functional_SNPs_TADs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.patches as mpatches\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy import stats\n",
    "import seaborn as sns; sns.set_theme(color_codes=True)\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import sklearn.cluster\n",
    "import sklearn.metrics\n",
    "import sklearn.datasets\n",
    "import warnings\n",
    "import random\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams[\"pdf.fonttype\"] = 42\n",
    "matplotlib.rcParams[\"ps.fonttype\"] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(style='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <th>1218429</th>\n",
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       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>-0.060888</td>\n",
       "      <td>1.812</td>\n",
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       "    <tr>\n",
       "      <th>1218430</th>\n",
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       "      <td>0.063025</td>\n",
       "      <td>3.642</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1218431 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0          1  2  3         4      5\n",
       "0         2      10572  A  C  0.187316  5.650\n",
       "1         2      18674  C  A -0.310182  0.341\n",
       "2         2      26844  C  T -0.348046  0.262\n",
       "3         2      27572  C  T -0.266129  0.461\n",
       "4         2      29068  C  G -0.210520  0.670\n",
       "...      ..        ... .. ..       ...    ...\n",
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       "1218428  15  102397836  C  T  0.059482  3.583\n",
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       "1218430  15  102400037  A  G  0.063025  3.642\n",
       "\n",
       "[1218431 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "functional_snps = pd.read_csv(\"Carter Lab Datasets/functional_meqtls_overlap2.txt\", delimiter = \"\\t\", header = None)\n",
    "functional_snps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [0, 1, 2, 3, 4, 5]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "print(functional_snps[(functional_snps[1] == 3395262)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  5 17  7  4 16 10 20  8 18  9 11  6  3 19 12  1 21 22 13 14 15]\n"
     ]
    }
   ],
   "source": [
    "print(pd.unique(functional_snps[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>snp</th>\n",
       "      <th>snp_position</th>\n",
       "      <th>alleles</th>\n",
       "      <th>probes</th>\n",
       "      <th>probe_position</th>\n",
       "      <th>probe_gene</th>\n",
       "      <th>beta</th>\n",
       "      <th>...</th>\n",
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       "      <th>chr</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>BLCA</td>\n",
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       "      <td>chr2:33952621</td>\n",
       "      <td>G/A</td>\n",
       "      <td>cg04131969</td>\n",
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       "      <td>MYADML</td>\n",
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       "      <td>1</td>\n",
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       "      <td>BLCA</td>\n",
       "      <td>rs12232965</td>\n",
       "      <td>chr2:33954560</td>\n",
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       "      <td>cg04131969</td>\n",
       "      <td>chr2:33951647</td>\n",
       "      <td>MYADML</td>\n",
       "      <td>-1.24</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>BLCA</td>\n",
       "      <td>rs7574695</td>\n",
       "      <td>chr2:33953186</td>\n",
       "      <td>C/T</td>\n",
       "      <td>cg04131969</td>\n",
       "      <td>chr2:33951647</td>\n",
       "      <td>MYADML</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr2</td>\n",
       "      <td>33953186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>BLCA</td>\n",
       "      <td>rs11777332</td>\n",
       "      <td>chr8:91676709</td>\n",
       "      <td>G/A</td>\n",
       "      <td>cg16814680</td>\n",
       "      <td>chr8:91681699</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>chr8</td>\n",
       "      <td>91676709</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>BLCA</td>\n",
       "      <td>rs4332092</td>\n",
       "      <td>chr8:91677926</td>\n",
       "      <td>G/A</td>\n",
       "      <td>cg16814680</td>\n",
       "      <td>chr8:91681699</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>chr8</td>\n",
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       "      <th>1236137</th>\n",
       "      <td>1236137</td>\n",
       "      <td>293433</td>\n",
       "      <td>UCEC</td>\n",
       "      <td>rs60260871</td>\n",
       "      <td>chr3:124711371</td>\n",
       "      <td>C/T</td>\n",
       "      <td>cg11029367</td>\n",
       "      <td>chr3:124705145</td>\n",
       "      <td>HEG1</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr3</td>\n",
       "      <td>124711371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236138</th>\n",
       "      <td>1236138</td>\n",
       "      <td>293434</td>\n",
       "      <td>UCEC</td>\n",
       "      <td>rs57574713</td>\n",
       "      <td>chr3:124711386</td>\n",
       "      <td>G/A</td>\n",
       "      <td>cg11029367</td>\n",
       "      <td>chr3:124705145</td>\n",
       "      <td>HEG1</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr3</td>\n",
       "      <td>124711386</td>\n",
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       "    <tr>\n",
       "      <th>1236139</th>\n",
       "      <td>1236139</td>\n",
       "      <td>293449</td>\n",
       "      <td>UCEC</td>\n",
       "      <td>rs10903612</td>\n",
       "      <td>chr10:2018353</td>\n",
       "      <td>C/T</td>\n",
       "      <td>cg16296438</td>\n",
       "      <td>chr10:1416569</td>\n",
       "      <td>ADARB2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr10</td>\n",
       "      <td>2018353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236140</th>\n",
       "      <td>1236140</td>\n",
       "      <td>293450</td>\n",
       "      <td>UCEC</td>\n",
       "      <td>rs4880953</td>\n",
       "      <td>chr10:2019373</td>\n",
       "      <td>C/T</td>\n",
       "      <td>cg16296438</td>\n",
       "      <td>chr10:1416569</td>\n",
       "      <td>ADARB2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr10</td>\n",
       "      <td>2019373</td>\n",
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       "    <tr>\n",
       "      <th>1236141</th>\n",
       "      <td>1236141</td>\n",
       "      <td>293451</td>\n",
       "      <td>UCEC</td>\n",
       "      <td>rs11002432</td>\n",
       "      <td>chr10:79931884</td>\n",
       "      <td>C/T</td>\n",
       "      <td>cg15849445</td>\n",
       "      <td>chr10:80635973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr10</td>\n",
       "      <td>79931884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236142 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Unnamed: 0.1 cancer_type         snp    snp_position  \\\n",
       "0                 0             0        BLCA  rs11684598   chr2:33952621   \n",
       "1                 1             1        BLCA  rs12232965   chr2:33954560   \n",
       "2                 2             2        BLCA   rs7574695   chr2:33953186   \n",
       "3                 3             3        BLCA  rs11777332   chr8:91676709   \n",
       "4                 4             4        BLCA   rs4332092   chr8:91677926   \n",
       "...             ...           ...         ...         ...             ...   \n",
       "1236137     1236137        293433        UCEC  rs60260871  chr3:124711371   \n",
       "1236138     1236138        293434        UCEC  rs57574713  chr3:124711386   \n",
       "1236139     1236139        293449        UCEC  rs10903612   chr10:2018353   \n",
       "1236140     1236140        293450        UCEC   rs4880953   chr10:2019373   \n",
       "1236141     1236141        293451        UCEC  rs11002432  chr10:79931884   \n",
       "\n",
       "        alleles      probes  probe_position probe_gene  beta  ...  \\\n",
       "0           G/A  cg04131969   chr2:33951647     MYADML -1.24  ...   \n",
       "1           C/T  cg04131969   chr2:33951647     MYADML -1.24  ...   \n",
       "2           C/T  cg04131969   chr2:33951647     MYADML -1.24  ...   \n",
       "3           G/A  cg16814680   chr8:91681699        NaN -1.20  ...   \n",
       "4           G/A  cg16814680   chr8:91681699        NaN -1.20  ...   \n",
       "...         ...         ...             ...        ...   ...  ...   \n",
       "1236137     C/T  cg11029367  chr3:124705145       HEG1  0.43  ...   \n",
       "1236138     G/A  cg11029367  chr3:124705145       HEG1  0.43  ...   \n",
       "1236139     C/T  cg16296438   chr10:1416569     ADARB2  0.36  ...   \n",
       "1236140     C/T  cg16296438   chr10:1416569     ADARB2  0.36  ...   \n",
       "1236141     C/T  cg15849445  chr10:80635973        NaN  0.58  ...   \n",
       "\n",
       "         Unnamed: 24  Unnamed: 25  Unnamed: 26 Unnamed: 27  Unnamed: 28  \\\n",
       "0                NaN          NaN          NaN         NaN          NaN   \n",
       "1                NaN          NaN          NaN         NaN          NaN   \n",
       "2                NaN          NaN          NaN         NaN          NaN   \n",
       "3                NaN          NaN          NaN         NaN          NaN   \n",
       "4                NaN          NaN          NaN         NaN          NaN   \n",
       "...              ...          ...          ...         ...          ...   \n",
       "1236137          NaN          NaN          NaN         NaN          NaN   \n",
       "1236138          NaN          NaN          NaN         NaN          NaN   \n",
       "1236139          NaN          NaN          NaN         NaN          NaN   \n",
       "1236140          NaN          NaN          NaN         NaN          NaN   \n",
       "1236141          NaN          NaN          NaN         NaN          NaN   \n",
       "\n",
       "         Unnamed: 29  Unnamed: 30  Unnamed: 31    chr         bp  \n",
       "0                NaN          NaN          NaN   chr2   33952621  \n",
       "1                NaN          NaN          NaN   chr2   33954560  \n",
       "2                NaN          NaN          NaN   chr2   33953186  \n",
       "3                NaN          NaN          NaN   chr8   91676709  \n",
       "4                NaN          NaN          NaN   chr8   91677926  \n",
       "...              ...          ...          ...    ...        ...  \n",
       "1236137          NaN          NaN          NaN   chr3  124711371  \n",
       "1236138          NaN          NaN          NaN   chr3  124711386  \n",
       "1236139          NaN          NaN          NaN  chr10    2018353  \n",
       "1236140          NaN          NaN          NaN  chr10    2019373  \n",
       "1236141          NaN          NaN          NaN  chr10   79931884  \n",
       "\n",
       "[1236142 rows x 37 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meqtls = pd.read_csv(\"Carter Lab Datasets/meQTL Data/unique_meqtls.csv\")\n",
    "meqtls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meqtl_dict[\"chr15:102397372\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n"
     ]
    }
   ],
   "source": [
    "meqtl_dict = {}\n",
    "for i, row in meqtls.iterrows():\n",
    "    if i % 100000 == 0:\n",
    "        print(i)\n",
    "    meqtl_dict[row[\"snp_position\"]] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1236096\n"
     ]
    }
   ],
   "source": [
    "print(len(meqtl_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>chr1</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>chr2</th>\n",
       "      <th>y1</th>\n",
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       "      <th>color</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>...</th>\n",
       "      <th>H3K27me3</th>\n",
       "      <th>H4K20me1</th>\n",
       "      <th>#meqtl</th>\n",
       "      <th>KMeans_State_Labels</th>\n",
       "      <th>#meqtl_pos</th>\n",
       "      <th>#meqtl_neg</th>\n",
       "      <th>domain_size</th>\n",
       "      <th>norm_meqtl_by_size</th>\n",
       "      <th>norm_pos_meqtl_by_size</th>\n",
       "      <th>norm_neg_meqtl_by_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.167622</td>\n",
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       "      <td>0.021654</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38485000</td>\n",
       "      <td>39310000</td>\n",
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       "      <td>38485000</td>\n",
       "      <td>39310000</td>\n",
       "      <td>255,255,0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>219</td>\n",
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       "      <td>825000</td>\n",
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       "      <td>0.042846</td>\n",
       "      <td>0.037893</td>\n",
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       "      <td>1.3679</td>\n",
       "      <td>0.065425</td>\n",
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       "      <td>0.275085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>283</td>\n",
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       "      <td>90</td>\n",
       "      <td>194</td>\n",
       "      <td>340000</td>\n",
       "      <td>0.238009</td>\n",
       "      <td>0.076695</td>\n",
       "      <td>0.183891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>64075000</td>\n",
       "      <td>64730000</td>\n",
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       "      <td>64075000</td>\n",
       "      <td>64730000</td>\n",
       "      <td>255,255,0</td>\n",
       "      <td>1.3300</td>\n",
       "      <td>0.074093</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244738</td>\n",
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       "      <td>0.131405</td>\n",
       "      <td>0.088911</td>\n",
       "      <td>0.092995</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>110530000</td>\n",
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       "      <td>110325000</td>\n",
       "      <td>110530000</td>\n",
       "      <td>255,255,0</td>\n",
       "      <td>1.5128</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.499037</td>\n",
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       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>1095</td>\n",
       "      <td>X</td>\n",
       "      <td>117750000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1096</td>\n",
       "      <td>X</td>\n",
       "      <td>37785000</td>\n",
       "      <td>38035000</td>\n",
       "      <td>X</td>\n",
       "      <td>37785000</td>\n",
       "      <td>38035000</td>\n",
       "      <td>255,255,0</td>\n",
       "      <td>1.5301</td>\n",
       "      <td>0.051187</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>1097</td>\n",
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       "      <td>109385000</td>\n",
       "      <td>X</td>\n",
       "      <td>109150000</td>\n",
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       "      <td>255,255,0</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.040047</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>1098</td>\n",
       "      <td>X</td>\n",
       "      <td>102790000</td>\n",
       "      <td>102945000</td>\n",
       "      <td>X</td>\n",
       "      <td>102790000</td>\n",
       "      <td>102945000</td>\n",
       "      <td>255,255,0</td>\n",
       "      <td>1.2129</td>\n",
       "      <td>0.041055</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>155000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>1099</td>\n",
       "      <td>X</td>\n",
       "      <td>24075000</td>\n",
       "      <td>24270000</td>\n",
       "      <td>X</td>\n",
       "      <td>24075000</td>\n",
       "      <td>24270000</td>\n",
       "      <td>255,255,0</td>\n",
       "      <td>1.5696</td>\n",
       "      <td>0.031361</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.706498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1100 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 chr1         x1         x2 chr2         y1         y2  \\\n",
       "0              0    1   36960000   37910000    1   36960000   37910000   \n",
       "1              1    1   38485000   39310000    1   38485000   39310000   \n",
       "2              2    1  157140000  157480000    1  157140000  157480000   \n",
       "3              3    1   64075000   64730000    1   64075000   64730000   \n",
       "4              4    1  110325000  110530000    1  110325000  110530000   \n",
       "...          ...  ...        ...        ...  ...        ...        ...   \n",
       "1095        1095    X  117750000  117955000    X  117750000  117955000   \n",
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       "1097        1097    X  109150000  109385000    X  109150000  109385000   \n",
       "1098        1098    X  102790000  102945000    X  102790000  102945000   \n",
       "1099        1099    X   24075000   24270000    X   24075000   24270000   \n",
       "\n",
       "          color      f1        f2  ...  H3K27me3  H4K20me1  #meqtl  \\\n",
       "0     255,255,0  1.8249  0.079690  ...  0.167622  0.705304     206   \n",
       "1     255,255,0  1.7513  0.051417  ...  0.226065  0.369001     219   \n",
       "2     255,255,0  1.3679  0.065425  ...  0.275085  0.000000     283   \n",
       "3     255,255,0  1.3300  0.074093  ...  0.244738  0.000000     301   \n",
       "4     255,255,0  1.5128  0.032783  ...  0.499037  0.704529      38   \n",
       "...         ...     ...       ...  ...       ...       ...     ...   \n",
       "1095  255,255,0  1.2678  0.032125  ...  0.000000  0.000000       0   \n",
       "1096  255,255,0  1.5301  0.051187  ...  1.000000  0.000000       0   \n",
       "1097  255,255,0  1.0945  0.040047  ...  0.000000  1.000000       0   \n",
       "1098  255,255,0  1.2129  0.041055  ...  0.000000  0.000000       0   \n",
       "1099  255,255,0  1.5696  0.031361  ...  0.000000  0.706498       0   \n",
       "\n",
       "      KMeans_State_Labels  #meqtl_pos  #meqtl_neg  domain_size  \\\n",
       "0                       4          71         141       950000   \n",
       "1                       4         122          97       825000   \n",
       "2                       4          90         194       340000   \n",
       "3                       4         201         189       655000   \n",
       "4                       0          26          22       205000   \n",
       "...                   ...         ...         ...          ...   \n",
       "1095                    0           0           0       205000   \n",
       "1096                    1           0           0       250000   \n",
       "1097                    1           0           0       235000   \n",
       "1098                    1           0           0       155000   \n",
       "1099                    0           0           0       195000   \n",
       "\n",
       "      norm_meqtl_by_size  norm_pos_meqtl_by_size  norm_neg_meqtl_by_size  \n",
       "0               0.062006                0.021654                0.047834  \n",
       "1               0.075906                0.042846                0.037893  \n",
       "2               0.238009                0.076695                0.183891  \n",
       "3               0.131405                0.088911                0.092995  \n",
       "4               0.053005                0.036747                0.034587  \n",
       "...                  ...                     ...                     ...  \n",
       "1095            0.000000                0.000000                0.000000  \n",
       "1096            0.000000                0.000000                0.000000  \n",
       "1097            0.000000                0.000000                0.000000  \n",
       "1098            0.000000                0.000000                0.000000  \n",
       "1099            0.000000                0.000000                0.000000  \n",
       "\n",
       "[1100 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_TADs = pd.read_csv(\"Carter Lab Datasets/common_TADs_meQTLs.csv\")\n",
    "common_TADs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7710000</td>\n",
       "      <td>7715000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8010000</td>\n",
       "      <td>8030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8410000</td>\n",
       "      <td>9170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9295000</td>\n",
       "      <td>9330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9550000</td>\n",
       "      <td>11075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>1072</td>\n",
       "      <td>X</td>\n",
       "      <td>108880000</td>\n",
       "      <td>108950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>1073</td>\n",
       "      <td>X</td>\n",
       "      <td>109125000</td>\n",
       "      <td>109150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>1074</td>\n",
       "      <td>X</td>\n",
       "      <td>109385000</td>\n",
       "      <td>117750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>1075</td>\n",
       "      <td>X</td>\n",
       "      <td>117955000</td>\n",
       "      <td>133635000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>1076</td>\n",
       "      <td>X</td>\n",
       "      <td>133905000</td>\n",
       "      <td>149110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1077 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 chr1         x1         x2\n",
       "0              0    1    7710000    7715000\n",
       "1              1    1    8010000    8030000\n",
       "2              2    1    8410000    9170000\n",
       "3              3    1    9295000    9330000\n",
       "4              4    1    9550000   11075000\n",
       "...          ...  ...        ...        ...\n",
       "1072        1072    X  108880000  108950000\n",
       "1073        1073    X  109125000  109150000\n",
       "1074        1074    X  109385000  117750000\n",
       "1075        1075    X  117955000  133635000\n",
       "1076        1076    X  133905000  149110000\n",
       "\n",
       "[1077 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundary_regions = pd.read_csv(\"Carter Lab Datasets/boundary_regions.csv\")\n",
    "boundary_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '6:155640000-155855000',\n",
       " '6:3265000-3730000',\n",
       " '6:3120000-3260000',\n",
       " '6:20285000-20540000',\n",
       " '6:138000000-138305000',\n",
       " '6:30885000-31060000',\n",
       " '6:12640000-12890000',\n",
       " '6:53355000-53590000',\n",
       " '6:150730000-151155000',\n",
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       " '6:119265000-119440000',\n",
       " '6:72080000-72225000',\n",
       " '6:17135000-17575000',\n",
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       " '6:17850000-18125000',\n",
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       " '6:129915000-130075000',\n",
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       " '6:32200000-32370000',\n",
       " '6:132595000-132825000',\n",
       " '6:26325000-26575000',\n",
       " '6:53030000-53350000',\n",
       " '6:105530000-105855000',\n",
       " '6:163835000-163985000',\n",
       " '6:97370000-97510000',\n",
       " '6:38105000-38470000',\n",
       " '6:111340000-111555000',\n",
       " '6:39945000-40280000',\n",
       " '6:112025000-112370000',\n",
       " '6:12290000-12610000',\n",
       " '6:13335000-13580000',\n",
       " '6:64295000-64480000',\n",
       " '6:42870000-43140000',\n",
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       " '6:56495000-56740000',\n",
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       " '6:150400000-150675000',\n",
       " '6:17585000-17820000',\n",
       " '6:41045000-41300000',\n",
       " '6:47060000-47300000',\n",
       " '6:138900000-139095000',\n",
       " '6:154995000-155175000',\n",
       " '6:3735000-4005000',\n",
       " '6:108860000-109155000',\n",
       " '6:71415000-71595000',\n",
       " '6:52275000-52535000',\n",
       " '6:130100000-130345000',\n",
       " '6:29815000-29995000',\n",
       " '6:42205000-42530000',\n",
       " '6:15245000-15460000',\n",
       " '6:31635000-31805000',\n",
       " '6:34410000-34580000',\n",
       " '6:44435000-44610000',\n",
       " '7:32800000-33170000',\n",
       " '7:45955000-46395000',\n",
       " '7:1240000-1485000',\n",
       " '7:2450000-2755000',\n",
       " '7:86550000-86785000',\n",
       " '7:140210000-140355000',\n",
       " '7:131985000-132290000',\n",
       " '7:117080000-117355000',\n",
       " '7:24845000-25160000',\n",
       " '7:139750000-140050000',\n",
       " '7:116550000-116780000',\n",
       " '7:142715000-142910000',\n",
       " '7:41760000-41980000',\n",
       " '7:121005000-121195000',\n",
       " '7:137320000-137585000',\n",
       " '7:37020000-37225000',\n",
       " '7:73475000-73600000',\n",
       " '7:95155000-95325000',\n",
       " '7:6185000-6380000',\n",
       " '7:129475000-129670000',\n",
       " '7:120620000-120745000',\n",
       " '7:100970000-101175000',\n",
       " '7:23310000-23575000',\n",
       " '7:26725000-26905000',\n",
       " '7:17020000-17340000',\n",
       " '7:116840000-117030000',\n",
       " '7:73000000-73155000',\n",
       " '7:131200000-131355000',\n",
       " '7:39605000-39795000',\n",
       " '7:23590000-23765000',\n",
       " '7:134305000-134645000',\n",
       " '7:95345000-95510000',\n",
       " '7:130570000-130740000',\n",
       " '7:104560000-104755000',\n",
       " '7:21295000-21560000',\n",
       " '7:28955000-29155000',\n",
       " '7:30195000-30505000',\n",
       " '7:127325000-127655000',\n",
       " '7:26415000-26700000',\n",
       " '7:76980000-77210000',\n",
       " '7:5530000-5760000',\n",
       " '7:116235000-116475000',\n",
       " '7:134105000-134300000',\n",
       " '7:92480000-92685000',\n",
       " '7:73615000-73820000',\n",
       " '7:73835000-73990000',\n",
       " '7:66270000-66465000',\n",
       " '7:73280000-73450000',\n",
       " '7:129305000-129465000',\n",
       " '7:99230000-99485000',\n",
       " '7:31020000-31170000',\n",
       " '8:40105000-40865000',\n",
       " '8:22920000-23290000',\n",
       " '8:19725000-19995000',\n",
       " '8:8610000-8860000',\n",
       " '8:39895000-40065000',\n",
       " '8:99160000-99520000',\n",
       " '8:42015000-42250000',\n",
       " '8:22280000-22430000',\n",
       " '8:119750000-120090000',\n",
       " '8:56685000-56945000',\n",
       " '8:103855000-104065000',\n",
       " '8:8885000-9020000',\n",
       " '8:135700000-135905000',\n",
       " '8:125550000-126010000',\n",
       " '8:25950000-26215000',\n",
       " '8:94700000-95000000',\n",
       " '8:52755000-52925000',\n",
       " '8:55085000-55410000',\n",
       " '8:121135000-121405000',\n",
       " '8:42545000-42670000',\n",
       " '8:126450000-126680000',\n",
       " '8:133885000-134215000',\n",
       " '8:70855000-71025000',\n",
       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tad_test = []\n",
    "for i, row in common_TADs.iterrows():\n",
    "    st = str(row[\"chr1\"])+ \":\" + str(row[\"x1\"]) + \"-\" + str(row[\"x2\"])\n",
    "    tad_test.append(st)\n",
    "tad_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1100\n"
     ]
    }
   ],
   "source": [
    "print(len(set(tad_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n"
     ]
    },
    {
     "data": {
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       " [2, 624524, 11.23],\n",
       " [2, 624581, 4.738],\n",
       " [2, 624678, 1.1320000000000001],\n",
       " [2, 625029, 0.537],\n",
       " [2, 625057, 0.02],\n",
       " [2, 625057, 0.028999999999999998],\n",
       " [2, 625347, 3.105],\n",
       " [2, 628504, 0.325],\n",
       " [2, 628524, 3.5589999999999997],\n",
       " [2, 628749, 4.426],\n",
       " [2, 628902, 8.964],\n",
       " [2, 629244, 7.629],\n",
       " [2, 629510, 0.008],\n",
       " [2, 629601, 0.195],\n",
       " [2, 629694, 5.249],\n",
       " [2, 629817, 0.58],\n",
       " [2, 629818, 4.008],\n",
       " [2, 629831, 6.624],\n",
       " [2, 629881, 3.7110000000000003],\n",
       " [2, 629914, 0.27699999999999997],\n",
       " [2, 629959, 6.3389999999999995],\n",
       " [2, 630024, 0.040999999999999995],\n",
       " [2, 630034, 4.423],\n",
       " [2, 630034, 4.165],\n",
       " [2, 630075, 2.133],\n",
       " [2, 630113, 0.147],\n",
       " [2, 630224, 0.366],\n",
       " [2, 630224, 0.384],\n",
       " [2, 630323, 1.906],\n",
       " [2, 630339, 6.222],\n",
       " [2, 630339, 6.321000000000001],\n",
       " [2, 630436, 5.54],\n",
       " [2, 630509, 0.5770000000000001],\n",
       " [2, 630555, 4.669],\n",
       " [2, 630569, 0.455],\n",
       " [2, 630662, 2.9789999999999996],\n",
       " [2, 630782, 1.089],\n",
       " [2, 630788, 1.7409999999999999],\n",
       " [2, 630788, 1.5759999999999998],\n",
       " [2, 630902, 1.6440000000000001],\n",
       " [2, 630903, 1.8869999999999998],\n",
       " [2, 630995, 4.985],\n",
       " [2, 631069, 0.9079999999999999],\n",
       " [2, 631096, 0.436],\n",
       " [2, 631099, 0.23600000000000002],\n",
       " [2, 631183, 2.151],\n",
       " [2, 631255, 4.678999999999999],\n",
       " [2, 631335, 0.16],\n",
       " [2, 631462, 1.756],\n",
       " [2, 631528, 0.377],\n",
       " [2, 631603, 0.711],\n",
       " [2, 631606, 5.037],\n",
       " [2, 631759, 3.045],\n",
       " [2, 632028, 7.0729999999999995],\n",
       " [2, 632101, 3.717],\n",
       " [2, 632116, 7.2589999999999995],\n",
       " [2, 632146, 2.151],\n",
       " [2, 632300, 1.311],\n",
       " [2, 632348, 3.485],\n",
       " [2, 632379, 0.9309999999999999],\n",
       " [2, 632529, 4.2860000000000005],\n",
       " [2, 632536, 0.34700000000000003],\n",
       " [2, 632550, 3.3089999999999997],\n",
       " [2, 632591, 5.763],\n",
       " [2, 632592, 1.067],\n",
       " [2, 632609, 0.261],\n",
       " [2, 632723, 0.324],\n",
       " [2, 632760, 9.166],\n",
       " [2, 632808, 4.727],\n",
       " [2, 632904, 0.475],\n",
       " [2, 632922, 0.841],\n",
       " [2, 632925, 4.4319999999999995],\n",
       " [2, 633055, 3.55],\n",
       " [2, 633055, 3.292],\n",
       " [2, 633063, 0.08800000000000001],\n",
       " [2, 633659, 3.676],\n",
       " [2, 633660, 2.6],\n",
       " [2, 633661, 0.419],\n",
       " [2, 634476, 1.29],\n",
       " [2, 634905, 1.969],\n",
       " [2, 635004, 3.8139999999999996],\n",
       " [2, 635154, 0.044000000000000004],\n",
       " [2, 635200, 0.195],\n",
       " [2, 635721, 5.073],\n",
       " [2, 635857, 0.627],\n",
       " [2, 635864, 2.113],\n",
       " [2, 635866, 4.295],\n",
       " [2, 636017, 1.81],\n",
       " [2, 636103, 0.081],\n",
       " [2, 636657, 6.542000000000001],\n",
       " [2, 636790, 7.565],\n",
       " [2, 636819, 1.295],\n",
       " [2, 636929, 1.5659999999999998],\n",
       " [2, 637498, 3.09],\n",
       " [2, 637508, 2.1590000000000003],\n",
       " [2, 637597, 3.8289999999999997],\n",
       " [2, 637830, 4.575],\n",
       " [2, 638107, 0.753],\n",
       " [2, 638144, 0.34700000000000003],\n",
       " [2, 638190, 3.117],\n",
       " [2, 638560, 0.149],\n",
       " [2, 638566, 0.175],\n",
       " [2, 638918, 5.877000000000001],\n",
       " [2, 639055, 6.76],\n",
       " [2, 639060, 2.9139999999999997],\n",
       " [2, 639061, 2.605],\n",
       " [2, 639900, 0.17300000000000001],\n",
       " [2, 640701, 4.048],\n",
       " [2, 641082, 2.773],\n",
       " [2, 641159, 0.875],\n",
       " [2, 641718, 1.765],\n",
       " [2, 641863, 5.357],\n",
       " [2, 642253, 0.414],\n",
       " [2, 642467, 1.3],\n",
       " [2, 642499, 0.392],\n",
       " [2, 642846, 0.248],\n",
       " [2, 643303, 0.304],\n",
       " [2, 643315, 3.18],\n",
       " [2, 643320, 0.067],\n",
       " [2, 643428, 1.831],\n",
       " [2, 643469, 4.533],\n",
       " [2, 644953, 2.149],\n",
       " [2, 645082, 5.347],\n",
       " [2, 645122, 0.765],\n",
       " [2, 645203, 1.0859999999999999],\n",
       " [2, 645372, 0.9440000000000001],\n",
       " [2, 645646, 0.2],\n",
       " [2, 646061, 6.4910000000000005],\n",
       " [2, 646081, 13.75],\n",
       " [2, 646110, 6.57],\n",
       " [2, 646145, 13.01],\n",
       " [2, 646345, 0.07200000000000001],\n",
       " [2, 646364, 4.84],\n",
       " [2, 646674, 0.042],\n",
       " [2, 646767, 0.539],\n",
       " [2, 646803, 1.761],\n",
       " [2, 646839, 1.922],\n",
       " [2, 646849, 0.17600000000000002],\n",
       " [2, 646857, 2.16],\n",
       " [2, 647577, 3.2310000000000003],\n",
       " [2, 647580, 0.344],\n",
       " [2, 647760, 1.528],\n",
       " [2, 647861, 3.451],\n",
       " [2, 648198, 1.001],\n",
       " [2, 648527, 2.989],\n",
       " [2, 648758, 6.35],\n",
       " [2, 648758, 6.002000000000001],\n",
       " [2, 648810, 7.106],\n",
       " [2, 648899, 0.042],\n",
       " [2, 649135, 0.6779999999999999],\n",
       " [2, 649347, 0.386],\n",
       " [2, 649638, 1.025],\n",
       " [2, 649867, 0.777],\n",
       " [2, 650012, 9.135],\n",
       " [2, 650143, 2.673],\n",
       " [2, 650479, 1.838],\n",
       " [2, 650479, 1.2429999999999999],\n",
       " [2, 650519, 2.234],\n",
       " [2, 650560, 6.611000000000001],\n",
       " [2, 650560, 6.749],\n",
       " [2, 650647, 3.8930000000000002],\n",
       " [2, 650828, 9.095],\n",
       " [2, 650973, 0.418],\n",
       " [2, 650974, 2.344],\n",
       " [2, 650980, 3.261],\n",
       " [2, 651030, 8.204],\n",
       " [2, 651105, 1.3869999999999998],\n",
       " [2, 651286, 4.822],\n",
       " [2, 651286, 5.18],\n",
       " [2, 651290, 4.827],\n",
       " [2, 651349, 4.356],\n",
       " [2, 651365, 0.276],\n",
       " [2, 651380, 0.28300000000000003],\n",
       " [2, 651382, 2.761],\n",
       " [2, 651407, 3.0439999999999996],\n",
       " [2, 651430, 0.289],\n",
       " [2, 651482, 6.685],\n",
       " [2, 651483, 6.487],\n",
       " [2, 651507, 6.215],\n",
       " [2, 651599, 3.804],\n",
       " [2, 652247, 0.004],\n",
       " [2, 652247, 0.003],\n",
       " [2, 652272, 1.6980000000000002],\n",
       " [2, 652542, 0.449],\n",
       " [2, 652542, 0.38],\n",
       " [2, 652565, 1.594],\n",
       " [2, 652576, 0.188],\n",
       " [2, 652607, 3.053],\n",
       " [2, 652797, 5.367000000000001],\n",
       " [2, 653093, 0.489],\n",
       " [2, 653195, 1.02],\n",
       " [2, 653245, 0.988],\n",
       " [2, 653250, 1.3030000000000002],\n",
       " [2, 653354, 1.837],\n",
       " [2, 653575, 0.44299999999999995],\n",
       " [2, 653581, 0.7609999999999999],\n",
       " [2, 653874, 0.324],\n",
       " [2, 656312, 0.9],\n",
       " [2, 656322, 0.8079999999999999],\n",
       " [2, 656450, 4.353],\n",
       " [2, 656723, 9.12],\n",
       " [2, 656741, 3.6319999999999997],\n",
       " [2, 656973, 3.485],\n",
       " [2, 657088, 2.056],\n",
       " [2, 657569, 0.7759999999999999],\n",
       " [2, 657954, 9.363],\n",
       " [2, 658149, 2.905],\n",
       " [2, 658157, 0.21],\n",
       " [2, 658385, 2.4530000000000003],\n",
       " [2, 658415, 0.27399999999999997],\n",
       " [2, 658419, 3.463],\n",
       " [2, 658443, 0.9359999999999999],\n",
       " [2, 658459, 5.672000000000001],\n",
       " [2, 658461, 1.76],\n",
       " [2, 658464, 0.141],\n",
       " [2, 658595, 3.467],\n",
       " ...]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_meqtls = []\n",
    "for i, row in functional_snps.iterrows():\n",
    "    if i % 100000 == 0:\n",
    "        print(i)\n",
    "    try:\n",
    "        st = \"chr\"+str(row[0])+\":\"+str(row[1])\n",
    "        if meqtl_dict[st]:\n",
    "            good_meqtls.append([int(row[0]), int(row[1]), float(row[5])])\n",
    "    except:\n",
    "        continue\n",
    "good_meqtls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1218431\n"
     ]
    }
   ],
   "source": [
    "print(len(good_meqtls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n"
     ]
    }
   ],
   "source": [
    "active_scores = []\n",
    "inactive_scores = []\n",
    "mixed_scores = []\n",
    "for i in range(len(good_meqtls)):\n",
    "    if i % 100000 == 0:\n",
    "        print(i)\n",
    "    overlap_chr = common_TADs[common_TADs[\"chr1\"] == str(good_meqtls[i][0])]\n",
    "    overlap_snp = overlap_chr[(overlap_chr[\"x1\"] <= good_meqtls[i][1]) & (good_meqtls[i][1] <= overlap_chr[\"x2\"])]\n",
    "    if len(overlap_snp)>0:\n",
    "        if overlap_snp.iloc[0][\"KMeans_State_Labels\"] == 0:\n",
    "            mixed_scores.append(good_meqtls[i][2])\n",
    "        elif overlap_snp.iloc[0][\"KMeans_State_Labels\"] == 1 and overlap_snp.iloc[0][\"KMeans_State_Labels\"] == 4:\n",
    "            inactive_scores.append(good_meqtls[i][2])\n",
    "        else:\n",
    "            active_scores.append(good_meqtls[i][2])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "1100000\n",
      "1200000\n"
     ]
    }
   ],
   "source": [
    "boundary_scores = []\n",
    "for i in range(len(good_meqtls)):\n",
    "    if i % 100000 == 0:\n",
    "        print(i)\n",
    "    overlap_chr2 = boundary_regions[boundary_regions[\"chr1\"] == str(good_meqtls[i][0])]\n",
    "    overlap_snp2 = overlap_chr2[(overlap_chr2[\"x1\"] <= good_meqtls[i][1]) & (good_meqtls[i][1] <= overlap_chr2[\"x2\"])]\n",
    "    if len(overlap_snp2)>0:\n",
    "        boundary_scores.append(good_meqtls[i][2])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [active_scores, boundary_scores, inactive_scores, mixed_scores]\n",
    "fig = plt.figure(figsize =(10, 7))\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "boxplt = ax.boxplot(data)\n",
    "plt.ylabel(\"CADD Scores\", fontsize=20)\n",
    "plt.title(\"Cadd Scores vs. TAD Type\", fontsize=20)\n",
    "ax.set_xticklabels([\"Active\", \"Boundary\", \"Inactive\", \"Mixed\"], fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}