tutorial/notebook.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Notebook Tutorial for the Node2VecHiC package\n",
    "## Introduction\n",
    "\n",
    "Welcome to this tutorial for the **Node2VecHiC** package.\n",
    "\n",
    "In this notebook, we will walk you through the process of using different classes and modules available in this repository. Let's get started by creating a fake dataset, which will be the foundation for our demonstration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of fake datasets\n",
    "\n",
    "At the moment, all the **Node2VecHiC** package relies on `.csv` files. In fact, we actually need to have two main objects in order to use this package:\n",
    "\n",
    "- a `.csv` Metadata file\n",
    "- a `.csv` Hi-C file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(123) # for reproducibility\n",
    "\n",
    "number_nodes = 300\n",
    "\n",
    "# Metadata\n",
    "metadata = {'chr': ['chr1', 'chr2', 'chr3', 'chr4'],\n",
    "            'start': [1, 31, 101, 201,],\n",
    "            'end': [30, 100, 200, number_nodes -1]} \n",
    "metadata = pd.DataFrame(metadata)\n",
    "metadata.to_csv('metadata.csv',\n",
    "                index = False)\n",
    "\n",
    "# Hi-C\n",
    "matrix = np.random.randint(low = 0,\n",
    "                           high = 1000,\n",
    "                           size = (number_nodes, number_nodes))\n",
    "matrix = (matrix + matrix.T) // 2\n",
    "np.fill_diagonal(matrix, 0)\n",
    "hi_c = pd.DataFrame(matrix)\n",
    "hi_c.to_csv('hic.csv',\n",
    "            index = False,\n",
    "            header = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We created a fake metadata file containing informations regarding three different chromosomes and 302 nodes. At the same time we created a fake Hi-C adjacency matrix. These are the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>start</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr3</td>\n",
       "      <td>101</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr4</td>\n",
       "      <td>201</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    chr  start  end\n",
       "0  chr1      1   30\n",
       "1  chr2     31  100\n",
       "2  chr3    101  200\n",
       "3  chr4    201  299"
      ]
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       "      <td>618</td>\n",
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       "      <td>819</td>\n",
       "      <td>240</td>\n",
       "      <td>444</td>\n",
       "      <td>915</td>\n",
       "      <td>454</td>\n",
       "      <td>212</td>\n",
       "      <td>729</td>\n",
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       "      <td>537</td>\n",
       "      <td>561</td>\n",
       "      <td>0</td>\n",
       "      <td>535</td>\n",
       "      <td>838</td>\n",
       "      <td>441</td>\n",
       "      <td>456</td>\n",
       "      <td>340</td>\n",
       "      <td>531</td>\n",
       "      <td>...</td>\n",
       "      <td>340</td>\n",
       "      <td>289</td>\n",
       "      <td>643</td>\n",
       "      <td>316</td>\n",
       "      <td>525</td>\n",
       "      <td>409</td>\n",
       "      <td>198</td>\n",
       "      <td>451</td>\n",
       "      <td>781</td>\n",
       "      <td>251</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>803</td>\n",
       "      <td>618</td>\n",
       "      <td>288</td>\n",
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       "      <td>0</td>\n",
       "      <td>371</td>\n",
       "      <td>260</td>\n",
       "      <td>306</td>\n",
       "      <td>619</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>713</td>\n",
       "      <td>360</td>\n",
       "      <td>692</td>\n",
       "      <td>599</td>\n",
       "      <td>330</td>\n",
       "      <td>209</td>\n",
       "      <td>355</td>\n",
       "      <td>388</td>\n",
       "      <td>943</td>\n",
       "      <td>439</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",
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       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>882</td>\n",
       "      <td>751</td>\n",
       "      <td>444</td>\n",
       "      <td>409</td>\n",
       "      <td>209</td>\n",
       "      <td>399</td>\n",
       "      <td>459</td>\n",
       "      <td>581</td>\n",
       "      <td>316</td>\n",
       "      <td>665</td>\n",
       "      <td>...</td>\n",
       "      <td>329</td>\n",
       "      <td>757</td>\n",
       "      <td>606</td>\n",
       "      <td>869</td>\n",
       "      <td>593</td>\n",
       "      <td>0</td>\n",
       "      <td>793</td>\n",
       "      <td>636</td>\n",
       "      <td>633</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>540</td>\n",
       "      <td>487</td>\n",
       "      <td>915</td>\n",
       "      <td>198</td>\n",
       "      <td>355</td>\n",
       "      <td>419</td>\n",
       "      <td>260</td>\n",
       "      <td>570</td>\n",
       "      <td>487</td>\n",
       "      <td>350</td>\n",
       "      <td>...</td>\n",
       "      <td>508</td>\n",
       "      <td>407</td>\n",
       "      <td>473</td>\n",
       "      <td>401</td>\n",
       "      <td>253</td>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>566</td>\n",
       "      <td>223</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>850</td>\n",
       "      <td>414</td>\n",
       "      <td>454</td>\n",
       "      <td>451</td>\n",
       "      <td>388</td>\n",
       "      <td>503</td>\n",
       "      <td>766</td>\n",
       "      <td>299</td>\n",
       "      <td>867</td>\n",
       "      <td>513</td>\n",
       "      <td>...</td>\n",
       "      <td>443</td>\n",
       "      <td>162</td>\n",
       "      <td>628</td>\n",
       "      <td>591</td>\n",
       "      <td>951</td>\n",
       "      <td>636</td>\n",
       "      <td>566</td>\n",
       "      <td>0</td>\n",
       "      <td>444</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>700</td>\n",
       "      <td>499</td>\n",
       "      <td>212</td>\n",
       "      <td>781</td>\n",
       "      <td>943</td>\n",
       "      <td>280</td>\n",
       "      <td>218</td>\n",
       "      <td>623</td>\n",
       "      <td>333</td>\n",
       "      <td>535</td>\n",
       "      <td>...</td>\n",
       "      <td>818</td>\n",
       "      <td>612</td>\n",
       "      <td>786</td>\n",
       "      <td>381</td>\n",
       "      <td>856</td>\n",
       "      <td>633</td>\n",
       "      <td>223</td>\n",
       "      <td>444</td>\n",
       "      <td>0</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>642</td>\n",
       "      <td>624</td>\n",
       "      <td>729</td>\n",
       "      <td>251</td>\n",
       "      <td>439</td>\n",
       "      <td>714</td>\n",
       "      <td>178</td>\n",
       "      <td>512</td>\n",
       "      <td>354</td>\n",
       "      <td>642</td>\n",
       "      <td>...</td>\n",
       "      <td>414</td>\n",
       "      <td>394</td>\n",
       "      <td>620</td>\n",
       "      <td>338</td>\n",
       "      <td>284</td>\n",
       "      <td>618</td>\n",
       "      <td>541</td>\n",
       "      <td>245</td>\n",
       "      <td>605</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9    ...  290  291  292  \\\n",
       "0      0  530  658  331  803   59  648  129  484  202  ...  384  527  643   \n",
       "1    530    0  607  537  618  712  351  325  277  397  ...  607  237  742   \n",
       "2    658  607    0  561  288  770  421  294  340  400  ...  328  618  528   \n",
       "3    331  537  561    0  535  838  441  456  340  531  ...  340  289  643   \n",
       "4    803  618  288  535    0  371  260  306  619  339  ...  713  360  692   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "295  882  751  444  409  209  399  459  581  316  665  ...  329  757  606   \n",
       "296  540  487  915  198  355  419  260  570  487  350  ...  508  407  473   \n",
       "297  850  414  454  451  388  503  766  299  867  513  ...  443  162  628   \n",
       "298  700  499  212  781  943  280  218  623  333  535  ...  818  612  786   \n",
       "299  642  624  729  251  439  714  178  512  354  642  ...  414  394  620   \n",
       "\n",
       "     293  294  295  296  297  298  299  \n",
       "0    412  421  882  540  850  700  642  \n",
       "1    183  388  751  487  414  499  624  \n",
       "2    819  240  444  915  454  212  729  \n",
       "3    316  525  409  198  451  781  251  \n",
       "4    599  330  209  355  388  943  439  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  \n",
       "295  869  593    0  793  636  633  618  \n",
       "296  401  253  793    0  566  223  541  \n",
       "297  591  951  636  566    0  444  245  \n",
       "298  381  856  633  223  444    0  605  \n",
       "299  338  284  618  541  245  605    0  \n",
       "\n",
       "[300 rows x 300 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hi_c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use of Metadata and HiC classes\n",
    "\n",
    "To define instances of these two classes, we need to import the classes and thereafter to define the path of the `.csv` file previously generated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Node2VecHiC.metadata import Metadata\n",
    "from Node2VecHiC.hic import HiC\n",
    "\n",
    "METADATA_PATH = 'metadata.csv'\n",
    "HIC_PATH = 'hic.csv'\n",
    "\n",
    "metadata = Metadata(METADATA_PATH)\n",
    "hic = HiC(metadata, HIC_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these instances, we can recall many attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    chr  start  end\n",
       "0  chr1      1   30\n",
       "1  chr2     31  100\n",
       "2  chr3    101  200\n",
       "3  chr4    201  299"
      ]
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     "metadata": {},
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    "metadata.data_frame"
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>530</td>\n",
       "      <td>658</td>\n",
       "      <td>331</td>\n",
       "      <td>803</td>\n",
       "      <td>59</td>\n",
       "      <td>648</td>\n",
       "      <td>129</td>\n",
       "      <td>484</td>\n",
       "      <td>202</td>\n",
       "      <td>...</td>\n",
       "      <td>384</td>\n",
       "      <td>527</td>\n",
       "      <td>643</td>\n",
       "      <td>412</td>\n",
       "      <td>421</td>\n",
       "      <td>882</td>\n",
       "      <td>540</td>\n",
       "      <td>850</td>\n",
       "      <td>700</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>530</td>\n",
       "      <td>0</td>\n",
       "      <td>607</td>\n",
       "      <td>537</td>\n",
       "      <td>618</td>\n",
       "      <td>712</td>\n",
       "      <td>351</td>\n",
       "      <td>325</td>\n",
       "      <td>277</td>\n",
       "      <td>397</td>\n",
       "      <td>...</td>\n",
       "      <td>607</td>\n",
       "      <td>237</td>\n",
       "      <td>742</td>\n",
       "      <td>183</td>\n",
       "      <td>388</td>\n",
       "      <td>751</td>\n",
       "      <td>487</td>\n",
       "      <td>414</td>\n",
       "      <td>499</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>658</td>\n",
       "      <td>607</td>\n",
       "      <td>0</td>\n",
       "      <td>561</td>\n",
       "      <td>288</td>\n",
       "      <td>770</td>\n",
       "      <td>421</td>\n",
       "      <td>294</td>\n",
       "      <td>340</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>328</td>\n",
       "      <td>618</td>\n",
       "      <td>528</td>\n",
       "      <td>819</td>\n",
       "      <td>240</td>\n",
       "      <td>444</td>\n",
       "      <td>915</td>\n",
       "      <td>454</td>\n",
       "      <td>212</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>331</td>\n",
       "      <td>537</td>\n",
       "      <td>561</td>\n",
       "      <td>0</td>\n",
       "      <td>535</td>\n",
       "      <td>838</td>\n",
       "      <td>441</td>\n",
       "      <td>456</td>\n",
       "      <td>340</td>\n",
       "      <td>531</td>\n",
       "      <td>...</td>\n",
       "      <td>340</td>\n",
       "      <td>289</td>\n",
       "      <td>643</td>\n",
       "      <td>316</td>\n",
       "      <td>525</td>\n",
       "      <td>409</td>\n",
       "      <td>198</td>\n",
       "      <td>451</td>\n",
       "      <td>781</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>803</td>\n",
       "      <td>618</td>\n",
       "      <td>288</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>371</td>\n",
       "      <td>260</td>\n",
       "      <td>306</td>\n",
       "      <td>619</td>\n",
       "      <td>339</td>\n",
       "      <td>...</td>\n",
       "      <td>713</td>\n",
       "      <td>360</td>\n",
       "      <td>692</td>\n",
       "      <td>599</td>\n",
       "      <td>330</td>\n",
       "      <td>209</td>\n",
       "      <td>355</td>\n",
       "      <td>388</td>\n",
       "      <td>943</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>882</td>\n",
       "      <td>751</td>\n",
       "      <td>444</td>\n",
       "      <td>409</td>\n",
       "      <td>209</td>\n",
       "      <td>399</td>\n",
       "      <td>459</td>\n",
       "      <td>581</td>\n",
       "      <td>316</td>\n",
       "      <td>665</td>\n",
       "      <td>...</td>\n",
       "      <td>329</td>\n",
       "      <td>757</td>\n",
       "      <td>606</td>\n",
       "      <td>869</td>\n",
       "      <td>593</td>\n",
       "      <td>0</td>\n",
       "      <td>793</td>\n",
       "      <td>636</td>\n",
       "      <td>633</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>540</td>\n",
       "      <td>487</td>\n",
       "      <td>915</td>\n",
       "      <td>198</td>\n",
       "      <td>355</td>\n",
       "      <td>419</td>\n",
       "      <td>260</td>\n",
       "      <td>570</td>\n",
       "      <td>487</td>\n",
       "      <td>350</td>\n",
       "      <td>...</td>\n",
       "      <td>508</td>\n",
       "      <td>407</td>\n",
       "      <td>473</td>\n",
       "      <td>401</td>\n",
       "      <td>253</td>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>566</td>\n",
       "      <td>223</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>850</td>\n",
       "      <td>414</td>\n",
       "      <td>454</td>\n",
       "      <td>451</td>\n",
       "      <td>388</td>\n",
       "      <td>503</td>\n",
       "      <td>766</td>\n",
       "      <td>299</td>\n",
       "      <td>867</td>\n",
       "      <td>513</td>\n",
       "      <td>...</td>\n",
       "      <td>443</td>\n",
       "      <td>162</td>\n",
       "      <td>628</td>\n",
       "      <td>591</td>\n",
       "      <td>951</td>\n",
       "      <td>636</td>\n",
       "      <td>566</td>\n",
       "      <td>0</td>\n",
       "      <td>444</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>700</td>\n",
       "      <td>499</td>\n",
       "      <td>212</td>\n",
       "      <td>781</td>\n",
       "      <td>943</td>\n",
       "      <td>280</td>\n",
       "      <td>218</td>\n",
       "      <td>623</td>\n",
       "      <td>333</td>\n",
       "      <td>535</td>\n",
       "      <td>...</td>\n",
       "      <td>818</td>\n",
       "      <td>612</td>\n",
       "      <td>786</td>\n",
       "      <td>381</td>\n",
       "      <td>856</td>\n",
       "      <td>633</td>\n",
       "      <td>223</td>\n",
       "      <td>444</td>\n",
       "      <td>0</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>642</td>\n",
       "      <td>624</td>\n",
       "      <td>729</td>\n",
       "      <td>251</td>\n",
       "      <td>439</td>\n",
       "      <td>714</td>\n",
       "      <td>178</td>\n",
       "      <td>512</td>\n",
       "      <td>354</td>\n",
       "      <td>642</td>\n",
       "      <td>...</td>\n",
       "      <td>414</td>\n",
       "      <td>394</td>\n",
       "      <td>620</td>\n",
       "      <td>338</td>\n",
       "      <td>284</td>\n",
       "      <td>618</td>\n",
       "      <td>541</td>\n",
       "      <td>245</td>\n",
       "      <td>605</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9    ...  290  291  292  \\\n",
       "0      0  530  658  331  803   59  648  129  484  202  ...  384  527  643   \n",
       "1    530    0  607  537  618  712  351  325  277  397  ...  607  237  742   \n",
       "2    658  607    0  561  288  770  421  294  340  400  ...  328  618  528   \n",
       "3    331  537  561    0  535  838  441  456  340  531  ...  340  289  643   \n",
       "4    803  618  288  535    0  371  260  306  619  339  ...  713  360  692   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "295  882  751  444  409  209  399  459  581  316  665  ...  329  757  606   \n",
       "296  540  487  915  198  355  419  260  570  487  350  ...  508  407  473   \n",
       "297  850  414  454  451  388  503  766  299  867  513  ...  443  162  628   \n",
       "298  700  499  212  781  943  280  218  623  333  535  ...  818  612  786   \n",
       "299  642  624  729  251  439  714  178  512  354  642  ...  414  394  620   \n",
       "\n",
       "     293  294  295  296  297  298  299  \n",
       "0    412  421  882  540  850  700  642  \n",
       "1    183  388  751  487  414  499  624  \n",
       "2    819  240  444  915  454  212  729  \n",
       "3    316  525  409  198  451  781  251  \n",
       "4    599  330  209  355  388  943  439  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  \n",
       "295  869  593    0  793  636  633  618  \n",
       "296  401  253  793    0  566  223  541  \n",
       "297  591  951  636  566    0  444  245  \n",
       "298  381  856  633  223  444    0  605  \n",
       "299  338  284  618  541  245  605    0  \n",
       "\n",
       "[300 rows x 300 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hic.data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'chr1',\n",
       " 1: 'chr1',\n",
       " 2: 'chr1',\n",
       " 3: 'chr1',\n",
       " 4: 'chr1',\n",
       " 5: 'chr1',\n",
       " 6: 'chr1',\n",
       " 7: 'chr1',\n",
       " 8: 'chr1',\n",
       " 9: 'chr1',\n",
       " 10: 'chr1',\n",
       " 11: 'chr1',\n",
       " 12: 'chr1',\n",
       " 13: 'chr1',\n",
       " 14: 'chr1',\n",
       " 15: 'chr1',\n",
       " 16: 'chr1',\n",
       " 17: 'chr1',\n",
       " 18: 'chr1',\n",
       " 19: 'chr1',\n",
       " 20: 'chr1',\n",
       " 21: 'chr1',\n",
       " 22: 'chr1',\n",
       " 23: 'chr1',\n",
       " 24: 'chr1',\n",
       " 25: 'chr1',\n",
       " 26: 'chr1',\n",
       " 27: 'chr1',\n",
       " 28: 'chr1',\n",
       " 29: 'chr1',\n",
       " 30: 'chr2',\n",
       " 31: 'chr2',\n",
       " 32: 'chr2',\n",
       " 33: 'chr2',\n",
       " 34: 'chr2',\n",
       " 35: 'chr2',\n",
       " 36: 'chr2',\n",
       " 37: 'chr2',\n",
       " 38: 'chr2',\n",
       " 39: 'chr2',\n",
       " 40: 'chr2',\n",
       " 41: 'chr2',\n",
       " 42: 'chr2',\n",
       " 43: 'chr2',\n",
       " 44: 'chr2',\n",
       " 45: 'chr2',\n",
       " 46: 'chr2',\n",
       " 47: 'chr2',\n",
       " 48: 'chr2',\n",
       " 49: 'chr2',\n",
       " 50: 'chr2',\n",
       " 51: 'chr2',\n",
       " 52: 'chr2',\n",
       " 53: 'chr2',\n",
       " 54: 'chr2',\n",
       " 55: 'chr2',\n",
       " 56: 'chr2',\n",
       " 57: 'chr2',\n",
       " 58: 'chr2',\n",
       " 59: 'chr2',\n",
       " 60: 'chr2',\n",
       " 61: 'chr2',\n",
       " 62: 'chr2',\n",
       " 63: 'chr2',\n",
       " 64: 'chr2',\n",
       " 65: 'chr2',\n",
       " 66: 'chr2',\n",
       " 67: 'chr2',\n",
       " 68: 'chr2',\n",
       " 69: 'chr2',\n",
       " 70: 'chr2',\n",
       " 71: 'chr2',\n",
       " 72: 'chr2',\n",
       " 73: 'chr2',\n",
       " 74: 'chr2',\n",
       " 75: 'chr2',\n",
       " 76: 'chr2',\n",
       " 77: 'chr2',\n",
       " 78: 'chr2',\n",
       " 79: 'chr2',\n",
       " 80: 'chr2',\n",
       " 81: 'chr2',\n",
       " 82: 'chr2',\n",
       " 83: 'chr2',\n",
       " 84: 'chr2',\n",
       " 85: 'chr2',\n",
       " 86: 'chr2',\n",
       " 87: 'chr2',\n",
       " 88: 'chr2',\n",
       " 89: 'chr2',\n",
       " 90: 'chr2',\n",
       " 91: 'chr2',\n",
       " 92: 'chr2',\n",
       " 93: 'chr2',\n",
       " 94: 'chr2',\n",
       " 95: 'chr2',\n",
       " 96: 'chr2',\n",
       " 97: 'chr2',\n",
       " 98: 'chr2',\n",
       " 99: 'chr2',\n",
       " 100: 'chr3',\n",
       " 101: 'chr3',\n",
       " 102: 'chr3',\n",
       " 103: 'chr3',\n",
       " 104: 'chr3',\n",
       " 105: 'chr3',\n",
       " 106: 'chr3',\n",
       " 107: 'chr3',\n",
       " 108: 'chr3',\n",
       " 109: 'chr3',\n",
       " 110: 'chr3',\n",
       " 111: 'chr3',\n",
       " 112: 'chr3',\n",
       " 113: 'chr3',\n",
       " 114: 'chr3',\n",
       " 115: 'chr3',\n",
       " 116: 'chr3',\n",
       " 117: 'chr3',\n",
       " 118: 'chr3',\n",
       " 119: 'chr3',\n",
       " 120: 'chr3',\n",
       " 121: 'chr3',\n",
       " 122: 'chr3',\n",
       " 123: 'chr3',\n",
       " 124: 'chr3',\n",
       " 125: 'chr3',\n",
       " 126: 'chr3',\n",
       " 127: 'chr3',\n",
       " 128: 'chr3',\n",
       " 129: 'chr3',\n",
       " 130: 'chr3',\n",
       " 131: 'chr3',\n",
       " 132: 'chr3',\n",
       " 133: 'chr3',\n",
       " 134: 'chr3',\n",
       " 135: 'chr3',\n",
       " 136: 'chr3',\n",
       " 137: 'chr3',\n",
       " 138: 'chr3',\n",
       " 139: 'chr3',\n",
       " 140: 'chr3',\n",
       " 141: 'chr3',\n",
       " 142: 'chr3',\n",
       " 143: 'chr3',\n",
       " 144: 'chr3',\n",
       " 145: 'chr3',\n",
       " 146: 'chr3',\n",
       " 147: 'chr3',\n",
       " 148: 'chr3',\n",
       " 149: 'chr3',\n",
       " 150: 'chr3',\n",
       " 151: 'chr3',\n",
       " 152: 'chr3',\n",
       " 153: 'chr3',\n",
       " 154: 'chr3',\n",
       " 155: 'chr3',\n",
       " 156: 'chr3',\n",
       " 157: 'chr3',\n",
       " 158: 'chr3',\n",
       " 159: 'chr3',\n",
       " 160: 'chr3',\n",
       " 161: 'chr3',\n",
       " 162: 'chr3',\n",
       " 163: 'chr3',\n",
       " 164: 'chr3',\n",
       " 165: 'chr3',\n",
       " 166: 'chr3',\n",
       " 167: 'chr3',\n",
       " 168: 'chr3',\n",
       " 169: 'chr3',\n",
       " 170: 'chr3',\n",
       " 171: 'chr3',\n",
       " 172: 'chr3',\n",
       " 173: 'chr3',\n",
       " 174: 'chr3',\n",
       " 175: 'chr3',\n",
       " 176: 'chr3',\n",
       " 177: 'chr3',\n",
       " 178: 'chr3',\n",
       " 179: 'chr3',\n",
       " 180: 'chr3',\n",
       " 181: 'chr3',\n",
       " 182: 'chr3',\n",
       " 183: 'chr3',\n",
       " 184: 'chr3',\n",
       " 185: 'chr3',\n",
       " 186: 'chr3',\n",
       " 187: 'chr3',\n",
       " 188: 'chr3',\n",
       " 189: 'chr3',\n",
       " 190: 'chr3',\n",
       " 191: 'chr3',\n",
       " 192: 'chr3',\n",
       " 193: 'chr3',\n",
       " 194: 'chr3',\n",
       " 195: 'chr3',\n",
       " 196: 'chr3',\n",
       " 197: 'chr3',\n",
       " 198: 'chr3',\n",
       " 199: 'chr3',\n",
       " 200: 'chr4',\n",
       " 201: 'chr4',\n",
       " 202: 'chr4',\n",
       " 203: 'chr4',\n",
       " 204: 'chr4',\n",
       " 205: 'chr4',\n",
       " 206: 'chr4',\n",
       " 207: 'chr4',\n",
       " 208: 'chr4',\n",
       " 209: 'chr4',\n",
       " 210: 'chr4',\n",
       " 211: 'chr4',\n",
       " 212: 'chr4',\n",
       " 213: 'chr4',\n",
       " 214: 'chr4',\n",
       " 215: 'chr4',\n",
       " 216: 'chr4',\n",
       " 217: 'chr4',\n",
       " 218: 'chr4',\n",
       " 219: 'chr4',\n",
       " 220: 'chr4',\n",
       " 221: 'chr4',\n",
       " 222: 'chr4',\n",
       " 223: 'chr4',\n",
       " 224: 'chr4',\n",
       " 225: 'chr4',\n",
       " 226: 'chr4',\n",
       " 227: 'chr4',\n",
       " 228: 'chr4',\n",
       " 229: 'chr4',\n",
       " 230: 'chr4',\n",
       " 231: 'chr4',\n",
       " 232: 'chr4',\n",
       " 233: 'chr4',\n",
       " 234: 'chr4',\n",
       " 235: 'chr4',\n",
       " 236: 'chr4',\n",
       " 237: 'chr4',\n",
       " 238: 'chr4',\n",
       " 239: 'chr4',\n",
       " 240: 'chr4',\n",
       " 241: 'chr4',\n",
       " 242: 'chr4',\n",
       " 243: 'chr4',\n",
       " 244: 'chr4',\n",
       " 245: 'chr4',\n",
       " 246: 'chr4',\n",
       " 247: 'chr4',\n",
       " 248: 'chr4',\n",
       " 249: 'chr4',\n",
       " 250: 'chr4',\n",
       " 251: 'chr4',\n",
       " 252: 'chr4',\n",
       " 253: 'chr4',\n",
       " 254: 'chr4',\n",
       " 255: 'chr4',\n",
       " 256: 'chr4',\n",
       " 257: 'chr4',\n",
       " 258: 'chr4',\n",
       " 259: 'chr4',\n",
       " 260: 'chr4',\n",
       " 261: 'chr4',\n",
       " 262: 'chr4',\n",
       " 263: 'chr4',\n",
       " 264: 'chr4',\n",
       " 265: 'chr4',\n",
       " 266: 'chr4',\n",
       " 267: 'chr4',\n",
       " 268: 'chr4',\n",
       " 269: 'chr4',\n",
       " 270: 'chr4',\n",
       " 271: 'chr4',\n",
       " 272: 'chr4',\n",
       " 273: 'chr4',\n",
       " 274: 'chr4',\n",
       " 275: 'chr4',\n",
       " 276: 'chr4',\n",
       " 277: 'chr4',\n",
       " 278: 'chr4',\n",
       " 279: 'chr4',\n",
       " 280: 'chr4',\n",
       " 281: 'chr4',\n",
       " 282: 'chr4',\n",
       " 283: 'chr4',\n",
       " 284: 'chr4',\n",
       " 285: 'chr4',\n",
       " 286: 'chr4',\n",
       " 287: 'chr4',\n",
       " 288: 'chr4',\n",
       " 289: 'chr4',\n",
       " 290: 'chr4',\n",
       " 291: 'chr4',\n",
       " 292: 'chr4',\n",
       " 293: 'chr4',\n",
       " 294: 'chr4',\n",
       " 295: 'chr4',\n",
       " 296: 'chr4',\n",
       " 297: 'chr4',\n",
       " 298: 'chr4'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hic.attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run algorithms\n",
    "\n",
    "At the moment, we can run two different algorithms:\n",
    "- node2vec\n",
    "- pca\n",
    "\n",
    "To run the node2vec algorithm, we need to import the function `run_node2vec`; then we need to arbitrarily define the following parameters, and thereafter pack them into a list. It is mandatory to also define an `embeddings_path`, that will save the learned embeddings in  `.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing transition probabilities: 100%|██████████| 300/300 [01:53<00:00,  2.64it/s]\n",
      "Generating walks (CPU: 1): 100%|██████████| 4/4 [00:03<00:00,  1.09it/s]\n"
     ]
    }
   ],
   "source": [
    "from Node2VecHiC.algorithms import run_node2vec\n",
    "\n",
    "EMBEDDINGS_PATH = 'embeddings.csv'\n",
    "\n",
    "# Node2Vec parameters\n",
    "N_DIMENSIONS = 4\n",
    "WALK_LENGTH = 50\n",
    "NUM_WALKS = 4\n",
    "WEIGHT_KEY = 'weight'\n",
    "WORKERS = 1\n",
    "P = 1\n",
    "Q = 0.5\n",
    "\n",
    "parameters = [N_DIMENSIONS, WALK_LENGTH, NUM_WALKS, WEIGHT_KEY, WORKERS, P, Q,]\n",
    "\n",
    "hic_graph = hic.graph\n",
    "embeddings, indexes = run_node2vec(hic_graph,\n",
    "                                   parameters,\n",
    "                                   EMBEDDINGS_PATH)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the pca algorithm, we need to import the `run_pca` function and then define the number of principal components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Node2VecHiC.algorithms import run_pca\n",
    "\n",
    "#PCA\n",
    "N_COMPONENTS = 2\n",
    "\n",
    "principal_df = run_pca(data_frame = embeddings,\n",
    "                       n_components = N_COMPONENTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot\n",
    "\n",
    "To visualize the result, we just need to import the `Graphics` class. The function `get_plot_chromosome()` will give us a visualization of the extracted principal components dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Node2VecHiC.graphics import Graphics\n",
    "\n",
    "hic_graphics = Graphics(hic)\n",
    "hic_graphics.get_plot_chromosome(principal_df,\n",
    "                                 title = \"PCA on Node2Vec embeddings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When interpreting the data in this context, it's important to qualitatively assess the potential presence or absence of clusters. In this particular case, we do not observe the formation of distinct clusters, leading us to infer the likely presence of chromosome translocations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blocks Graph\n",
    "\n",
    "If one would like to isolate the different chromosomes, it is possible to isolate nodes using a combination of the `get_block_graph()` method of the `HiC` class. With this method we obtain a list of different graphs, related to all the possible combination of chromosome couples.\n",
    "\n",
    "At the moment, this method works with the variable `selected_chromosome = 1` associated with the chromosome `chr2` in the specific case of the fake dataset used. This mean that the output graph list will be composed by three graphs:\n",
    " \n",
    "$$\n",
    "\\left[ G(\\text{chr}_2, \\text{chr}_1), G(\\text{chr}_2, \\text{chr}_3), G(\\text{chr}_2, \\text{chr}_4) \\right]\n",
    "$$\n",
    "\n",
    "where $ G(\\text{chr}_i, \\text{chr}_j) $ is the graph containing just the index nodes associated with chromosome $i$ and chromosome $j$.\n",
    "\n",
    "Then, the idea is to run the node2vec algorithm and the pca as before, on these new graphs structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing transition probabilities: 100%|██████████| 99/99 [00:05<00:00, 17.17it/s]\n",
      "Generating walks (CPU: 1): 100%|██████████| 4/4 [00:00<00:00,  6.88it/s]\n",
      "Computing transition probabilities: 100%|██████████| 169/169 [00:31<00:00,  5.33it/s]\n",
      "Generating walks (CPU: 1): 100%|██████████| 4/4 [00:01<00:00,  2.70it/s]\n",
      "Computing transition probabilities: 100%|██████████| 168/168 [00:23<00:00,  7.29it/s]\n",
      "Generating walks (CPU: 1): 100%|██████████| 4/4 [00:01<00:00,  3.84it/s]\n"
     ]
    }
   ],
   "source": [
    "selected_chromosome = 1 #chr2\n",
    "\n",
    "graph_block_lists = hic.get_block_graph(selected_chromosome)\n",
    "\n",
    "EMBEDDINGS_chr2_chr1 = 'embeddings_chr2_chr1.csv'\n",
    "EMBEDDINGS_chr2_chr3 = 'embeddings_chr2_chr3.csv'\n",
    "EMBEDDINGS_chr2_chr4 = 'embeddings_chr2_chr4.csv'\n",
    "\n",
    "embeddings_path_block = [EMBEDDINGS_chr2_chr1, EMBEDDINGS_chr2_chr3, EMBEDDINGS_chr2_chr4]\n",
    "\n",
    "embeddings = []\n",
    "for idx, graph in enumerate(graph_block_lists):\n",
    "    embeddings_found, _ = run_node2vec(graph,\n",
    "                                       parameters,\n",
    "                                       embeddings_path_block[idx])\n",
    "    embeddings.append(embeddings_found)\n",
    "\n",
    "block = []\n",
    "for model in embeddings:\n",
    "    principal_df = run_pca(data_frame = model,\n",
    "                           n_components = N_COMPONENTS)\n",
    "    block.append(principal_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blocks plot\n",
    "\n",
    "To visualize the blocks result, use the `get_plot_blocks()` method of the `Graphics` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hic_graphics.get_plot_blocks(block,\n",
    "                             selected_chromosome,\n",
    "                             save = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we could hypothesize chromosome translocations, as we do not visually observe the presence of clusters.\n",
    "\n",
    "However, it must be noted that this blocks method is purely experimental. The primary goal of this procedure was to investigate the feasibility of refining the method for tracing chromosome translocations in the most challenging cases where they were not detectable using the traditional approach of applying algorithms to the entire structure of the initial graph."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}