26.umap-transform-node2vec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert STRING node2vec embeddings to a 2d embedding using UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import umap\n",
    "import scipy.sparse\n",
    "import pandas as pd\n",
    "\n",
    "from utils import get_string_dataset_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 949 ms, sys: 27 ms, total: 976 ms\n",
      "Wall time: 975 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>emb_1</th>\n",
       "      <th>emb_2</th>\n",
       "      <th>emb_3</th>\n",
       "      <th>emb_4</th>\n",
       "      <th>emb_5</th>\n",
       "      <th>emb_6</th>\n",
       "      <th>emb_7</th>\n",
       "      <th>emb_8</th>\n",
       "      <th>emb_9</th>\n",
       "      <th>emb_10</th>\n",
       "      <th>...</th>\n",
       "      <th>emb_119</th>\n",
       "      <th>emb_120</th>\n",
       "      <th>emb_121</th>\n",
       "      <th>emb_122</th>\n",
       "      <th>emb_123</th>\n",
       "      <th>emb_124</th>\n",
       "      <th>emb_125</th>\n",
       "      <th>emb_126</th>\n",
       "      <th>emb_127</th>\n",
       "      <th>emb_128</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_id</th>\n",
       "      <th>preferred_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>ARF5</th>\n",
       "      <td>0.495522</td>\n",
       "      <td>-0.264478</td>\n",
       "      <td>0.383956</td>\n",
       "      <td>-1.48516</td>\n",
       "      <td>1.13501</td>\n",
       "      <td>-0.687379</td>\n",
       "      <td>-0.092868</td>\n",
       "      <td>-0.428224</td>\n",
       "      <td>-0.148345</td>\n",
       "      <td>-0.028783</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002594</td>\n",
       "      <td>0.308740</td>\n",
       "      <td>-0.062943</td>\n",
       "      <td>0.140117</td>\n",
       "      <td>0.177642</td>\n",
       "      <td>0.132789</td>\n",
       "      <td>0.032162</td>\n",
       "      <td>0.093262</td>\n",
       "      <td>-0.260608</td>\n",
       "      <td>0.014748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>M6PR</th>\n",
       "      <td>0.371978</td>\n",
       "      <td>-0.189449</td>\n",
       "      <td>0.336587</td>\n",
       "      <td>-1.45652</td>\n",
       "      <td>1.08911</td>\n",
       "      <td>-0.992916</td>\n",
       "      <td>-0.500337</td>\n",
       "      <td>-0.429294</td>\n",
       "      <td>0.179531</td>\n",
       "      <td>-0.128398</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.213474</td>\n",
       "      <td>-0.102929</td>\n",
       "      <td>0.170698</td>\n",
       "      <td>-0.014626</td>\n",
       "      <td>0.150232</td>\n",
       "      <td>0.078505</td>\n",
       "      <td>0.129128</td>\n",
       "      <td>0.193226</td>\n",
       "      <td>-0.458337</td>\n",
       "      <td>0.285326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           emb_1     emb_2     emb_3    emb_4    emb_5  \\\n",
       "node_id preferred_name                                                   \n",
       "0       ARF5            0.495522 -0.264478  0.383956 -1.48516  1.13501   \n",
       "1       M6PR            0.371978 -0.189449  0.336587 -1.45652  1.08911   \n",
       "\n",
       "                           emb_6     emb_7     emb_8     emb_9    emb_10  ...  \\\n",
       "node_id preferred_name                                                    ...   \n",
       "0       ARF5           -0.687379 -0.092868 -0.428224 -0.148345 -0.028783  ...   \n",
       "1       M6PR           -0.992916 -0.500337 -0.429294  0.179531 -0.128398  ...   \n",
       "\n",
       "                         emb_119   emb_120   emb_121   emb_122   emb_123  \\\n",
       "node_id preferred_name                                                     \n",
       "0       ARF5           -0.002594  0.308740 -0.062943  0.140117  0.177642   \n",
       "1       M6PR           -0.213474 -0.102929  0.170698 -0.014626  0.150232   \n",
       "\n",
       "                         emb_124   emb_125   emb_126   emb_127   emb_128  \n",
       "node_id preferred_name                                                    \n",
       "0       ARF5            0.132789  0.032162  0.093262 -0.260608  0.014748  \n",
       "1       M6PR            0.078505  0.129128  0.193226 -0.458337  0.285326  \n",
       "\n",
       "[2 rows x 128 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "embedding_high_df = pd.read_csv(\"data/embeddings/node2vec-128d.tsv.xz\", sep=\"\\t\", index_col=[0, 1])\n",
    "embedding_high_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>index</th>\n",
       "      <th>protein_external_id</th>\n",
       "      <th>preferred_name</th>\n",
       "      <th>protein_size</th>\n",
       "      <th>annotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19564</th>\n",
       "      <td>19564</td>\n",
       "      <td>9606.ENSP00000485675</td>\n",
       "      <td>ENSG00000280116</td>\n",
       "      <td>84</td>\n",
       "      <td>annotation not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19565</th>\n",
       "      <td>19565</td>\n",
       "      <td>9606.ENSP00000485678</td>\n",
       "      <td>OR6Q1</td>\n",
       "      <td>317</td>\n",
       "      <td>Olfactory receptor 6Q1; Odorant receptor; Olfa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   protein_external_id   preferred_name  protein_size  \\\n",
       "19564  19564  9606.ENSP00000485675  ENSG00000280116            84   \n",
       "19565  19565  9606.ENSP00000485678            OR6Q1           317   \n",
       "\n",
       "                                              annotation  \n",
       "19564                           annotation not available  \n",
       "19565  Olfactory receptor 6Q1; Odorant receptor; Olfa...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the head of protein info\n",
    "gene_path = get_string_dataset_path(\"protein.info\")\n",
    "gene_df = pd.read_csv(gene_path, sep='\\t')\n",
    "gene_df = gene_df.reset_index()\n",
    "gene_df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "reducer = umap.UMAP(\n",
    "    n_components=2,\n",
    "    random_state=0,\n",
    "    init=\"random\", # https://github.com/lmcinnes/umap/issues/381\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29.8 s, sys: 432 ms, total: 30.3 s\n",
      "Wall time: 30.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mapper = reducer.fit(embedding_high_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>index</th>\n",
       "      <th>protein_external_id</th>\n",
       "      <th>preferred_name</th>\n",
       "      <th>protein_size</th>\n",
       "      <th>annotation</th>\n",
       "      <th>umap_0</th>\n",
       "      <th>umap_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9606.ENSP00000000233</td>\n",
       "      <td>ARF5</td>\n",
       "      <td>180</td>\n",
       "      <td>ADP-ribosylation factor 5; GTP-binding protein...</td>\n",
       "      <td>12.868138</td>\n",
       "      <td>2.650041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9606.ENSP00000000412</td>\n",
       "      <td>M6PR</td>\n",
       "      <td>277</td>\n",
       "      <td>Cation-dependent mannose-6-phosphate receptor;...</td>\n",
       "      <td>5.868094</td>\n",
       "      <td>7.865096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   protein_external_id preferred_name  protein_size  \\\n",
       "0      0  9606.ENSP00000000233           ARF5           180   \n",
       "1      1  9606.ENSP00000000412           M6PR           277   \n",
       "\n",
       "                                          annotation     umap_0    umap_1  \n",
       "0  ADP-ribosylation factor 5; GTP-binding protein...  12.868138  2.650041  \n",
       "1  Cation-dependent mannose-6-phosphate receptor;...   5.868094  7.865096  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create embedding dataframe\n",
    "umap_df = gene_df.merge(\n",
    "    pd.DataFrame(mapper.transform(embedding_high_df))\n",
    "    .add_prefix(\"umap_\")\n",
    "    .reset_index(drop = False)\n",
    "    .rename(columns={\"node_id\": \"index\"})\n",
    ")\n",
    "umap_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19354"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(umap_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# from sklearn.cluster import DBSCAN\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "# # https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html\n",
    "# cluster_model = DBSCAN(eps=0.5, min_samples=5)\n",
    "# clusters = cluster_model.fit_predict(StandardScaler().fit_transform(embedding_high_df))\n",
    "# clusters[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cluster\n",
    "\n",
    "cluster high dimensional embedding for coloring the 2d scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 57.4 s, sys: 29.7 s, total: 1min 27s\n",
      "Wall time: 27.9 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>protein_external_id</th>\n",
       "      <th>preferred_name</th>\n",
       "      <th>protein_size</th>\n",
       "      <th>annotation</th>\n",
       "      <th>umap_0</th>\n",
       "      <th>umap_1</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9606.ENSP00000000233</td>\n",
       "      <td>ARF5</td>\n",
       "      <td>180</td>\n",
       "      <td>ADP-ribosylation factor 5; GTP-binding protein...</td>\n",
       "      <td>12.868138</td>\n",
       "      <td>2.650041</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9606.ENSP00000000412</td>\n",
       "      <td>M6PR</td>\n",
       "      <td>277</td>\n",
       "      <td>Cation-dependent mannose-6-phosphate receptor;...</td>\n",
       "      <td>5.868094</td>\n",
       "      <td>7.865096</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   protein_external_id preferred_name  protein_size  \\\n",
       "0      0  9606.ENSP00000000233           ARF5           180   \n",
       "1      1  9606.ENSP00000000412           M6PR           277   \n",
       "\n",
       "                                          annotation     umap_0    umap_1  \\\n",
       "0  ADP-ribosylation factor 5; GTP-binding protein...  12.868138  2.650041   \n",
       "1  Cation-dependent mannose-6-phosphate receptor;...   5.868094  7.865096   \n",
       "\n",
       "   cluster  \n",
       "0       45  \n",
       "1       77  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.cluster import DBSCAN, KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html\n",
    "cluster_model = KMeans(n_clusters=100, random_state=0)\n",
    "clusters = cluster_model.fit_predict(StandardScaler().fit_transform(embedding_high_df))\n",
    "umap_df[\"cluster\"] = clusters\n",
    "umap_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "umap_df.to_csv(\"data/embeddings/node2vec-128d-to-umap-2d.tsv.xz\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import umap.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap.plot.points(mapper, labels=clusters, show_legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.renderers.GlyphRenderer\">GlyphRenderer</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'1036', <span id=\"1039\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">data_source&nbsp;=&nbsp;ColumnDataSource(id='1001', ...),</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">glyph&nbsp;=&nbsp;Circle(id='1034', ...),</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hover_glyph&nbsp;=&nbsp;None,</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">level&nbsp;=&nbsp;'glyph',</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted&nbsp;=&nbsp;False,</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted_glyph&nbsp;=&nbsp;None,</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">nonselection_glyph&nbsp;=&nbsp;Circle(id='1035', ...),</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">selection_glyph&nbsp;=&nbsp;None,</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">view&nbsp;=&nbsp;CDSView(id='1037', ...),</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range_name&nbsp;=&nbsp;'default',</div></div><div class=\"1038\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range_name&nbsp;=&nbsp;'default')</div></div></div>\n",
       "<script>\n",
       "(function() {\n",
       "  var expanded = false;\n",
       "  var ellipsis = document.getElementById(\"1039\");\n",
       "  ellipsis.addEventListener(\"click\", function() {\n",
       "    var rows = document.getElementsByClassName(\"1038\");\n",
       "    for (var i = 0; i < rows.length; i++) {\n",
       "      var el = rows[i];\n",
       "      el.style.display = expanded ? \"none\" : \"table-row\";\n",
       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "GlyphRenderer(id='1036', ...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on example at https://umap-learn.readthedocs.io/en/latest/basic_usage.html\n",
    "import bokeh.plotting\n",
    "import bokeh.models\n",
    "import bokeh.palettes\n",
    "import colorcet\n",
    "\n",
    "umap_df[\"cluster_color\"] = umap_df.cluster.map(dict(enumerate(colorcet.glasbey_dark)))\n",
    "datasource = bokeh.models.ColumnDataSource(umap_df)\n",
    "\n",
    "plot_title = 'UMAP projection of STRING proteins (via node2vec)'\n",
    "figure = bokeh.plotting.figure(\n",
    "    title=plot_title,\n",
    "    plot_width=600,\n",
    "    plot_height=600,\n",
    "    tools='box_zoom, pan, wheel_zoom, reset'\n",
    ")\n",
    "\n",
    "hover_tool = bokeh.models.HoverTool(\n",
    "    tooltips=[\n",
    "        ('symbol', '<i>@preferred_name</i>'),\n",
    "        ('protein_size',  '@protein_size{,}'),\n",
    "        ('info', '@annotation'),\n",
    "        ('cluster', '@cluster'),\n",
    "    ],\n",
    ")\n",
    "figure.add_tools(hover_tool)\n",
    "\n",
    "figure.circle(\n",
    "    x='umap_0',\n",
    "    y='umap_1',\n",
    "    source=datasource,\n",
    "    color=\"cluster_color\",\n",
    "    line_alpha=0.6,\n",
    "    fill_alpha=0.6,\n",
    "    radius=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment below to display plot in notebook\n",
    "# bokeh.plotting.output_notebook()\n",
    "# bokeh.plotting.show(figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/user/jupyter/data/embeddings/node2vec-128d-to-umap-2d.html'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bokeh.resources\n",
    "bokeh.plotting.save(\n",
    "    obj=figure,\n",
    "    resources=bokeh.resources.CDN,\n",
    "    title=plot_title,\n",
    "    filename=\"data/embeddings/node2vec-128d-to-umap-2d.html\",\n",
    ")"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}