cluster_conn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5x/9dwn0lbx633gccy22ts5bskh0000gn/T/ipykernel_81321/3622504503.py:2: DtypeWarning: Columns (7,8,12,13,14,19,20,24,27,31,42,43,44,49,51,59,60,62,64,65,68,101,121,163) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  pheno = pd.read_csv(path_pheno,index_col=0)\n"
     ]
    }
   ],
   "source": [
    "path_pheno = '/Users/harveyaa/Documents/masters/data/pheno_26-01-22.csv'\n",
    "pheno = pd.read_csv(path_pheno,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_conn = '/Users/harveyaa/Documents/masters/data/connectomes/connectome_{}_cambridge64.npy'\n",
    "connectomes = []\n",
    "for idx in pheno.index:\n",
    "    connectomes.append(np.load(path_conn.format(idx)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_conn = pd.DataFrame(np.mean(connectomes,axis=(0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mean_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'icoord': [[5.0, 5.0, 15.0, 15.0],\n",
       "  [25.0, 25.0, 35.0, 35.0],\n",
       "  [55.0, 55.0, 65.0, 65.0],\n",
       "  [45.0, 45.0, 60.0, 60.0],\n",
       "  [30.0, 30.0, 52.5, 52.5],\n",
       "  [10.0, 10.0, 41.25, 41.25],\n",
       "  [75.0, 75.0, 85.0, 85.0],\n",
       "  [105.0, 105.0, 115.0, 115.0],\n",
       "  [95.0, 95.0, 110.0, 110.0],\n",
       "  [125.0, 125.0, 135.0, 135.0],\n",
       "  [145.0, 145.0, 155.0, 155.0],\n",
       "  [130.0, 130.0, 150.0, 150.0],\n",
       "  [102.5, 102.5, 140.0, 140.0],\n",
       "  [80.0, 80.0, 121.25, 121.25],\n",
       "  [175.0, 175.0, 185.0, 185.0],\n",
       "  [165.0, 165.0, 180.0, 180.0],\n",
       "  [195.0, 195.0, 205.0, 205.0],\n",
       "  [172.5, 172.5, 200.0, 200.0],\n",
       "  [235.0, 235.0, 245.0, 245.0],\n",
       "  [225.0, 225.0, 240.0, 240.0],\n",
       "  [215.0, 215.0, 232.5, 232.5],\n",
       "  [186.25, 186.25, 223.75, 223.75],\n",
       "  [100.625, 100.625, 205.0, 205.0],\n",
       "  [25.625, 25.625, 152.8125, 152.8125],\n",
       "  [275.0, 275.0, 285.0, 285.0],\n",
       "  [265.0, 265.0, 280.0, 280.0],\n",
       "  [255.0, 255.0, 272.5, 272.5],\n",
       "  [295.0, 295.0, 305.0, 305.0],\n",
       "  [315.0, 315.0, 325.0, 325.0],\n",
       "  [300.0, 300.0, 320.0, 320.0],\n",
       "  [263.75, 263.75, 310.0, 310.0],\n",
       "  [365.0, 365.0, 375.0, 375.0],\n",
       "  [355.0, 355.0, 370.0, 370.0],\n",
       "  [345.0, 345.0, 362.5, 362.5],\n",
       "  [335.0, 335.0, 353.75, 353.75],\n",
       "  [385.0, 385.0, 395.0, 395.0],\n",
       "  [405.0, 405.0, 415.0, 415.0],\n",
       "  [425.0, 425.0, 435.0, 435.0],\n",
       "  [445.0, 445.0, 455.0, 455.0],\n",
       "  [430.0, 430.0, 450.0, 450.0],\n",
       "  [410.0, 410.0, 440.0, 440.0],\n",
       "  [390.0, 390.0, 425.0, 425.0],\n",
       "  [344.375, 344.375, 407.5, 407.5],\n",
       "  [465.0, 465.0, 475.0, 475.0],\n",
       "  [485.0, 485.0, 495.0, 495.0],\n",
       "  [505.0, 505.0, 515.0, 515.0],\n",
       "  [490.0, 490.0, 510.0, 510.0],\n",
       "  [470.0, 470.0, 500.0, 500.0],\n",
       "  [535.0, 535.0, 545.0, 545.0],\n",
       "  [525.0, 525.0, 540.0, 540.0],\n",
       "  [575.0, 575.0, 585.0, 585.0],\n",
       "  [565.0, 565.0, 580.0, 580.0],\n",
       "  [555.0, 555.0, 572.5, 572.5],\n",
       "  [532.5, 532.5, 563.75, 563.75],\n",
       "  [595.0, 595.0, 605.0, 605.0],\n",
       "  [625.0, 625.0, 635.0, 635.0],\n",
       "  [615.0, 615.0, 630.0, 630.0],\n",
       "  [600.0, 600.0, 622.5, 622.5],\n",
       "  [548.125, 548.125, 611.25, 611.25],\n",
       "  [485.0, 485.0, 579.6875, 579.6875],\n",
       "  [375.9375, 375.9375, 532.34375, 532.34375],\n",
       "  [286.875, 286.875, 454.140625, 454.140625],\n",
       "  [89.21875, 89.21875, 370.5078125, 370.5078125]],\n",
       " 'dcoord': [[0.0, 0.8224790504626862, 0.8224790504626862, 0.0],\n",
       "  [0.0, 0.9069306701542151, 0.9069306701542151, 0.0],\n",
       "  [0.0, 0.8437766349153766, 0.8437766349153766, 0.0],\n",
       "  [0.0, 1.0438306650926819, 1.0438306650926819, 0.8437766349153766],\n",
       "  [0.9069306701542151,\n",
       "   1.3983488347427897,\n",
       "   1.3983488347427897,\n",
       "   1.0438306650926819],\n",
       "  [0.8224790504626862,\n",
       "   2.080824394962745,\n",
       "   2.080824394962745,\n",
       "   1.3983488347427897],\n",
       "  [0.0, 1.0473315021673528, 1.0473315021673528, 0.0],\n",
       "  [0.0, 1.0907413908504828, 1.0907413908504828, 0.0],\n",
       "  [0.0, 1.2070864301230446, 1.2070864301230446, 1.0907413908504828],\n",
       "  [0.0, 1.0723947336653916, 1.0723947336653916, 0.0],\n",
       "  [0.0, 1.126606511567701, 1.126606511567701, 0.0],\n",
       "  [1.0723947336653916,\n",
       "   1.3380498093625985,\n",
       "   1.3380498093625985,\n",
       "   1.126606511567701],\n",
       "  [1.2070864301230446,\n",
       "   1.6736996627740455,\n",
       "   1.6736996627740455,\n",
       "   1.3380498093625985],\n",
       "  [1.0473315021673528, 1.87569837304191, 1.87569837304191, 1.6736996627740455],\n",
       "  [0.0, 0.9368719646309213, 0.9368719646309213, 0.0],\n",
       "  [0.0, 1.0735224648774906, 1.0735224648774906, 0.9368719646309213],\n",
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       "  'C2',\n",
       "  'C2',\n",
       "  'C2',\n",
       "  'C2']}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram, fcluster\n",
    "#from scipy.spatial.distance import squareform\n",
    "\n",
    "\n",
    "Z = linkage(mean_conn, 'ward')\n",
    "\n",
    "dendrogram(Z, orientation='top', \n",
    "           leaf_rotation=90)\n",
    "\n",
    "#from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "#cluster = AgglomerativeClustering(linkage='ward',n_clusters=3)\n",
    "#clustering = cluster.fit(mean_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 34, 53,  5, 54, 55,  4,  1, 15, 17,  8, 58, 49, 35, 56, 30,  2,\n",
       "       37, 12, 10,  6, 16, 36, 13,  7, 43, 28, 62, 59, 26, 11, 44, 27, 22,\n",
       "       31, 47, 63, 29, 19, 60, 14, 23, 32,  9, 41, 45, 42, 57, 50, 20, 38,\n",
       "       24, 21, 51, 18, 39, 61, 64, 33, 46, 25, 40, 52, 48], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clusterize the data\n",
    "threshold = 0.8\n",
    "labels = fcluster(Z, threshold, criterion='distance')\n",
    "\n",
    "# Show the cluster\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>7</th>\n",
       "      <th>16</th>\n",
       "      <th>0</th>\n",
       "      <th>6</th>\n",
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       "      <td>0.239614</td>\n",
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       "      <td>0.398735</td>\n",
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       "      <td>0.239847</td>\n",
       "      <td>0.250710</td>\n",
       "      <td>0.271364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363321</td>\n",
       "      <td>0.344114</td>\n",
       "      <td>0.340683</td>\n",
       "      <td>0.235338</td>\n",
       "      <td>0.395748</td>\n",
       "      <td>0.383538</td>\n",
       "      <td>0.295802</td>\n",
       "      <td>0.343958</td>\n",
       "      <td>0.317767</td>\n",
       "      <td>0.292601</td>\n",
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       "      <td>0.387862</td>\n",
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       "      <td>0.234447</td>\n",
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       "      <td>0.525246</td>\n",
       "      <td>0.307190</td>\n",
       "      <td>0.141641</td>\n",
       "      <td>0.155592</td>\n",
       "      <td>0.229764</td>\n",
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       "      <td>0.394634</td>\n",
       "      <td>0.379525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.485921</td>\n",
       "      <td>0.433232</td>\n",
       "      <td>0.532729</td>\n",
       "      <td>0.423766</td>\n",
       "      <td>0.547725</td>\n",
       "      <td>0.459522</td>\n",
       "      <td>0.411295</td>\n",
       "      <td>0.500036</td>\n",
       "      <td>0.495474</td>\n",
       "      <td>0.468829</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.252695</td>\n",
       "      <td>0.172999</td>\n",
       "      <td>0.383538</td>\n",
       "      <td>0.383725</td>\n",
       "      <td>0.459522</td>\n",
       "      <td>0.343274</td>\n",
       "      <td>0.464544</td>\n",
       "      <td>0.318819</td>\n",
       "      <td>0.364673</td>\n",
       "      <td>0.289308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429203</td>\n",
       "      <td>0.571157</td>\n",
       "      <td>0.530266</td>\n",
       "      <td>0.459154</td>\n",
       "      <td>0.627754</td>\n",
       "      <td>0.202174</td>\n",
       "      <td>1.042771</td>\n",
       "      <td>0.921631</td>\n",
       "      <td>0.691021</td>\n",
       "      <td>0.576318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.216543</td>\n",
       "      <td>0.158232</td>\n",
       "      <td>0.295802</td>\n",
       "      <td>0.320366</td>\n",
       "      <td>0.411295</td>\n",
       "      <td>0.344385</td>\n",
       "      <td>0.427320</td>\n",
       "      <td>0.327545</td>\n",
       "      <td>0.382082</td>\n",
       "      <td>0.356656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.366303</td>\n",
       "      <td>0.520987</td>\n",
       "      <td>0.516453</td>\n",
       "      <td>0.452979</td>\n",
       "      <td>0.622657</td>\n",
       "      <td>1.042771</td>\n",
       "      <td>0.236602</td>\n",
       "      <td>0.809621</td>\n",
       "      <td>0.958706</td>\n",
       "      <td>0.834957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.275424</td>\n",
       "      <td>0.252643</td>\n",
       "      <td>0.343958</td>\n",
       "      <td>0.321788</td>\n",
       "      <td>0.500036</td>\n",
       "      <td>0.378320</td>\n",
       "      <td>0.473492</td>\n",
       "      <td>0.532185</td>\n",
       "      <td>0.548541</td>\n",
       "      <td>0.502063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.431830</td>\n",
       "      <td>0.703472</td>\n",
       "      <td>0.567500</td>\n",
       "      <td>0.718498</td>\n",
       "      <td>0.645951</td>\n",
       "      <td>0.921631</td>\n",
       "      <td>0.809621</td>\n",
       "      <td>0.137340</td>\n",
       "      <td>0.732456</td>\n",
       "      <td>0.551190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.256547</td>\n",
       "      <td>0.227423</td>\n",
       "      <td>0.317767</td>\n",
       "      <td>0.441791</td>\n",
       "      <td>0.495474</td>\n",
       "      <td>0.529681</td>\n",
       "      <td>0.389835</td>\n",
       "      <td>0.377292</td>\n",
       "      <td>0.497659</td>\n",
       "      <td>0.477539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382861</td>\n",
       "      <td>0.552232</td>\n",
       "      <td>0.522107</td>\n",
       "      <td>0.594632</td>\n",
       "      <td>0.596154</td>\n",
       "      <td>0.691021</td>\n",
       "      <td>0.958706</td>\n",
       "      <td>0.732456</td>\n",
       "      <td>0.324649</td>\n",
       "      <td>0.920194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.288309</td>\n",
       "      <td>0.197239</td>\n",
       "      <td>0.292601</td>\n",
       "      <td>0.382485</td>\n",
       "      <td>0.468829</td>\n",
       "      <td>0.525434</td>\n",
       "      <td>0.541335</td>\n",
       "      <td>0.322915</td>\n",
       "      <td>0.374827</td>\n",
       "      <td>0.487854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.364481</td>\n",
       "      <td>0.382976</td>\n",
       "      <td>0.516542</td>\n",
       "      <td>0.392672</td>\n",
       "      <td>0.500746</td>\n",
       "      <td>0.576318</td>\n",
       "      <td>0.834957</td>\n",
       "      <td>0.551190</td>\n",
       "      <td>0.920194</td>\n",
       "      <td>0.239672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          7         16        0         6         3         20        24  \\\n",
       "7   0.125473  0.369351  0.360696  0.331302  0.297065  0.278732  0.371319   \n",
       "16  0.369351  0.091637  0.536653  0.387862  0.400799  0.253959  0.196005   \n",
       "0   0.360696  0.536653  0.105411  0.558625  0.597459  0.398735  0.358451   \n",
       "6   0.331302  0.387862  0.558625  0.234447  0.506425  0.525246  0.307190   \n",
       "3   0.297065  0.400799  0.597459  0.506425  0.111896  0.490748  0.453573   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "39  0.252695  0.172999  0.383538  0.383725  0.459522  0.343274  0.464544   \n",
       "56  0.216543  0.158232  0.295802  0.320366  0.411295  0.344385  0.427320   \n",
       "27  0.275424  0.252643  0.343958  0.321788  0.500036  0.378320  0.473492   \n",
       "36  0.256547  0.227423  0.317767  0.441791  0.495474  0.529681  0.389835   \n",
       "57  0.288309  0.197239  0.292601  0.382485  0.468829  0.525434  0.541335   \n",
       "\n",
       "          10        43        19  ...        5         14        47        11  \\\n",
       "7   0.164535  0.185436  0.227752  ...  0.190007  0.223893  0.262737  0.187526   \n",
       "16  0.239614  0.213867  0.269198  ...  0.153289  0.165720  0.155204  0.239342   \n",
       "0   0.239847  0.250710  0.271364  ...  0.363321  0.344114  0.340683  0.235338   \n",
       "6   0.141641  0.155592  0.229764  ...  0.352673  0.270229  0.316842  0.213097   \n",
       "3   0.368513  0.394634  0.379525  ...  0.485921  0.433232  0.532729  0.423766   \n",
       "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
       "39  0.318819  0.364673  0.289308  ...  0.429203  0.571157  0.530266  0.459154   \n",
       "56  0.327545  0.382082  0.356656  ...  0.366303  0.520987  0.516453  0.452979   \n",
       "27  0.532185  0.548541  0.502063  ...  0.431830  0.703472  0.567500  0.718498   \n",
       "36  0.377292  0.497659  0.477539  ...  0.382861  0.552232  0.522107  0.594632   \n",
       "57  0.322915  0.374827  0.487854  ...  0.364481  0.382976  0.516542  0.392672   \n",
       "\n",
       "          28        39        56        27        36        57  \n",
       "7   0.183569  0.252695  0.216543  0.275424  0.256547  0.288309  \n",
       "16  0.232139  0.172999  0.158232  0.252643  0.227423  0.197239  \n",
       "0   0.395748  0.383538  0.295802  0.343958  0.317767  0.292601  \n",
       "6   0.399104  0.383725  0.320366  0.321788  0.441791  0.382485  \n",
       "3   0.547725  0.459522  0.411295  0.500036  0.495474  0.468829  \n",
       "..       ...       ...       ...       ...       ...       ...  \n",
       "39  0.627754  0.202174  1.042771  0.921631  0.691021  0.576318  \n",
       "56  0.622657  1.042771  0.236602  0.809621  0.958706  0.834957  \n",
       "27  0.645951  0.921631  0.809621  0.137340  0.732456  0.551190  \n",
       "36  0.596154  0.691021  0.958706  0.732456  0.324649  0.920194  \n",
       "57  0.500746  0.576318  0.834957  0.551190  0.920194  0.239672  \n",
       "\n",
       "[64 rows x 64 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep the indices to sort labels\n",
    "labels_order = np.argsort(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(round(mean_conn[labels_order].loc[labels_order],2), cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fmri",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}