exploring_spokesig_features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de430df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "import pandas as pd\n",
    "from paths import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc5f650f",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "CPU times: user 9.09 s, sys: 33.8 s, total: 42.9 s\n",
      "Wall time: 42.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with open(os.path.join(OUTPUT_PATH, \"spoke_embedding_analysis\", \"ppr_features_dict_gzip_compressed.pickle\"), \"rb\") as f:\n",
    "    features = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12c5fe9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'features': array(['Anatomy:UBERON:0001255', 'Gene:8938', 'Gene:2812', ...,\n",
       "        'Variant:rs72991939', 'Variant:rs113428065', 'Variant:rs1056914'],\n",
       "       dtype='<U191')}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00ecd909",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df = pd.DataFrame(features[\"features\"], columns=[\"node_id\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "076f4eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df[\"node_type\"] = features_df.node_id.apply(lambda x:x.split(\":\")[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01685578",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.node_id = features_df.node_id.apply(lambda x:\":\".join(x.split(\":\")[1:]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d9a835d",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.to_csv(os.path.join(OUTPUT_PATH, \"spoke_embedding_analysis\", \"spoke35M_ppr_features.csv\"), index=False, header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9399ebb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.node_type.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "33e67a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_id</th>\n",
       "      <th>node_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8938</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2812</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>127391</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>222008</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>147323</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665854</th>\n",
       "      <td>101927844</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665857</th>\n",
       "      <td>101928120</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665866</th>\n",
       "      <td>107303344</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665875</th>\n",
       "      <td>441490</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665888</th>\n",
       "      <td>101929372</td>\n",
       "      <td>Gene</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          node_id node_type\n",
       "1            8938      Gene\n",
       "2            2812      Gene\n",
       "3          127391      Gene\n",
       "4          222008      Gene\n",
       "5          147323      Gene\n",
       "...           ...       ...\n",
       "665854  101927844      Gene\n",
       "665857  101928120      Gene\n",
       "665866  107303344      Gene\n",
       "665875     441490      Gene\n",
       "665888  101929372      Gene\n",
       "\n",
       "[20730 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df[features_df.node_type==\"Gene\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca7583ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "From an implicit embedding vector of MS, extract top N biological nodes (both top positive and top negative nodes)\n",
    "Check the proximity of these biological nodes with respect to MS disease node in graph space\n",
    "\"\"\""
   ]
  }
 ],
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