HPO_clustering_Freq.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('PhenoTool/')\n",
    "\n",
    "import networkx\n",
    "import obonet\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import DeepPhenotyping_functions as func\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import OBO object HPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.52 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "url = 'phenopy_mod/.phenopy/data/hp.obo'\n",
    "graph = obonet.read_obo(url)\n",
    "\n",
    "id_to_name = {id_: data.get('name') for id_, data in graph.nodes(data=True)}\n",
    "name_to_id = {data['name']: id_ for id_, data in graph.nodes(data=True) if 'name' in data}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Abnormality of the head'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hpo_id = id_to_name['HP:0000234']\n",
    "hpo_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Reduced alpha/beta synthesis ratio ⟶ Imbalanced hemoglobin synthesis ⟶ Abnormal hemoglobin ⟶ Abnormal erythrocyte morphology ⟶ Abnormality of blood and blood-forming tissues ⟶ Phenotypic abnormality\n",
      "• HP:0000234 ⟶ HP:0000152 ⟶ HP:0000118\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>HPO id</th>\n",
       "      <th>Phenotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>HP:0000118</td>\n",
       "      <td>Phenotypic abnormality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>HP:0000152</td>\n",
       "      <td>Abnormality of head or neck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP:0000234</td>\n",
       "      <td>Abnormality of the head</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      HPO id                    Phenotype\n",
       "0      0  HP:0000118       Phenotypic abnormality\n",
       "1      1  HP:0000152  Abnormality of head or neck\n",
       "2      2  HP:0000234      Abnormality of the head"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df_hpo = get_superclass(graph, hpo_id, name_to_id, id_to_name)\n",
    "df_hpo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Id</th>\n",
       "      <th>Protein</th>\n",
       "      <th>High frequency hearing loss</th>\n",
       "      <th>Short stature</th>\n",
       "      <th>microcephaly</th>\n",
       "      <th>hearing loss</th>\n",
       "      <th>ID</th>\n",
       "      <th>Developmental delay (Global)</th>\n",
       "      <th>Motor Delay</th>\n",
       "      <th>...</th>\n",
       "      <th>Recurrent infections</th>\n",
       "      <th>Photosensitivity</th>\n",
       "      <th>Polyarthralgia</th>\n",
       "      <th>Hepatomegaly</th>\n",
       "      <th>Hypothyroidism</th>\n",
       "      <th>Leukocytose</th>\n",
       "      <th>Trombocytopenie</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>Strabismus</th>\n",
       "      <th>Growth hormone deficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NAT6</td>\n",
       "      <td>Man</td>\n",
       "      <td>L130P</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NAT6</td>\n",
       "      <td>Ant</td>\n",
       "      <td>L130P</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category   Id Protein  High frequency hearing loss  Short stature  \\\n",
       "0     NAT6  Man   L130P                            1              0   \n",
       "1     NAT6  Ant   L130P                            1              0   \n",
       "\n",
       "    microcephaly   hearing loss    ID  Developmental delay (Global)  \\\n",
       "0              1              1     1                             1   \n",
       "1              1              1     1                             1   \n",
       "\n",
       "    Motor Delay  ...  Recurrent infections  Photosensitivity  Polyarthralgia  \\\n",
       "0             1  ...                     0               0.0             0.0   \n",
       "1             1  ...                     0               0.0             0.0   \n",
       "\n",
       "   Hepatomegaly  Hypothyroidism  Leukocytose  Trombocytopenie  Asthma  \\\n",
       "0           0.0               0            0                0       0   \n",
       "1           0.0               0            0                0       0   \n",
       "\n",
       "   Strabismus  Growth hormone deficiency  \n",
       "0           1                          0  \n",
       "1           0                          0  \n",
       "\n",
       "[2 rows x 96 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actb = pd.read_csv(r'data/ACTB_data.csv', sep=',', header=0)\n",
    "df_actg1 = pd.read_csv(r'data/ACTG1_data.csv', sep=',', header=0)\n",
    "df_nat6 = pd.read_csv(r'data/NAT6_data.csv', sep=',', header=0)\n",
    "df_nat6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actg1['Category'] = df_actg1['Category'] + ' (ACTG1)'\n",
    "df_actb['Category'] = df_actb['Category'] + ' (ACTB)'\n",
    "df_actb['Origin'] = 'ACTB'\n",
    "df_actg1['Origin'] = 'ACTG1'\n",
    "df_nat6['Origin'] = 'NAT6'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_all = df_all.rename(columns={\"Retrognatia/micrognathia\" : \"Retrognathia/micrognathia\", \"Leukocytose\": \"Leukocytosis\", \"Trombocytopenie\": \"thrombocytopenia\", \"Other Psychiatric diagnosis (shizofrenia, depression, other, not autism, or ADHD/ADD)\" : \"Other Psychiatric diagnosis (schizophrenia, depression, other, not autism, or ADHD/ADD)\", \"Mid face hypoplasia\": \"Midface hypoplasia\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all['Category'] = df_all['Category'].replace({'Other/Myosin binding (ACTG1)': 'Other (ACTG1)', 'Other/Fimbrin binding (ACTG1)': 'Other (ACTG1)', 'N-terminus (ACTG1)': 'Other (ACTG1)', 'N-terminus (ACTB)': 'Other (ACTB)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'columns_linked_to_hpo.column_to_hpo'; 'columns_linked_to_hpo' is not a package",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-e6111728169c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mcolumns_linked_to_hpo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn_to_hpo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'columns_linked_to_hpo.column_to_hpo'; 'columns_linked_to_hpo' is not a package"
     ]
    }
   ],
   "source": [
    "import columns_linked_to_hpo_actin.column_to_hpo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer HPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "persistent embryonic sructures False\n",
      "persistent embryonic sructures False\n",
      "persistent embryonic sructures False\n",
      "polyarthralgia False\n",
      "persistent embryonic sructures False\n",
      "persistent embryonic sructures False\n"
     ]
    }
   ],
   "source": [
    "import columns_linked_to_hpo_actin\n",
    "def infer_HPO(row, column_to_hpo):\n",
    "    hpo_list = []\n",
    "    cols = list(row.index)\n",
    "    for i in cols:\n",
    "        val = row[i]\n",
    "        #print(val)\n",
    "        if val == 1:\n",
    "            col = i.lower().strip()\n",
    "            if (col in column_to_hpo.keys()) == False :\n",
    "                print(col, col in column_to_hpo.keys())\n",
    "            if col in column_to_hpo.keys():\n",
    "                if type(column_to_hpo[col]) == list:\n",
    "                    hpo_list.extend(column_to_hpo[col])\n",
    "                else :\n",
    "                    hpo_list.append(column_to_hpo[col])\n",
    "    return hpo_list\n",
    "\n",
    "hp_patients = list(df_all.apply(lambda x: infer_HPO(x, columns_linked_to_hpo_actin.column_to_hpo) , axis= 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## write to csv to calculate patient similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/deepdata/actg_patpheno.csv', 'w') as f:\n",
    "    cnt = 0\n",
    "    for pat in df_all['Id']:\n",
    "        f.write(str(pat) + ',' + ','.join(hp_patients[cnt]) + '\\n')\n",
    "        cnt +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CLUSTERING 1 : HRSS clustering\n",
    "\n",
    "## phenotype similarity scoring by semantic similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-11-25 22:06:01,829 - phenopy - INFO - checking if config file exists: \\Users\\levrex\\.phenopy\n",
      "2020-11-25 22:06:01,887 - phenopy - INFO - phenopy 0.4.2\n",
      "2020-11-25 22:06:01,890 - phenopy - INFO - Using configuration file: \\Users\\levrex\\.phenopy\\phenopy.ini\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from phenopy import generate_annotated_hpo_network\n",
    "from phenopy.score import Scorer\n",
    "\n",
    "# data directory\n",
    "phenopy_data_directory = os.path.join(os.getenv('HOMEPATH'), '.phenopy\\\\data')\n",
    "\n",
    "# files used in building the annotated HPO network\n",
    "obo_file = os.path.join(phenopy_data_directory, 'hp.obo')\n",
    "disease_to_phenotype_file = os.path.join(phenopy_data_directory, 'phenotype.hpoa')\n",
    "\n",
    "# if you have a custom ages_distribution_file, you can set it here.\n",
    "#ages_distribution_file = os.path.join(phenopy_data_directory, 'xa_age_stats_oct052019.tsv')\n",
    "\n",
    "hpo_network, alt2prim, disease_records = \\\n",
    "    generate_annotated_hpo_network(obo_file,\n",
    "                                   disease_to_phenotype_file,\n",
    "                                   #ages_distribution_file=ages_distribution_file\n",
    "                                   )\n",
    "\n",
    "scorer = Scorer(hpo_network)\n",
    "\n",
    "terms_a = ['HP:0000377']# wide nasal bridge  #hp_patients[0]\n",
    "terms_b = ['HP:0000153']# abnormality #hp_patients[1]\n",
    "\n",
    "print(scorer.score_term_sets_basic(terms_a, terms_b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the HRSS between patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\levrex\\Anaconda3\\envs\\my_env\\lib\\site-packages\\numpy\\lib\\function_base.py:380: RuntimeWarning: Mean of empty slice.\n",
      "  avg = a.mean(axis)\n",
      "C:\\Users\\levrex\\Anaconda3\\envs\\my_env\\lib\\site-packages\\numpy\\core\\_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "l = []\n",
    "for j in range(len(hp_patients)):\n",
    "    for i in range(len(hp_patients)):\n",
    "        l.append(scorer.score_term_sets_basic(hp_patients[i], hp_patients[j]))\n",
    "\n",
    "l_cleaned = [x if str(x) != 'nan' else 0 for x in l ]\n",
    "\n",
    "l2 = []\n",
    "n = len(hp_patients)\n",
    "for j in range(len(hp_patients)):\n",
    "    l2.append(l_cleaned[n*j:n+n*j])\n",
    "df_hrss = pd.DataFrame(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hrss.to_csv('data/deepdata/distance_matrix_hrss.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read HRSS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 113 columns</p>\n",
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       "          0         1         2         3         4         5         6  \\\n",
       "0  0.744947  0.511291  0.466504  0.020417  0.326188  0.580029  0.386291   \n",
       "1  0.511291  0.638106  0.493442  0.076861  0.322993  0.587144  0.544356   \n",
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       "3  0.020417  0.076861  0.022064  0.303196  0.028209  0.017701  0.074936   \n",
       "4  0.326188  0.322993  0.255252  0.028209  0.805445  0.342018  0.250878   \n",
       "\n",
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       "3  0.019176  0.016625  0.074875  ...  0.033455  0.061744  0.023645  0.059784   \n",
       "4  0.313839  0.333496  0.322993  ...  0.080899  0.218222  0.275649  0.178895   \n",
       "\n",
       "        107       108       109       110       111       112  \n",
       "0  0.092692  0.035364  0.018605  0.027927  0.021298  0.023234  \n",
       "1  0.134187  0.076685  0.062309  0.122363  0.097920  0.104040  \n",
       "2  0.097482  0.031724  0.015605  0.012276  0.014236  0.015530  \n",
       "3  0.097985  0.040378  0.033129  0.078666  0.125674  0.137099  \n",
       "4  0.132292  0.093256  0.100644  0.087971  0.015784  0.016502  \n",
       "\n",
       "[5 rows x 113 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hrss = pd.read_csv('data/deepdata/distance_matrix_hrss.csv', index_col=0)\n",
    "df_hrss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of clusters = 4\n"
     ]
    }
   ],
   "source": [
    "X_trans = df_hrss[df_hrss.columns[:113]]\n",
    "k = func.elbowMethod(X_trans, method='kmeans', n=30)\n",
    "print('Nr of clusters = %s' % (k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAelUlEQVR4nO3df5BV9Znn8ffj7QZuZxIgipvYQiQOanDBkDRqht1ETVwENwNEs/FHbUrHWgqMs5upoitQWjFuYkEWajeZWn8MayxqKtkYN5AeMmI6k2E1GyMuTVBaYjCEZJDGLdoIONINdDfP/nFud18u994+t++vc879vKoo+57zvec+XuXhy/P9Ze6OiIjE33n1DkBERCpDCV1EJCGU0EVEEkIJXUQkIZTQRUQSoqleH3zBBRf4JZdcUq+PFxGJpV27dr3l7tPy3atbQr/kkkvo6uqq18eLiMSSmf1ToXsquYiIJIQSuohIQiihi4gkhBK6iEhCKKGLiCRE3Wa5iEj9dezuYX3nPg4f6+eiKWnaF17O0nmt9Q5LxkkJXaRBdezuYc2WbvoHhgDoOdbPmi3dAErqMaWSi0iDWt+5bySZD+sfGGJ95746RSTlUkIXaVCHj/WXdF2iTyUXkQbyQEc333/pDYaKHGxz0ZR0DSOSSlJCF2kQD3R0890dB4u2STenaF94eY0ikkpTQhdpEN9/6Y2C9ww0yyUBlNBFGkSxMsvv191cw0ikWjQoKtIgUmYlXZf4UUIXaRC3XzO9pOtR1LG7hwXrtjNz9TMsWLedjt099Q4pUlRyEUmAMCs+v7F0DsDILJeUGbdfM33ketRpIdTYzIvU1aqpra3NdcCFSPlyEx0Es1XWfm5OohLdgnXb6ckzR751SpoXVt9Qh4jqw8x2uXtbvnvqoYvEXLEVn9kJPe77tmgh1NhUQxeJuTCJbrgX33OsH2e0XBGnGnShBU9aCDVKPXSRmBrucRcqmmYnukK9+Id+vPesXvv9889n8a5O2LMHjh+HyZNh7ly4+26Ylvdc4pppX3h53tKSFkKNUkIXiaF8dfNsuYmuUC/+aN8AR/sGmPvm69y75Wmuf2AXg00pmk6dHG20ZQs8+CAsWgRr1sD8+RX9dwlruDwU57JRtYVK6GZ2E/BtIAU84e7r8rS5DvgW0Ay85e6fqmCcIpIlX497WGueRHfRlHTeAUWAO3dv4/7t32Hi4GlSOAwNnN2gP/O+jg7o7IQNG2Dlyor8e5Rq6bzWqiXwuI8xQIgaupmlgEeARcBs4HYzm53TZgrwKPDn7n4l8PkqxCoiGYV63Aa8sPqGcxJR+8LLSTenzmk/nMxbBk8FybwYd+jrg1Wr4LHHxht6JCVhjAHCDYpeDex39wPufhp4CliS0+YOYIu7HwRw9yOVDVNEspU6QLh0XitrPzeH1ilpjKAXv+DtAyPJvCTDST1B046Tsjd8mJJLK5C9q88h4JqcNpcBzWb2HPBe4Nvu/re5DzKz5cBygBkzZownXhFhfAOEueWKwzesZ+Lg6fEF0N8Pa9fC5s3je3/EJGVKZJiEnm+jh9y/mzUBHwc+DaSBF81sh7u/ftab3DcCGyFYWFR6uCICFRggPHKEi158jnN/K4fkDtu2QW9v0dkvcalLFxpjiNuUyDAJ/RCQvdnDxcDhPG3ecvcTwAkz+zlwFfA6IlIVZQ0QbtpUfgBmwXPa2/PejtNS/aRMiQxTQ98JzDKzmWY2AbgN2JrT5u+Af21mTWbWQlCSea2yoYpIxezZAydPjt2umP5+6O4ueDtOdel8Ywxx3DphzB66uw+a2X1AJ8G0xSfdfa+Zrcjcf9zdXzOznwB7gDMEUxtfrWbgIlKG48cr85yjRwveiltduppTImsl1Dx0d98GbMu59njO6/XA+sqFJiJVM3lyZZ4zdWrBW0mpS8eJ9nIRaURz58KkSeU9I52GOYW33s039z2Odek4UUIXaUR33VX+M9yLPicpdek40V4uIo3owguDvVk6OoLEXCozWLx4zA27Kl2Xjss0yHpRD12kUa1ZE5RNxiOdDt5fQ0lZnl9NSugidRCJszHnzw822mppKeltg5PSwfva8h6aUzVxmgZZL0roIjUWqZ7mypWjSd3yLQofNYTR1zSR/7ZweV12W4zbNMh6UEIXqbHI9TRXroTnn4dly4KZLzllmP6mCZxMNfPTy67lC3es49ErbqxLmDqxaGwaFBWpsUj2NNvago22enuD5fzd3fxi5285kkrz2rRL2DznM7zdEsxdb61TAk3K8vxqUkIXGUOlZ1ZEesHNtGkje7O8tbuH+yOUQHVi0diU0EWKqMYGU3HpaUYxgSZheX41KaGLFFGs3p2dWErpxUcxURaiBBovSugiRYSpd4+nF69EKdWgWS4iRYSZWRG5WSvSsJTQRYoIs8FUJGetSENSQhcpIswGU5ofLVGhGrrIGMaqd8dl1ooknxK6SJniNGtFkk0JXaQCNGtFokAJXUSkRqq9n7sSuohIDVRj1XEuzXIREamBWqxXCJXQzewmM9tnZvvNbHWe+9eZ2XEzeznz66sVi1BEJAFqsV5hzJKLmaWAR4AbgUPATjPb6u6/zmn6f9z931YsMhGRBKnFLptheuhXA/vd/YC7nwaeApZULAIRGVMkjqyTsoRZdVyuMAm9FXgj6/WhzLVcnzCzV8zsWTO7Mt+DzGy5mXWZWVdvb+84whVpPJE6sk7GLcyq43KFmeWS76BBz3n9K+BD7v6umS0GOoBZ57zJfSOwEaCtrS33GSKSR9gtfCX6qr1eIUwP/RAwPev1xcDh7Abu/o67v5v5eRvQbGYXVCxKkQamzb8krDAJfScwy8xmmtkE4DZga3YDM/uAWXBkuJldnXnuHysdrEgj0uZfEtaYCd3dB4H7gE7gNeBpd99rZivMbEWm2a3Aq2b2CvDXwG3urpKKSAXUYjBNksHqlXfb2tq8q6urLp8tEjfVXjIu8WFmu9y9Ld89Lf0XiQFt/iVhaOm/iEhCKKGLiCSEErqISEIooYuIJIQSuohIQiihi4gkhBK6iEhCKKGLiCSEErqISEIooYuIJIQSuohIQiihi4gkhBK6iEhCKKGLiCSEErqISEIooYuIJIQSuohIQiihi4gkhBK6iEhCKKGLiCREqIRuZjeZ2T4z229mq4u0m29mQ2Z2a+VCFBGRMMZM6GaWAh4BFgGzgdvNbHaBdt8EOisdpIiIjC1MD/1qYL+7H3D308BTwJI87f4S2AwcqWB8IiISUpiE3gq8kfX6UObaCDNrBZYBj1cuNBERKUWYhG55rnnO628BX3H3oaIPMltuZl1m1tXb2xs2RhERCaEpRJtDwPSs1xcDh3PatAFPmRnABcBiMxt0947sRu6+EdgI0NbWlvuHgoiIlCFMQt8JzDKzmUAPcBtwR3YDd585/LOZbQL+PjeZi4hIdY2Z0N190MzuI5i9kgKedPe9ZrYic191cxGRCAjTQ8fdtwHbcq7lTeTuflf5YYmISKm0UlREJCGU0EVEEkIJXUQkIZTQRUQSQgldRCQhlNBFRBJCCV1EJCFCzUMXqZeO3T2s79zH4WP9XDQlTfvCy1k6r3XsN4o0ICV0iayO3T2s2dJN/0Cw51vPsX7WbOkGUFIXyUMJXWqqlB73+s59I8l8WP/AEOs79ymhi+ShhC41U2qP+/Cx/rzPKXRdpNFpUFRqpliPO5+LpqRLui7S6JTQpWZK7XG3L7ycdHPqrGvp5hTtCy+veGwiSaCELjVTao976bxW1n5uDq1T0hjQOiXN2s/NUf1cpADV0KVm2hdeflYNHcbucS+d16oELhKSErrUzHBirvS8cs1VFwkooUtNVbrHrbnqIqNUQ5dYK3XmjEiSqYcuZ+nY3cNDP97L0b4BAKakm/nan18Z2d6u5qqLjFIPXUZ07O6h/YevjCRzgGP9A7T/r1fo2N1Tx8gK01x1kVFK6DJifec+Bob8nOsDZzyyJQzNVRcZpZKLjChWpohqCaNaM2dE4ihUQjezm4BvAyngCXdfl3N/CfB14AwwCHzZ3X9R4Vilyi6akqanQOIuVMKIwpRBzVUXCYxZcjGzFPAIsAiYDdxuZrNzmv0jcJW7fxT4C+CJSgcq1de+8HKaU3bO9ebzLG8JY3jKYM+xfpzRKYNRrbeLJF2YGvrVwH53P+Dup4GngCXZDdz9XXcfLr6+Bzi3ECuRt3ReK+tvvYqpLc0j16akm1n/+avy9oA1ZVAkWsKUXFqBN7JeHwKuyW1kZsuAtcCFwM35HmRmy4HlADNmzCg1VqmBUsoXmjIoEi1heujn/h08Tw/c3X/k7lcASwnq6ee+yX2ju7e5e9u0adNKi1QiR1MGRaIlTEI/BEzPen0xcLhQY3f/OXCpmV1QZmwScZoyKBItYRL6TmCWmc00swnAbcDW7AZm9qdmZpmfPwZMAP5Y6WAlWrS9rUi0jFlDd/dBM7sP6CSYtviku+81sxWZ+48DtwBfNLMBoB/4QtYgqVRQFKYJZtOUQZHosHrl3ba2Nu/q6qrLZ8dV7s6CEJQ41CsWaRxmtsvd2/Ld09L/GNE0QREpRkv/Y6TS0wSjVr4RkfKohx4jlZwmqFWeIsmjhB4jlZwmqPKNSPKo5BIjldxZUKs8RZJHCT1mKjVNsNDOilrlKRJfKrk0qLiu8uzY3cOCdduZufoZFqzbrpq/SBYl9Aa1dF4rt3y8lVSwwJeUGbd8PNqLhDSQK1KcEnqD6tjdw+ZdPQxlFpYNubN5V0+kk6MGckWKU0JvUHFMjhrIFSlOCb1BxTE5artekeKU0BtUHJNjXAdyRWpFCb1BxTE5artekeI0D71EUd//JGx8lVykVEvarlekMCX0EuRuXzs8bQ6obpI5cgQ2bYI9e+D4cZg8GebOhbvvhqyj/EqNT8lRJFm0H3oJFqzbnnd1ZeuUNC+svqHyH7hzJ6xdy9Az2xg440waPD16L50Gd1i0CNasgfnzax+fiNRcsf3Q1UMvQU1nhjz2GKxaxZn+flLupHLv92c+s6MDOjthwwYOH5tRu/hEJHI0KFqCms0MySRz+vo4b6y/QblDXx+sWsW9v/mH2sQnIpGkhF6CmswM2blzJJmXpK+P//STv6Gt93fVjU9EIksJvQQ1mTa3du1oOaVEqVOneOjXWzWtT6RBaVA0So4cgQ99CE6eHPcjTjVNYOLhQ2fNfhGR5Cj7kGgzu8nM9pnZfjNbnef+nWa2J/Prl2Z2VblBN6RNmxgs88/XM5nniEjjGTOhm1kKeARYBMwGbjez2TnNfg98yt3nAl8HNlY60EZw8LkdNJ0af+8cID14Grq7KxSRiMRJmB761cB+dz/g7qeBp4Al2Q3c/ZfufjTzcgdwcWXDbAwHf/9mZR509OjYbUQkccIk9FbgjazXhzLXCrkHeDbfDTNbbmZdZtbV29sbPsoG0XvepMo8aOrUyjxHRGIlTEK3PNfyVnrN7HqChP6VfPfdfaO7t7l72zQN2p3j8IxZ9DdNKO8h6TTMmVOZgEQkVsIk9EPA9KzXFwOHcxuZ2VzgCWCJu/+xMuE1lpmrvjT2QqKxuMNdd1UkHhGJlzBL/3cCs8xsJtAD3Abckd3AzGYAW4B/7+6vVzzKcXqgo5vvv/QGQ+6kzLj9mul8Y2l0e6+LP30Vhz/5af7Fc52kxpPYzWDxYk1ZFGlQY/bQ3X0QuA/oBF4Dnnb3vWa2wsxWZJp9FTgfeNTMXjazuk8wf6Cjm+/uOHjWmZnf3XGQBzqiPQPkom/+Z1LpcS7VT6eDjbpEpCEldmHRpWu2jSTzbCkzfrd2cdU+tyKy9nIJraUFNmyAlSurF5eI1F1D7raYL5kXux4pw0l51apgG4BiMZsFPXMlc5GGl9i9XFKWb3JO4euRs3IlPP88LFsGkyYFSTtbOh1cX7YsaKdkLtLwEttDv/2a6Xx3x8G812OjrQ02b4be3mA5f3d3sGho6tRgauJdd2kAVERGJDahD89mqcQsl3LOEa3IGaTTpkF7e8lxi0hjSeygaKXkntMJwR7jYbalLee9IiL5lL3bYhx07O5hwbrtzFz9DAvWbadjd09Fnru+c99ZCRmgf2CI9Z37qvpeEZFSJSKhD/eEe4714wSn3X/5By/z0Yd+WnZiL+cc0ZqeQSoiDS8RNfR8PWGAY/0DrNkSLCQab4njoilpevIk4DDndJbz3mqoSD1fRCIrET30Yj3ecksc5ZwjWpMzSEPK97eYNVu6K1aaEpH6S0RCH6vHW06Jo5xzRGtyBmlIqueLJF8iSi7tCy8/ZzZJtnJLHEvntY47CZfz3kpSPV8k+RKR0IcT5kM/3svRvoGz7uWWOKpRR45DbTpq9XwRqbxElFyGtUwI/nwaXt6fW+KoRh35gY5u/uoHL0e+Nh2ler6IVEciEnp2ooZgA67hZJXdU650Hbljdw/f23HwnOObolibjlI9X0SqIxEll2KJOjthVbqOvL5zX/6z+Mp4ZjVFpZ4vItWRiIReKHn2HOtnwbrtI7XtyelmjvUPnNNuvHXkYklbtWkRqbVYJfRCg4+FBvyAkes9x/ppThnN5xkDZ0b71eXUkQt9roFq0yJSc7GpoRcb0Lz+inBbyA4MOROazmNqS/PItYlN4/8K8g00GnDntTNU2hCRmotND71QnfxrW/dyavBM3vecf+IYt3T/jI/0/oH3njrBP098D69Nu4S/++i/gUnvA8rbHmC4fdSnLIpIY4jN9rkzVz9TcAAy19w3X+feF5/mugO7cDPSg6dH7vU3TcDcee7DbTz6ic+z54OXAcGsjxdW31DKv4KISM0l4kzRYnXybHfu3sb927/DxMHTpPL8ETCc3G/87Q4++ftf8fAN9/C9eYsjOStFRKQUoQrIZnaTme0zs/1mtjrP/SvM7EUzO2VmqyofZuF6dbbhZN4yeCpvMs+WwmkZPMX927/Dnbu3aVaKiMTemAndzFLAI8AiYDZwu5nNzmn2NvAfgQ0VjzAj38KY7JQ9983XR5J5KYaT+sPTT1Y0XhGRWgtTcrka2O/uBwDM7ClgCfDr4QbufgQ4YmY3VyXKjNyFMQvWbR8pw9z74tNMzKqVl2Li4Gmm/ff/CncurkicIiL1EKbk0gq8kfX6UOZa3Q1PVzz/xDGuO7BrzDJLISmcS3f+nG3b95T0vmodeyciMh5heui5pWpgfJnTzJYDywFmzJhR8vtzFxadODUIwC3dP8MtX5jhuRkHNjwCN/xN6Fiyt+wdnhcPwd8k4rADo4gkS5iEfgiYnvX6YuDweD7M3TcCGyGYtljKe/Ml0GEf6f3DWVMTxyM9eJqLDv42dPuxNvoqluxFRKohTMllJzDLzGaa2QTgNmBrdcM6V6FzQwHee+pERT5jcgnPKbbRl04HEpF6GLOH7u6DZnYf0AmkgCfdfa+Zrcjcf9zMPgB0Ae8DzpjZl4HZ7v5OpQItNk/8nye+pyKfcbyE5xQ7MEKnA4lIPYSah+7u29z9Mne/1N0fzlx73N0fz/z8/9z9Ynd/n7tPyfxcsWQOhXcvnNrSzOEZs+hvmlDW8/ubJnB4xqzQ7YsdGFEoVs11F5Fqis3mXIUWFh3tG2Dbxxcy4bzyBkXPc+fDq74Uun2xAyN0OpCI1ENslv5nb4TVc6wfY3Sqzd7BSfzDh+Zx429fGtfUxSGMtz71GRbfMLfkmPINcmrTLhGph9hszpUte0HRsLlvvs5T/3NNyStFAfqaJtLy4i+gLe9+NyIikVFsc67YlFyy5Rtc3PPBy3j4hnvoa5pY0rP6mibyyM0rlMxFJPZiU3LJVmiGyffmBUv3i+22OGwI41TTBP7Ljf+Bjz7YXvTztEhIROIglj30fIOOw743bzFfuGMdP73sWk6mms+Z/dLfNIGTqWZ+etm13HHnN/now6uLJudiJyWJiERJLHvowwn4r55+mXxDAN0fnMXKZffz/r7jIycWve/kCd6ZFJxYtHnOZ3i7ZTLG2Cs3iy0SUi9dRKIklgkdRhNx+w9fYWAof2nl7ZbJPHntrbx3UhPH+gfOuR9mXrgWCYlIXMSy5DJs6bxW1t96Fa2ZxJxvJvqQOydOD5I7Tb05ZaHmhWuRkIjERawTOgRJ/YXVN/CHdTdz57X5d3AcGHLO5Hbis14X2wZXi4REJC5iW3LJ53//pjd024Ezzpd/8DJf27qXE6cHR8o2uTsjapGQiMRFohL6eOra+WrruYOehVaEiohESexLLtkqWdfWoKeIxE2iEnqhDbwWXPr+gvPWC9Ggp4jETaJKLsXq3cOrPfOtMM2lQU8RiaN4bc515Ahs2gR79sDx4zB5MsydC3ffDdOmhXpE7lF2AM3nGX8yqYljfQMa9BSRSCu2OVc8eug7d8LatfDss8HrkydH723ZAg8+CIsWwZo1MH9+0Udp1oqIJFX0e+iPPQarVkF/P3nX+WecsWCzrW9cfw/fm7eYqS3NPPjZK5WoRSRR4ttDH07mfX1jNj3PnfTAKe7f/h0g2KSr/YevAGPv1yIikgTRneWyc2foZJ6tZTBI6nPe/C0DQ876zn1VClBEJFqim9DXrg3KLOMwcfA09+54GtB8chFpHNFM6EeOBAOg46zvp3Cu/10X7+87rvnkItIwQiV0M7vJzPaZ2X4zW53nvpnZX2fu7zGzj5UV1aZNZb0dwM34d6/+TPPJRaRhjDkoamYp4BHgRuAQsNPMtrr7r7OaLQJmZX5dAzyW+ef47Nlz9tTEcUgPnuaOlneYoQFREWkQYXroVwP73f2Au58GngKW5LRZAvytB3YAU8zsg+OO6vjxcb812ww7VZHniIjEQZiE3gq8kfX6UOZaqW0ws+Vm1mVmXb29Rba6nTw5RFghTJ1ameeIiMRAmISe7yCg3NHKMG1w943u3ububdOKLdWfOxcmTQoRWhHpNMyZU94zRERiJExCPwRMz3p9MXB4HG3Cu+uucb91hHtlniMiEhNhEvpOYJaZzTSzCcBtwNacNluBL2Zmu1wLHHf3N8cd1YUXBnuzWL6OfwhmsHhx6A27RESSYMyE7u6DwH1AJ/Aa8LS77zWzFWa2ItNsG3AA2A/8D+DesiNbsyYom4xHOh28X0SkgYTay8XdtxEk7exrj2f97MCXKhrZ/PmwYUPpy/9bWoL3teXdu0ZEJLGivTnXypXBP0PstohZ0DPfsGH0fSIiDSSaS/+zrVwJzz8Py5YFM19yyzDpdHB92bKgnZK5iDSoaPfQh7W1webN0NsbbAvQ3Q1HjwbzzOfMCWazaABURBpcPBL6sGnToL293lGIiERS3U4sMrNe4J/G+fYLgLcqGE41KMbKUIyVoRjLF5X4PuTueUsSdUvo5TCzrkJHMEWFYqwMxVgZirF8UY8P4jAoKiIioSihi4gkRFwT+sZ6BxCCYqwMxVgZirF8UY8vnjV0ERE5V1x76CIikkMJXUQkISKd0Gt+OHV1YrzCzF40s1NmtqrW8YWM8c7M97fHzH5pZldFMMYlmfhezpx69a+iFF9Wu/lmNmRmt9Yyvsxnj/UdXmdmxzPf4ctm9tWoxZgV58tmttfMno9ajGbWnvUdvpr57/3+WseZl7tH8heQAn4HfBiYALwCzM5psxh4luDEpGuBlyIY44XAfOBhYFVEv8c/A6Zmfl4U0e/xTxgd85kL/CZK8WW1206wM+mtEfwOrwP+vtb/D5YY4xTg18CMzOsLoxZjTvvPAtvr9Z3m/opyD732h1NXIUZ3P+LuO4GBGsaVLUyMv3T3o5mXOwhOnIpajO965ncQ8B7yHHFYz/gy/hLYDBypYWzDwsZYT2FivAPY4u4HIfj9E8EYs90OfL8mkYUQ5YRescOpq6jenx9GqTHeQ/C3nloKe8j4MjP7DfAM8Bc1ig1CxGdmrcAy4HHqI+x/50+Y2Stm9qyZXVmb0EaEifEyYKqZPWdmu8zsizWLLhD694uZtQA3EfwhHglR3pyrYodTV1G9Pz+M0DGa2fUECb2m9WnCHzL+I+BHZvZJ4OvAZ6odWEaY+L4FfMXdh2y8RyeWJ0yMvyLYB+RdM1sMdACzqh7ZqDAxNgEfBz4NpIEXzWyHu79e7eAySvk9/VngBXd/u4rxlCTKCb32h1OXrt6fH0aoGM1sLvAEsMjd/1ij2IaV9D26+8/N7FIzu8Dda7FZUpj42oCnMsn8AmCxmQ26e0cN4oMQMbr7O1k/bzOzR2v4HYaKMdPmLXc/AZwws58DVwG1Suil/L94GxEqtwCRHhRtIjindCajgxNX5rS5mbMHRf9v1GLMavs16jMoGuZ7nEFwHuyfRfi/9Z8yOij6MaBn+HUU4stpv4naD4qG+Q4/kPUdXg0crNV3WEKMHwH+MdO2BXgV+JdRijHTbjLwNvCeWv53HutXZHvo7j5oZsOHU6eAJz1zOHXm/uMEswkWEySjPuDuqMVoZh8AuoD3AWfM7MsEo+bvFHxwjWMEvgqcDzya6WEOeg13lQsZ4y3AF81sAOgHvuCZ31kRia+uQsZ4K7DSzAYJvsPbavUdho3R3V8zs58Ae4AzwBPu/mqUYsw0XQb81IO/SUSGlv6LiCRElGe5iIhICZTQRUQSQgldRCQhlNBFRBJCCV1EJCGU0EVEEkIJXUQkIf4/UzrsMQVZOhMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=k, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "pred_y = kmeans.fit_predict(X_trans)\n",
    "plt.scatter(X_trans.values[:,0], X_trans.values[:,1])\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')\n",
    "plt.show()\n",
    "\n",
    "df_hrss['cluster'] = kmeans.fit_predict(X_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 113 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 113 samples in 0.022s...\n",
      "[t-SNE] Computed conditional probabilities for sample 113 / 113\n",
      "[t-SNE] Mean sigma: 0.692538\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 56.851517\n",
      "[t-SNE] KL divergence after 800 iterations: 0.140663\n",
      "KMeans(n_clusters=4, random_state=2)\n",
      "\n",
      "TSNE figure saved under location: TSNE/Kmeans_phenoMap_tsne_All_vs.html\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from bokeh.palettes import Colorblind8\n",
    "from bokeh.palettes import Spectral10\n",
    "func.makeTSNE_Cluster2(df_hrss[df_hrss.columns[:113]].values,df_all['Id'],  df_hrss['cluster'].astype(str), df_all['Category'],\n",
    "                      title='All_vs', clusters=4, pal=[Colorblind8, Colorblind8], seed=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Spectral8' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-9e15ec9a6bc6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakePCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_hrss\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf_hrss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m113\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_all\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_all\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Category'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'All'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSpectral8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'Spectral8' is not defined"
     ]
    }
   ],
   "source": [
    "func.makePCA(df_hrss[df_hrss.columns[:113]].values, df_all['Id'], df_all['Category'], title='All', pal=Spectral8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1301e42f710>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#X = df_actg1.loc[:, cols].values # df_actg1.loc[:, cols].values # \n",
    "#X_trans = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "d = {'cat': df_all['Category'], 'cluster': df_hrss['cluster']}\n",
    "df_bar = pd.DataFrame(data=d)\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "df_bar.groupby(['cluster', 'cat']).size().unstack().plot(ax=ax, kind = 'bar') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = pd.concat([df_all[['Category', 'Id', 'Origin']], df_hrss], axis=1)\n",
    "#sub_df = sub_df.sort_values('cluster')\n",
    "#sub_df = sub_df[list(sub_df.loc[:, ~sub_df.columns.isin(['cluster', 'Origin'])].columns)]\n",
    "#func.makeDistanceHeatmap(sub_df, col_id='Id', col_label='Category', dist='HRSS', title='All')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore clusters made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "explore_df =  pd.concat([df_all, df_hrss['cluster']], axis=1)\n",
    "l_ignore = ['Category', 'Id', 'Protein', 'Origin'] # , 'cluster'\n",
    "columns = list(explore_df.loc[:, ~explore_df.columns.isin(l_ignore)])\n",
    "#explore_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Order phenotypes by occurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import importlib as imp\n",
    "\n",
    "imp.reload(func)\n",
    "\n",
    "func.visualizeMostOccuringFeatures(explore_df[columns], cluster=0, cutoff=0.05)\n",
    "\n",
    "#plt.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Order by information content\n",
    "- maybe also consider level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from phenopy.ic import calculate_information_content\n",
    "#from phenopy.d2p import load as load_d2p\n",
    "\n",
    "#hpo_network = calculateIC(hpo_network, len(disease_records))\n",
    "func.visualizeIC_Features(explore_df[columns], l_ic_weight, l_depth_weight, cluster=2, cutoff=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Order by phenotypic specificity\n",
    "\n",
    "#### load HPO_to_Genes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>#Format: HPO-id</th>\n",
       "      <th>HPO label</th>\n",
       "      <th>entrez-gene-id</th>\n",
       "      <th>entrez-gene-symbol</th>\n",
       "      <th>Additional Info from G-D source</th>\n",
       "      <th>G-D source</th>\n",
       "      <th>disease-ID for link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>26229</td>\n",
       "      <td>B3GAT3</td>\n",
       "      <td>-</td>\n",
       "      <td>mim2gene</td>\n",
       "      <td>OMIM:245600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>2892</td>\n",
       "      <td>GRIA3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orphadata</td>\n",
       "      <td>ORPHA:364028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>64374</td>\n",
       "      <td>SIL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orphadata</td>\n",
       "      <td>ORPHA:559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>10588</td>\n",
       "      <td>MTHFS</td>\n",
       "      <td>-</td>\n",
       "      <td>mim2gene</td>\n",
       "      <td>OMIM:618367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>51633</td>\n",
       "      <td>OTUD6B</td>\n",
       "      <td>-</td>\n",
       "      <td>mim2gene</td>\n",
       "      <td>OMIM:617452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #Format: HPO-id                   HPO label  entrez-gene-id  \\\n",
       "0      HP:0000002  Abnormality of body height           26229   \n",
       "1      HP:0000002  Abnormality of body height            2892   \n",
       "2      HP:0000002  Abnormality of body height           64374   \n",
       "3      HP:0000002  Abnormality of body height           10588   \n",
       "4      HP:0000002  Abnormality of body height           51633   \n",
       "\n",
       "  entrez-gene-symbol Additional Info from G-D source G-D source  \\\n",
       "0             B3GAT3                               -   mim2gene   \n",
       "1              GRIA3                             NaN  orphadata   \n",
       "2               SIL1                             NaN  orphadata   \n",
       "3              MTHFS                               -   mim2gene   \n",
       "4             OTUD6B                               -   mim2gene   \n",
       "\n",
       "  disease-ID for link  \n",
       "0         OMIM:245600  \n",
       "1        ORPHA:364028  \n",
       "2           ORPHA:559  \n",
       "3         OMIM:618367  \n",
       "4         OMIM:617452  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_hpo = pd.read_table(r'hpo/util/annotation/phenotype_to_genes.txt', sep='\\t|<tab>', engine='python') # phenotype_to_genes.txt\n",
    "df_hpo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate weights for phenotypic specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import columns_linked_to_hpo_actin\n",
    "l_cols = df_all.columns\n",
    "\n",
    "weight_list = func.calculatePhenotypicSpecificity(df_all.columns[3:-1], col2hpo=columns_linked_to_hpo_actin.column_to_hpo, df_hpo=df_hpo)\n",
    "#print(coef_list)\n",
    "#if (col in column_to_hpo.keys()) == False :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw probabilities:\n",
      "  Motor Delay            0.050093\n",
      "Recurrent infections    0.079545\n",
      "  ID                    0.148325\n",
      "Short stature           0.201754\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.visualizePhenotypicSpecificity(explore_df[columns], weights=weight_list, cluster=0, cutoff=0.05, pat=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokehheat import heat\n",
    "\n",
    "# generate test data\n",
    "ls_sample = df_all['Id']\n",
    "ls_variable = df_all['Id']\n",
    "\n",
    "\n",
    "df_matrix = pd.DataFrame(df_hrss[df_hrss.columns[:113]].values)\n",
    "df_matrix.index = ls_variable\n",
    "df_matrix.columns = ls_sample\n",
    "df_matrix.index.name = 'y'\n",
    "df_matrix.columns.name = 'x'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113, 113, 113)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from bokeh.palettes import Spectral8\n",
    "from bokeh.palettes import viridis\n",
    "#col_list2= viridis(len(list(df_all['cluster'].unique())))\n",
    "\n",
    "colors = ['Cyan','Green', 'Red','Yellow', 'Silver','Magenta','Orange', 'ForestGreen', 'Darkblue', 'Lime', 'Gray', 'Gold']\n",
    "#color_list = [col_list2[list(df_all['cluster'].unique()).index(i)] for i in df_all['Origin']]\n",
    "color_list2 = [colors[list(df_all['Category'].unique()).index(i)] for i in df_all['Category']]\n",
    "color_list3 = [['Pink', 'Purple', 'Blue'][list(df_all['Origin'].unique()).index(i)] for i in df_all['Origin']]\n",
    "\n",
    "len(df_all['Origin']), len(df_all['Category']), len(df_all['Id'])\n",
    "#color_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate some gene annotation\n",
    "df_variable = pd.DataFrame({\n",
    "    'y': ls_variable,\n",
    "    'category': list(df_all['Category']),\n",
    "    'origin' : list(df_all['Origin']),\n",
    "    'category_color':color_list2,\n",
    "    'origin_color': color_list3,\n",
    "})\n",
    "df_variable.index = df_variable.y\n",
    "\n",
    "# generate some sample annotation\n",
    "df_sample = pd.DataFrame({\n",
    "    'x': ls_sample,\n",
    "    'category': list(df_all['Category']),\n",
    "    'origin' : list(df_all['Origin']),\n",
    "    'category_color':color_list2,\n",
    "    'origin_color': color_list3,\n",
    "})\n",
    "df_sample.index = df_sample.x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ds_xcolor: {'Cyan': {'Gain Of Function'}, 'Green': {'Loss of function'}, 'Red': {'N-terminus'}, 'Yellow': {'Other'}, 'Silver': {'Other/Fimbrin binding'}, 'Magenta': {'Other/Myosin binding'}, 'Orange': {'NAT6'}, 'Pink': {'ACTG1'}, 'Purple': {'NAT6'}, 'Blue': {'ACTB'}}\n",
      "ds_ycolor: {'Cyan': {'Gain Of Function'}, 'Green': {'Loss of function'}, 'Red': {'N-terminus'}, 'Yellow': {'Other'}, 'Silver': {'Other/Fimbrin binding'}, 'Magenta': {'Other/Myosin binding'}, 'Orange': {'NAT6'}, 'Pink': {'ACTG1'}, 'Purple': {'NAT6'}, 'Blue': {'ACTB'}}\n"
     ]
    }
   ],
   "source": [
    "#t_yboole = (df_variable,['geneboole'],'Red','Maroon') # True, False\n",
    "#t_ycat = (df_variable, ['cluster'], ['cluster_color'])\n",
    "t_zcat = (df_variable, ['origin'], ['origin_color'])\n",
    "#t_yquant = (df_variable, ['genereal'], [0], [k], [Colorblind8][::-1])\n",
    "#t_xboole = (df_sample,['sampleboole'],'Red','Maroon') # True, False\n",
    "t_xcat = (df_sample, ['category'], ['category_color'])\n",
    "#t_xquant = (df_sample, ['age_year'], [0], [k], [YlGn8][::-1])\n",
    "tt_boolecatquant = (t_xcat, t_zcat) # (t_ycat, t_yquant, t_xcat, t_xquant)\n",
    "\n",
    "s_file = \"clusterheatmap/heatmap_%s_All.html\" % ('semanticHRSS')  # or \"theclustermap.png\"\n",
    "o_clustermap, ls_xaxis, ls_yaxis = heat.clustermap(\n",
    "    df_matrix = df_matrix,\n",
    "    ls_color_palette = 'Magma256',\n",
    "    r_low = 0,\n",
    "    r_high = df_matrix.values.max(),\n",
    "    s_z = \"log2\",\n",
    "    tt_axis_annot = tt_boolecatquant,\n",
    "    b_ydendo = True,\n",
    "    b_xdendo = True,\n",
    "    s_method= 'average',\n",
    "    #s_metric= 'distance',\n",
    "    #s_method= 'ward', #'average',\n",
    "    s_metric= 'euclidean', #distance,\n",
    "    b_optimal_ordering=True,\n",
    "    #i_px = 64,\n",
    "    #i_height = 12,\n",
    "    #i_width = 12,\n",
    "    #i_min_border_px = 128,\n",
    "    s_filename=s_file,\n",
    "    s_filetitel=\"the Clustermap\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Unused function] Import previous HPO translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_pat = []\n",
    "hp_patients = []\n",
    "with open('data/deepdata/actg_patpheno.csv', 'r') as f:\n",
    "    for line in f:\n",
    "        txt = line.split(',')\n",
    "        txt[-1] = txt[-1].strip()\n",
    "        l_pat.append(txt[0])\n",
    "        hp_patients.append(txt[1:])\n",
    "        #hp_patients.append()\n",
    "        #f.write(str(pat) + ',' + ','.join(hp_patients[cnt]) + '\\n')\n",
    "        #cnt +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Pathophysiology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import HPOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of HPO codes: 199197\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#DatabaseID</th>\n",
       "      <th>DiseaseName</th>\n",
       "      <th>Qualifier</th>\n",
       "      <th>HPO_ID</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Evidence</th>\n",
       "      <th>Onset</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Modifier</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Biocuration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OMIM:210100</td>\n",
       "      <td>BETA-AMINOISOBUTYRIC ACID, URINARY EXCRETION OF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HP:0000007</td>\n",
       "      <td>OMIM:210100</td>\n",
       "      <td>IEA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I</td>\n",
       "      <td>HPO:iea[2009-02-17]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OMIM:210100</td>\n",
       "      <td>BETA-AMINOISOBUTYRIC ACID, URINARY EXCRETION OF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HP:0003355</td>\n",
       "      <td>OMIM:210100</td>\n",
       "      <td>IEA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>HPO:skoehler[2009-02-17]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OMIM:163600</td>\n",
       "      <td>NIPPLES INVERTED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HP:0000006</td>\n",
       "      <td>OMIM:163600</td>\n",
       "      <td>IEA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I</td>\n",
       "      <td>HPO:iea[2009-02-17]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OMIM:163600</td>\n",
       "      <td>NIPPLES INVERTED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HP:0003186</td>\n",
       "      <td>OMIM:163600</td>\n",
       "      <td>IEA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>HPO:iea[2009-02-17]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OMIM:615763</td>\n",
       "      <td>#615763 CORTICAL DYSPLASIA, COMPLEX, WITH OTHE...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HP:0002365</td>\n",
       "      <td>OMIM:615763</td>\n",
       "      <td>TAS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HP:0040283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>HPO:skoehler[2014-08-24]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #DatabaseID                                        DiseaseName Qualifier  \\\n",
       "0  OMIM:210100    BETA-AMINOISOBUTYRIC ACID, URINARY EXCRETION OF       NaN   \n",
       "1  OMIM:210100    BETA-AMINOISOBUTYRIC ACID, URINARY EXCRETION OF       NaN   \n",
       "2  OMIM:163600                                   NIPPLES INVERTED       NaN   \n",
       "3  OMIM:163600                                   NIPPLES INVERTED       NaN   \n",
       "4  OMIM:615763  #615763 CORTICAL DYSPLASIA, COMPLEX, WITH OTHE...       NaN   \n",
       "\n",
       "       HPO_ID    Reference Evidence Onset   Frequency  Sex Modifier Aspect  \\\n",
       "0  HP:0000007  OMIM:210100      IEA   NaN         NaN  NaN      NaN      I   \n",
       "1  HP:0003355  OMIM:210100      IEA   NaN         NaN  NaN      NaN      P   \n",
       "2  HP:0000006  OMIM:163600      IEA   NaN         NaN  NaN      NaN      I   \n",
       "3  HP:0003186  OMIM:163600      IEA   NaN         NaN  NaN      NaN      P   \n",
       "4  HP:0002365  OMIM:615763      TAS   NaN  HP:0040283  NaN      NaN      P   \n",
       "\n",
       "                Biocuration  \n",
       "0       HPO:iea[2009-02-17]  \n",
       "1  HPO:skoehler[2009-02-17]  \n",
       "2       HPO:iea[2009-02-17]  \n",
       "3       HPO:iea[2009-02-17]  \n",
       "4  HPO:skoehler[2014-08-24]  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hpoa = pd.read_table(r'hpo/phenotype.hpoa', sep='\\t', header=4, encoding = \"ISO-8859-1\")\n",
    "print('Nr of HPO codes:', len(df_hpoa))\n",
    "df_hpoa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_assoc_genes(hpo_id, df_hpo, df_hpoa):\n",
    "    \"\"\"\n",
    "    Get genes that correspond to the provided phenotype (HPO)\n",
    "    \"\"\"\n",
    "    output_genes = list(df_hpo[df_hpo['#Format: HPO-id']==hpo_id]['entrez-gene-symbol'].unique())\n",
    "    l_diseases = list(df_hpo[df_hpo['#Format: HPO-id']==hpo_id]['disease-ID for link'].unique())\n",
    "    output_diseases = list(df_hpoa[df_hpoa['Reference'].isin(l_diseases)]['DiseaseName'].unique())\n",
    "    #output_genes = list(df_hpo[df_hpo['Reference'].isin(l_diseases)]['entrez-gene-symbol'].unique())\n",
    "    return output_diseases, output_genes\n",
    "\n",
    "\n",
    "col = 'hypertelorism' # 'congenital ptosis'\n",
    "hpo = columns_linked_to_hpo.column_to_hpo[col]\n",
    "output_diseases, output_genes = get_assoc_genes(hpo, df_hpo, df_hpoa)\n",
    "#print(col, output_diseases, output_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93\n",
      "93\n"
     ]
    }
   ],
   "source": [
    "#weight_list\n",
    "print(len(df_all.columns[3:-1]))\n",
    "print(len(weight_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>#Format: HPO-id</th>\n",
       "      <th>HPO label</th>\n",
       "      <th>entrez-gene-id</th>\n",
       "      <th>entrez-gene-symbol</th>\n",
       "      <th>Additional Info from G-D source</th>\n",
       "      <th>G-D source</th>\n",
       "      <th>disease-ID for link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>26229</td>\n",
       "      <td>B3GAT3</td>\n",
       "      <td>-</td>\n",
       "      <td>mim2gene</td>\n",
       "      <td>OMIM:245600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>2892</td>\n",
       "      <td>GRIA3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orphadata</td>\n",
       "      <td>ORPHA:364028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>64374</td>\n",
       "      <td>SIL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orphadata</td>\n",
       "      <td>ORPHA:559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>10588</td>\n",
       "      <td>MTHFS</td>\n",
       "      <td>-</td>\n",
       "      <td>mim2gene</td>\n",
       "      <td>OMIM:618367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP:0000002</td>\n",
       "      <td>Abnormality of body height</td>\n",
       "      <td>51633</td>\n",
       "      <td>OTUD6B</td>\n",
       "      <td>-</td>\n",
       "      <td>mim2gene</td>\n",
       "      <td>OMIM:617452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  #Format: HPO-id                   HPO label  entrez-gene-id  \\\n",
       "0      HP:0000002  Abnormality of body height           26229   \n",
       "1      HP:0000002  Abnormality of body height            2892   \n",
       "2      HP:0000002  Abnormality of body height           64374   \n",
       "3      HP:0000002  Abnormality of body height           10588   \n",
       "4      HP:0000002  Abnormality of body height           51633   \n",
       "\n",
       "  entrez-gene-symbol Additional Info from G-D source G-D source  \\\n",
       "0             B3GAT3                               -   mim2gene   \n",
       "1              GRIA3                             NaN  orphadata   \n",
       "2               SIL1                             NaN  orphadata   \n",
       "3              MTHFS                               -   mim2gene   \n",
       "4             OTUD6B                               -   mim2gene   \n",
       "\n",
       "  disease-ID for link  \n",
       "0         OMIM:245600  \n",
       "1        ORPHA:364028  \n",
       "2           ORPHA:559  \n",
       "3         OMIM:618367  \n",
       "4         OMIM:617452  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hpo.head() ['disease-ID for link']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Category</th>\n",
       "      <th>Id</th>\n",
       "      <th>Protein</th>\n",
       "      <th>High frequency hearing loss</th>\n",
       "      <th>Short stature</th>\n",
       "      <th>microcephaly</th>\n",
       "      <th>hearing loss</th>\n",
       "      <th>ID</th>\n",
       "      <th>Developmental delay (Global)</th>\n",
       "      <th>Motor Delay</th>\n",
       "      <th>...</th>\n",
       "      <th>Photosensitivity</th>\n",
       "      <th>Polyarthralgia</th>\n",
       "      <th>Hepatomegaly</th>\n",
       "      <th>Hypothyroidism</th>\n",
       "      <th>Leukocytosis</th>\n",
       "      <th>thrombocytopenia</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>Strabismus</th>\n",
       "      <th>Growth hormone deficiency</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gain Of Function</td>\n",
       "      <td>LR04-298</td>\n",
       "      <td>p.Ser155Phe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ACTG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gain Of Function</td>\n",
       "      <td>LP98-096</td>\n",
       "      <td>p.Ser155Phe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ACTG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gain Of Function</td>\n",
       "      <td>11-10857</td>\n",
       "      <td>p.Ser155Phe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ACTG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Loss of function</td>\n",
       "      <td>LR11 334</td>\n",
       "      <td>Deletion 17q25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004785</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>0.015385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ACTG1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N-terminus</td>\n",
       "      <td>LR16?163</td>\n",
       "      <td>p.Asn12Asp</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ACTG1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category        Id           Protein  High frequency hearing loss  \\\n",
       "0  Gain Of Function  LR04-298       p.Ser155Phe                          0.0   \n",
       "1  Gain Of Function  LP98-096       p.Ser155Phe                          0.0   \n",
       "2  Gain Of Function  11-10857       p.Ser155Phe                          0.0   \n",
       "3  Loss of function  LR11 334  Deletion 17q25.3                          0.0   \n",
       "4        N-terminus  LR16?163        p.Asn12Asp                          0.0   \n",
       "\n",
       "   Short stature   microcephaly   hearing loss        ID  \\\n",
       "0       0.000000       0.000000            0.0  0.000000   \n",
       "1       0.142857       0.008772            0.0  0.000779   \n",
       "2       0.000000       0.000000            0.0  0.000000   \n",
       "3       0.142857       0.000000            0.0  0.000000   \n",
       "4       0.000000       0.000000            0.0  0.000779   \n",
       "\n",
       "   Developmental delay (Global)   Motor Delay  ...  Photosensitivity  \\\n",
       "0                      0.000000           0.0  ...               0.0   \n",
       "1                      0.000000           0.0  ...               0.0   \n",
       "2                      0.000000           0.0  ...               0.0   \n",
       "3                      0.004785           0.0  ...               0.0   \n",
       "4                      0.000000           0.0  ...               0.0   \n",
       "\n",
       "   Polyarthralgia  Hepatomegaly  Hypothyroidism  Leukocytosis  \\\n",
       "0             0.0           0.0             0.0           0.0   \n",
       "1             0.0           0.0             0.0           0.0   \n",
       "2             0.0           0.0             0.0           0.0   \n",
       "3             0.0           0.0             0.0           0.0   \n",
       "4             0.0           0.0             0.0           0.0   \n",
       "\n",
       "   thrombocytopenia  Asthma  Strabismus  Growth hormone deficiency  Origin  \n",
       "0               0.0     0.0    0.000000                        0.0   ACTG1  \n",
       "1               0.0     0.0    0.000000                        0.0   ACTG1  \n",
       "2               0.0     0.0    0.000000                        0.0   ACTG1  \n",
       "3               0.0     0.0    0.015385                        0.0   ACTG1  \n",
       "4               0.0     0.0    0.000000                        0.0   ACTG1  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt = 0\n",
    "df_occ = df_all.copy()\n",
    "for column in df_all.columns[3:-1]:\n",
    "    df_occ[column] = df_all[column] *coef_list[cnt] \n",
    "    cnt += 1\n",
    "df_occ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist\n",
    "from scipy.spatial.distance import squareform\n",
    "\n",
    "cols = list(df_occ.loc[:, ~df_occ.columns.isin(['Category', 'Id', 'Protein', 'Origin'])].columns)\n",
    "\n",
    "bool_dist = ['dice', 'hamming', 'jaccard',  'kulsinski', 'rogerstanimoto', 'russellrao', 'sokalmichener', 'sokalsneath', 'yule']\n",
    "discrete_dist = ['braycurtis', 'canberra', 'chebyshev', 'cityblock','correlation', 'cosine','euclidean', 'jensenshannon', 'mahalanobis', 'minkowski', 'seuclidean', 'sqeuclidean',  'wminkowski', 'hamming']\n",
    "\n",
    "distance = bool_dist[0]\n",
    "\n",
    "df = df_occ.copy()\n",
    "df = df.sample(frac=1)\n",
    "pairwise = pd.DataFrame(\n",
    "    squareform(pdist(df[cols], metric=distance)),\n",
    "    columns = df['Id'],\n",
    "    index = df['Id']\n",
    ")\n",
    "\n",
    "X_trans = pairwise.values \n",
    "#X_trans = df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of clusters = 4\n"
     ]
    }
   ],
   "source": [
    "k = func.elbowMethod(X_trans, method='kmeans', n=30)\n",
    "print('Nr of clusters = %s' % (k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#func.makeTSNE_Cluster(X_trans, df_occ['Id'], df_occ['Category'], title='All_GeneOccurrance_rogers', clusters=6, pal=Colorblind8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAdfElEQVR4nO3df5BV5Z3n8ffXBuSSuKBDOxVbW1jKMO5s4zhzEXbZ2SFmLYQxK2o24w+S0nXXkkmyW5uBkk5ZSazBQKIzZbZioIjrMKkwWqlA9bDRhM0sScyayHYzJHbUMUE02G1qaccfmUiLdPvdP07f5vbt++Pc2+fe8+N+XlUU9Lmn73me2833Pvd7vs/zmLsjIiLpd1bcDRARkWgooIuIZIQCuohIRiigi4hkhAK6iEhGzIrrwgsXLvRFixbFdXkRkVQ6fPjwq+7eWe6x2AL6okWLGBgYiOvyIiKpZGa/rPSYUi4iIhmhgC4ikhEK6CIiGaGALiKSEQroIiIZUbPKxcweBq4BTrj7vyzzuAFfAtYBJ4Fb3f3vo26oiEja3d03yCOHXmbcnQ4zblpxEVvX90T2/GFG6LuBq6s8vha4ZOLPHcCOmTdLRCRb7u4b5OtPHWd8YoXbcXe+/tRx7u4bjOwaNQO6uz8BvFbllGuBr3ngKWCBmb0vqgaKiGTBI4derut4I6LIoXcBxS0amjg2jZndYWYDZjYwMjISwaVFRNJhvMLeE5WONyKKgG5ljpVtobvvcve8u+c7O8vOXBURyaQOKxcqKx9vRBQBfQi4qOjrC4FXInheEZHMuGnFRXUdb0QUAX0/8DELrATedPdfRfC8IiKZsXV9DxtWdk+OyDvM2LCyO9IqF6u1p6iZPQKsBhYC/w/4LDAbwN13TpQtfpmgEuYkcJu711x1K5/PuxbnEhGpj5kddvd8ucdq1qG7+001Hnfg4w22TUREIqKZoiIiGaGALiKSEQroIiIZoYAuIpIRCugiIhmhgC4ikhEK6CIiGaGALiKSEQroIiIZoYAuIpIRCugiIhmhgC4ikhE1F+cSEWmGviPD3HfgeV55Y5QLFuTYvGYp6y8vu9mZhKSALiJNU2mX+74jw/TuG2T09DgAw2+M0rsv2CxZQb1xCugi0rBqo+zCLvcFhV3uAb73DyOTwbxg9PQ49x14XgF9BhTQRaQhtUbZ1Xa5f7fCxjqvvDHanMa2CQV0EZmiUpqk1H0Hnq86yq62y33XghzDZYL3BQty0XSiTanKRUQmFdIkhWBcSJPc3Tc47dxKo+nC8Wq73G9es5Tc7I4px3OzO9i8ZulMmt/2FNBFMqjvyDCrth9k8ZbHWLX9IH1HhkN9X7U0SalKo+nC8Wq73K+/vItt1/fQtSCHAV0Lcmy7vkf58xlSyiXhwn78FSmYSQVJtTRJqc1rlk65DkwdZRd+Tyv9/q6/vCuWAJ7lckkF9ASrViWgoC6V1MptV9NhVjZ4l0ufFJ6rWnDcur4nUb+rWS+XVEBPsGoff5P0n0SSpVZuu5qbVlw0ZRBRfLycuEbZjZrJm10aKIeeYPV8/BUpqJXbrmbr+h42rOyeHJF3mLFhZXdmBhAzebNLA43QE6yej78iBbVy27UkLU0SpQsyXi6pEXqCVasSEKlEFSSVZb1cUiP0BKtVJSBSSdpy260S5kZumpnHlI/N5/M+MDAQy7VFJBuyXIJYiZkddvd8ucc0QheRVMp6CWIjlEMXkVSqVoLYrhTQRSSVsl6C2AgFdBFJpZnU22dVqBy6mV0NfAnoAB5y9+0lj88Hvg50Tzzn/e7+VxG3VUTI9o3AetYummm9fRbVDOhm1gE8CFwFDAH9Zrbf3Z8tOu3jwLPu/iEz6wSeN7M97v5OU1ot0qayfCOw3rWLsl6C2IgwI/QrgKPufgzAzB4FrgWKA7oD55iZAe8FXgPGIm6rSNvL8lokjaxdpHr7qcIE9C6g+JUeAlaUnPNlYD/wCnAO8Cfu/m7pE5nZHcAdAN3d3Y20V6StZflGYKU1ihb85nX44hfh6afhzTdh/nxYtgxuuw06O1vcymQLE9DLLRxS+sqvAX4CXAksAb5rZj90919P+Sb3XcAuCCYW1d9ckfaW5bVIStcuWvarn/OnP/4Gq48dhtkd8PbbZ07etw8++1lYuxZ6e2H58hhaXL9m3/8IU+UyBBQvHnIhwUi82G3APg8cBV4EfieaJopIQZbXIileo+iWI4/z6N/0ctUvDjF3/PTUYA4wOhoc6+uD1athx47WNrYBhfsfw2+M4py5/xF2N6kwwozQ+4FLzGwxMAzcCNxccs5x4IPAD83st4GlwLHIWikiQLZvBBby5LZjJ70H/wfzxk7V/iZ3OHkSNm0Kvt64ccrDSdrxqxX3P2oGdHcfM7NPAAcIyhYfdvdnzOzOicd3An8O7DazQYIUzV3u/mokLWzUiROwe7fybpI5Wb4RuLXrbfg/uyFMMC9WCOrLl0M+WOYkaTt+teL+R/YW5+rvh23b4NvfDr4u/qiWywXv6CnLu4m0jeuvD9IojcQlM7juOti7F4AlvY9X3E/ghW3rZtrSuq3afrDs/Y+uBTme3HJl6OeptjhXtmaK7tgR5NP6+oJAnoG8m0jbOHEiGIg1Osh0h8cfh5ERIHk7frXi/kd2AvqOHcFHrpMna/9CFOfdFNRFkmH37pk/h9nk81Ta2SuuHb9asfFINpbP7e8/E8zrUSbvJiIxefrp6Z+q6zU6CoPBzNl6N7xuhWbf/8jGCH3btuAH2YjR0eD7RSReb74ZzfO8/jqQ/Q2vy0nVCH3RlsemHXvpU8ujy7up+kUkPvPnR/M85547+c9qG15ncZGz1IzQywVzgM9/5K6ZP3lR3k1E6nDiRDAtf8MG+NCHgr+/+MXJG5N1WbYM5s6dWXtyOeipPQJvxSSfOKQmoFdy6chLkebdRCSE/v6gxPDii4Mp+Hv2wLe+Ffz9uc9Bd3fweH9/+Oe89daZt8s91PNkdbej1Af0c069Fc0TTeTdRKSGZpUHn39+MEek0SoUM1i3LlTqNKuLnKUqh17OP539nmieqCjvliRZzPMlWVyvd9TXbVo/isuDa6kxLb+s3l44cKD+ijUI0i29vaFOzeoiZ6kfoT/XuahlebdWy2qeL6nier2jvm7T+jHT8uAwM8OXL4f774d58+q7xrx5wfeFLD/O6iJnqQnoL23/47LHP/2NL8z8yUPm3Votq3m+pIrr9Y76uk3rR6vKgzdunAzq47XSL2ZngnmVTwB9R4ZZtf0gi7c8xqrtBwGaPsknDqlKuVQK6qxdO7P1H0Lm3Votq3m+pIrr9a73urXSKU3pR5TT8sP8X9u4EZYv53/d8l/4wAsDuBm5sTM7Wo7OmkNu1lnB/93e3qoj80rb9m27vqeuNVTSIFUBvaIW5d1aLat5vqSK6/Wu57ph9hRtSj+inJa/eXO48/N5Pn3L57BXX+WGwb/j0pGX+Gdvv8Wv576HX16whP+25/Oh3hyyvG1fqdSkXKpqUd6t1bKa50uquF7veq4bJp3SlH5EPC0/LHd4bd58vrriBj51zZ/xnz78GT51zZ/x16v+Q+hP1e30STcbI3Q4kz/btCn4xan20dAsGJnXyLvFLcubGSRRXK93PdcNE5ya0o+Ip+VDuEqcN0dPl29OhePltNMn3ewEdJjMu7FtW5CvM5t6E6ewHnqIvFtSZHkzgySK6/UOe92wwSnyfkQ8LT9M6giiCcab1yydci0484kla2XB2Ui5FMvngwXujx+He+6Bj34Urrkm+Puee4Lje/emIpiLlIotDRfxtPywlThR9LfSsrVA5sqCszVCL9bZGf7mi0hKxJaGu/XWYIr/DLz9zhh/ef4VfJrwee16+ltttF3uE8uq7Qczd7M0uwFdJKNiSQsVpuU3WB48jvG9JXl2PfcbTvYN1pVKCdPfsCmcYlm8WZq9lIuINEdvb5A2acCpWXP4ysqPAPDIoZcjTx01MpmqUh4+zTdLNUIXkSnu7hvkkUMvM+5Ohxk3rbgoWFO8UB5c5/T/k7PO5t4rb2fwfZcAwZ6eUaWObvnqj3nyhdcqPl5ttF3tZmlaKaCLyKS7+wanbNs27j759db1PXWVB49jnJo1h3uvvJ09l6+bPF7YQWimqaNawRyqj7azWBasgC4ikx459HLF45M7/0yUBz/zyS0s6X9i2rT803POZvZZxj/83iq2vP+ayZF5QVR7etYK5uVG2+U+fWRp+r8CukgbqlQRMl5hxD3teD7Pv1/9KeYv/4/TpuU/37mI3m98gd/t7OSyvkGeLZe+iaDt1XSVGW1X+/SRv/i8TIzUFdBF2ky1ipAOs7JBvaPMqofj7pPT8kv1TkzLr7anZxRtr6R01N13ZHhKMC+259Bx9h4erqtCJqlU5SLSZqpVhFRKh5Q7Xi7IVzsehXJtL7VqyXlTvi68CVTiTmaWqVZAF2kz1eqvt67vYcPK7smg3GHGhpXdZUfZ9QT/qNSqEV+15Dz2/Od/NeVYmDeBRq6VREq5iBTJ2toe5dSa1BM2TVI4p2yJY4Su+svv84sT1fcO7lqQq3hzs1Zgnjf7LE6efnfa8TTWoyugi0xoZLZhGkVZfx11jrxUmGA++yyr2vZKb2AAG1Z2k7/4vMzUoyvlIjKhXbb8q7RYVRLftGoFc4D3zp1Vte2VZqU+8Ce/x9b1Pal6PWoJNUI3s6uBLwEdwEPuvr3MOauBB4DZwKvu/kcRtlOk6dKytkcUaaGkLstc2rcw3jhZfW30MBOIkvp61KtmQDezDuBB4CpgCOg3s/3u/mzROQuArwBXu/txMzu/WQ0WaZY0bISQ5bRQub6FEebnk5WAXUuYlMsVwFF3P+bu7wCPAteWnHMzsM/djwO4+4lomynSfGnY8i/LaaFGq1HeOjWW6jXMoxQmoHcBxfOBhyaOFXs/cK6Zfd/MDpvZx8o9kZndYWYDZjYwMjLSWItFmiQNudS0pIUaEbYPpVXub4yeTv3GFFEJk0MvN0ugdCrZLOAPgA8COeDHZvaUu/98yje57wJ2AeTz+foXVRZpsqR/NI8zLdTsks5KfSstSVy1/eC089K+MUVUwozQh4DimQIXAq+UOec77v6Wu78KPAFcFk0TRaQgrrRQIb/dzO3awvYty59SZipMQO8HLjGzxWY2B7gR2F9yzt8Cf2hms8xsHrACeC7apopIXGmhVuTuw/YtixtTRKVmysXdx8zsE8ABgrLFh939GTO7c+Lxne7+nJl9B3gaeJegtPFnzWy4SLtqdlqo3BKzrRoVh+lbFjemiIp5A/sDRiGfz/vAwEAs1xaR8kqXmC3IzT6L0TLT46tNuW+mdliioRIzO+zu+XKPaeq/iEyqtMHFqbF3yc3uSMyoOOk3r+Oiqf8iMqnSBhfvOokv6RSN0EWkSLUNLjQqnrlmp4o0QheRSXGscd4uWlH6qYAuIpPq2eBC6tOK0k+lXERkimavcd6otFe2tKL0UwFdRBLt7r5B9hw6TnFqP42rTLZi2QalXEQksQp18eWKb9K2ymQrlm3QCF1EEqtSXXxBmtZvCbPRxkwpoItIYlWqiy9I2/otzS79VMpFRBKrUG1TjtZvmU4jdBFJrJtWXFR2bZl5s8/i8w3MVC238FgSK3oapYAuIolVCLZRBOHShcfG3Se/zkpQ12qLItIWlvQ+XnFZgxe2rYuhRY2pttqicugi0hYq3WCtdeM1TZRyEZHUCpsTr7ZeSrUbr2mjgC6SAGmf1t6IQp9PDf+K247+gA/Zq3R3nIb582HZMrjtNujsrPj9YXPihUWxKmnlwmPN/jkrhy4Ss0LAKd08IsvrjfcdGWbPl7/J7T98lNXHDuNm5MbeOXNCLgfusHYt9PbC8uXTniNsTnzV9oNlp9wDLV14LKqfs3LoIgnWilX4kuYX99zHX3/tLq76xSHmjp+eGswBRkfh7behrw9Wr4YdO6Y9R9iceKXZpEZrq1ta8XNWQBeJWas2YE6MHTv4+GM7mTd2ig5qZAjc4eRJ2LRpWlCvlPsuPV5pNmmrZ5m24uesgC4Ss6QEnJbo74dNm5g3dqq+7ysE9YEB+o4Ms2r7wYoj9NKceCsWxQqjFT9nBXSRmCUl4LTEtm1BOqURo6MM3/WZyV1/SlXajGP95V2J2A9Vqy1KKrRjhUaUWrEKX1yKywrPP/kmT37rMWY3WojhzsIn/je5393A6Lz5Ux7qWpDjyS1XVvzWJOyHqtUWJfFK79ynceOBJEhCwIlaaVnh+qe/y5jD7Bk857vADYN/x1dX3DDleFruN2i1RUm0dqzQkHBK1zK/dOSl6dUsdcqNvcOlIy9NO57J+w0NUECXGWm7Cg0JrfSm5Tmn3orkeeeXPE9m7zc0QAFdZqStKjSkLqXlg/909nsied5fz30P586bHesNzqRSQJcZaasKDalLafngc52LGJ01Z0bPOTprDs8uXMS8ObN4cfsf8+SWKxXMiyigy4wkpSRMkmfr+h42rOyeHKn3LbuKWTNcB8vc2dvz75TSq0BVLjJjWazQkHBqlaxuXd8ztS586JvBdP4GShfHMb63JM9r8+bTpZReWRqhi0hDCiWrw2+M4pwpWa22VC29vcHCWw04NWsOX1n5EaX0qggV0M3sajN73syOmtmWKuctN7NxM/twdE0UkSRqqGR1+XK4/36YN6+ua43OPpt7r7yd1y5dppReFTVTLmbWATwIXAUMAf1mtt/dny1z3heAA81oqIgkS8Mlqxs3Bn9v2hQsA1At/WIGuRy5++/n3sL3SUVhRuhXAEfd/Zi7vwM8Clxb5rxPAnuBExG2T0QSakYlqxs3wg9+ANddB3PnTk/D5HLB8euuC85TMA8lzE3RLqB4ytcQsKL4BDPrAq4DrgSmr0R/5rw7gDsAuru7622riCTI5jVLy27YEDq/nc/D3r0wMgK7d8PgILz+Opx7LvT0wK23Vt2xSKYLE9DLFRqVfkZ6ALjL3cetyv587r4L2AXBjkVhGykiyRPZYlOdnbB5cxNa2H7CBPQhoHiGwIXAKyXn5IFHJ4L5QmCdmY25e18krRSRRFLJarKECej9wCVmthgYBm4Ebi4+wd0XF/5tZruBbymYi4QXdvd6SbdmLzVdM6C7+5iZfYKgeqUDeNjdnzGzOyce3xlZa0TaUNjd6yWQ1je/Viw1bd7oYvMzlM/nfWBgIJZriyRJ2N3rZfqbX0HpTkWVgn6cm7Gs2n6w7E5LtTbnKGVmh909X+4xTf0XiVnY3etl+hrrxccLAb3SJ54XR37D3x9/M7bNWLRJtEgbCLt7vYR786sU9J984bVYN2PRJtEibaB0mdlax9tZmDe/ej/ZtGrlxlYsNa2ALhKz0mVmK+1eL+He/Or9ZNOqzVhasdS0boqKSChx3VAsve6i38rx1LHXK1a5VLpxumrJeVNy6BCMkNO22JduiorIjLSi5C7sdV976x3+4iOXVbxuIbgnrcqlFTRCF5Gaoiq5S8t1k6zaCF05dBGpqRUld0m6blopoItITa0ouUvSddNKAV1EampFyV2SrptWuikqIjVFtlRuxNfN+k3OeummqIikUmkFDKSzDLFeuikqIpnT0CbVGaeALiKppAqY6RTQRSSVVAEznQK6iKSSKmCmU5WLiKRSXJU3SaaALiKppU2qp1JAF5FYqIY8egroItJyca3emHUK6CLSctVqyBsN6BrxK6CLSAyiriHXiD+gskURabmoa8g1azSggC4iLRd1DblmjQaUchGRyITNY0ddQ37BglzZnY3abdaoArqIRKLePHaUNeSb1ywtu/Jiu80aVcpFRCIRZx57/eVdbLu+h64FOYxgz9GsL6NbjkboIhKJuPPYmjWqEbqIRESrH8ZPAV1EIqHVD+MXKuViZlcDXwI6gIfcfXvJ47cAd018+Rtgo7v/NMqGikggqTMik7T6YVJfo2arGdDNrAN4ELgKGAL6zWy/uz9bdNqLwB+5++tmthbYBaxoRoNF2lnSZ0QmIY+d9NeomcKkXK4Ajrr7MXd/B3gUuLb4BHf/kbu/PvHlU8CF0TZTREAzIsNo59coTEDvAl4u+npo4lgltwPfLveAmd1hZgNmNjAyMhK+lSICxF9Jkgbt/BqFCehW5piXPdHsAwQB/a5yj7v7LnfPu3u+s7MzfCtFBFAlSRjt/BqFCehDwEVFX18IvFJ6kpktAx4CrnX3f4ymeSJSTJUktbXzaxSmyqUfuMTMFgPDwI3AzcUnmFk3sA/4qLv/PPJWigiQrEqSpGrn18jcy2ZPpp5ktg54gKBs8WF3v9fM7gRw951m9hBwA/DLiW8Zc/d8tefM5/M+MDAwo8aLiLQbMztcKb6GCujNoIAuIlK/agFdM0VFRDJCAV1EJCMU0EVEMkIBXUQkIxTQRUQyQgFdRCQjFNBFRDJCAV1EJCMU0EVEMkIBXUQkIxTQRUQyQgFdRCQjFNBFRDJCAV1EJCMU0EVEMkIBXUQkIxTQRUQyQgFdRCQjFNBFRDJCAV1EJCMU0EVEMkIBXUQkIxTQRUQyQgFdRCQjFNBFRDJCAV1EJCMU0EVEMkIBXUQkIxTQRUQyQgFdRCQjFNBFRDIiVEA3s6vN7HkzO2pmW8o8bmb23ycef9rMfj/6popInPqODLNq+0EWb3mMVdsP0ndkOO4mSYlZtU4wsw7gQeAqYAjoN7P97v5s0WlrgUsm/qwAdkz8LSIZ0HdkmN59g4yeHgdg+I1RevcNArD+8q44myZFwozQrwCOuvsxd38HeBS4tuSca4GveeApYIGZvS/itopITO478PxkMC8YPT3OfQeej6lFUk6YgN4FvFz09dDEsXrPwczuMLMBMxsYGRmpt60iEpNX3hit67jEI0xAtzLHvIFzcPdd7p5393xnZ2eY9olIAlywIFfXcYlHmIA+BFxU9PWFwCsNnCMiKbV5zVJyszumHMvN7mDzmqUxtUjKCRPQ+4FLzGyxmc0BbgT2l5yzH/jYRLXLSuBNd/9VxG0VkZisv7yLbdf30LUghwFdC3Jsu75HN0QTpmaVi7uPmdkngANAB/Cwuz9jZndOPL4TeBxYBxwFTgK3Na/JIhKH9Zd3KYAnXM2ADuDujxME7eJjO4v+7cDHo22aiIjUQzNFRUQyQgFdRCQjFNBFRDJCAV1EJCMsuJ8Zw4XNRoBfNvjtC4FXI2xOGqjP7UF9bg8z6fPF7l52ZmZsAX0mzGzA3fNxt6OV1Of2oD63h2b1WSkXEZGMUEAXEcmItAb0XXE3IAbqc3tQn9tDU/qcyhy6iIhMl9YRuoiIlFBAFxHJiEQH9HbcnDpEn2+Z6OvTZvYjM7ssjnZGqVafi85bbmbjZvbhVravGcL02cxWm9lPzOwZM/tBq9sYtRC/2/PN7H+a2U8n+pzqVVvN7GEzO2FmP6vwePTxy90T+Ydgqd4XgH8OzAF+CvyLknPWAd8m2DFpJXAo7na3oM//Gjh34t9r26HPRecdJFj188Nxt7sFP+cFwLNA98TX58fd7hb0+dPAFyb+3Qm8BsyJu+0z6PO/BX4f+FmFxyOPX0keobfj5tQ1++zuP3L31ye+fIpgd6g0C/NzBvgksBc40crGNUmYPt8M7HP34wDunvZ+h+mzA+eYmQHvJQjoY61tZnTc/QmCPlQSefxKckCPbHPqFKm3P7cTvMOnWc0+m1kXcB2wk2wI83N+P3CumX3fzA6b2cda1rrmCNPnLwOXEmxfOQj8V3d/tzXNi0Xk8SvUBhcxiWxz6hQJ3R8z+wBBQP83TW1R84Xp8wPAXe4+HgzeUi9Mn2cBfwB8EMgBPzazp9z9581uXJOE6fMa4CfAlcAS4Ltm9kN3/3WzGxeTyONXkgN6O25OHao/ZrYMeAhY6+7/2KK2NUuYPueBRyeC+UJgnZmNuXtfa5oYubC/26+6+1vAW2b2BHAZkNaAHqbPtwHbPUgwHzWzF4HfAf5va5rYcpHHrySnXNpxc+qafTazbmAf8NEUj9aK1eyzuy9290Xuvgj4JvCnKQ7mEO53+2+BPzSzWWY2D1gBPNfidkYpTJ+PE3wiwcx+G1gKHGtpK1sr8viV2BG6t+Hm1CH7/Bngt4CvTIxYxzzFK9WF7HOmhOmzuz9nZt8BngbeBR5y97Llb2kQ8uf858BuMxskSEfc5e6pXVbXzB4BVgMLzWwI+CwwG5oXvzT1X0QkI5KcchERkToooIuIZIQCuohIRiigi4hkhAK6iEhGKKCLiGSEArqISEb8fwk86Vk8WSvtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=k, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "pred_y = kmeans.fit_predict(X_trans)\n",
    "plt.scatter(X_trans[:,0], X_trans[:,1])\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')\n",
    "plt.show()\n",
    "\n",
    "df_occ['cluster'] = kmeans.fit_predict(X_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x180f548afd0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nT1atWrXk8/k0Z84c5ebmKiMjQ2vXrpXP55PH49GcOXMkSUeOHFFsbKzWr1+vDh066MiRI0pOTlZOTo7q1KmjX/7yl1q+fLkWLlyoCRMmBJxvy5YtWrZsmbKysjRx4kSdOHHinMcwefJkbdiwQV988YWmT59e8Q/jEsdpjQAAAIDD6tatq/vvv1/p6emqVatWmfpkZmYqJydHN998syTp6NGjatiwoSTJ4/FowIAB/rZXXHGFevbsKUmKi4tTaGioatasqbi4OOXl5QUc//bbb1doaKhCQ0PVsGFD7dq165z1xMfHa/Dgwbrzzjt15513lukYLkesnAEAAACOe+KJJ/Tqq6/qyJEjkk5eK+b1euX1egOubllrNXToUPl8Pvl8Pv3jH//wn2IYFhZ2xnVmNWvWlDFGklSjRg2Fhob6XxcWFgas51Qb6WTYKywsVEhIiIqLi/3bjx075n/9wQcfaPTo0crJyVFCQsJZx73cEc4AAAAAx9WvX1/33HOPXn31VUknA9Gp4PXcc89JOhmyTp1e2K1bN82bN0+7d++WJO3fv1/btm27oDU2atRIu3fv1r59+/Tvf/9b77//viSpuLhY27dvV5cuXfTb3/5WBw4c0OHDhy9oLdUVpzUCAAAA1cDYsWP1xz/+8az7R44cqfj4eLVp00Zz5szR888/rx49eqi4uFg1a9bUtGnTdMMNN1yw+mrWrOm/0UjTpk3VsmVLSSdX+YYMGaL8/HxZa/Xkk0/qqquuumB1VGfGWnvuBsb8VNJsST+RVCxphrX298aY+pIyJEVKypN0j7X2h3ONlZiYaLOzs4NQNgAAqLTUepXoe/YbEwCXitzcXEVHR1d1GajGAv0MGWNyrLUBn0tQltMaCyWNtdZGS2onabQxJkbSeEmZ1tooSZkl7wEAAAAAFXDecGat3Wmt/azk9SFJuZKul9RP0uslzV6XxG1XAAAAAKCCynVDEGNMpKSbJK2X1Mhau1M6GeAkNTxLn5HGmGxjTPaePXsqVy0AAAAAXKLKHM6MMeGS5kt6wlp7sKz9rLUzrLWJ1trEBg0aVKRGAAAAALjklSmcGWNq6mQwm2OtXVCyeZcx5tqS/ddK2n1hSgQAAACAS995w5k5+US6VyXlWmtfPm3XYklDS14PlbQo+OUBAAAAwOWhLCtnt0q6T1JXY4yv5E9vSZMldTfGfC2pe8l7AAAAAEFijNHYsWP976dMmaLU1NQz2rRu3VqDBg2SJM2cOVNer1der1dXXHGF4uLi5PV6NX78yRurr1y5Ul6vV61atVLnzp0v2nGgbM77EGpr7RpJ5iy7uwW3HAAAAMBNuS2D+8yz6C25520TGhqqBQsW6Oc//7kiIiJK15Sbq+LiYn388cc6cuSIhg8fruHDh0uSIiMj9dFHH/n7HThwQI888oiWLl2qJk2aaPdurkpyTbnu1ggAAADg4gkJCdHIkSOVlpYWcP9bb72l++67Tz169NDixYvPOdZbb72llJQUNWnSRJLUsGHAm62jChHOAAAAAIeNHj1ac+bMUX5+fql9GRkZGjhwoAYNGqS5c+eec5yvvvpKP/zwg5KTk5WQkKDZs2dfqJJRQec9rREAAABA1albt67uv/9+paenq1atWv7tn376qRo0aKAbbrhBjRs31gMPPKAffvhBV199dcBxCgsLlZOTo8zMTB09elTt27dXu3bt1KJFi4t1KDgPVs4AAAAAxz3xxBN69dVXdeTIEf+2uXPnasuWLYqMjFTz5s118OBBzZ8//6xjNG7cWD179lTt2rUVERGhTp066fPPP78Y5aOMCGcAAACA4+rXr6977rlHr776qiSpuLhY77zzjr744gvl5eUpLy9PixYtOuepjf369dPq1atVWFiogoICrV+/XtHRwb3JCSqHcAYAAABUA2PHjtXevXslSR9//LGuv/56XX/99f79nTp10ubNm7Vz586A/aOjo9WzZ0/Fx8crKSlJI0aMUGxs7EWpHWVjrLUXbbLExESbnZ190eYDAADnkFqvEn1L35gAuNTk5uaysoRKCfQzZIzJsdYmBmrPyhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAI7asWOH+vXrp6ioKDVv3lyPP/64jh8/Lp/PpyVLlvjbpaamasqUKVVYKYIhpKoLAAAAAKqDaaNWBHW80dO7nnO/tVYpKSl6+OGHtWjRIhUVFWnkyJF69tln1apVK2VnZ6t3795BqaWoqEgejycoY6HiWDkDAAAAHLRixQqFhYVp+PDhkiSPx6O0tDS98soreuaZZ5SRkSGv16uMjAxJ0ubNm5WcnKxmzZopPT3dP86bb76ppKQkeb1ePfTQQyoqKpIkhYeHa8KECWrbtq3WrVt38Q8QpRDOAAAAAAdt2rRJCQkJZ2yrW7euIiMj9ctf/lIDBw6Uz+fTwIEDJUlbtmzRsmXLlJWVpYkTJ+rEiRPKzc1VRkaG1q5dK5/PJ4/Hozlz5kiSjhw5otjYWK1fv14dOnS46MeH0jitEQAAAHCQtVbGmDJvv/322xUaGqrQ0FA1bNhQu3btUmZmpnJycnTzzTdLko4ePaqGDRtKOrkSN2DAgAt7ECgXwhkAAADgoFatWmn+/PlnbDt48KC2b98e8Pqw0NBQ/2uPx6PCwkJZazV06FBNmjSpVPuwsDCuM3MMpzUCAAAADurWrZsKCgo0e/ZsSSdv2jF27FgNGzZMjRo10qFDh8o0xrx587R7925J0v79+7Vt27YLWjcqjnAGAAAAOMgYo4ULF+qdd95RVFSUWrRoobCwML344ovq0qWLNm/efMYNQQKJiYnR888/rx49eig+Pl7du3fXzp07L+JRoDyMtfaiTZaYmGizs7Mv2nwAAOAcUutVom9+8OoAHJWbm6vo6OiqLgPVWKCfIWNMjrU2MVB7Vs4AAAAAwAGEMwAAAABwAOEMAAAAABxAOAMAAAAABxDOAAAAAMABhDMAAAAAcADhDAAAAHDUjh071K9fP0VFRal58+Z6/PHHdfz4cUmSz+fTkiVL/G1TU1M1ZcqUSs85d+5cvfDCC5o1a5YaNGggr9crr9er+++/X4sXL9bkyZPLNV54eHiZ2k2fPt3/wO2yWLlypfr06RNwX+/evXXgwIEyj5WXl6fY2FhJUnZ2th577LEy9w2mkCqZFQAAAKhmXhoYOAhU1NiM98+531qrlJQUPfzww1q0aJGKioo0cuRIPfvss/rd734nn8+n7Oxs9e7dOyj1FBUVyePxaOnSpXrssce0ceNGDRw4UH/84x/PaNe3b9+gzHe6wsJCjRo1KmjjnR5ayysxMVGJiQEfQ3bBsXIGAAAAOGjFihUKCwvT8OHDJUkej0dpaWl67bXXdPDgQU2YMEEZGRnyer3KyMiQJG3evFnJyclq1qyZ0tPT/WO9+eabSkpKktfr1UMPPaSioiJJJ1e1JkyYoLZt22rdunWy1srn86lNmzYBa5o1a5bGjBkjSRo2bJgefvhhdenSRc2aNdOqVav0wAMPKDo6WsOGDTuj39ixY9WmTRt169ZNe/bskSQlJyfrF7/4hTp37qzf//73Z6z8JScna9y4cUpKSlKLFi20evXqgPUcPHhQ/fv3V0xMjEaNGqXi4mJJUmRkpPbu3au8vDxFR0frwQcfVKtWrdSjRw8dPXpUkpSTk6PWrVurffv2mjZtmn/M01fkUlNT9cADDwT8TH/zm9+oZcuW6t69uwYNGhSUVUvCGQAAAOCgTZs2KSEh4YxtdevWVZMmTZSXl6fnnntOAwcOlM/n08CBAyVJW7Zs0bJly5SVlaWJEyfqxIkTys3NVUZGhtauXSufzyePx6M5c+ZIko4cOaLY2FitX79eHTp00IYNG9S6dWsZYyTJH/68Xq9mzpxZqsYffvhBK1asUFpamu644w49+eST2rRpkzZu3Cifz+efo02bNvrss8/UuXNnTZw40d//wIEDWrVqlcaOHVtq7MLCQmVlZWnq1Kln9DldVlaWXnrpJW3cuFHffPONFixYUKrN119/rdGjR2vTpk266qqrNH/+fEnS8OHDlZ6ernXr1p3zv0OgzzQ7O1vz58/Xhg0btGDBAmVnZ59zjLIinAEAAAAOstb6Q1JZtkvS7bffrtDQUEVERKhhw4batWuXMjMzlZOTo5tvvller1eZmZnaunWrpJOrcQMGDPD3X7p0qXr16uV/fyr8+Xw+/wre6e644w4ZYxQXF6dGjRopLi5ONWrUUKtWrZSXlydJqlGjhj88DhkyRGvWrDlj/LNJSUmRJCUkJPjH+rGkpCQ1a9ZMHo9HgwYNOmPsU5o2bSqv13vGWPn5+Tpw4IA6d+4sSbrvvvvOWkegz3TNmjXq16+fatWqpTp16uiOO+44a//y4JozAAAAwEGtWrXyr/KccvDgQW3fvl3NmzdXTk5OqT6hoaH+1x6PR4WFhbLWaujQoZo0aVKp9mFhYfJ4PP73f/vb30rNeS6n5qtRo8YZc9eoUUOFhYUB+5weLGvXrn3esU8dx/nGCvT+9HFOjXX06NFzBtzz9T/1mV4IrJwBAAAADurWrZsKCgr8dzAsKirS2LFjNWzYMF155ZWqU6eODh06VKZx5s2bp927d0uS9u/fr23btpVql5+fr8LCQl1zzTVBPY7i4mLNmzdPkvTWW2+pQ4cOQRs7KytL3377rYqLi5WRkVHmsa+66irVq1fPv9J26jTPsurQoYPee+89HTt2TIcPH9YHH3xQ7toDIZwBAAAADjLGaOHChXrnnXcUFRWlFi1aKCwsTC+++KIkqUuXLtq8efMZNwQJJCYmRs8//7x69Oih+Ph4de/eXTt37izVbvny5brtttuCfhy1a9f2Xz+3YsUKTZgwIWhjt2/fXuPHj1dsbKyaNm2q/v37l7nvzJkzNXr0aLVv3161atUq17w333yz+vbtq9atWyslJUWJiYmqV69eecsvxVyoJblAEhMTbbAulgMAAJWUWol/SKTmB68OwFG5ubmKjo6u6jIumhEjRmjEiBFq165dVZdSLRw+fFjh4eEqKChQp06dNGPGjFJ3uQz0M2SMybHWBrxXP9ecAQAAANArr7xS1SVUKyNHjtTmzZt17NgxDR069KyPHygPwhkAAAAAlNNbb70V9DG55gwAAAAAHEA4AwAAAAAHEM4AAAAAwAGEMwAAAABwAOEMAAAAcNSOHTvUr18/RUVFqXnz5nr88cd1/PhxSZLP59OSJUv8bVNTUzVlypRKzzl37ly98MILmjVrlowxyszM9O9buHChjDH+h0oHw4QJE/Thhx+Wuf2sWbM0ZsyYgPtuueWWcs29cuVK9enTR5K0ePFiTZ48uVz9g427NQIAAABlsGP86qCO13hyx3Put9YqJSVFDz/8sBYtWqSioiKNHDlSzz77rH73u9/J5/MpOztbvXv3Dko9RUVF8ng8Wrp0qR577DFt3LhRcXFxmjt3rrp16yZJevvtt9W6deugzHfKc889F7SxPvnkkwr37du3r/r27Ru0WiqClTMAAADAQStWrFBYWJiGDx8uSfJ4PEpLS9Nrr72mgwcPasKECcrIyJDX61VGRoYkafPmzUpOTlazZs2Unp7uH+vNN99UUlKSvF6vHnroIRUVFUmSwsPDNWHCBLVt21br1q2TtVY+n8//zK6OHTsqKytLJ06c0OHDh/XPf/5TXq9XkpSZman+/fv751i+fLlSUlIknVx9i4uLU2xsrMaNGyfpZPgbNmyYYmNjFRcXp7S0NEnSsGHD/CtxkZGR+vWvf602bdooLi5OW7ZsCfjZbN++XT179tSNN96oiRMn+reHh4dLOrkilpycrLvuukstW7bU4MGDZa2VJC1dulQtW7ZUhw4dtGDBAn/f01fkhg0bpscee0y33HKLmjVr5q+vuLhYjzzyiFq1aqU+ffqod+/eQV1FJJwBAAAADtq0aZMSEhLO2Fa3bl01adJEeXl5eu655zRw4ED5fD4NHDhQkrRlyxYtW7ZMWVlZmjhxok6cOKHc3FxlZGRo7dq18vl88ng8mjNnjiTpyJEjio2N1fr169WhQwdt2LBBrVu3ljFGkmSM0W233Sw2DaMAABS4SURBVKZly5Zp0aJFZ6wsde3aVbm5udqzZ48kaebMmRo+fLi+++47jRs3TitWrJDP59Onn36qd999Vz6fT//617/05ZdfauPGjf7Q+WMRERH67LPP9PDDD5/1NM2srCzNmTNHPp9P77zzjrKzs0u12bBhg6ZOnarNmzdr69atWrt2rY4dO6YHH3xQ7733nlavXq3vv//+rJ//zp07tWbNGr3//vsaP368JGnBggXKy8vTxo0b9corr2jdunVn7V8RhDMAAADAQdZaf0gqy3ZJuv322xUaGqqIiAg1bNhQu3btUmZmpnJycnTzzTfL6/UqMzNTW7dulXRyNW7AgAH+/kuXLlWvXr3OGPPee+/V22+/rbfffluDBg3ybzfG6L777tObb76pAwcOaN26derVq5c+/fRTJScnq0GDBgoJCdHgwYP18ccfq1mzZtq6daseffRRLV26VHXr1g14DKdW3xISEpSXlxewTffu3XXNNdeoVq1aSklJ0Zo1a0q1SUpKUuPGjVWjRg15vV7l5eVpy5Ytatq0qaKiomSM0ZAhQwKOL0l33nmnatSooZiYGO3atUuStGbNGt19992qUaOGfvKTn6hLly5n7V8RXHMGAAAAOKhVq1aaP3/+GdsOHjyo7du3q3nz5srJySnVJzQ01P/a4/GosLBQ1loNHTpUkyZNKtU+LCxMHo/H//5vf/tbqTmTkpL05ZdfqlatWmrRosUZ+4YPH6477rhDYWFhuvvuuxUSEuI/ffDHrr76an3++edatmyZpk2bpr/85S967bXXznoMp+oP5MfhNFBYDfRZnK1tIKf3P3VMZzu2YGHlDAAAAHBQt27dVFBQoNmzZ0s6ec3W2LFjNWzYMF155ZWqU6eODh06VKZx5s2bp927d0uS9u/fr23btpVql5+fr8LCQl1zzTWl9k2aNEkvvvhiqe3XXXedrrvuOj3//PMaNmyYJKlt27ZatWqV9u7dq6KiIs2dO1edO3fW3r17VVxcrAEDBug3v/mNPvvss/J8HGdYvny59u/fr6NHj+rdd9/VrbfeWqZ+LVu21LfffqtvvvlG0slr48qjQ4cOmj9/voqLi7Vr1y6tXLmyvKWfE+EMAAAAcJAxRgsXLtQ777yjqKgotWjRQmFhYf6Q1KVLF23evPmMG4IEEhMTo+eff149evRQfHy8unfvrp07d5Zqt3z5ct12220Bx+jVq9dZT+EbPHiwfvrTnyomJkaSdO2112rSpEnq0qWLWrdurTZt2qhfv37617/+peTkZHm9Xg0bNizgSl5ZdejQQffdd5+8Xq8GDBigxMTEMvULCwvTjBkzdPvtt6tDhw664YYbyjXvgAED1LhxY8XGxuqhhx5S27ZtVa9evYocQkDmQi/NnS4xMdEGulgPAABUgdRK/IMiNT94dQCOys3NVXR0dFWXcdGMGDFCI0aMULt27crVb8yYMbrpppv0n//5nxeoMrccPnxY4eHh2rdvn5KSkrR27Vr95Cc/Cdg20M+QMSbHWhswTXLNGQAAAAC98sor5e6TkJCg2rVr66WXXroAFbmpT58+OnDggI4fP65f/epXZw1mFUE4AwAAAFAhgW5KcqkL9nVmp+OaMwAAAABwAOEMAAAAOIuLeX8GXFoq8rNDOAMAAAACCAsL0759+whoKDdrrfbt26ewsLBy9eOaMwAAACCAxo0ba8eOHdqzZ09Vl4JqKCwsTI0bNy5XH8IZAAAAEEDNmjXVtGnTqi4DlxFOawQAAAAABxDOAAAAAMABhDMAAAAAcADhDAAAAAAcQDgDAAAAAAcQzgAAAADAAYQzAAAAAHAA4QwAAAAAHEA4AwAAAAAHEM4AAAAAwAHnDWfGmNeMMbuNMV+eti3VGPMvY4yv5E/vC1smAAAAAFzayrJyNktSzwDb06y13pI/S4JbFgAAAABcXs4bzqy1H0vafxFqAQAAAIDLVmWuORtjjPmi5LTHq4NWEQAAAABchioazv4sqbkkr6Sdkl46W0NjzEhjTLYxJnvPnj0VnA4AAAAALm0VCmfW2l3W2iJrbbGk/5GUdI62M6y1idbaxAYNGlS0TgAAAAC4pFUonBljrj3tbX9JX56tLQAAAADg/ELO18AYM1dSsqQIY8wOSb+WlGyM8UqykvIkPXQBawQAAACAS955w5m1dlCAza9egFoAAAAA4LJVmbs1AgAAAACChHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAA0KqugBUH9NGrahw39HTuwaxEgAAAODSw8oZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADgip6gIAAADKatqoFRXqN3p61yBXAgDBx8oZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADzhvOjDGvGWN2G2O+PG1bfWPMcmPM1yV/X31hywQAAACAS1tZVs5mSer5o23jJWVaa6MkZZa8BwAAAABU0HnDmbX2Y0n7f7S5n6TXS16/LunOINcFAAAAAJeVil5z1shau1OSSv5uGLySAAAAAODyc8FvCGKMGWmMyTbGZO/Zs+dCTwcAAAAA1VJFw9kuY8y1klTy9+6zNbTWzrDWJlprExs0aFDB6QAAAADg0lbRcLZY0tCS10MlLQpOOQAAAABweSrLrfTnSlon6UZjzA5jzH9KmiypuzHma0ndS94DAAAAACoo5HwNrLWDzrKrW5BrAQAAAIDL1gW/IQgAAAAA4PwIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOCCkqgsAgMvFtFErKtx39PSuQawEAAC4iJUzAAAAAHAA4QwAAAAAHEA4AwAAAAAHEM4AAAAAwAGEMwAAAABwAOEMAAAAABxAOAMAAAAABxDOAAAAAMABhDMAAAAAcADhDAAAAAAcQDgDAAAAAAcQzgAAAADAAYQzAAAAAHAA4QwAAAAAHBBS1QUAQIWk1qtE3/zg1QEAuHxVw99F00atqHDf0dO7BrESBMLKGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAA0KquoDLVmq9SvTND14dAAAAAJzAyhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgANCKtPZGJMn6ZCkIkmF1trEYBQFAAAAAJebSoWzEl2stXuDMA4AAAAAXLY4rREAAAAAHFDZcGYl/c0Yk2OMGRmMggAAAADgclTZ0xpvtdZ+Z4xpKGm5MWaLtfbj0xuUhLaRktSkSZNKTgcAAE4XOf6DCvfNCwtiIQCASqvUypm19ruSv3dLWigpKUCbGdbaRGttYoMGDSozHQAAAABcsioczowxtY0xdU69ltRD0pfBKgwAAAAALieVOa2xkaSFxphT47xlrV0alKoAAAAA4DJT4XBmrd0qqXUQawEAAACAyxa30gcAAAAABxDOAAAAAMABhDMAAAAAcADhDAAAAAAcQDgDAAAAAAcQzgAAAADAAYQzAAAAAHAA4QwAAAAAHEA4AwAAAAAHEM4AAAAAwAGEMwAAAABwAOEMAAAAABxAOAMAAAAABxDOAAAAAMABhDMAAAAAcADhDAAAAAAcQDgDAAAAAAcQzgAAAADAAYQzAAAAAHAA4QwAAAAAHEA4AwAAAAAHEM4AAAAAwAGEMwAAAABwAOEMAAAAABxAOAMAAAAABxDOAAAAAMABIVVdQHUWOf6DCvfNC6v4vHGvx1W478ahGys+MQAAJfhdBADBx8oZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADQqq6AACXt8jxH1SoX15YxeeMez2uwn3/Mqmw4hMnT6t4XwDABVHR30NS5X4XAYGwcgYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ICQqi4AAHB+Lw3sU+G+YzPeD2IlQPXEdwioWjvGr65w38aTOwaxErexcgYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4gHAGAAAAAA4gnAEAAACAAwhnAAAAAOAAwhkAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADggEqFM2NMT2PMP4wx/zTGjA9WUQAAAABwualwODPGeCRNk9RLUoykQcaYmGAVBgAAAACXk8qsnCVJ+qe1dqu19riktyX1C05ZAAAAAHB5MdbainU05i5JPa21I0re3yeprbV2zI/ajZQ0suTtjZL+UfFycRFESNpb1UUA1RjfIaBy+A4BlcN3yH03WGsbBNoRUolBTYBtpZKetXaGpBmVmAcXkTEm21qbWNV1ANUV3yGgcvgOAZXDd6h6q8xpjTsk/fS0940lfVe5cgAAAADg8lSZcPappChjTFNjzBWS7pW0ODhlAQAAAMDlpcKnNVprC40xYyQtk+SR9Jq1dlPQKkNV4RRUoHL4DgGVw3cIqBy+Q9VYhW8IAgAAAAAInko9hBoAAAAAEByEMwAAAABwAOEMAAAAABxQmeecoZozxrSU1E/S9Tr5jLrvJC221uZWaWEAgMtGye+i6yWtt9YePm17T2vt0qqrDKgejDFJkqy19lNjTIyknpK2WGuXVHFpqABWzi5Txphxkt7WyYeJZ+nkoxGMpLnGmPFVWRtwKTDGDK/qGgDXGWMek7RI0qOSvjTG9Dtt94tVUxVQfRhjfi0pXdKfjTGTJP1RUrik8caYZ6u0OFQId2u8TBljvpLUylp74kfbr5C0yVobVTWVAZcGY8z/WmubVHUdgMuMMRsltbfWHjbGREqaJ+kNa+3vjTEbrLU3VWmBgONKvkNeSaGSvpfU2Fp70BhTSydXo+OrtECUG6c1Xr6KJV0naduPtl9bsg/AeRhjvjjbLkmNLmYtQDXlOXUqo7U2zxiTLGmeMeYGnfweATi3QmttkaQCY8w31tqDkmStPWqM4d9z1RDh7PL1hKRMY8zXkraXbGsi6WeSxlRZVUD10kjS/5H0w4+2G0mfXPxygGrne2OM11rrk6SSFbQ+kl6TFFe1pQHVwnFjzJXW2gJJCac2GmPqif/ZXi1xWuNlzBhTQ1KSTl6IbSTtkPRpyf+BAXAexphXJc201q4JsO8ta+1/VEFZQLVhjGmsk//n//sA+2611q6tgrKAasMYE2qt/XeA7RGSrrXWbqyCslAJhDMAAAAAcAB3awQAAAAABxDOAAAAAMABhDMAQLVmjEk1xjxdgX5XGWMeuRA1AQBQEYQzAMDl6ipJ5Qpn5iR+dwIALgh+wQAAqhVjzP3GmC+MMZ8bY9740b6VxpjEktcRxpi8ktetjDFZxhhfSd8oSZMlNS/Z9ruSdv/XGPNpSZuJJdsijTG5xpg/SfpM0k8v4uECAC4jPOcMAFBtGGNaSXpW0q3W2r3GmPqSHitD11GSfm+tnWOMuUKSR9J4SbHWWm/J2D0kRenkI0aMpMXGmE6S/lfSjZKGW2s5DRIAcMEQzgAA1UlXSfOstXslyVq73xhTln7rJD1b8lytBdbarwP061HyZ0PJ+3CdDGv/K2mbtfbvQagfAICz4rRGAEB1YiSd6wGdhfr/v9vCTm201r4lqa+ko5KWGWO6nmXsSdZab8mfn1lrXy3Zd6TypQMAcG6EMwBAdZIp6R5jzDWSVHJa4+nyJCWUvL7r1EZjTDNJW6216ZIWS4qXdEhSndP6LpP0gDEmvKTP9caYhhfiIAAACITTGgEA1Ya1dpMx5gVJq4wxRTp5CmLeaU2mSPqLMeY+SStO2z5Q0hBjzAlJ30t6ruSUyLXGmC8l/dVa+3+NMdGS1pWc8nhY0hBJRRf8wAAAkGSsPdfZIQAAAACAi4HTGgEAAADAAYQzAAAAAHAA4QwAAAAAHEA4AwAAAAAHEM4AAAAAwAGEMwAAAABwAOEMAAAAABxAOAMAAAAAB/w/+4RzVz79G9UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#X = df_actg1.loc[:, cols].values # df_actg1.loc[:, cols].values # \n",
    "#X_trans = pca.fit_transform(X)\n",
    "\n",
    "\n",
    "d = {'cat': df_occ['Category'], 'cluster': df_occ['cluster']}\n",
    "df_bar = pd.DataFrame(data=d)\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "df_bar.groupby(['cluster', 'cat']).size().unstack().plot(ax=ax, kind = 'bar') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained PCA:\tPC1= 0.9242048741274692 \tPC2= 0.04813958741541355\n",
      "\n",
      "PCA figure saved under location: PCA/phenoMap_pca_All_GeneOccurrance_rogers.html\n"
     ]
    }
   ],
   "source": [
    "import importlib as imp\n",
    "\n",
    "imp.reload(func)\n",
    "func.makePCA2(X_trans,df_occ['Id'],  df_occ['cluster'].astype(str), df_occ['Category'],  df_occ['Origin'],\n",
    "                      title='All_GeneOccurrance_rogers', clusters=k, pal=[Colorblind8, Colorblind8, Colorblind8], seed=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 113 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 113 samples in 0.012s...\n",
      "[t-SNE] Computed conditional probabilities for sample 113 / 113\n",
      "[t-SNE] Mean sigma: 0.685076\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 54.717361\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.194912\n",
      "KMeans(n_clusters=5, random_state=2)\n",
      "\n",
      "TSNE figure saved under location: TSNE/Kmeans_phenoMap_tsne_All_GeneOccurrance_rogers.html\n"
     ]
    }
   ],
   "source": [
    "func.makeTSNE_Cluster3(X_trans,df_occ['Id'],  df_occ['cluster'].astype(str), df_occ['Category'],  df_occ['Origin'],\n",
    "                      title='All_GeneOccurrance_rogers', clusters=k, pal=[Colorblind8, Colorblind8, Colorblind8], seed=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CLUSTER 3 - GROUPBASED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>HPOcode</th>\n",
       "      <th>HP:0000119</th>\n",
       "      <th>HP:0000079</th>\n",
       "      <th>HP:0010935</th>\n",
       "      <th>HP:0000077</th>\n",
       "      <th>HP:0012210</th>\n",
       "      <th>HP:0000107</th>\n",
       "      <th>HP:0100542</th>\n",
       "      <th>HP:0000085</th>\n",
       "      <th>HP:0000152</th>\n",
       "      <th>...</th>\n",
       "      <th>HP:0002012</th>\n",
       "      <th>HP:0001392</th>\n",
       "      <th>HP:0410042</th>\n",
       "      <th>HP:0025032</th>\n",
       "      <th>HP:0011458</th>\n",
       "      <th>HP:0011968</th>\n",
       "      <th>HP:0008872</th>\n",
       "      <th>HP:0040064</th>\n",
       "      <th>HP:0002817</th>\n",
       "      <th>HP:0001155</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>actbgain</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.039154268</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>actbloss</td>\n",
       "      <td>0.008458192</td>\n",
       "      <td>0.011393229</td>\n",
       "      <td>0.016385768</td>\n",
       "      <td>0.016778523</td>\n",
       "      <td>0.018541409</td>\n",
       "      <td>0.034722222</td>\n",
       "      <td>0.098425197</td>\n",
       "      <td>0.211864407</td>\n",
       "      <td>0.026429131</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02173913</td>\n",
       "      <td>0.026840491</td>\n",
       "      <td>0.045992116</td>\n",
       "      <td>0.09383378</td>\n",
       "      <td>0.009982886</td>\n",
       "      <td>0.00400641</td>\n",
       "      <td>0.004355401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>actbnterminus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.039154268</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>actbabs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.031323414</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019379845</td>\n",
       "      <td>0.022962113</td>\n",
       "      <td>0.027662517</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011409013</td>\n",
       "      <td>0.016025641</td>\n",
       "      <td>0.017421603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>actbother</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.039154268</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005704507</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 284 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         HPOcode   HP:0000119   HP:0000079   HP:0010935   HP:0000077  \\\n",
       "0       actbgain            0            0            0            0   \n",
       "1       actbloss  0.008458192  0.011393229  0.016385768  0.016778523   \n",
       "2  actbnterminus            0            0            0            0   \n",
       "3        actbabs            0            0            0            0   \n",
       "4      actbother            0            0            0            0   \n",
       "\n",
       "    HP:0012210   HP:0000107   HP:0100542   HP:0000085   HP:0000152  ...  \\\n",
       "0            0            0            0            0  0.039154268  ...   \n",
       "1  0.018541409  0.034722222  0.098425197  0.211864407  0.026429131  ...   \n",
       "2            0            0            0            0  0.039154268  ...   \n",
       "3            0            0            0            0  0.031323414  ...   \n",
       "4            0            0            0            0  0.039154268  ...   \n",
       "\n",
       "    HP:0002012   HP:0001392   HP:0410042  HP:0025032   HP:0011458  \\\n",
       "0            0            0            0           0            0   \n",
       "1            0            0            0  0.02173913  0.026840491   \n",
       "2            0            0            0           0            0   \n",
       "3  0.019379845  0.022962113  0.027662517           0            0   \n",
       "4            0            0            0           0            0   \n",
       "\n",
       "    HP:0011968  HP:0008872   HP:0040064   HP:0002817   HP:0001155  \n",
       "0            0           0            0            0            0  \n",
       "1  0.045992116  0.09383378  0.009982886   0.00400641  0.004355401  \n",
       "2            0           0            0            0            0  \n",
       "3            0           0  0.011409013  0.016025641  0.017421603  \n",
       "4            0           0  0.005704507            0            0  \n",
       "\n",
       "[5 rows x 284 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_group = pd.read_csv(r'data/deep_group/occurance_ratio_N6_and_ACTB_and_ACTG1.txt', sep='\\t', header=0)\n",
    "df_group = df_group.apply(lambda x: x.astype('str').str.replace(',','.'))\n",
    "df_group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_remove = ['actg1nterminus', 'actbnterminus', 'actbabs', 'actbtotal', 'NAT6']\n",
    "df_group = df_group[~(df_group['HPOcode'].isin(l_remove))].reset_index(drop=True)\n",
    "df_group['HPOcode'] = df_group['HPOcode'].replace({'actbgain': 'Gain of Function (ACTB)', 'actbloss': 'Loss of Function (ACTB)', 'actbother': 'Other (ACTB)', 'actg1gain': 'Gain of Function (ACTG1)', 'actg1loss': 'Loss of Function (ACTG1)', 'actg1other': 'Other (ACTG1)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist\n",
    "from scipy.spatial.distance import squareform\n",
    "\n",
    "#cols = list(df_group.loc[:, ~df_group.columns.isin(['Category', 'Id', 'Protein', 'Origin'])].columns)\n",
    "\n",
    "bool_dist = ['dice', 'hamming', 'jaccard',  'kulsinski', 'rogerstanimoto', 'russellrao', 'sokalmichener', 'sokalsneath', 'yule']\n",
    "discrete_dist = ['braycurtis', 'canberra', 'chebyshev', 'cityblock','correlation', 'cosine','euclidean', 'jensenshannon', 'mahalanobis', 'minkowski', 'seuclidean', 'sqeuclidean',  'wminkowski', 'hamming']\n",
    "\n",
    "#distance = bool_dist[1]\n",
    "distance = 'euclidean' # discrete_dist[4]\n",
    "\n",
    "df = df_group.copy()\n",
    "df = df.sample(frac=1)\n",
    "pairwise = pd.DataFrame(\n",
    "    squareform(pdist(df_group[df_group.columns[1:]], metric=distance)),\n",
    "    columns = df['HPOcode'],\n",
    "    index = df['HPOcode']\n",
    ")\n",
    "\n",
    "\n",
    "X_trans = pairwise.values \n",
    "#X_trans=df_group[df_group.columns[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0.0000011', '0.0000000', '0.0000000', ..., '0.0000000',\n",
       "        '0.0000000', '0.0000000'],\n",
       "       ['0.00106', '0.00000', '0.00000', ..., '0.00000', '0.00000',\n",
       "        '0.00000'],\n",
       "       ['0.00212', '0.00000', '0.00000', ..., '0.00433', '0.00758',\n",
       "        '0.00000'],\n",
       "       ...,\n",
       "       ['0.0095', '0.0118', '0.0370', ..., '0.0346', '0.0530', '0.0588'],\n",
       "       ['0.0021', '0.0029', '0.0000', ..., '0.0303', '0.0530', '0.0000'],\n",
       "       ['0.0011', '0.0029', '0.0000', ..., '0.0000', '0.0000', '0.0000']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group[columns].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trans = [i[1:].astype(float) for i in df_group[columns].values]\n",
    "# X_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained PCA:\tPC1= 0.7239552990617878 \tPC2= 0.2251234088678058\n",
      "PCA figure saved under location: PCA/phenoMap_pca__test_group_COG.html\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('src/') # add source with all scripts\n",
    "\n",
    "from bokeh.palettes import Colorblind8\n",
    "import importlib as imp\n",
    "#import DeepPhenotyping_functions as func\n",
    "#imp.reload(func)\n",
    "\n",
    "# ensure same order\n",
    "l_order = ['Gain of Function (ACTG1)', 'Loss of Function (ACTG1)', 'Other (ACTG1)', 'Gain of Function (ACTB)', 'Loss of Function (ACTB)', 'Other (ACTB)']\n",
    "\n",
    "func.makePCA(X_trans, df_group['HPOcode'],  df_group['HPOcode'], title='_test_group_COG', pal=Colorblind8, l_order=l_order, radius=.1, seed=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained PCA:\tPC1= 0.7239552990617878 \tPC2= 0.2251234088678058\n",
      "PCA figure saved under location: PCA/phenoMap_pca__ACTBACTG.html\n"
     ]
    }
   ],
   "source": [
    "#from bokeh.palettes import Paired12\n",
    "func.makePCA(X_trans, df_group['HPOcode'],  df_group['HPOcode'], title='_ACTBACTG', pal=Colorblind8, l_order=l_order, radius=5, seed=2)\n",
    "#func.makePCA(X_trans, df_group['HPOcode'],  df_group['HPOcode'], title='_correlation_group', pal=Paired12, seed=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Category</th>\n",
       "      <th>Id</th>\n",
       "      <th>Protein</th>\n",
       "      <th>High frequency hearing loss</th>\n",
       "      <th>Short stature</th>\n",
       "      <th>microcephaly</th>\n",
       "      <th>hearing loss</th>\n",
       "      <th>ID</th>\n",
       "      <th>Developmental delay (Global)</th>\n",
       "      <th>Motor Delay</th>\n",
       "      <th>...</th>\n",
       "      <th>Recurrent infections</th>\n",
       "      <th>Photosensitivity</th>\n",
       "      <th>Polyarthralgia</th>\n",
       "      <th>Hepatomegaly</th>\n",
       "      <th>Hypothyroidism</th>\n",
       "      <th>Leukocytose</th>\n",
       "      <th>Trombocytopenie</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>Strabismus</th>\n",
       "      <th>Growth hormone deficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gain Of Function</td>\n",
       "      <td>LR04-298</td>\n",
       "      <td>p.Ser155Phe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gain Of Function</td>\n",
       "      <td>LP98-096</td>\n",
       "      <td>p.Ser155Phe</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gain Of Function</td>\n",
       "      <td>11-10857</td>\n",
       "      <td>p.Ser155Phe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Loss of function</td>\n",
       "      <td>LR11 334</td>\n",
       "      <td>Deletion 17q25.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N-terminus</td>\n",
       "      <td>LR16?163</td>\n",
       "      <td>p.Asn12Asp</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category        Id           Protein  High frequency hearing loss  \\\n",
       "0  Gain Of Function  LR04-298       p.Ser155Phe                            0   \n",
       "1  Gain Of Function  LP98-096       p.Ser155Phe                            0   \n",
       "2  Gain Of Function  11-10857       p.Ser155Phe                            0   \n",
       "3  Loss of function  LR11 334  Deletion 17q25.3                            0   \n",
       "4        N-terminus  LR16?163        p.Asn12Asp                            0   \n",
       "\n",
       "   Short stature   microcephaly   hearing loss    ID  \\\n",
       "0              0              0              0     0   \n",
       "1              1              1              0     1   \n",
       "2              0              0              0     0   \n",
       "3              1              0              0     0   \n",
       "4              0              0              0     1   \n",
       "\n",
       "   Developmental delay (Global)   Motor Delay  ...  Recurrent infections  \\\n",
       "0                             0             0  ...                     0   \n",
       "1                             0             0  ...                     0   \n",
       "2                             0             0  ...                     0   \n",
       "3                             1             0  ...                     0   \n",
       "4                             0             0  ...                     0   \n",
       "\n",
       "   Photosensitivity  Polyarthralgia  Hepatomegaly  Hypothyroidism  \\\n",
       "0               0.0             0.0           0.0               0   \n",
       "1               0.0             0.0           0.0               0   \n",
       "2               0.0             0.0           0.0               0   \n",
       "3               0.0             0.0           0.0               0   \n",
       "4               0.0             0.0           0.0               0   \n",
       "\n",
       "   Leukocytose  Trombocytopenie  Asthma  Strabismus  Growth hormone deficiency  \n",
       "0            0                0       0           0                          0  \n",
       "1            0                0       0           0                          0  \n",
       "2            0                0       0           0                          0  \n",
       "3            0                0       0           1                          0  \n",
       "4            0                0       0           0                          0  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_actg1 = pd.read_csv(r'data/ACTG1/ACTG1_data.csv', sep=',', header=0)\n",
    "df_actg1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Id</th>\n",
       "      <th>Protein</th>\n",
       "      <th>High frequency hearing loss</th>\n",
       "      <th>Short stature</th>\n",
       "      <th>microcephaly</th>\n",
       "      <th>hearing loss</th>\n",
       "      <th>ID</th>\n",
       "      <th>Developmental delay (Global)</th>\n",
       "      <th>Motor Delay</th>\n",
       "      <th>...</th>\n",
       "      <th>Recurrent infections</th>\n",
       "      <th>Photosensitivity</th>\n",
       "      <th>Polyarthralgia</th>\n",
       "      <th>Hepatomegaly</th>\n",
       "      <th>Hypothyroidism</th>\n",
       "      <th>Leukocytose</th>\n",
       "      <th>Trombocytopenie</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>Strabismus</th>\n",
       "      <th>Growth hormone deficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Loss of function</td>\n",
       "      <td>LR11 334</td>\n",
       "      <td>Deletion 17q25.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>1 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category        Id           Protein  High frequency hearing loss  \\\n",
       "3  Loss of function  LR11 334  Deletion 17q25.3                            0   \n",
       "\n",
       "   Short stature   microcephaly   hearing loss    ID  \\\n",
       "3              1              0              0     0   \n",
       "\n",
       "   Developmental delay (Global)   Motor Delay  ...  Recurrent infections  \\\n",
       "3                             1             0  ...                     0   \n",
       "\n",
       "   Photosensitivity  Polyarthralgia  Hepatomegaly  Hypothyroidism  \\\n",
       "3               0.0             0.0           0.0               0   \n",
       "\n",
       "   Leukocytose  Trombocytopenie  Asthma  Strabismus  Growth hormone deficiency  \n",
       "3            0                0       0           1                          0  \n",
       "\n",
       "[1 rows x 96 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actg1[df_actg1['Category']=='Loss of function']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cog_split = pd.read_csv(r'data/COG/JMID_split_patients.csv', sep=';', header=0,index_col=0)\n",
    "df_cog_split = df_cog_split.apply(lambda x: x.str.replace(',','.'))\n",
    "columns = df_cog_split.columns\n",
    "df_group = df_cog_split.copy()\n",
    "df_group = df_group.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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