jacc_distance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_ranks = pd.read_pickle('data/gene_dependency_ranks.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_matrix = gene_ranks.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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    "correlation_matrix.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_matrix.to_pickle(\"data/gene_depedency_rank_corr.pkl\")\n",
    "correlation_matrix.to_csv(\"data/gene_depedency_rank_corr.csv\", round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "                  ACH-000004  ACH-000005  ACH-000007  ACH-000009  ACH-000011  \\\n",
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       "A2ML1 (144568)        7531.0      3271.0      3164.0      4521.0       110.0   \n",
       "A3GALT2 (127550)      5630.0     12276.0      4953.0      5208.0      8739.0   \n",
       "\n",
       "                  ACH-000012  ACH-000013  ACH-000014  ACH-000015  ACH-000017  \\\n",
       "A1BG (1)              6645.0      4814.0      2770.0     10265.0      2005.0   \n",
       "A1CF (29974)          1772.0      5660.0      7878.0      6771.0      3759.0   \n",
       "A2M (2)               3804.0      8849.0      6429.0     12913.0      9835.0   \n",
       "A2ML1 (144568)         257.0      1096.0      1930.0      3348.0      3683.0   \n",
       "A3GALT2 (127550)      6374.0      9834.0      7816.0      8266.0     13207.0   \n",
       "\n",
       "                  ...  ACH-001736  ACH-001737  ACH-001740  ACH-001745  \\\n",
       "A1BG (1)          ...      2263.0      8525.0      6952.0      1150.0   \n",
       "A1CF (29974)      ...      6572.0      3726.0       298.0      2155.0   \n",
       "A2M (2)           ...     11600.0      9162.0     12895.0     11569.0   \n",
       "A2ML1 (144568)    ...      5372.0      5305.0      1319.0      1853.0   \n",
       "A3GALT2 (127550)  ...     13344.0      4162.0      8927.0     11428.0   \n",
       "\n",
       "                  ACH-001750  ACH-001765  ACH-001814  ACH-001838  ACH-001956  \\\n",
       "A1BG (1)              5339.0      2401.0      2387.0      9692.0     10047.0   \n",
       "A1CF (29974)          2890.0      6289.0     11554.0      4134.0      2873.0   \n",
       "A2M (2)               9665.0     10940.0     13219.0     11490.0      6062.0   \n",
       "A2ML1 (144568)        2738.0      4210.0      6047.0       773.0       858.0   \n",
       "A3GALT2 (127550)     12201.0     11227.0      4192.0      8704.0     10192.0   \n",
       "\n",
       "                  ACH-001957  \n",
       "A1BG (1)              4749.0  \n",
       "A1CF (29974)          6567.0  \n",
       "A2M (2)               4342.0  \n",
       "A2ML1 (144568)        1154.0  \n",
       "A3GALT2 (127550)      8376.0  \n",
       "\n",
       "[5 rows x 625 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_ranks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_stats = pd.read_pickle(\"data/gene_dependency_rank_stats.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG (1)</th>\n",
       "      <td>3035.358185</td>\n",
       "      <td>4427.3760</td>\n",
       "      <td>3753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF (29974)</th>\n",
       "      <td>3055.178004</td>\n",
       "      <td>5107.5984</td>\n",
       "      <td>4626.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M (2)</th>\n",
       "      <td>2897.901026</td>\n",
       "      <td>8900.2928</td>\n",
       "      <td>9198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1 (144568)</th>\n",
       "      <td>2210.593643</td>\n",
       "      <td>2611.4336</td>\n",
       "      <td>1881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2 (127550)</th>\n",
       "      <td>3425.762390</td>\n",
       "      <td>8661.8576</td>\n",
       "      <td>8722.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          std       mean  median\n",
       "A1BG (1)          3035.358185  4427.3760  3753.0\n",
       "A1CF (29974)      3055.178004  5107.5984  4626.0\n",
       "A2M (2)           2897.901026  8900.2928  9198.0\n",
       "A2ML1 (144568)    2210.593643  2611.4336  1881.0\n",
       "A3GALT2 (127550)  3425.762390  8661.8576  8722.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "differences = pd.DataFrame(index=gene_ranks.index, columns=gene_ranks.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in gene_ranks.columns:\n",
    "    differences[c] = gene_ranks[c] - gene_stats[\"median\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACH-000004</th>\n",
       "      <th>ACH-000005</th>\n",
       "      <th>ACH-000007</th>\n",
       "      <th>ACH-000009</th>\n",
       "      <th>ACH-000011</th>\n",
       "      <th>ACH-000012</th>\n",
       "      <th>ACH-000013</th>\n",
       "      <th>ACH-000014</th>\n",
       "      <th>ACH-000015</th>\n",
       "      <th>ACH-000017</th>\n",
       "      <th>...</th>\n",
       "      <th>ACH-001736</th>\n",
       "      <th>ACH-001737</th>\n",
       "      <th>ACH-001740</th>\n",
       "      <th>ACH-001745</th>\n",
       "      <th>ACH-001750</th>\n",
       "      <th>ACH-001765</th>\n",
       "      <th>ACH-001814</th>\n",
       "      <th>ACH-001838</th>\n",
       "      <th>ACH-001956</th>\n",
       "      <th>ACH-001957</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG (1)</th>\n",
       "      <td>-1596.0</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>2221.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>-3027.0</td>\n",
       "      <td>2892.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>-983.0</td>\n",
       "      <td>6512.0</td>\n",
       "      <td>-1748.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1490.0</td>\n",
       "      <td>4772.0</td>\n",
       "      <td>3199.0</td>\n",
       "      <td>-2603.0</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>-1352.0</td>\n",
       "      <td>-1366.0</td>\n",
       "      <td>5939.0</td>\n",
       "      <td>6294.0</td>\n",
       "      <td>996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF (29974)</th>\n",
       "      <td>-529.0</td>\n",
       "      <td>-2475.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>2346.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-2854.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>3252.0</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>-867.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>-900.0</td>\n",
       "      <td>-4328.0</td>\n",
       "      <td>-2471.0</td>\n",
       "      <td>-1736.0</td>\n",
       "      <td>1663.0</td>\n",
       "      <td>6928.0</td>\n",
       "      <td>-492.0</td>\n",
       "      <td>-1753.0</td>\n",
       "      <td>1941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M (2)</th>\n",
       "      <td>3139.0</td>\n",
       "      <td>-6336.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>-688.0</td>\n",
       "      <td>-1968.0</td>\n",
       "      <td>-5394.0</td>\n",
       "      <td>-349.0</td>\n",
       "      <td>-2769.0</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2402.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>3697.0</td>\n",
       "      <td>2371.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>4021.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>-3136.0</td>\n",
       "      <td>-4856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1 (144568)</th>\n",
       "      <td>5650.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>2640.0</td>\n",
       "      <td>-1771.0</td>\n",
       "      <td>-1624.0</td>\n",
       "      <td>-785.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>-562.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>2329.0</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>-1108.0</td>\n",
       "      <td>-1023.0</td>\n",
       "      <td>-727.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2 (127550)</th>\n",
       "      <td>-3092.0</td>\n",
       "      <td>3554.0</td>\n",
       "      <td>-3769.0</td>\n",
       "      <td>-3514.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-2348.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>-906.0</td>\n",
       "      <td>-456.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4622.0</td>\n",
       "      <td>-4560.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>2706.0</td>\n",
       "      <td>3479.0</td>\n",
       "      <td>2505.0</td>\n",
       "      <td>-4530.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>-346.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ACH-000004  ACH-000005  ACH-000007  ACH-000009  ACH-000011  \\\n",
       "A1BG (1)             -1596.0      5530.0      2221.0       794.0     -3027.0   \n",
       "A1CF (29974)          -529.0     -2475.0       491.0      2346.0        47.0   \n",
       "A2M (2)               3139.0     -6336.0       303.0      -688.0     -1968.0   \n",
       "A2ML1 (144568)        5650.0      1390.0      1283.0      2640.0     -1771.0   \n",
       "A3GALT2 (127550)     -3092.0      3554.0     -3769.0     -3514.0        17.0   \n",
       "\n",
       "                  ACH-000012  ACH-000013  ACH-000014  ACH-000015  ACH-000017  \\\n",
       "A1BG (1)              2892.0      1061.0      -983.0      6512.0     -1748.0   \n",
       "A1CF (29974)         -2854.0      1034.0      3252.0      2145.0      -867.0   \n",
       "A2M (2)              -5394.0      -349.0     -2769.0      3715.0       637.0   \n",
       "A2ML1 (144568)       -1624.0      -785.0        49.0      1467.0      1802.0   \n",
       "A3GALT2 (127550)     -2348.0      1112.0      -906.0      -456.0      4485.0   \n",
       "\n",
       "                  ...  ACH-001736  ACH-001737  ACH-001740  ACH-001745  \\\n",
       "A1BG (1)          ...     -1490.0      4772.0      3199.0     -2603.0   \n",
       "A1CF (29974)      ...      1946.0      -900.0     -4328.0     -2471.0   \n",
       "A2M (2)           ...      2402.0       -36.0      3697.0      2371.0   \n",
       "A2ML1 (144568)    ...      3491.0      3424.0      -562.0       -28.0   \n",
       "A3GALT2 (127550)  ...      4622.0     -4560.0       205.0      2706.0   \n",
       "\n",
       "                  ACH-001750  ACH-001765  ACH-001814  ACH-001838  ACH-001956  \\\n",
       "A1BG (1)              1586.0     -1352.0     -1366.0      5939.0      6294.0   \n",
       "A1CF (29974)         -1736.0      1663.0      6928.0      -492.0     -1753.0   \n",
       "A2M (2)                467.0      1742.0      4021.0      2292.0     -3136.0   \n",
       "A2ML1 (144568)         857.0      2329.0      4166.0     -1108.0     -1023.0   \n",
       "A3GALT2 (127550)      3479.0      2505.0     -4530.0       -18.0      1470.0   \n",
       "\n",
       "                  ACH-001957  \n",
       "A1BG (1)               996.0  \n",
       "A1CF (29974)          1941.0  \n",
       "A2M (2)              -4856.0  \n",
       "A2ML1 (144568)        -727.0  \n",
       "A3GALT2 (127550)      -346.0  \n",
       "\n",
       "[5 rows x 625 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "differences.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using 5% of the top rankings for all genes. 5% out of 18333 -> 917"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_matrix = gene_ranks < 918"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "top5_matrix = gene_ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_matrix = filter_matrix.values\n",
    "top5_matrix = top5_matrix.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrows = top5_matrix.shape[0]\n",
    "ncols = top5_matrix.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with cell line:  287\n",
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      "Working with cell line:  570\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with cell line:  571\n",
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      "Working with cell line:  622\n",
      "Working with cell line:  623\n",
      "Working with cell line:  624\n"
     ]
    }
   ],
   "source": [
    "for j in range(ncols):\n",
    "    print(\"Working with cell line: \", j)\n",
    "    for i in range(nrows):\n",
    "        top5_matrix[i][j] = top5_matrix[i][j] * filter_matrix[i][j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_top5 = top5_matrix[np.any(top5_matrix != 0, axis= 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   0.,   0., 726.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0., 110., 257.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [271.,   0.,   0.,   0.,   0., 501.,   0.,   0., 836., 775.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [552.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0., 250., 380.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0., 520.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 729.,   0.,\n",
       "          0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_top5[0:15, 0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff842f488d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(filtered_top5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexes = np.where(np.any(top5_matrix != 0, axis= 1) == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "top5_genes = [list(gene_ranks.index)[i] for i in indexes[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = pd.DataFrame(filtered_top5, index=top5_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACH-000004</th>\n",
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       "      <th>ACH-000011</th>\n",
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       "      <th>ACH-000017</th>\n",
       "      <th>...</th>\n",
       "      <th>ACH-001736</th>\n",
       "      <th>ACH-001737</th>\n",
       "      <th>ACH-001740</th>\n",
       "      <th>ACH-001745</th>\n",
       "      <th>ACH-001750</th>\n",
       "      <th>ACH-001765</th>\n",
       "      <th>ACH-001814</th>\n",
       "      <th>ACH-001838</th>\n",
       "      <th>ACH-001956</th>\n",
       "      <th>ACH-001957</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG (1)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>A1CF (29974)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>A2M (2)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1 (144568)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>773.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2 (127550)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 625 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                  ACH-000004  ACH-000005  ACH-000007  ACH-000009  ACH-000011  \\\n",
       "A1BG (1)                 0.0         0.0         0.0         0.0       726.0   \n",
       "A1CF (29974)             0.0         0.0         0.0         0.0         0.0   \n",
       "A2M (2)                  0.0         0.0         0.0         0.0         0.0   \n",
       "A2ML1 (144568)           0.0         0.0         0.0         0.0       110.0   \n",
       "A3GALT2 (127550)         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                  ACH-000012  ACH-000013  ACH-000014  ACH-000015  ACH-000017  \\\n",
       "A1BG (1)                 0.0         0.0         0.0         0.0         0.0   \n",
       "A1CF (29974)             0.0         0.0         0.0         0.0         0.0   \n",
       "A2M (2)                  0.0         0.0         0.0         0.0         0.0   \n",
       "A2ML1 (144568)         257.0         0.0         0.0         0.0         0.0   \n",
       "A3GALT2 (127550)         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                  ...  ACH-001736  ACH-001737  ACH-001740  ACH-001745  \\\n",
       "A1BG (1)          ...         0.0         0.0         0.0         0.0   \n",
       "A1CF (29974)      ...         0.0         0.0       298.0         0.0   \n",
       "A2M (2)           ...         0.0         0.0         0.0         0.0   \n",
       "A2ML1 (144568)    ...         0.0         0.0         0.0         0.0   \n",
       "A3GALT2 (127550)  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                  ACH-001750  ACH-001765  ACH-001814  ACH-001838  ACH-001956  \\\n",
       "A1BG (1)                 0.0         0.0         0.0         0.0         0.0   \n",
       "A1CF (29974)             0.0         0.0         0.0         0.0         0.0   \n",
       "A2M (2)                  0.0         0.0         0.0         0.0         0.0   \n",
       "A2ML1 (144568)           0.0         0.0         0.0       773.0       858.0   \n",
       "A3GALT2 (127550)         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "                  ACH-001957  \n",
       "A1BG (1)                 0.0  \n",
       "A1CF (29974)             0.0  \n",
       "A2M (2)                  0.0  \n",
       "A2ML1 (144568)           0.0  \n",
       "A3GALT2 (127550)         0.0  \n",
       "\n",
       "[5 rows x 625 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered.to_pickle(\"data/top5ranking_genes.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = pd.read_pickle(\"data/top5ranking_genes.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered.columns = gene_ranks.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered[np.isnan(df_filtered)] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jaccard_similarity(list1, list2):\n",
    "    s1 = set(list1)\n",
    "    s2 = set(list2)\n",
    "    return len(s1.intersection(s2)) / len(s1.union(s2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncols = len(df_filtered.columns)\n",
    "jacc_sim = np.zeros((ncols, ncols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with column:  320\n",
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     ]
    }
   ],
   "source": [
    "for i in range(ncols):\n",
    "    print(\"Working with column: \", i)\n",
    "    for j in range(ncols):\n",
    "        l1 = list(df_filtered.iloc[:,i][df_filtered.iloc[:,i] != 0].index)\n",
    "        l2 = list(df_filtered.iloc[:,j][df_filtered.iloc[:,j] != 0].index)\n",
    "        jacc_sim[i][j] = jaccard_similarity(l1, l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.13209877, 0.1441048 , ..., 0.15782828, 0.19478827,\n",
       "        0.16296766],\n",
       "       [0.13209877, 1.        , 0.12239902, ..., 0.12377451, 0.15345912,\n",
       "        0.141257  ],\n",
       "       [0.1441048 , 0.12239902, 1.        , ..., 0.15709779, 0.19245774,\n",
       "        0.17488789],\n",
       "       ...,\n",
       "       [0.18398967, 0.13349815, 0.18782383, ..., 0.19478827, 0.2622161 ,\n",
       "        0.24170616],\n",
       "       [0.18552036, 0.14696685, 0.16964286, ..., 0.20183486, 0.27715877,\n",
       "        0.23584906],\n",
       "       [0.17263427, 0.12792128, 0.16444444, ..., 0.18246293, 0.21941489,\n",
       "        0.21056106]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jacc_sim[0:15][0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f161efed780>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(jacc_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "jacc_sim_df = pd.DataFrame(jacc_sim, index = df_filtered.columns, columns = df_filtered.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>ACH-000004</th>\n",
       "      <th>ACH-000005</th>\n",
       "      <th>ACH-000007</th>\n",
       "      <th>ACH-000009</th>\n",
       "      <th>ACH-000011</th>\n",
       "      <th>ACH-000012</th>\n",
       "      <th>ACH-000013</th>\n",
       "      <th>ACH-000014</th>\n",
       "      <th>ACH-000015</th>\n",
       "      <th>ACH-000017</th>\n",
       "      <th>...</th>\n",
       "      <th>ACH-001736</th>\n",
       "      <th>ACH-001737</th>\n",
       "      <th>ACH-001740</th>\n",
       "      <th>ACH-001745</th>\n",
       "      <th>ACH-001750</th>\n",
       "      <th>ACH-001765</th>\n",
       "      <th>ACH-001814</th>\n",
       "      <th>ACH-001838</th>\n",
       "      <th>ACH-001956</th>\n",
       "      <th>ACH-001957</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACH-000004</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132099</td>\n",
       "      <td>0.144105</td>\n",
       "      <td>0.174888</td>\n",
       "      <td>0.141257</td>\n",
       "      <td>0.175641</td>\n",
       "      <td>0.190136</td>\n",
       "      <td>0.147685</td>\n",
       "      <td>0.190909</td>\n",
       "      <td>0.159292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.162968</td>\n",
       "      <td>0.065660</td>\n",
       "      <td>0.168897</td>\n",
       "      <td>0.170389</td>\n",
       "      <td>0.159292</td>\n",
       "      <td>0.194010</td>\n",
       "      <td>0.118975</td>\n",
       "      <td>0.157828</td>\n",
       "      <td>0.194788</td>\n",
       "      <td>0.162968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000005</th>\n",
       "      <td>0.132099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122399</td>\n",
       "      <td>0.134901</td>\n",
       "      <td>0.127921</td>\n",
       "      <td>0.139130</td>\n",
       "      <td>0.146250</td>\n",
       "      <td>0.114894</td>\n",
       "      <td>0.150565</td>\n",
       "      <td>0.130006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147685</td>\n",
       "      <td>0.071262</td>\n",
       "      <td>0.122399</td>\n",
       "      <td>0.123086</td>\n",
       "      <td>0.144819</td>\n",
       "      <td>0.156368</td>\n",
       "      <td>0.098204</td>\n",
       "      <td>0.123775</td>\n",
       "      <td>0.153459</td>\n",
       "      <td>0.141257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000007</th>\n",
       "      <td>0.144105</td>\n",
       "      <td>0.122399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.176395</td>\n",
       "      <td>0.156368</td>\n",
       "      <td>0.177150</td>\n",
       "      <td>0.190136</td>\n",
       "      <td>0.146250</td>\n",
       "      <td>0.186287</td>\n",
       "      <td>0.163706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164444</td>\n",
       "      <td>0.063805</td>\n",
       "      <td>0.155640</td>\n",
       "      <td>0.160759</td>\n",
       "      <td>0.160759</td>\n",
       "      <td>0.182463</td>\n",
       "      <td>0.121713</td>\n",
       "      <td>0.157098</td>\n",
       "      <td>0.192458</td>\n",
       "      <td>0.174888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000009</th>\n",
       "      <td>0.174888</td>\n",
       "      <td>0.134901</td>\n",
       "      <td>0.176395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.164444</td>\n",
       "      <td>0.244233</td>\n",
       "      <td>0.228399</td>\n",
       "      <td>0.171137</td>\n",
       "      <td>0.231699</td>\n",
       "      <td>0.193234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.183226</td>\n",
       "      <td>0.080731</td>\n",
       "      <td>0.189364</td>\n",
       "      <td>0.195567</td>\n",
       "      <td>0.177906</td>\n",
       "      <td>0.225117</td>\n",
       "      <td>0.149123</td>\n",
       "      <td>0.186287</td>\n",
       "      <td>0.239189</td>\n",
       "      <td>0.197910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000011</th>\n",
       "      <td>0.141257</td>\n",
       "      <td>0.127921</td>\n",
       "      <td>0.156368</td>\n",
       "      <td>0.164444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.179421</td>\n",
       "      <td>0.197910</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.192458</td>\n",
       "      <td>0.173385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179421</td>\n",
       "      <td>0.081368</td>\n",
       "      <td>0.160759</td>\n",
       "      <td>0.165184</td>\n",
       "      <td>0.184755</td>\n",
       "      <td>0.199477</td>\n",
       "      <td>0.139838</td>\n",
       "      <td>0.168153</td>\n",
       "      <td>0.208169</td>\n",
       "      <td>0.210561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ACH-000004  ACH-000005  ACH-000007  ACH-000009  ACH-000011  \\\n",
       "ACH-000004    1.000000    0.132099    0.144105    0.174888    0.141257   \n",
       "ACH-000005    0.132099    1.000000    0.122399    0.134901    0.127921   \n",
       "ACH-000007    0.144105    0.122399    1.000000    0.176395    0.156368   \n",
       "ACH-000009    0.174888    0.134901    0.176395    1.000000    0.164444   \n",
       "ACH-000011    0.141257    0.127921    0.156368    0.164444    1.000000   \n",
       "\n",
       "            ACH-000012  ACH-000013  ACH-000014  ACH-000015  ACH-000017  ...  \\\n",
       "ACH-000004    0.175641    0.190136    0.147685    0.190909    0.159292  ...   \n",
       "ACH-000005    0.139130    0.146250    0.114894    0.150565    0.130006  ...   \n",
       "ACH-000007    0.177150    0.190136    0.146250    0.186287    0.163706  ...   \n",
       "ACH-000009    0.244233    0.228399    0.171137    0.231699    0.193234  ...   \n",
       "ACH-000011    0.179421    0.197910    0.166667    0.192458    0.173385  ...   \n",
       "\n",
       "            ACH-001736  ACH-001737  ACH-001740  ACH-001745  ACH-001750  \\\n",
       "ACH-000004    0.162968    0.065660    0.168897    0.170389    0.159292   \n",
       "ACH-000005    0.147685    0.071262    0.122399    0.123086    0.144819   \n",
       "ACH-000007    0.164444    0.063805    0.155640    0.160759    0.160759   \n",
       "ACH-000009    0.183226    0.080731    0.189364    0.195567    0.177906   \n",
       "ACH-000011    0.179421    0.081368    0.160759    0.165184    0.184755   \n",
       "\n",
       "            ACH-001765  ACH-001814  ACH-001838  ACH-001956  ACH-001957  \n",
       "ACH-000004    0.194010    0.118975    0.157828    0.194788    0.162968  \n",
       "ACH-000005    0.156368    0.098204    0.123775    0.153459    0.141257  \n",
       "ACH-000007    0.182463    0.121713    0.157098    0.192458    0.174888  \n",
       "ACH-000009    0.225117    0.149123    0.186287    0.239189    0.197910  \n",
       "ACH-000011    0.199477    0.139838    0.168153    0.208169    0.210561  \n",
       "\n",
       "[5 rows x 625 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jacc_sim_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "jacc_sim_df.to_csv(\"data/jaccard_indexes.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "links = []\n",
    "for i in range(ncols):\n",
    "    for j in range(i+1, ncols):\n",
    "        if(jacc_sim[i][j] > 0.20):\n",
    "            links.append([jacc_sim_df.columns[i], jacc_sim_df.columns[j], jacc_sim[i][j]] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ['ACH-000012', 'ACH-000888', 0.25961538461538464],\n",
       " ['ACH-000012', 'ACH-000889', 0.24338983050847457],\n",
       " ['ACH-000012', 'ACH-000890', 0.2292225201072386],\n",
       " ['ACH-000012', 'ACH-000896', 0.2527322404371585],\n",
       " ['ACH-000012', 'ACH-000898', 0.21136063408190225],\n",
       " ['ACH-000012', 'ACH-000900', 0.22266666666666668],\n",
       " ['ACH-000012', 'ACH-000901', 0.2604810996563574],\n",
       " ['ACH-000012', 'ACH-000905', 0.2467709041468389],\n",
       " ['ACH-000012', 'ACH-000906', 0.2861150070126227],\n",
       " ['ACH-000012', 'ACH-000907', 0.20341207349081364],\n",
       " ['ACH-000012', 'ACH-000910', 0.25530458590006844],\n",
       " ['ACH-000012', 'ACH-000911', 0.25102319236016374],\n",
       " ['ACH-000012', 'ACH-000912', 0.2587508579272478],\n",
       " ['ACH-000012', 'ACH-000913', 0.23087248322147652],\n",
       " ['ACH-000012', 'ACH-000914', 0.2789400278940028],\n",
       " ['ACH-000012', 'ACH-000915', 0.21618037135278514],\n",
       " ['ACH-000012', 'ACH-000917', 0.2467709041468389],\n",
       " ['ACH-000012', 'ACH-000921', 0.22185209860093272],\n",
       " ['ACH-000012', 'ACH-000924', 0.22429906542056074],\n",
       " ['ACH-000012', 'ACH-000926', 0.22022621423819028],\n",
       " ['ACH-000012', 'ACH-000932', 0.2467709041468389],\n",
       " ['ACH-000012', 'ACH-000936', 0.21618037135278514],\n",
       " ['ACH-000012', 'ACH-000937', 0.2587508579272478],\n",
       " ['ACH-000012', 'ACH-000938', 0.21216126900198282],\n",
       " ['ACH-000012', 'ACH-000941', 0.21941489361702127],\n",
       " ['ACH-000012', 'ACH-000943', 0.29064039408866993],\n",
       " ['ACH-000012', 'ACH-000946', 0.23835246455097908],\n",
       " ['ACH-000012', 'ACH-000947', 0.25530458590006844],\n",
       " ['ACH-000012', 'ACH-000948', 0.24002704530087898],\n",
       " ['ACH-000012', 'ACH-000953', 0.2358490566037736],\n",
       " ['ACH-000012', 'ACH-000960', 0.22185209860093272],\n",
       " ['ACH-000012', 'ACH-000965', 0.24592391304347827],\n",
       " ['ACH-000012', 'ACH-000969', 0.21618037135278514],\n",
       " ['ACH-000012', 'ACH-000973', 0.27715877437325903],\n",
       " ['ACH-000012', 'ACH-000974', 0.26657458563535913],\n",
       " ['ACH-000012', 'ACH-000975', 0.24423337856173677],\n",
       " ['ACH-000012', 'ACH-000976', 0.21698739216987392],\n",
       " ['ACH-000012', 'ACH-000980', 0.27449617790132036],\n",
       " ['ACH-000012', 'ACH-000983', 0.23751686909581646],\n",
       " ['ACH-000012', 'ACH-000984', 0.22593582887700533],\n",
       " ['ACH-000012', 'ACH-000986', 0.24931880108991825],\n",
       " ['ACH-000012', 'ACH-000988', 0.2186046511627907],\n",
       " ['ACH-000012', 'ACH-000996', 0.21056105610561057],\n",
       " ['ACH-000012', 'ACH-001001', 0.2587508579272478],\n",
       " ['ACH-000012', 'ACH-001028', 0.28162124388539483],\n",
       " ['ACH-000012', 'ACH-001038', 0.2527322404371585],\n",
       " ['ACH-000012', 'ACH-001048', 0.24423337856173677],\n",
       " ['ACH-000012', 'ACH-001050', 0.21779548472775564],\n",
       " ['ACH-000012', 'ACH-001054', 0.21779548472775564],\n",
       " ['ACH-000012', 'ACH-001061', 0.2527322404371585],\n",
       " ['ACH-000012', 'ACH-001096', 0.21537442014579192],\n",
       " ['ACH-000012', 'ACH-001134', 0.21216126900198282],\n",
       " ['ACH-000012', 'ACH-001151', 0.27096327096327094],\n",
       " ['ACH-000012', 'ACH-001163', 0.24423337856173677],\n",
       " ['ACH-000012', 'ACH-001173', 0.2861150070126227],\n",
       " ['ACH-000012', 'ACH-001183', 0.2186046511627907],\n",
       " ['ACH-000012', 'ACH-001184', 0.20104780615586115],\n",
       " ['ACH-000012', 'ACH-001190', 0.20816864295125165],\n",
       " ['ACH-000012', 'ACH-001232', 0.2292225201072386],\n",
       " ['ACH-000012', 'ACH-001275', 0.21537442014579192],\n",
       " ['ACH-000012', 'ACH-001283', 0.21376571806750497],\n",
       " ['ACH-000012', 'ACH-001295', 0.26657458563535913],\n",
       " ['ACH-000012', 'ACH-001300', 0.20262295081967213],\n",
       " ['ACH-000012', 'ACH-001306', 0.2210386151797603],\n",
       " ['ACH-000012', 'ACH-001318', 0.23751686909581646],\n",
       " ['ACH-000012', 'ACH-001328', 0.25961538461538464],\n",
       " ['ACH-000012', 'ACH-001329', 0.26657458563535913],\n",
       " ['ACH-000012', 'ACH-001336', 0.2613480055020633],\n",
       " ['ACH-000012', 'ACH-001340', 0.27008310249307477],\n",
       " ['ACH-000012', 'ACH-001346', 0.2467709041468389],\n",
       " ['ACH-000012', 'ACH-001353', 0.26744989633724947],\n",
       " ['ACH-000012', 'ACH-001366', 0.22348232154769845],\n",
       " ['ACH-000012', 'ACH-001368', 0.27983251919050944],\n",
       " ['ACH-000012', 'ACH-001377', 0.22266666666666668],\n",
       " ['ACH-000012', 'ACH-001380', 0.2727272727272727],\n",
       " ['ACH-000012', 'ACH-001407', 0.24338983050847457],\n",
       " ['ACH-000012', 'ACH-001408', 0.21456953642384105],\n",
       " ['ACH-000012', 'ACH-001412', 0.26832641770401106],\n",
       " ['ACH-000012', 'ACH-001413', 0.22266666666666668],\n",
       " ['ACH-000012', 'ACH-001414', 0.20816864295125165],\n",
       " ['ACH-000012', 'ACH-001496', 0.2210386151797603],\n",
       " ['ACH-000012', 'ACH-001523', 0.2300469483568075],\n",
       " ['ACH-000012', 'ACH-001524', 0.2578875171467764],\n",
       " ['ACH-000012', 'ACH-001529', 0.23835246455097908],\n",
       " ['ACH-000012', 'ACH-001532', 0.24761904761904763],\n",
       " ['ACH-000012', 'ACH-001554', 0.25616438356164384],\n",
       " ['ACH-000012', 'ACH-001568', 0.2300469483568075],\n",
       " ['ACH-000012', 'ACH-001645', 0.20657894736842106],\n",
       " ['ACH-000012', 'ACH-001650', 0.24846834581347857],\n",
       " ['ACH-000012', 'ACH-001654', 0.25102319236016374],\n",
       " ['ACH-000012', 'ACH-001735', 0.24592391304347827],\n",
       " ['ACH-000012', 'ACH-001736', 0.2292225201072386],\n",
       " ['ACH-000012', 'ACH-001740', 0.20341207349081364],\n",
       " ['ACH-000012', 'ACH-001745', 0.23169912693082606],\n",
       " ['ACH-000012', 'ACH-001750', 0.20262295081967213],\n",
       " ['ACH-000012', 'ACH-001765', 0.24846834581347857],\n",
       " ['ACH-000012', 'ACH-001838', 0.2073732718894009],\n",
       " ['ACH-000012', 'ACH-001956', 0.2762700069589422],\n",
       " ['ACH-000012', 'ACH-001957', 0.23835246455097908],\n",
       " ['ACH-000013', 'ACH-000015', 0.26832641770401106],\n",
       " ['ACH-000013', 'ACH-000017', 0.24170616113744076],\n",
       " ['ACH-000013', 'ACH-000018', 0.20026178010471204],\n",
       " ['ACH-000013', 'ACH-000021', 0.26920415224913496],\n",
       " ['ACH-000013', 'ACH-000022', 0.25616438356164384],\n",
       " ['ACH-000013', 'ACH-000025', 0.2073732718894009],\n",
       " ['ACH-000013', 'ACH-000028', 0.22348232154769845],\n",
       " ['ACH-000013', 'ACH-000029', 0.2834149755073478],\n",
       " ['ACH-000013', 'ACH-000030', 0.31281317108088763],\n",
       " ['ACH-000013', 'ACH-000035', 0.21941489361702127],\n",
       " ['ACH-000013', 'ACH-000036', 0.23335574983187626],\n",
       " ['ACH-000013', 'ACH-000037', 0.20816864295125165],\n",
       " ['ACH-000013', 'ACH-000040', 0.22675585284280936],\n",
       " ['ACH-000013', 'ACH-000041', 0.2210386151797603],\n",
       " ['ACH-000013', 'ACH-000042', 0.23668240053944706],\n",
       " ['ACH-000013', 'ACH-000045', 0.21056105610561057],\n",
       " ['ACH-000013', 'ACH-000047', 0.26920415224913496],\n",
       " ['ACH-000013', 'ACH-000053', 0.26920415224913496],\n",
       " ['ACH-000013', 'ACH-000054', 0.2527322404371585],\n",
       " ['ACH-000013', 'ACH-000055', 0.20262295081967213],\n",
       " ['ACH-000013', 'ACH-000078', 0.20262295081967213],\n",
       " ['ACH-000013', 'ACH-000082', 0.2408660351826793],\n",
       " ['ACH-000013', 'ACH-000086', 0.24846834581347857],\n",
       " ['ACH-000013', 'ACH-000092', 0.2888264230498946],\n",
       " ['ACH-000013', 'ACH-000095', 0.27184466019417475],\n",
       " ['ACH-000013', 'ACH-000097', 0.27184466019417475],\n",
       " ['ACH-000013', 'ACH-000098', 0.2604810996563574],\n",
       " ['ACH-000013', 'ACH-000099', 0.2613480055020633],\n",
       " ['ACH-000013', 'ACH-000108', 0.23335574983187626],\n",
       " ['ACH-000013', 'ACH-000113', 0.2391891891891892],\n",
       " ['ACH-000013', 'ACH-000116', 0.27983251919050944],\n",
       " ['ACH-000013', 'ACH-000118', 0.26221610461114936],\n",
       " ['ACH-000013', 'ACH-000120', 0.2210386151797603],\n",
       " ['ACH-000013', 'ACH-000123', 0.24170616113744076],\n",
       " ['ACH-000013', 'ACH-000127', 0.3194244604316547],\n",
       " ['ACH-000013', 'ACH-000132', 0.22593582887700533],\n",
       " ['ACH-000013', 'ACH-000133', 0.25444596443228457],\n",
       " ['ACH-000013', 'ACH-000137', 0.21779548472775564],\n",
       " ['ACH-000013', 'ACH-000138', 0.23835246455097908],\n",
       " ['ACH-000013', 'ACH-000141', 0.21618037135278514],\n",
       " ['ACH-000013', 'ACH-000142', 0.24507807196198234],\n",
       " ['ACH-000013', 'ACH-000143', 0.21056105610561057],\n",
       " ['ACH-000013', 'ACH-000146', 0.2300469483568075],\n",
       " ['ACH-000013', 'ACH-000148', 0.25961538461538464],\n",
       " ['ACH-000013', 'ACH-000150', 0.20262295081967213],\n",
       " ['ACH-000013', 'ACH-000152', 0.2535885167464115],\n",
       " ['ACH-000013', 'ACH-000159', 0.2535885167464115],\n",
       " ['ACH-000013', 'ACH-000163', 0.22429906542056074],\n",
       " ['ACH-000013', 'ACH-000169', 0.25102319236016374],\n",
       " ['ACH-000013', 'ACH-000178', 0.25187713310580206],\n",
       " ['ACH-000013', 'ACH-000182', 0.2587508579272478],\n",
       " ['ACH-000013', 'ACH-000183', 0.22757697456492637],\n",
       " ['ACH-000013', 'ACH-000188', 0.2535885167464115],\n",
       " ['ACH-000013', 'ACH-000198', 0.24846834581347857],\n",
       " ['ACH-000013', 'ACH-000211', 0.27184466019417475],\n",
       " ['ACH-000013', 'ACH-000215', 0.2780487804878049],\n",
       " ['ACH-000013', 'ACH-000217', 0.2727272727272727],\n",
       " ['ACH-000013', 'ACH-000221', 0.24254742547425473],\n",
       " ['ACH-000013', 'ACH-000223', 0.24846834581347857],\n",
       " ['ACH-000013', 'ACH-000226', 0.23751686909581646],\n",
       " ['ACH-000013', 'ACH-000231', 0.2762700069589422],\n",
       " ['ACH-000013', 'ACH-000232', 0.26832641770401106],\n",
       " ['ACH-000013', 'ACH-000233', 0.2630853994490358],\n",
       " ...]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80685"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "links_df = pd.DataFrame(links, columns = [\"source\", \"target\", \"JaccSim\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>source</th>\n",
       "      <th>target</th>\n",
       "      <th>JaccSim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACH-000004</td>\n",
       "      <td>ACH-000030</td>\n",
       "      <td>0.210561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACH-000004</td>\n",
       "      <td>ACH-000053</td>\n",
       "      <td>0.209763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACH-000004</td>\n",
       "      <td>ACH-000127</td>\n",
       "      <td>0.213766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACH-000004</td>\n",
       "      <td>ACH-000215</td>\n",
       "      <td>0.204993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACH-000004</td>\n",
       "      <td>ACH-000235</td>\n",
       "      <td>0.223482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source      target   JaccSim\n",
       "0  ACH-000004  ACH-000030  0.210561\n",
       "1  ACH-000004  ACH-000053  0.209763\n",
       "2  ACH-000004  ACH-000127  0.213766\n",
       "3  ACH-000004  ACH-000215  0.204993\n",
       "4  ACH-000004  ACH-000235  0.223482"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "links_df.to_csv(\"data/edges_p2.txt\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(ncols):\n",
    "    l = len(list(df_filtered.iloc[:,i][df_filtered.iloc[:,i] != 0].index))\n",
    "    if(l != 917):\n",
    "        print(l)"
   ]
  }
 ],
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