development.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nethome/mbafna6/miniconda3/envs/CeSpGRN/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import igviz as ig\n",
    "from node2vec import Node2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "# import src.preprocess as pre\n",
    "# import src.visualize as vis\n",
    "# pio.renderers.default = \"png\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1597, 45, 45)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('./out/preprocessing_output/initial_grns.npy').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_df = pd.read_csv(\"./data/celltalk_human_lr_pair.txt\", sep=\"\\t\")\n",
    "receptors = set(LR_df[\"receptor_gene_symbol\"].to_list())\n",
    "ligands = set(LR_df[\"ligand_gene_symbol\"].to_list())\n",
    "\n",
    "sample_counts = pd.read_csv(\"./data/seqFISH/counts.csv\")\n",
    "gene_names = set(map(lambda x: x.upper(), sample_counts.columns.to_numpy()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib_venn._common.VennDiagram at 0x7fecac2b79d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib_venn import venn3\n",
    "\n",
    "venn3((ligands, gene_names,receptors),(\"Known Ligands\", \"Candidate Genes\", \"Known Receptors\") )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1597, 127)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(\"./out/preprocessing_output/celllevel_features.npy\").shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './out/preprocessing_output/genelevel_features'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m wv_from_text \u001b[39m=\u001b[39m KeyedVectors\u001b[39m.\u001b[39;49mload_word2vec_format(\u001b[39m\"\u001b[39;49m\u001b[39m./out/preprocessing_output/genelevel_features\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniconda3/envs/CeSpGRN/lib/python3.8/site-packages/gensim/models/keyedvectors.py:1723\u001b[0m, in \u001b[0;36mKeyedVectors.load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header)\u001b[0m\n\u001b[1;32m   1676\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m   1677\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mload_word2vec_format\u001b[39m(\n\u001b[1;32m   1678\u001b[0m         \u001b[39mcls\u001b[39m, fname, fvocab\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, binary\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, encoding\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mutf8\u001b[39m\u001b[39m'\u001b[39m, unicode_errors\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mstrict\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m   1679\u001b[0m         limit\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, datatype\u001b[39m=\u001b[39mREAL, no_header\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m   1680\u001b[0m     ):\n\u001b[1;32m   1681\u001b[0m     \u001b[39m\"\"\"Load KeyedVectors from a file produced by the original C word2vec-tool format.\u001b[39;00m\n\u001b[1;32m   1682\u001b[0m \n\u001b[1;32m   1683\u001b[0m \u001b[39m    Warnings\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1721\u001b[0m \n\u001b[1;32m   1722\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1723\u001b[0m     \u001b[39mreturn\u001b[39;00m _load_word2vec_format(\n\u001b[1;32m   1724\u001b[0m         \u001b[39mcls\u001b[39;49m, fname, fvocab\u001b[39m=\u001b[39;49mfvocab, binary\u001b[39m=\u001b[39;49mbinary, encoding\u001b[39m=\u001b[39;49mencoding, unicode_errors\u001b[39m=\u001b[39;49municode_errors,\n\u001b[1;32m   1725\u001b[0m         limit\u001b[39m=\u001b[39;49mlimit, datatype\u001b[39m=\u001b[39;49mdatatype, no_header\u001b[39m=\u001b[39;49mno_header,\n\u001b[1;32m   1726\u001b[0m     )\n",
      "File \u001b[0;32m~/miniconda3/envs/CeSpGRN/lib/python3.8/site-packages/gensim/models/keyedvectors.py:2052\u001b[0m, in \u001b[0;36m_load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header, binary_chunk_size)\u001b[0m\n\u001b[1;32m   2049\u001b[0m             counts[word] \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(count)\n\u001b[1;32m   2051\u001b[0m logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mloading projection weights from \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, fname)\n\u001b[0;32m-> 2052\u001b[0m \u001b[39mwith\u001b[39;00m utils\u001b[39m.\u001b[39;49mopen(fname, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m fin:\n\u001b[1;32m   2053\u001b[0m     \u001b[39mif\u001b[39;00m no_header:\n\u001b[1;32m   2054\u001b[0m         \u001b[39m# deduce both vocab_size & vector_size from 1st pass over file\u001b[39;00m\n\u001b[1;32m   2055\u001b[0m         \u001b[39mif\u001b[39;00m binary:\n",
      "File \u001b[0;32m~/miniconda3/envs/CeSpGRN/lib/python3.8/site-packages/smart_open/smart_open_lib.py:177\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(uri, mode, buffering, encoding, errors, newline, closefd, opener, compression, transport_params)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[39mif\u001b[39;00m transport_params \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    175\u001b[0m     transport_params \u001b[39m=\u001b[39m {}\n\u001b[0;32m--> 177\u001b[0m fobj \u001b[39m=\u001b[39m _shortcut_open(\n\u001b[1;32m    178\u001b[0m     uri,\n\u001b[1;32m    179\u001b[0m     mode,\n\u001b[1;32m    180\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m    181\u001b[0m     buffering\u001b[39m=\u001b[39;49mbuffering,\n\u001b[1;32m    182\u001b[0m     encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[1;32m    183\u001b[0m     errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m    184\u001b[0m     newline\u001b[39m=\u001b[39;49mnewline,\n\u001b[1;32m    185\u001b[0m )\n\u001b[1;32m    186\u001b[0m \u001b[39mif\u001b[39;00m fobj \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    187\u001b[0m     \u001b[39mreturn\u001b[39;00m fobj\n",
      "File \u001b[0;32m~/miniconda3/envs/CeSpGRN/lib/python3.8/site-packages/smart_open/smart_open_lib.py:363\u001b[0m, in \u001b[0;36m_shortcut_open\u001b[0;34m(uri, mode, compression, buffering, encoding, errors, newline)\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[39mif\u001b[39;00m errors \u001b[39mand\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mb\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[1;32m    361\u001b[0m     open_kwargs[\u001b[39m'\u001b[39m\u001b[39merrors\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m errors\n\u001b[0;32m--> 363\u001b[0m \u001b[39mreturn\u001b[39;00m _builtin_open(local_path, mode, buffering\u001b[39m=\u001b[39;49mbuffering, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mopen_kwargs)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './out/preprocessing_output/genelevel_features'"
     ]
    }
   ],
   "source": [
    "wv_from_text = KeyedVectors.load_word2vec_format(\"./out/preprocessing_output/genelevel_features\")\n",
    "# np.load(\"./out/preprocessing_output/genelevel_features\", allow_pickle=True).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.array([vector for vector in wv_from_text])\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "model = Word2Vec.load(\"./out/preprocessing_output/genelevel_feature_model\")\n",
    "np.save(\"./out/preprocessing_output/genelevel_features.npy\", model.wv.vectors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 0., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('./out/preprocessing_output/celllevel_adjacencymatrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-2.29</td>\n",
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       "      <td>3.20</td>\n",
       "      <td>2.30</td>\n",
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       "      <td>-0.30</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.30</td>\n",
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       "      <td>-1.16</td>\n",
       "      <td>-1.29</td>\n",
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       "      <td>0.99</td>\n",
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       "      <td>-0.97</td>\n",
       "      <td>-1.29</td>\n",
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       "      <td>1.09</td>\n",
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       "      <td>0.36</td>\n",
       "      <td>2.47</td>\n",
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       "      <td>1.01</td>\n",
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       "      <td>-2.60</td>\n",
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       "      <td>-1.97</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.54</td>\n",
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       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.26</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.23</td>\n",
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       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>-1.06</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>-1.99</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>1.58</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.27</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>-1.69</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1597 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2     3     4     5     6     7     8     9    ...   115  \\\n",
       "0     1.83  1.52  2.41 -0.02 -0.28  1.95  0.04  0.10  0.91 -0.24  ...  0.12   \n",
       "1     0.38 -0.04  0.59  0.03  0.93  0.49  0.08  2.05 -0.17 -0.11  ... -1.60   \n",
       "2     0.82  0.12  0.67 -0.29 -0.42 -0.04  1.35  0.68  0.08  0.05  ... -0.66   \n",
       "3     0.04 -1.27 -0.30 -0.52 -2.08  0.92 -0.03  0.42 -0.27  0.30  ... -0.85   \n",
       "4     0.52  0.44 -1.05  0.48 -0.49  2.29  0.27  0.67 -1.00  0.43  ... -0.65   \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "1592 -0.17 -0.50  1.03  0.90  0.23  0.48  0.73  0.96  1.01 -0.66  ... -0.79   \n",
       "1593  0.20  0.95  0.57  0.77 -2.34 -0.29  0.13  0.84  1.26 -0.09  ... -0.57   \n",
       "1594 -1.06 -0.18 -0.33 -0.05 -0.26 -0.22  0.32  0.18  0.20  0.45  ...  1.04   \n",
       "1595 -1.99 -0.25  1.34  0.00 -0.01  0.95  1.23  0.72  0.31 -0.15  ... -1.28   \n",
       "1596 -1.69  0.94 -0.12 -0.21 -0.82  0.36 -1.60  0.98  0.39 -1.35  ... -0.28   \n",
       "\n",
       "       116   117   118   119   120   121   122   123   124  \n",
       "0    -1.32 -2.06 -1.32  0.19 -2.20 -1.54 -1.40  2.51  0.60  \n",
       "1    -0.64 -2.47 -1.34  0.00  0.22 -1.46  0.22  1.88  2.05  \n",
       "2    -0.78 -1.07 -2.09  0.09  0.32 -2.29  0.15  3.20  2.30  \n",
       "3    -1.16 -1.29 -0.57  0.99 -0.19 -1.49  0.65  3.04  2.00  \n",
       "4    -0.97 -1.29 -1.77  1.09 -1.17 -2.11  0.36  2.47  1.83  \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "1592 -0.31 -3.45 -2.60 -1.13 -1.97 -0.82 -1.53  2.21  0.54  \n",
       "1593 -0.33 -1.13 -0.68 -0.06 -1.55 -0.43 -0.60  2.66  0.23  \n",
       "1594  0.54  1.38  0.90 -0.41 -1.69  0.36 -0.49  0.80  0.39  \n",
       "1595 -1.25  1.58 -2.68 -0.77  0.27 -0.60 -0.14 -2.66  0.37  \n",
       "1596 -2.20  0.24 -1.44  0.16 -1.36 -0.04  0.04 -0.61 -0.14  \n",
       "\n",
       "[1597 rows x 125 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.load('./out/preprocessing_output/celllevel_features.npy'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <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>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.004634</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>-0.006738</td>\n",
       "      <td>-0.002148</td>\n",
       "      <td>0.004101</td>\n",
       "      <td>-0.011146</td>\n",
       "      <td>-0.004890</td>\n",
       "      <td>-0.004959</td>\n",
       "      <td>-0.003431</td>\n",
       "      <td>-0.004909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010438</td>\n",
       "      <td>0.017101</td>\n",
       "      <td>0.009317</td>\n",
       "      <td>0.011760</td>\n",
       "      <td>-0.004696</td>\n",
       "      <td>0.004759</td>\n",
       "      <td>0.016287</td>\n",
       "      <td>0.003582</td>\n",
       "      <td>0.001202</td>\n",
       "      <td>-0.001816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.004863</td>\n",
       "      <td>1.003174</td>\n",
       "      <td>1.009325</td>\n",
       "      <td>1.002433</td>\n",
       "      <td>0.995672</td>\n",
       "      <td>1.014559</td>\n",
       "      <td>1.005069</td>\n",
       "      <td>1.006324</td>\n",
       "      <td>1.004245</td>\n",
       "      <td>1.012613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.987435</td>\n",
       "      <td>0.972709</td>\n",
       "      <td>0.975218</td>\n",
       "      <td>0.979422</td>\n",
       "      <td>1.015804</td>\n",
       "      <td>0.984703</td>\n",
       "      <td>0.965073</td>\n",
       "      <td>0.987810</td>\n",
       "      <td>0.990594</td>\n",
       "      <td>1.001660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.290000</td>\n",
       "      <td>-4.110000</td>\n",
       "      <td>-4.500000</td>\n",
       "      <td>-4.370000</td>\n",
       "      <td>-4.330000</td>\n",
       "      <td>-4.100000</td>\n",
       "      <td>-4.460000</td>\n",
       "      <td>-4.260000</td>\n",
       "      <td>-4.170000</td>\n",
       "      <td>-4.320000</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.170000</td>\n",
       "      <td>-4.600000</td>\n",
       "      <td>-3.580000</td>\n",
       "      <td>-3.810000</td>\n",
       "      <td>-4.520000</td>\n",
       "      <td>-4.700000</td>\n",
       "      <td>-6.160000</td>\n",
       "      <td>-6.030000</td>\n",
       "      <td>-3.610000</td>\n",
       "      <td>-4.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.690000</td>\n",
       "      <td>-0.660000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>-0.640000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.650000</td>\n",
       "      <td>-0.690000</td>\n",
       "      <td>-0.690000</td>\n",
       "      <td>-0.660000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.610000</td>\n",
       "      <td>-0.630000</td>\n",
       "      <td>-0.590000</td>\n",
       "      <td>-0.590000</td>\n",
       "      <td>-0.620000</td>\n",
       "      <td>-0.510000</td>\n",
       "      <td>-0.560000</td>\n",
       "      <td>-0.450000</td>\n",
       "      <td>-0.590000</td>\n",
       "      <td>-0.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.570000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>2.930000</td>\n",
       "      <td>2.620000</td>\n",
       "      <td>2.630000</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>2.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.790000</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>3.070000</td>\n",
       "      <td>2.980000</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>2.370000</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>5.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "count  1597.000000  1597.000000  1597.000000  1597.000000  1597.000000   \n",
       "mean     -0.004634     0.000795    -0.006738    -0.002148     0.004101   \n",
       "std       1.004863     1.003174     1.009325     1.002433     0.995672   \n",
       "min      -4.290000    -4.110000    -4.500000    -4.370000    -4.330000   \n",
       "25%      -0.670000    -0.690000    -0.660000    -0.700000    -0.640000   \n",
       "50%       0.110000     0.120000     0.080000     0.140000     0.170000   \n",
       "75%       0.740000     0.700000     0.720000     0.730000     0.740000   \n",
       "max       2.570000     2.570000     2.410000     2.930000     2.620000   \n",
       "\n",
       "               5            6            7            8            9    ...  \\\n",
       "count  1597.000000  1597.000000  1597.000000  1597.000000  1597.000000  ...   \n",
       "mean     -0.011146    -0.004890    -0.004959    -0.003431    -0.004909  ...   \n",
       "std       1.014559     1.005069     1.006324     1.004245     1.012613  ...   \n",
       "min      -4.100000    -4.460000    -4.260000    -4.170000    -4.320000  ...   \n",
       "25%      -0.670000    -0.650000    -0.690000    -0.690000    -0.660000  ...   \n",
       "50%       0.060000     0.120000     0.090000     0.150000     0.060000  ...   \n",
       "75%       0.780000     0.720000     0.760000     0.740000     0.720000  ...   \n",
       "max       2.630000     2.480000     2.470000     2.430000     2.920000  ...   \n",
       "\n",
       "               115          116          117          118          119  \\\n",
       "count  1597.000000  1597.000000  1597.000000  1597.000000  1597.000000   \n",
       "mean      0.010438     0.017101     0.009317     0.011760    -0.004696   \n",
       "std       0.987435     0.972709     0.975218     0.979422     1.015804   \n",
       "min      -3.170000    -4.600000    -3.580000    -3.810000    -4.520000   \n",
       "25%      -0.610000    -0.630000    -0.590000    -0.590000    -0.620000   \n",
       "50%       0.020000     0.070000     0.150000     0.090000     0.030000   \n",
       "75%       0.630000     0.700000     0.710000     0.710000     0.640000   \n",
       "max       3.790000     2.740000     2.410000     3.070000     2.980000   \n",
       "\n",
       "               120          121          122          123          124  \n",
       "count  1597.000000  1597.000000  1597.000000  1597.000000  1597.000000  \n",
       "mean      0.004759     0.016287     0.003582     0.001202    -0.001816  \n",
       "std       0.984703     0.965073     0.987810     0.990594     1.001660  \n",
       "min      -4.700000    -6.160000    -6.030000    -3.610000    -4.360000  \n",
       "25%      -0.510000    -0.560000    -0.450000    -0.590000    -0.580000  \n",
       "50%       0.150000     0.140000     0.090000    -0.020000     0.080000  \n",
       "75%       0.680000     0.700000     0.600000     0.500000     0.620000  \n",
       "max       2.470000     2.370000     3.920000     4.180000     5.150000  \n",
       "\n",
       "[8 rows x 125 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Data(x=[1597, 125], edge_index=[2, 7985], y=[1597, 1597]),\n",
       " Data(x=[71865, 64], edge_index=[2, 1824841]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src import models, training\n",
    "from torch_geometric.nn import GAE\n",
    "import torch\n",
    "import os\n",
    "\n",
    "preprocess_output_path = \"./out/preprocessing_output/\"\n",
    "training_output_path = \"./out/training_output/\"\n",
    "celllevel_data, genelevel_data = training.create_pyg_data(preprocess_output_path)\n",
    "data = (celllevel_data, genelevel_data)\n",
    "\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GAE(\n",
       "  (encoder): MultiviewEncoder(\n",
       "    (encoder_g): SubgraphEncoder(\n",
       "      (conv1): GCNConv(64, 32)\n",
       "      (conv2): GCNConv(32, 32)\n",
       "      (linear): Linear(in_features=1440, out_features=32, bias=True)\n",
       "    )\n",
       "    (encoder_c): GraphEncoder(\n",
       "      (conv1): GCNConv(125, 32)\n",
       "      (conv2): GCNConv(32, 32)\n",
       "    )\n",
       "  )\n",
       "  (decoder): InnerProductDecoder()\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_clarifyGAE_pytorch(data, hyperparams = None):\n",
    "    num_cells, num_cellfeatures = data[0].x.shape[0], data[0].x.shape[1]\n",
    "    num_genes, num_genefeatures = data[1].x.shape[0], data[1].x.shape[1]\n",
    "    hidden_dim = hyperparams[\"concat_hidden_dim\"] // 2\n",
    "    num_genespercell = hyperparams[\"num_genespercell\"]\n",
    "\n",
    "    cellEncoder = models.GraphEncoder(num_cellfeatures, hidden_dim)\n",
    "    geneEncoder = models.SubgraphEncoder(num_features=num_genefeatures, hidden_dim=hidden_dim, num_vertices = num_cells, num_subvertices = num_genespercell)\n",
    "    \n",
    "    multiviewEncoder = models.MultiviewEncoder(SubgraphEncoder = geneEncoder, GraphEncoder = cellEncoder)\n",
    "    gae = GAE(multiviewEncoder)\n",
    "\n",
    "    return gae\n",
    "\n",
    "\n",
    "hyperparameters = {\n",
    "    \"num_genespercell\": 45,\n",
    "    \"concat_hidden_dim\": 64,\n",
    "    \"optimizer\" : \"adam\",\n",
    "    \"criterion\" : torch.nn.BCELoss(),\n",
    "    \"num_epochs\": 400\n",
    "}\n",
    "\n",
    "trained_gae = build_clarifyGAE_pytorch(data, hyperparameters)\n",
    "trained_gae.load_state_dict(torch.load(os.path.join(training_output_path,f'trained_gae_model.pth')))\n",
    "trained_gae.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_level_encoder = trained_gae.encoder.encoder_c\n",
    "gene_level_encoder = trained_gae.encoder.encoder_g\n",
    "z = trained_gae.encode(data[0].x,data[1].x, data[0].edge_index, data[1].edge_index)\n",
    "z_c = cell_level_encoder(data[0].x, data[0].edge_index)\n",
    "z_g = gene_level_encoder.embed(data[1].x, data[1].edge_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[-0.1112,  0.1229, -0.2332,  ...,  0.0674,  0.0020, -0.0342],\n",
       "         [-0.1296,  0.1299, -0.2572,  ...,  0.0346,  0.0223, -0.0537],\n",
       "         [-0.0744,  0.1145, -0.1754,  ...,  0.0124,  0.0714, -0.0825],\n",
       "         ...,\n",
       "         [ 0.0970,  0.0127,  0.1533,  ...,  0.0845, -0.0419,  0.0275],\n",
       "         [-0.0340, -0.0606, -0.3503,  ..., -0.0693,  0.0326, -0.0410],\n",
       "         [ 0.0226, -0.0130, -0.4287,  ..., -0.1413,  0.1442, -0.1397]],\n",
       "        grad_fn=<CatBackward0>),\n",
       " torch.Size([1597, 64]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z,z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[-0.1112,  0.1229, -0.2332,  ...,  0.0888, -0.2396, -0.3305],\n",
       "         [-0.1296,  0.1299, -0.2572,  ...,  0.0953, -0.2569, -0.3565],\n",
       "         [-0.0744,  0.1145, -0.1754,  ...,  0.1117, -0.1832, -0.2226],\n",
       "         ...,\n",
       "         [ 0.0970,  0.0127,  0.1533,  ..., -0.1178, -0.5628,  0.2156],\n",
       "         [-0.0340, -0.0606, -0.3503,  ...,  0.1191,  0.2159,  0.6880],\n",
       "         [ 0.0226, -0.0130, -0.4287,  ...,  0.1142,  0.1929,  0.5404]],\n",
       "        grad_fn=<AddBackward0>),\n",
       " torch.Size([1597, 32]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_c,z_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[-2.1153e-02, -6.9548e-03,  1.8205e-05,  ...,  1.0911e-02,\n",
       "           7.8208e-03,  4.7564e-02],\n",
       "         [-1.0616e-02, -1.1525e-02,  5.1655e-03,  ...,  2.8392e-02,\n",
       "           7.4270e-04,  4.9644e-02],\n",
       "         [ 5.0740e-03, -2.7834e-02,  1.7913e-03,  ...,  2.4759e-02,\n",
       "          -2.4404e-04,  4.3329e-02],\n",
       "         ...,\n",
       "         [-4.9639e-02, -2.9899e-02, -4.0271e-02,  ..., -4.8675e-02,\n",
       "           7.9036e-03,  1.3046e-02],\n",
       "         [-5.2377e-02, -3.2574e-02, -3.8744e-02,  ..., -4.4512e-02,\n",
       "           1.4279e-02,  1.8906e-02],\n",
       "         [-1.6025e-02, -3.2065e-02, -3.3183e-02,  ..., -1.9597e-02,\n",
       "           6.1737e-03,  4.5368e-02]], grad_fn=<AddBackward0>),\n",
       " torch.Size([71865, 32]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_g, z_g.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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