.397039e-21  \n",
       "\n",
       "[98 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.sort_values('seg_size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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