code/TCGA/maf_groups_combine_and_associate_with_groups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "476f3f9a-dd38-4986-ae88-428d40de8b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "27c02011-e07d-4819-a821-9020bfcb4453",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_maf = pd.read_csv(\"maf_matched.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fcfdcfab-b9b4-412a-93e2-23d487e7f4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_groups = pd.read_csv(\"Allgroups_noDupIDs.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1c82e0b8-0c35-4581-90af-4e0df954e6bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((121, 26), (121, 24))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_maf.shape, df_groups.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7f58d6f8-81d4-4dc0-9830-c76a9ccd2f01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121\n"
     ]
    }
   ],
   "source": [
    "common = list(set(df_maf.index.tolist())&set(df_groups.index.tolist()))\n",
    "print(len(common))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d3dfa129-4540-4833-ae54-14b203c86ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_maf, df_groups = df_maf.loc[common], df_groups.loc[common]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c0f3a774-6243-4ba4-95cd-ef1ff61921d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print((df_maf.index==df_groups.index).all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd155518-7327-480a-8c2d-18b9a1e00f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_groups, df_maf], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "13164a90-66c9-4f85-81ed-bafda4945338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['barcode', 'patient', 'paper_Supervised.DNA.Methylation.Cluster',\n",
       "       'paper_Original.Subtype', 'paper_Transcriptome.Subtype',\n",
       "       'paper_IDH.specific.DNA.Methylation.Cluster', 'paper_IDH.status',\n",
       "       'paper_IDH.specific.RNA.Expression.Cluster', 'paper_IDH.codel.subtype',\n",
       "       'Project ID', 'Case ID', 'Case UUID', 'ID', 'File Name',\n",
       "       'Data Category', 'Data Type', 'Sample Type', 'year of diagnosis',\n",
       "       'age at index', 'days to death', 'gender',\n",
       "       'vital status (0=alive, 1=dead)', 'Neural signature score',\n",
       "       'Neural subgroup (0=low, 1=high)', 'Project ID', 'Case ID', 'Case UUID',\n",
       "       'ID', 'File Name', 'Data Category', 'Data Type', 'Sample Type',\n",
       "       'year of diagnosis', 'age at index', 'days to death', 'gender',\n",
       "       'vital status (0=alive, 1=dead)', 'Neural signature score',\n",
       "       'Neural subgroup (0=low, 1=high)', 'PTEN', 'EGFR', 'RB1', 'TP53', 'NF1',\n",
       "       'PIK3R1', 'ATRX', 'PIK3CA', 'TERT', 'CIC', 'FUBP1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b7ad51b-b7d9-426c-ac3a-2d4602b2cdb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated(keep=\"first\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da011902-f4e4-4e89-a852-a59fbe8cf352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121, 35)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53ae865b-c398-4904-a972-6ab75c8baedc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"merged_groups_maf.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97f23b31-44c7-4978-ad03-15c4cf7eeb70",
   "metadata": {},
   "outputs": [],
   "source": [
    "crosstab = pd.crosstab(df[\"Neural subgroup (0=low, 1=high)\"], df[\"paper_Transcriptome.Subtype\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "442e5be6-2aa5-4998-ac73-844a306e2655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CL', 'ME', 'NE', 'PN'], dtype='object', name='paper_Transcriptome.Subtype')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ccf23d8-8d22-4b0b-a2a2-320e7404678f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>paper_Transcriptome.Subtype</th>\n",
       "      <th>CL</th>\n",
       "      <th>ME</th>\n",
       "      <th>NE</th>\n",
       "      <th>PN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural subgroup (0=low, 1=high)</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",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "paper_Transcriptome.Subtype      CL  ME  NE  PN\n",
       "Neural subgroup (0=low, 1=high)                \n",
       "0                                19  18   2   1\n",
       "1                                12   5   1   9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "861be1b8-892a-4d99-b759-3a203d8e3318",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "colors = sns.color_palette(\"colorblind\", 2+4).as_hex()\n",
    "df_nodes = pd.DataFrame(index=list(range(2+4)),\n",
    "                     columns = [\"ID\", \"Label\", \"Color\"])\n",
    "df_nodes[\"ID\"] = df_nodes.index.tolist()\n",
    "df_nodes[\"Color\"] = colors\n",
    "df_nodes[\"Label\"] = crosstab.index.tolist()+crosstab.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5302260-736f-4351-a89f-5efdfae7cab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Label</th>\n",
       "      <th>Color</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>#0173b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>#de8f05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CL</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ME</td>\n",
       "      <td>#d55e00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NE</td>\n",
       "      <td>#cc78bc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>PN</td>\n",
       "      <td>#ca9161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Label    Color\n",
       "0   0     0  #0173b2\n",
       "1   1     1  #de8f05\n",
       "2   2    CL  #029e73\n",
       "3   3    ME  #d55e00\n",
       "4   4    NE  #cc78bc\n",
       "5   5    PN  #ca9161"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0743aaa-8223-4bf9-b60e-950d2ded89bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nodes[\"Label\"].replace({0: \"Low\", 1: \"High\",\n",
    "                         \"CL\": \"Classical\",\n",
    "                         \"ME\": \"Mesenchymal\",\n",
    "                         \"NE\": \"Neural\",\n",
    "                         \"PN\": \"Proneural\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a5c82fbb-0626-45a7-bfef-54d93e61d40a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nodes.Color = [\"#f68612\", \"#91191e\"] + df_nodes.Color.tolist()[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88f03f59-3e1e-4602-96bc-daa54987b8e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_links = pd.read_table(\"links_transcriptomic.txt\", index_col=None, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d834fcc-331e-4f3e-916e-dab508ca2642",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_links.columns = [\"Source\", \"Target\", \"Value\", \"Link Color\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9a23d79-5c95-4558-a0dc-67c3cb1430e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>paper_Transcriptome.Subtype</th>\n",
       "      <th>CL</th>\n",
       "      <th>ME</th>\n",
       "      <th>NE</th>\n",
       "      <th>PN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural subgroup (0=low, 1=high)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "paper_Transcriptome.Subtype      CL  ME  NE  PN\n",
       "Neural subgroup (0=low, 1=high)                \n",
       "0                                19  18   2   1\n",
       "1                                12   5   1   9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99d5a19d-41cc-4f06-8ec5-bdd5dbb15093",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Low</td>\n",
       "      <td>#f68612</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Classical</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>#d55e00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Neural</td>\n",
       "      <td>#cc78bc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Proneural</td>\n",
       "      <td>#ca9161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID        Label    Color\n",
       "0   0          Low  #f68612\n",
       "1   1         High  #91191e\n",
       "2   2    Classical  #029e73\n",
       "3   3  Mesenchymal  #d55e00\n",
       "4   4       Neural  #cc78bc\n",
       "5   5    Proneural  #ca9161"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88a6a9a4-9348-41f9-99ce-3a16e4d66839",
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in crosstab.index:\n",
    "    if row==0:\n",
    "        l = \"Low\"\n",
    "    else:\n",
    "        l = \"High\"\n",
    "    e = crosstab.loc[row].sum()\n",
    "    df_nodes[\"Label\"].replace({f\"{l}\": f\"{l} (n={e})\"}, inplace=True)\n",
    "for col in crosstab.columns:\n",
    "    if col==\"CL\":\n",
    "        l = \"Classical\"\n",
    "    elif col==\"ME\":\n",
    "        l = \"Mesenchymal\"\n",
    "    elif col==\"NE\":\n",
    "        l = \"Neural\"\n",
    "    else:\n",
    "        l = \"Proneural\"\n",
    "    e = crosstab[col].sum()\n",
    "    df_nodes[\"Label\"].replace({f\"{l}\": f\"{l} (n={e})\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6fd7115a-84c1-45d5-888c-fb7886d1a8c0",
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Low (n=40)</td>\n",
       "      <td>#f68612</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>High (n=27)</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Classical (n=31)</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mesenchymal (n=23)</td>\n",
       "      <td>#d55e00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Neural (n=3)</td>\n",
       "      <td>#cc78bc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Proneural (n=10)</td>\n",
       "      <td>#ca9161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   ID               Label    Color\n",
       "0   0          Low (n=40)  #f68612\n",
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       "2   2    Classical (n=31)  #029e73\n",
       "3   3  Mesenchymal (n=23)  #d55e00\n",
       "4   4        Neural (n=3)  #cc78bc\n",
       "5   5    Proneural (n=10)  #ca9161"
      ]
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    "# Sankey plot setup\n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "layout = dict(\n",
    "        title = \"Correspondence between neural and transcriptomic subgroups (TCGA)\",\n",
    "        height = 700,\n",
    "        width=700,\n",
    "        font = dict(size = 10),)\n",
    "\n",
    "fig = dict(data=[data_trace], layout=layout)\n",
    "fig = go.Figure(fig)\n",
    "iplot(fig, validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "865d9aab-cf65-4d9f-8d19-c3b1198fdecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image(\"tcga_tx.png\")\n",
    "fig.write_image(\"tcga_tx.pdf\", engine=\"kaleido\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "98f69f28-6ff1-424a-99e8-3cd2a0b649c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>barcode</th>\n",
       "      <th>patient</th>\n",
       "      <th>paper_Supervised.DNA.Methylation.Cluster</th>\n",
       "      <th>paper_Original.Subtype</th>\n",
       "      <th>paper_Transcriptome.Subtype</th>\n",
       "      <th>paper_IDH.specific.DNA.Methylation.Cluster</th>\n",
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       "      <th>paper_IDH.codel.subtype</th>\n",
       "      <th>Project ID</th>\n",
       "      <th>...</th>\n",
       "      <th>Data Category</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Sample Type</th>\n",
       "      <th>year of diagnosis</th>\n",
       "      <th>age at index</th>\n",
       "      <th>days to death</th>\n",
       "      <th>gender</th>\n",
       "      <th>vital status (0=alive, 1=dead)</th>\n",
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       "      <th>Neural subgroup (0=low, 1=high)</th>\n",
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       "    <tr>\n",
       "      <th>Sample ID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-06-0152-01A</th>\n",
       "      <td>TCGA-06-0152-01A-02D-A45W-05</td>\n",
       "      <td>TCGA-06-0152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>1995</td>\n",
       "      <td>68</td>\n",
       "      <td>375</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>TCGA-06-6693-01A</th>\n",
       "      <td>TCGA-06-6693-01A-11D-1844-05</td>\n",
       "      <td>TCGA-06-6693</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>1993</td>\n",
       "      <td>64</td>\n",
       "      <td>3667</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-A60I-01A</th>\n",
       "      <td>TCGA-19-A60I-01A-12D-A33U-05</td>\n",
       "      <td>TCGA-19-A60I</td>\n",
       "      <td>Classic-like</td>\n",
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       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
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       "      <td>2012</td>\n",
       "      <td>39</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.405</td>\n",
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       "      <th>TCGA-76-6193-01A</th>\n",
       "      <td>TCGA-76-6193-01A-11D-1697-05</td>\n",
       "      <td>TCGA-76-6193</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
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       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
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       "      <td>2010</td>\n",
       "      <td>78</td>\n",
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       "      <td>male</td>\n",
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       "      <td>0.380</td>\n",
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       "      <th>TCGA-06-6698-01A</th>\n",
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       "      <td>TCGA-06-6698</td>\n",
       "      <td>LGm6-GBM</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2011</td>\n",
       "      <td>53</td>\n",
       "      <td>'--</td>\n",
       "      <td>female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
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       "      <td>TCGA-76-6663</td>\n",
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       "      <td>0.417</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-5134-01A</th>\n",
       "      <td>TCGA-26-5134-01A-01D-1481-05</td>\n",
       "      <td>TCGA-26-5134</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Proneural</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R3</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2009</td>\n",
       "      <td>74</td>\n",
       "      <td>'--</td>\n",
       "      <td>male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-RR-A6KC-01A</th>\n",
       "      <td>TCGA-RR-A6KC-01A-31D-A33U-05</td>\n",
       "      <td>TCGA-RR-A6KC</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>55</td>\n",
       "      <td>625</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5209-01A</th>\n",
       "      <td>TCGA-28-5209-01A-01D-1481-05</td>\n",
       "      <td>TCGA-28-5209</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>CL</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R1</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>66</td>\n",
       "      <td>'--</td>\n",
       "      <td>female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4928-01B</th>\n",
       "      <td>TCGA-76-4928-01B-01D-1481-05</td>\n",
       "      <td>TCGA-76-4928</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Classical</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2005</td>\n",
       "      <td>85</td>\n",
       "      <td>94</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       barcode       patient  \\\n",
       "Sample ID                                                      \n",
       "TCGA-06-0152-01A  TCGA-06-0152-01A-02D-A45W-05  TCGA-06-0152   \n",
       "TCGA-06-6693-01A  TCGA-06-6693-01A-11D-1844-05  TCGA-06-6693   \n",
       "TCGA-19-A60I-01A  TCGA-19-A60I-01A-12D-A33U-05  TCGA-19-A60I   \n",
       "TCGA-76-6193-01A  TCGA-76-6193-01A-11D-1697-05  TCGA-76-6193   \n",
       "TCGA-06-6698-01A  TCGA-06-6698-01A-11D-1844-05  TCGA-06-6698   \n",
       "...                                        ...           ...   \n",
       "TCGA-76-6663-01A  TCGA-76-6663-01A-11D-1844-05  TCGA-76-6663   \n",
       "TCGA-26-5134-01A  TCGA-26-5134-01A-01D-1481-05  TCGA-26-5134   \n",
       "TCGA-RR-A6KC-01A  TCGA-RR-A6KC-01A-31D-A33U-05  TCGA-RR-A6KC   \n",
       "TCGA-28-5209-01A  TCGA-28-5209-01A-01D-1481-05  TCGA-28-5209   \n",
       "TCGA-76-4928-01B  TCGA-76-4928-01B-01D-1481-05  TCGA-76-4928   \n",
       "\n",
       "                 paper_Supervised.DNA.Methylation.Cluster  \\\n",
       "Sample ID                                                   \n",
       "TCGA-06-0152-01A                                      NaN   \n",
       "TCGA-06-6693-01A                             Classic-like   \n",
       "TCGA-19-A60I-01A                             Classic-like   \n",
       "TCGA-76-6193-01A                         Mesenchymal-like   \n",
       "TCGA-06-6698-01A                                 LGm6-GBM   \n",
       "...                                                   ...   \n",
       "TCGA-76-6663-01A                             Classic-like   \n",
       "TCGA-26-5134-01A                         Mesenchymal-like   \n",
       "TCGA-RR-A6KC-01A                             Classic-like   \n",
       "TCGA-28-5209-01A                             Classic-like   \n",
       "TCGA-76-4928-01B                         Mesenchymal-like   \n",
       "\n",
       "                 paper_Original.Subtype paper_Transcriptome.Subtype  \\\n",
       "Sample ID                                                             \n",
       "TCGA-06-0152-01A            Mesenchymal                          ME   \n",
       "TCGA-06-6693-01A                    NaN                         NaN   \n",
       "TCGA-19-A60I-01A                    NaN                         NaN   \n",
       "TCGA-76-6193-01A            Mesenchymal                          ME   \n",
       "TCGA-06-6698-01A                    NaN                         NaN   \n",
       "...                                 ...                         ...   \n",
       "TCGA-76-6663-01A                    NaN                         NaN   \n",
       "TCGA-26-5134-01A              Proneural                         NaN   \n",
       "TCGA-RR-A6KC-01A                    NaN                         NaN   \n",
       "TCGA-28-5209-01A            Mesenchymal                          CL   \n",
       "TCGA-76-4928-01B              Classical                          ME   \n",
       "\n",
       "                 paper_IDH.specific.DNA.Methylation.Cluster paper_IDH.status  \\\n",
       "Sample ID                                                                      \n",
       "TCGA-06-0152-01A                                        NaN               WT   \n",
       "TCGA-06-6693-01A                                   IDHwt-K1               WT   \n",
       "TCGA-19-A60I-01A                                   IDHwt-K1               WT   \n",
       "TCGA-76-6193-01A                                   IDHwt-K2               WT   \n",
       "TCGA-06-6698-01A                                   IDHwt-K3               WT   \n",
       "...                                                     ...              ...   \n",
       "TCGA-76-6663-01A                                   IDHwt-K1               WT   \n",
       "TCGA-26-5134-01A                                   IDHwt-K2               WT   \n",
       "TCGA-RR-A6KC-01A                                   IDHwt-K1               WT   \n",
       "TCGA-28-5209-01A                                   IDHwt-K1               WT   \n",
       "TCGA-76-4928-01B                                   IDHwt-K2               WT   \n",
       "\n",
       "                 paper_IDH.specific.RNA.Expression.Cluster  \\\n",
       "Sample ID                                                    \n",
       "TCGA-06-0152-01A                                       NaN   \n",
       "TCGA-06-6693-01A                                       NaN   \n",
       "TCGA-19-A60I-01A                                       NaN   \n",
       "TCGA-76-6193-01A                                       NaN   \n",
       "TCGA-06-6698-01A                                       NaN   \n",
       "...                                                    ...   \n",
       "TCGA-76-6663-01A                                       NaN   \n",
       "TCGA-26-5134-01A                                  IDHwt-R3   \n",
       "TCGA-RR-A6KC-01A                                       NaN   \n",
       "TCGA-28-5209-01A                                  IDHwt-R1   \n",
       "TCGA-76-4928-01B                                  IDHwt-R2   \n",
       "\n",
       "                 paper_IDH.codel.subtype Project ID  ...    Data Category  \\\n",
       "Sample ID                                            ...                    \n",
       "TCGA-06-0152-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-06-6693-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-19-A60I-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-76-6193-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-06-6698-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "...                                  ...        ...  ...              ...   \n",
       "TCGA-76-6663-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-26-5134-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-RR-A6KC-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-28-5209-01A                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "TCGA-76-4928-01B                   IDHwt   TCGA-GBM  ...  DNA Methylation   \n",
       "\n",
       "                               Data Type    Sample Type year of diagnosis  \\\n",
       "Sample ID                                                                   \n",
       "TCGA-06-0152-01A  Methylation Beta Value  Primary Tumor              1995   \n",
       "TCGA-06-6693-01A  Methylation Beta Value  Primary Tumor              1993   \n",
       "TCGA-19-A60I-01A  Methylation Beta Value  Primary Tumor              2012   \n",
       "TCGA-76-6193-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-06-6698-01A  Methylation Beta Value  Primary Tumor              2011   \n",
       "...                                  ...            ...               ...   \n",
       "TCGA-76-6663-01A  Methylation Beta Value  Primary Tumor              2011   \n",
       "TCGA-26-5134-01A  Methylation Beta Value  Primary Tumor              2009   \n",
       "TCGA-RR-A6KC-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-28-5209-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-76-4928-01B  Methylation Beta Value  Primary Tumor              2005   \n",
       "\n",
       "                 age at index days to death  gender  \\\n",
       "Sample ID                                             \n",
       "TCGA-06-0152-01A           68           375    male   \n",
       "TCGA-06-6693-01A           64          3667  female   \n",
       "TCGA-19-A60I-01A           39           '--    male   \n",
       "TCGA-76-6193-01A           78            82    male   \n",
       "TCGA-06-6698-01A           53           '--  female   \n",
       "...                       ...           ...     ...   \n",
       "TCGA-76-6663-01A           44           567  female   \n",
       "TCGA-26-5134-01A           74           '--    male   \n",
       "TCGA-RR-A6KC-01A           55           625    male   \n",
       "TCGA-28-5209-01A           66           '--  female   \n",
       "TCGA-76-4928-01B           85            94  female   \n",
       "\n",
       "                  vital status (0=alive, 1=dead)  Neural signature score  \\\n",
       "Sample ID                                                                  \n",
       "TCGA-06-0152-01A                             1.0                   0.372   \n",
       "TCGA-06-6693-01A                             1.0                   0.392   \n",
       "TCGA-19-A60I-01A                             0.0                   0.405   \n",
       "TCGA-76-6193-01A                             1.0                   0.380   \n",
       "TCGA-06-6698-01A                             0.0                   0.400   \n",
       "...                                          ...                     ...   \n",
       "TCGA-76-6663-01A                             1.0                   0.417   \n",
       "TCGA-26-5134-01A                             0.0                   0.442   \n",
       "TCGA-RR-A6KC-01A                             1.0                   0.336   \n",
       "TCGA-28-5209-01A                             0.0                   0.372   \n",
       "TCGA-76-4928-01B                             1.0                   0.257   \n",
       "\n",
       "                 Neural subgroup (0=low, 1=high)  \n",
       "Sample ID                                         \n",
       "TCGA-06-0152-01A                               0  \n",
       "TCGA-06-6693-01A                               0  \n",
       "TCGA-19-A60I-01A                               0  \n",
       "TCGA-76-6193-01A                               0  \n",
       "TCGA-06-6698-01A                               0  \n",
       "...                                          ...  \n",
       "TCGA-76-6663-01A                               1  \n",
       "TCGA-26-5134-01A                               1  \n",
       "TCGA-RR-A6KC-01A                               0  \n",
       "TCGA-28-5209-01A                               0  \n",
       "TCGA-76-4928-01B                               0  \n",
       "\n",
       "[121 rows x 24 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2ff127a8-0ecd-410c-ac1c-f3582d2c145d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\", font_scale=1.2)\n",
    "plt.rcParams[\"font.family\"] = [\"Inter\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aac9068f-02c0-4951-a926-e0eadd3514c9",
   "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>barcode</th>\n",
       "      <th>patient</th>\n",
       "      <th>paper_Supervised.DNA.Methylation.Cluster</th>\n",
       "      <th>paper_Original.Subtype</th>\n",
       "      <th>paper_Transcriptome.Subtype</th>\n",
       "      <th>paper_IDH.specific.DNA.Methylation.Cluster</th>\n",
       "      <th>paper_IDH.status</th>\n",
       "      <th>paper_IDH.specific.RNA.Expression.Cluster</th>\n",
       "      <th>paper_IDH.codel.subtype</th>\n",
       "      <th>Project ID</th>\n",
       "      <th>...</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Sample Type</th>\n",
       "      <th>year of diagnosis</th>\n",
       "      <th>age at index</th>\n",
       "      <th>days to death</th>\n",
       "      <th>gender</th>\n",
       "      <th>vital status (0=alive, 1=dead)</th>\n",
       "      <th>Neural signature score</th>\n",
       "      <th>Neural subgroup (0=low, 1=high)</th>\n",
       "      <th>months</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample ID</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>TCGA-06-0152-01A</th>\n",
       "      <td>TCGA-06-0152-01A-02D-A45W-05</td>\n",
       "      <td>TCGA-06-0152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>1995</td>\n",
       "      <td>68</td>\n",
       "      <td>375</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0</td>\n",
       "      <td>12.328632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6193-01A</th>\n",
       "      <td>TCGA-76-6193-01A-11D-1697-05</td>\n",
       "      <td>TCGA-76-6193</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>78</td>\n",
       "      <td>82</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0</td>\n",
       "      <td>2.695861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-5135-01A</th>\n",
       "      <td>TCGA-26-5135-01A-01D-1481-05</td>\n",
       "      <td>TCGA-26-5135</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Proneural</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R3</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2009</td>\n",
       "      <td>72</td>\n",
       "      <td>270</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.457</td>\n",
       "      <td>1</td>\n",
       "      <td>8.876615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5947-01A</th>\n",
       "      <td>TCGA-19-5947-01A-11D-1697-05</td>\n",
       "      <td>TCGA-19-5947</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>47</td>\n",
       "      <td>202</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0</td>\n",
       "      <td>6.641023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-1389-01A</th>\n",
       "      <td>TCGA-19-1389-01A-01D-0595-05</td>\n",
       "      <td>TCGA-19-1389</td>\n",
       "      <td>LGm6-GBM</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K3</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2008</td>\n",
       "      <td>51</td>\n",
       "      <td>141</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0</td>\n",
       "      <td>4.635566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6282-01A</th>\n",
       "      <td>TCGA-76-6282-01A-11D-1697-05</td>\n",
       "      <td>TCGA-76-6282</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2007</td>\n",
       "      <td>63</td>\n",
       "      <td>519</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0</td>\n",
       "      <td>17.062827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5214-01A</th>\n",
       "      <td>TCGA-28-5214-01A-01D-1481-05</td>\n",
       "      <td>TCGA-28-5214</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>53</td>\n",
       "      <td>713</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0</td>\n",
       "      <td>23.440839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-0650-01A</th>\n",
       "      <td>TCGA-06-0650-01A-02D-1697-05</td>\n",
       "      <td>TCGA-06-0650</td>\n",
       "      <td>LGm6-GBM</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K3</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2007</td>\n",
       "      <td>39</td>\n",
       "      <td>717</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0</td>\n",
       "      <td>23.572344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-5136-01B</th>\n",
       "      <td>TCGA-26-5136-01B-01D-1481-05</td>\n",
       "      <td>TCGA-26-5136</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2009</td>\n",
       "      <td>78</td>\n",
       "      <td>577</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0</td>\n",
       "      <td>18.969655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5418-01A</th>\n",
       "      <td>TCGA-06-5418-01A-01D-1481-05</td>\n",
       "      <td>TCGA-06-5418</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>75</td>\n",
       "      <td>83</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.424</td>\n",
       "      <td>1</td>\n",
       "      <td>2.728737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-0210-01A</th>\n",
       "      <td>TCGA-06-0210-01A-01D-A45W-05</td>\n",
       "      <td>TCGA-06-0210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>1997</td>\n",
       "      <td>72</td>\n",
       "      <td>225</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0</td>\n",
       "      <td>7.397179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5955-01A</th>\n",
       "      <td>TCGA-19-5955-01A-11D-1697-05</td>\n",
       "      <td>TCGA-19-5955</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2010</td>\n",
       "      <td>83</td>\n",
       "      <td>54</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>1.775323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-12-5295-01A</th>\n",
       "      <td>TCGA-12-5295-01A-01D-1481-05</td>\n",
       "      <td>TCGA-12-5295</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>Neural</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R1</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2008</td>\n",
       "      <td>60</td>\n",
       "      <td>454</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0</td>\n",
       "      <td>14.925864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-14-0736-01A</th>\n",
       "      <td>TCGA-14-0736-01A-01D-0521-05</td>\n",
       "      <td>TCGA-14-0736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>1999</td>\n",
       "      <td>49</td>\n",
       "      <td>460</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0</td>\n",
       "      <td>15.123122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-0190-01A</th>\n",
       "      <td>TCGA-06-0190-01A-01D-A45W-05</td>\n",
       "      <td>TCGA-06-0190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mesenchymal</td>\n",
       "      <td>ME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2006</td>\n",
       "      <td>62</td>\n",
       "      <td>317</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0</td>\n",
       "      <td>10.421804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4928-01B</th>\n",
       "      <td>TCGA-76-4928-01B-01D-1481-05</td>\n",
       "      <td>TCGA-76-4928</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Classical</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Methylation Beta Value</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>2005</td>\n",
       "      <td>85</td>\n",
       "      <td>94</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0</td>\n",
       "      <td>3.090377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       barcode       patient  \\\n",
       "Sample ID                                                      \n",
       "TCGA-06-0152-01A  TCGA-06-0152-01A-02D-A45W-05  TCGA-06-0152   \n",
       "TCGA-76-6193-01A  TCGA-76-6193-01A-11D-1697-05  TCGA-76-6193   \n",
       "TCGA-26-5135-01A  TCGA-26-5135-01A-01D-1481-05  TCGA-26-5135   \n",
       "TCGA-19-5947-01A  TCGA-19-5947-01A-11D-1697-05  TCGA-19-5947   \n",
       "TCGA-19-1389-01A  TCGA-19-1389-01A-01D-0595-05  TCGA-19-1389   \n",
       "TCGA-76-6282-01A  TCGA-76-6282-01A-11D-1697-05  TCGA-76-6282   \n",
       "TCGA-28-5214-01A  TCGA-28-5214-01A-01D-1481-05  TCGA-28-5214   \n",
       "TCGA-06-0650-01A  TCGA-06-0650-01A-02D-1697-05  TCGA-06-0650   \n",
       "TCGA-26-5136-01B  TCGA-26-5136-01B-01D-1481-05  TCGA-26-5136   \n",
       "TCGA-06-5418-01A  TCGA-06-5418-01A-01D-1481-05  TCGA-06-5418   \n",
       "TCGA-06-0210-01A  TCGA-06-0210-01A-01D-A45W-05  TCGA-06-0210   \n",
       "TCGA-19-5955-01A  TCGA-19-5955-01A-11D-1697-05  TCGA-19-5955   \n",
       "TCGA-12-5295-01A  TCGA-12-5295-01A-01D-1481-05  TCGA-12-5295   \n",
       "TCGA-14-0736-01A  TCGA-14-0736-01A-01D-0521-05  TCGA-14-0736   \n",
       "TCGA-06-0190-01A  TCGA-06-0190-01A-01D-A45W-05  TCGA-06-0190   \n",
       "TCGA-76-4928-01B  TCGA-76-4928-01B-01D-1481-05  TCGA-76-4928   \n",
       "\n",
       "                 paper_Supervised.DNA.Methylation.Cluster  \\\n",
       "Sample ID                                                   \n",
       "TCGA-06-0152-01A                                      NaN   \n",
       "TCGA-76-6193-01A                         Mesenchymal-like   \n",
       "TCGA-26-5135-01A                         Mesenchymal-like   \n",
       "TCGA-19-5947-01A                         Mesenchymal-like   \n",
       "TCGA-19-1389-01A                                 LGm6-GBM   \n",
       "TCGA-76-6282-01A                         Mesenchymal-like   \n",
       "TCGA-28-5214-01A                             Classic-like   \n",
       "TCGA-06-0650-01A                                 LGm6-GBM   \n",
       "TCGA-26-5136-01B                         Mesenchymal-like   \n",
       "TCGA-06-5418-01A                         Mesenchymal-like   \n",
       "TCGA-06-0210-01A                                      NaN   \n",
       "TCGA-19-5955-01A                         Mesenchymal-like   \n",
       "TCGA-12-5295-01A                             Classic-like   \n",
       "TCGA-14-0736-01A                                      NaN   \n",
       "TCGA-06-0190-01A                                      NaN   \n",
       "TCGA-76-4928-01B                         Mesenchymal-like   \n",
       "\n",
       "                 paper_Original.Subtype paper_Transcriptome.Subtype  \\\n",
       "Sample ID                                                             \n",
       "TCGA-06-0152-01A            Mesenchymal                          ME   \n",
       "TCGA-76-6193-01A            Mesenchymal                          ME   \n",
       "TCGA-26-5135-01A              Proneural                          ME   \n",
       "TCGA-19-5947-01A            Mesenchymal                          ME   \n",
       "TCGA-19-1389-01A            Mesenchymal                          ME   \n",
       "TCGA-76-6282-01A            Mesenchymal                          ME   \n",
       "TCGA-28-5214-01A            Mesenchymal                          ME   \n",
       "TCGA-06-0650-01A            Mesenchymal                          ME   \n",
       "TCGA-26-5136-01B            Mesenchymal                          ME   \n",
       "TCGA-06-5418-01A            Mesenchymal                          ME   \n",
       "TCGA-06-0210-01A            Mesenchymal                          ME   \n",
       "TCGA-19-5955-01A            Mesenchymal                          ME   \n",
       "TCGA-12-5295-01A                 Neural                          ME   \n",
       "TCGA-14-0736-01A            Mesenchymal                          ME   \n",
       "TCGA-06-0190-01A            Mesenchymal                          ME   \n",
       "TCGA-76-4928-01B              Classical                          ME   \n",
       "\n",
       "                 paper_IDH.specific.DNA.Methylation.Cluster paper_IDH.status  \\\n",
       "Sample ID                                                                      \n",
       "TCGA-06-0152-01A                                        NaN               WT   \n",
       "TCGA-76-6193-01A                                   IDHwt-K2               WT   \n",
       "TCGA-26-5135-01A                                   IDHwt-K2               WT   \n",
       "TCGA-19-5947-01A                                   IDHwt-K2               WT   \n",
       "TCGA-19-1389-01A                                   IDHwt-K3               WT   \n",
       "TCGA-76-6282-01A                                   IDHwt-K2               WT   \n",
       "TCGA-28-5214-01A                                   IDHwt-K1               WT   \n",
       "TCGA-06-0650-01A                                   IDHwt-K3               WT   \n",
       "TCGA-26-5136-01B                                   IDHwt-K2               WT   \n",
       "TCGA-06-5418-01A                                   IDHwt-K2               WT   \n",
       "TCGA-06-0210-01A                                        NaN               WT   \n",
       "TCGA-19-5955-01A                                   IDHwt-K2               WT   \n",
       "TCGA-12-5295-01A                                   IDHwt-K1               WT   \n",
       "TCGA-14-0736-01A                                        NaN              NaN   \n",
       "TCGA-06-0190-01A                                        NaN               WT   \n",
       "TCGA-76-4928-01B                                   IDHwt-K2               WT   \n",
       "\n",
       "                 paper_IDH.specific.RNA.Expression.Cluster  \\\n",
       "Sample ID                                                    \n",
       "TCGA-06-0152-01A                                       NaN   \n",
       "TCGA-76-6193-01A                                       NaN   \n",
       "TCGA-26-5135-01A                                  IDHwt-R3   \n",
       "TCGA-19-5947-01A                                       NaN   \n",
       "TCGA-19-1389-01A                                       NaN   \n",
       "TCGA-76-6282-01A                                       NaN   \n",
       "TCGA-28-5214-01A                                       NaN   \n",
       "TCGA-06-0650-01A                                       NaN   \n",
       "TCGA-26-5136-01B                                  IDHwt-R2   \n",
       "TCGA-06-5418-01A                                  IDHwt-R2   \n",
       "TCGA-06-0210-01A                                  IDHwt-R2   \n",
       "TCGA-19-5955-01A                                       NaN   \n",
       "TCGA-12-5295-01A                                  IDHwt-R1   \n",
       "TCGA-14-0736-01A                                       NaN   \n",
       "TCGA-06-0190-01A                                  IDHwt-R2   \n",
       "TCGA-76-4928-01B                                  IDHwt-R2   \n",
       "\n",
       "                 paper_IDH.codel.subtype Project ID  ...  \\\n",
       "Sample ID                                            ...   \n",
       "TCGA-06-0152-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-76-6193-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-26-5135-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-19-5947-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-19-1389-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-76-6282-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-28-5214-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-06-0650-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-26-5136-01B                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-06-5418-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-06-0210-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-19-5955-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-12-5295-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-14-0736-01A                     NaN   TCGA-GBM  ...   \n",
       "TCGA-06-0190-01A                   IDHwt   TCGA-GBM  ...   \n",
       "TCGA-76-4928-01B                   IDHwt   TCGA-GBM  ...   \n",
       "\n",
       "                               Data Type    Sample Type year of diagnosis  \\\n",
       "Sample ID                                                                   \n",
       "TCGA-06-0152-01A  Methylation Beta Value  Primary Tumor              1995   \n",
       "TCGA-76-6193-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-26-5135-01A  Methylation Beta Value  Primary Tumor              2009   \n",
       "TCGA-19-5947-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-19-1389-01A  Methylation Beta Value  Primary Tumor              2008   \n",
       "TCGA-76-6282-01A  Methylation Beta Value  Primary Tumor              2007   \n",
       "TCGA-28-5214-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-06-0650-01A  Methylation Beta Value  Primary Tumor              2007   \n",
       "TCGA-26-5136-01B  Methylation Beta Value  Primary Tumor              2009   \n",
       "TCGA-06-5418-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-06-0210-01A  Methylation Beta Value  Primary Tumor              1997   \n",
       "TCGA-19-5955-01A  Methylation Beta Value  Primary Tumor              2010   \n",
       "TCGA-12-5295-01A  Methylation Beta Value  Primary Tumor              2008   \n",
       "TCGA-14-0736-01A  Methylation Beta Value  Primary Tumor              1999   \n",
       "TCGA-06-0190-01A  Methylation Beta Value  Primary Tumor              2006   \n",
       "TCGA-76-4928-01B  Methylation Beta Value  Primary Tumor              2005   \n",
       "\n",
       "                 age at index days to death  gender  \\\n",
       "Sample ID                                             \n",
       "TCGA-06-0152-01A           68           375    male   \n",
       "TCGA-76-6193-01A           78            82    male   \n",
       "TCGA-26-5135-01A           72           270  female   \n",
       "TCGA-19-5947-01A           47           202  female   \n",
       "TCGA-19-1389-01A           51           141    male   \n",
       "TCGA-76-6282-01A           63           519    male   \n",
       "TCGA-28-5214-01A           53           713    male   \n",
       "TCGA-06-0650-01A           39           717  female   \n",
       "TCGA-26-5136-01B           78           577  female   \n",
       "TCGA-06-5418-01A           75            83  female   \n",
       "TCGA-06-0210-01A           72           225  female   \n",
       "TCGA-19-5955-01A           83            54    male   \n",
       "TCGA-12-5295-01A           60           454  female   \n",
       "TCGA-14-0736-01A           49           460    male   \n",
       "TCGA-06-0190-01A           62           317    male   \n",
       "TCGA-76-4928-01B           85            94  female   \n",
       "\n",
       "                 vital status (0=alive, 1=dead)  Neural signature score  \\\n",
       "Sample ID                                                                 \n",
       "TCGA-06-0152-01A                            1.0                   0.372   \n",
       "TCGA-76-6193-01A                            1.0                   0.380   \n",
       "TCGA-26-5135-01A                            1.0                   0.457   \n",
       "TCGA-19-5947-01A                            1.0                   0.245   \n",
       "TCGA-19-1389-01A                            1.0                   0.314   \n",
       "TCGA-76-6282-01A                            1.0                   0.286   \n",
       "TCGA-28-5214-01A                            1.0                   0.406   \n",
       "TCGA-06-0650-01A                            1.0                   0.345   \n",
       "TCGA-26-5136-01B                            1.0                   0.406   \n",
       "TCGA-06-5418-01A                            1.0                   0.424   \n",
       "TCGA-06-0210-01A                            1.0                   0.297   \n",
       "TCGA-19-5955-01A                            1.0                   0.244   \n",
       "TCGA-12-5295-01A                            1.0                   0.297   \n",
       "TCGA-14-0736-01A                            1.0                   0.286   \n",
       "TCGA-06-0190-01A                            1.0                   0.301   \n",
       "TCGA-76-4928-01B                            1.0                   0.257   \n",
       "\n",
       "                  Neural subgroup (0=low, 1=high)     months  \n",
       "Sample ID                                                     \n",
       "TCGA-06-0152-01A                                0  12.328632  \n",
       "TCGA-76-6193-01A                                0   2.695861  \n",
       "TCGA-26-5135-01A                                1   8.876615  \n",
       "TCGA-19-5947-01A                                0   6.641023  \n",
       "TCGA-19-1389-01A                                0   4.635566  \n",
       "TCGA-76-6282-01A                                0  17.062827  \n",
       "TCGA-28-5214-01A                                0  23.440839  \n",
       "TCGA-06-0650-01A                                0  23.572344  \n",
       "TCGA-26-5136-01B                                0  18.969655  \n",
       "TCGA-06-5418-01A                                1   2.728737  \n",
       "TCGA-06-0210-01A                                0   7.397179  \n",
       "TCGA-19-5955-01A                                0   1.775323  \n",
       "TCGA-12-5295-01A                                0  14.925864  \n",
       "TCGA-14-0736-01A                                0  15.123122  \n",
       "TCGA-06-0190-01A                                0  10.421804  \n",
       "TCGA-76-4928-01B                                0   3.090377  \n",
       "\n",
       "[16 rows x 25 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3780e53c-d1d3-44ef-b450-0c0d8aa12e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for gr in sorted(['CL', 'ME']):\n",
    "    \n",
    "    tmp = df_groups.loc[df[df[\"paper_Transcriptome.Subtype\"]==gr].index]\n",
    "    tmp = tmp[tmp[\"vital status (0=alive, 1=dead)\"]==1]\n",
    "    tmp[\"days to death\"] = tmp[\"days to death\"].astype(int)\n",
    "    tmp[\"months\"] = tmp[\"days to death\"]/30.417\n",
    "    groups = tmp[\"Neural subgroup (0=low, 1=high)\"]\n",
    "\n",
    "    i1 = (groups == 0)\n",
    "    i2 = (groups == 1)\n",
    "\n",
    "    durations = pd.DataFrame(tmp[\"months\"], index=tmp.index)\n",
    "    event_observed = tmp[\"vital status (0=alive, 1=dead)\"]\n",
    "\n",
    "    ## fit the model for 1st cohort\n",
    "    kmf1 = KaplanMeierFitter()\n",
    "\n",
    "    n = i1.sum()\n",
    "    kmf1.fit(durations.loc[i1.tolist()], event_observed.loc[i1.tolist()], label=f'Low (n={n})')\n",
    "    a1 = kmf1.plot(ci_show=True, color=\"#f68612\")\n",
    "    n = durations.loc[i1.tolist()].mean()[0]\n",
    "    plt.text(40, 0.4, f\"median survival: {round(n,2)} months\", color=\"#f68612\")\n",
    "\n",
    "    ## fit the model for 2nd cohort\n",
    "    n = i2.sum()\n",
    "    kmf1.fit(durations.loc[i2.tolist()], event_observed.loc[i2.tolist()], label=f'High (n={n})')\n",
    "    kmf1.plot(ax=a1, ci_show=True, color=\"#91191e\")\n",
    "    n = durations.loc[i2.tolist()].mean()[0]\n",
    "\n",
    "    plt.text(40, 0.1, f\"median survival: {round(n,2)} months\", color=\"#91191e\")\n",
    "\n",
    "\n",
    "    plt.xlim(0,80)\n",
    "    plt.title(gr)\n",
    "    plt.savefig(f\"{gr}_KM.pdf\", bbox_inches=\"tight\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a09daf71-c8a5-4e33-a835-aa8204c26ab5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e69cfc6c-cef4-4681-b7d5-cc50a177f6df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7a5977c3-0977-4ae4-b15d-6841969f3ed3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4ca061e8-c9e7-4a0b-9bdc-7d78b1f9f201",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "73cd064c-dfee-4f0b-bd72-ff9c27d86ce4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4d95aeb3-dafa-4ebf-9d91-be8122ddc41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CL')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e3954e8d-1631-4692-bfd9-4cb931233bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = df_groups.loc[df[df[\"paper_Transcriptome.Subtype\"]==\"ME\"].index]\n",
    "tmp = tmp[tmp[\"vital status (0=alive, 1=dead)\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bd93ab20-b709-4edb-ad76-330574524434",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp[\"days to death\"] = tmp[\"days to death\"].astype(int)\n",
    "tmp[\"months\"] = tmp[\"days to death\"]/30.417"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8f6a022f-3aaf-469e-92aa-89594cd9a5d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "1     2\n",
       "Name: Neural subgroup (0=low, 1=high), dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[\"Neural subgroup (0=low, 1=high)\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "40bab2e5-eef8-4578-838f-bd9999958b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59088/3355537109.py:1: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "\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>year of diagnosis</th>\n",
       "      <th>age at index</th>\n",
       "      <th>days to death</th>\n",
       "      <th>vital status (0=alive, 1=dead)</th>\n",
       "      <th>Neural signature score</th>\n",
       "      <th>months</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural subgroup (0=low, 1=high)</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",
       "      <td>2005.785714</td>\n",
       "      <td>63.428571</td>\n",
       "      <td>352.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.316857</td>\n",
       "      <td>11.577173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>176.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.440500</td>\n",
       "      <td>5.802676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 year of diagnosis  age at index  \\\n",
       "Neural subgroup (0=low, 1=high)                                    \n",
       "0                                      2005.785714     63.428571   \n",
       "1                                      2009.500000     73.500000   \n",
       "\n",
       "                                 days to death  \\\n",
       "Neural subgroup (0=low, 1=high)                  \n",
       "0                                   352.142857   \n",
       "1                                   176.500000   \n",
       "\n",
       "                                 vital status (0=alive, 1=dead)  \\\n",
       "Neural subgroup (0=low, 1=high)                                   \n",
       "0                                                           1.0   \n",
       "1                                                           1.0   \n",
       "\n",
       "                                 Neural signature score     months  \n",
       "Neural subgroup (0=low, 1=high)                                     \n",
       "0                                              0.316857  11.577173  \n",
       "1                                              0.440500   5.802676  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.groupby(\"Neural subgroup (0=low, 1=high)\").agg(\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0393c585-2679-4aea-a3af-e7f50d1aff27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifelines import KaplanMeierFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "68440394-71ff-4461-8747-d9bbed1dd521",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = tmp[\"Neural subgroup (0=low, 1=high)\"]\n",
    "\n",
    "i1 = (groups == 0)\n",
    "i2 = (groups == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9cdda00c-52c7-48ef-989e-f077f1ad44c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "durations = pd.DataFrame(tmp[\"months\"], index=tmp.index)\n",
    "event_observed = tmp[\"vital status (0=alive, 1=dead)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "066a481e-5993-4215-9368-b1742b2d40fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\", font_scale=1.2)\n",
    "plt.rcParams[\"font.family\"] = [\"Inter\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3dffd894-84f0-4baa-a49a-d4a3e1bb9281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ME')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## fit the model for 1st cohort\n",
    "kmf1 = KaplanMeierFitter()\n",
    "\n",
    "n = i1.sum()\n",
    "kmf1.fit(durations.loc[i1.tolist()], event_observed.loc[i1.tolist()], label=f'Low (n={n})')\n",
    "a1 = kmf1.plot(ci_show=True, color=\"#f68612\")\n",
    "n = durations.loc[i1.tolist()].mean()[0]\n",
    "plt.text(40, 0.4, f\"median survival: {round(n,2)} months\", color=\"#f68612\")\n",
    "\n",
    "## fit the model for 2nd cohort\n",
    "n = i2.sum()\n",
    "kmf1.fit(durations.loc[i2.tolist()], event_observed.loc[i2.tolist()], label=f'High (n={n})')\n",
    "kmf1.plot(ax=a1, ci_show=True, color=\"#91191e\")\n",
    "n = durations.loc[i2.tolist()].mean()[0]\n",
    "\n",
    "plt.text(40, 0.1, f\"median survival: {round(n,2)} months\", color=\"#91191e\")\n",
    "\n",
    "\n",
    "plt.xlim(0,80)\n",
    "plt.title(\"ME\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77cc0fb-605d-49fd-866e-ede7930c035f",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea64ff54-bc4f-4f74-b8d3-eb534ed86ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "crosstab = pd.crosstab(df[\"Neural subgroup (0=low, 1=high)\"], df[\"paper_Supervised.DNA.Methylation.Cluster\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e288131e-6fb4-4436-a85f-35a93f3750d2",
   "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>paper_Supervised.DNA.Methylation.Cluster</th>\n",
       "      <th>Classic-like</th>\n",
       "      <th>LGm6-GBM</th>\n",
       "      <th>Mesenchymal-like</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural subgroup (0=low, 1=high)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "paper_Supervised.DNA.Methylation.Cluster  Classic-like  LGm6-GBM  \\\n",
       "Neural subgroup (0=low, 1=high)                                    \n",
       "0                                                   35         3   \n",
       "1                                                   17         3   \n",
       "\n",
       "paper_Supervised.DNA.Methylation.Cluster  Mesenchymal-like  \n",
       "Neural subgroup (0=low, 1=high)                             \n",
       "0                                                       26  \n",
       "1                                                       27  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c264f18c-724c-4615-a53f-f2d2a67c8ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "colors = sns.color_palette(\"colorblind\", 2+3).as_hex()\n",
    "df_nodes = pd.DataFrame(index=list(range(2+3)),\n",
    "                     columns = [\"ID\", \"Label\", \"Color\"])\n",
    "df_nodes[\"ID\"] = df_nodes.index.tolist()\n",
    "df_nodes[\"Color\"] = colors\n",
    "df_nodes[\"Label\"] = crosstab.index.tolist()+crosstab.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb011981-428b-42b3-9527-85046ccce152",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nodes[\"Label\"].replace({0: \"Low\", 1: \"High\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "55d4a5ab-703a-45f6-895c-9a2d58c3a69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_links = pd.DataFrame(index=list(range(2*3)),\n",
    "                        columns=[\"Source\", \"Target\", \"Value\", \"Link Color\"])\n",
    "df_links[\"Link Color\"] = [\"#f68612\"]*3 + [\"#91191e\"]*3\n",
    "df_links[\"Target\"] = crosstab.columns.tolist() + crosstab.columns.tolist()\n",
    "df_links[\"Source\"] = [\"Low\"]*3 + [\"High\"]*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "17ede082-1a87-4c43-8d63-8527c7c20cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nodes.Color = [\"#f68612\", \"#91191e\"] + df_nodes.Color.tolist()[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c424c5c6-4652-4378-8912-c98274490786",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx in crosstab.index:\n",
    "    for col in crosstab.columns:\n",
    "        val = crosstab.loc[idx, col]\n",
    "        # print(val)\n",
    "        if idx==0:\n",
    "            gr=\"Low\"\n",
    "        else:\n",
    "            gr=\"High\"\n",
    "        df_links.loc[(df_links.Source==gr)&(df_links.Target==col), \"Value\"] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1c2c724-159f-46f6-aef9-3de94828bbc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = dict(zip(df_nodes.Label.tolist(),\n",
    "         df_nodes.ID.tolist()))\n",
    "df_links.replace(mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "922ccaa5-4e09-4283-ad2e-e4e8f8409195",
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in crosstab.index:\n",
    "    if row==0:\n",
    "        l = \"Low\"\n",
    "    else:\n",
    "        l = \"High\"\n",
    "    e = crosstab.loc[row].sum()\n",
    "    df_nodes[\"Label\"].replace({f\"{l}\": f\"{l} (n={e})\"}, inplace=True)\n",
    "for col in crosstab.columns:\n",
    "\n",
    "    e = crosstab[col].sum()\n",
    "    df_nodes[\"Label\"].replace({f\"{col}\": f\"{col} (n={e})\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbf3b7f2-3dea-48a4-96c3-5cde03e7b105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Value</th>\n",
       "      <th>Link Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source  Target  Value Link Color\n",
       "0       0       2     35    #f68612\n",
       "1       0       3      3    #f68612\n",
       "2       0       4     26    #f68612\n",
       "3       1       2     17    #91191e\n",
       "4       1       3      3    #91191e\n",
       "5       1       4     27    #91191e"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "60e532b5-ed72-4d56-ac20-b6a4af5fb49e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.14.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3302f2f3-2f39-4357-9cbf-272d41df04b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Value</th>\n",
       "      <th>Link Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source  Target  Value Link Color\n",
       "0       0       2     35    #f68612\n",
       "1       0       3      3    #f68612\n",
       "2       0       4     26    #f68612\n",
       "3       1       2     17    #91191e\n",
       "4       1       3      3    #91191e\n",
       "5       1       4     27    #91191e"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "32f933d7-320b-4c86-831a-1456174bea31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_links[\"Source\"] = df_links[\"Source\"].astype(\"category\").cat.reorder_categories([0,1])\n",
    "df_links[\"Target\"] = df_links[\"Target\"].astype(\"category\").cat.reorder_categories([2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7819f9f4-c459-4bb7-8b7c-6f01d601bffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Value</th>\n",
       "      <th>Link Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Target  Value Link Color\n",
       "0      0      2     35    #f68612\n",
       "1      0      3      3    #f68612\n",
       "2      0      4     26    #f68612\n",
       "3      1      2     17    #91191e\n",
       "4      1      3      3    #91191e\n",
       "5      1      4     27    #91191e"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "db1a80e7-35ba-4970-b3de-33a909eb921e",
   "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>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Low (n=64)</td>\n",
       "      <td>#f68612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>High (n=47)</td>\n",
       "      <td>#91191e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Classic-like (n=52)</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>LGm6-GBM (n=6)</td>\n",
       "      <td>#d55e00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Mesenchymal-like (n=53)</td>\n",
       "      <td>#cc78bc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                    Label    Color\n",
       "0   0               Low (n=64)  #f68612\n",
       "1   1              High (n=47)  #91191e\n",
       "2   2      Classic-like (n=52)  #029e73\n",
       "3   3           LGm6-GBM (n=6)  #d55e00\n",
       "4   4  Mesenchymal-like (n=53)  #cc78bc"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3462a3f5-b05d-40f4-a503-9b45d00a53b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sankey plot setup\n",
    "data_trace = dict(\n",
    "    type='sankey',\n",
    "    domain = dict(\n",
    "      x =  (0.,1.),\n",
    "      y =  (0.,1.)\n",
    "    ),\n",
    "    orientation = \"h\",\n",
    "    valueformat = \".0f\",\n",
    "    node = dict(\n",
    "      pad = 10,\n",
    "    # thickness = 30,\n",
    "      line = dict(\n",
    "        color = \"black\",\n",
    "        width = 0\n",
    "      ),\n",
    "      label =  df_nodes['Label'].dropna(axis=0, how='any'),\n",
    "      color = df_nodes['Color']\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = df_links['Source'].dropna(axis=0, how='any'),\n",
    "      target = df_links['Target'].dropna(axis=0, how='any'),\n",
    "      value = df_links['Value'].dropna(axis=0, how='any'),\n",
    "      color = df_links['Link Color'].dropna(axis=0, how='any'),\n",
    "  )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0533c134-e9e9-448d-b579-8263efd3d539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "link": {
          "color": [
           "#f68612",
           "#f68612",
           "#f68612",
           "#91191e",
           "#91191e",
           "#91191e"
          ],
          "source": [
           0,
           0,
           0,
           1,
           1,
           1
          ],
          "target": [
           2,
           3,
           4,
           2,
           3,
           4
          ],
          "value": [
           35,
           3,
           26,
           17,
           3,
           27
          ]
         },
         "node": {
          "color": [
           "#f68612",
           "#91191e",
           "#029e73",
           "#d55e00",
           "#cc78bc"
          ],
          "label": [
           "Low (n=64)",
           "High (n=47)",
           "Classic-like (n=52)",
           "LGm6-GBM (n=6)",
           "Mesenchymal-like (n=53)"
          ],
          "line": {
           "color": "black",
           "width": 0
          },
          "pad": 10
         },
         "orientation": "h",
         "type": "sankey",
         "valueformat": ".0f"
        }
       ],
       "layout": {
        "font": {
         "size": 10
        },
        "height": 700,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout = dict(\n",
    "        title = \"Correspondence between neural and \\nsupervised methylation subgroups (TCGA)\",\n",
    "    height = 700,\n",
    "    width=700,\n",
    "    font = dict(\n",
    "      size = 10),)\n",
    "\n",
    "fig = dict(data=[data_trace], layout=layout, arrangement='snap',)\n",
    "fig = go.Figure(fig, )\n",
    "iplot(fig, validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7ee6db9-aeff-464c-8587-8f5c5465582f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image(\"tcga_methSup.png\")\n",
    "fig.write_image(\"tcga_methSup.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4014c7ea-740c-4adc-a9db-126fa9d42deb",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7cce5c30-2874-4b9c-bd0d-739354d2ece3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>barcode</th>\n",
       "      <th>patient</th>\n",
       "      <th>paper_Supervised.DNA.Methylation.Cluster</th>\n",
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       "      <th>TP53</th>\n",
       "      <th>NF1</th>\n",
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       "    <tr>\n",
       "      <th>Sample ID</th>\n",
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       "      <th>TCGA-28-5213-01A</th>\n",
       "      <td>TCGA-28-5213-01A-01D-1481-05</td>\n",
       "      <td>TCGA-28-5213</td>\n",
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       "      <td>Mesenchymal</td>\n",
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       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
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       "      <td>...</td>\n",
       "      <td>Missense_Mutation</td>\n",
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       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
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       "    <tr>\n",
       "      <th>TCGA-19-5959-01A</th>\n",
       "      <td>TCGA-19-5959-01A-11D-1697-05</td>\n",
       "      <td>TCGA-19-5959</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>Classical</td>\n",
       "      <td>CL</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
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       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
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       "      <th>TCGA-28-5209-01A</th>\n",
       "      <td>TCGA-28-5209-01A-01D-1481-05</td>\n",
       "      <td>TCGA-28-5209</td>\n",
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       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
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       "    <tr>\n",
       "      <th>TCGA-19-4065-01A</th>\n",
       "      <td>TCGA-19-4065-01A-01D-2004-05</td>\n",
       "      <td>TCGA-19-4065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ME</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TCGA-GBM</td>\n",
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       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
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       "    <tr>\n",
       "      <th>TCGA-4W-AA9T-01A</th>\n",
       "      <td>TCGA-4W-AA9T-01A-11D-A392-05</td>\n",
       "      <td>TCGA-4W-AA9T</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
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       "      <td>Missense_Mutation</td>\n",
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       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>TCGA-19-5958-01A</th>\n",
       "      <td>TCGA-19-5958-01A-11D-1697-05</td>\n",
       "      <td>TCGA-19-5958</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Classical</td>\n",
       "      <td>NE</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5954-01A</th>\n",
       "      <td>TCGA-19-5954-01A-11D-1697-05</td>\n",
       "      <td>TCGA-19-5954</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Classical</td>\n",
       "      <td>CL</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-6694-01A</th>\n",
       "      <td>TCGA-06-6694-01A-12D-1844-05</td>\n",
       "      <td>TCGA-06-6694</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-12-5299-01A</th>\n",
       "      <td>TCGA-12-5299-01A-02D-1481-05</td>\n",
       "      <td>TCGA-12-5299</td>\n",
       "      <td>Mesenchymal-like</td>\n",
       "      <td>Classical</td>\n",
       "      <td>CL</td>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5859-01A</th>\n",
       "      <td>TCGA-06-5859-01A-01D-1697-05</td>\n",
       "      <td>TCGA-06-5859</td>\n",
       "      <td>Classic-like</td>\n",
       "      <td>Neural</td>\n",
       "      <td>ME</td>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R3</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       barcode       patient  \\\n",
       "Sample ID                                                      \n",
       "TCGA-28-5213-01A  TCGA-28-5213-01A-01D-1481-05  TCGA-28-5213   \n",
       "TCGA-19-5959-01A  TCGA-19-5959-01A-11D-1697-05  TCGA-19-5959   \n",
       "TCGA-28-5209-01A  TCGA-28-5209-01A-01D-1481-05  TCGA-28-5209   \n",
       "TCGA-19-4065-01A  TCGA-19-4065-01A-01D-2004-05  TCGA-19-4065   \n",
       "TCGA-4W-AA9T-01A  TCGA-4W-AA9T-01A-11D-A392-05  TCGA-4W-AA9T   \n",
       "...                                        ...           ...   \n",
       "TCGA-19-5958-01A  TCGA-19-5958-01A-11D-1697-05  TCGA-19-5958   \n",
       "TCGA-19-5954-01A  TCGA-19-5954-01A-11D-1697-05  TCGA-19-5954   \n",
       "TCGA-06-6694-01A  TCGA-06-6694-01A-12D-1844-05  TCGA-06-6694   \n",
       "TCGA-12-5299-01A  TCGA-12-5299-01A-02D-1481-05  TCGA-12-5299   \n",
       "TCGA-06-5859-01A  TCGA-06-5859-01A-01D-1697-05  TCGA-06-5859   \n",
       "\n",
       "                 paper_Supervised.DNA.Methylation.Cluster  \\\n",
       "Sample ID                                                   \n",
       "TCGA-28-5213-01A                         Mesenchymal-like   \n",
       "TCGA-19-5959-01A                             Classic-like   \n",
       "TCGA-28-5209-01A                             Classic-like   \n",
       "TCGA-19-4065-01A                                      NaN   \n",
       "TCGA-4W-AA9T-01A                             Classic-like   \n",
       "...                                                   ...   \n",
       "TCGA-19-5958-01A                         Mesenchymal-like   \n",
       "TCGA-19-5954-01A                         Mesenchymal-like   \n",
       "TCGA-06-6694-01A                         Mesenchymal-like   \n",
       "TCGA-12-5299-01A                         Mesenchymal-like   \n",
       "TCGA-06-5859-01A                             Classic-like   \n",
       "\n",
       "                 paper_Original.Subtype paper_Transcriptome.Subtype  \\\n",
       "Sample ID                                                             \n",
       "TCGA-28-5213-01A            Mesenchymal                          ME   \n",
       "TCGA-19-5959-01A              Classical                          CL   \n",
       "TCGA-28-5209-01A            Mesenchymal                          CL   \n",
       "TCGA-19-4065-01A                    NaN                          ME   \n",
       "TCGA-4W-AA9T-01A                    NaN                         NaN   \n",
       "...                                 ...                         ...   \n",
       "TCGA-19-5958-01A              Classical                          NE   \n",
       "TCGA-19-5954-01A              Classical                          CL   \n",
       "TCGA-06-6694-01A                    NaN                         NaN   \n",
       "TCGA-12-5299-01A              Classical                          CL   \n",
       "TCGA-06-5859-01A                 Neural                          ME   \n",
       "\n",
       "                 paper_IDH.specific.DNA.Methylation.Cluster paper_IDH.status  \\\n",
       "Sample ID                                                                      \n",
       "TCGA-28-5213-01A                                   IDHwt-K2               WT   \n",
       "TCGA-19-5959-01A                                   IDHwt-K1               WT   \n",
       "TCGA-28-5209-01A                                   IDHwt-K1               WT   \n",
       "TCGA-19-4065-01A                                        NaN              NaN   \n",
       "TCGA-4W-AA9T-01A                                   IDHwt-K1               WT   \n",
       "...                                                     ...              ...   \n",
       "TCGA-19-5958-01A                                   IDHwt-K2               WT   \n",
       "TCGA-19-5954-01A                                   IDHwt-K2               WT   \n",
       "TCGA-06-6694-01A                                   IDHwt-K2               WT   \n",
       "TCGA-12-5299-01A                                   IDHwt-K2               WT   \n",
       "TCGA-06-5859-01A                                   IDHwt-K1               WT   \n",
       "\n",
       "                 paper_IDH.specific.RNA.Expression.Cluster  \\\n",
       "Sample ID                                                    \n",
       "TCGA-28-5213-01A                                  IDHwt-R2   \n",
       "TCGA-19-5959-01A                                       NaN   \n",
       "TCGA-28-5209-01A                                  IDHwt-R1   \n",
       "TCGA-19-4065-01A                                       NaN   \n",
       "TCGA-4W-AA9T-01A                                       NaN   \n",
       "...                                                    ...   \n",
       "TCGA-19-5958-01A                                       NaN   \n",
       "TCGA-19-5954-01A                                       NaN   \n",
       "TCGA-06-6694-01A                                       NaN   \n",
       "TCGA-12-5299-01A                                  IDHwt-R2   \n",
       "TCGA-06-5859-01A                                  IDHwt-R3   \n",
       "\n",
       "                 paper_IDH.codel.subtype Project ID  ...               EGFR  \\\n",
       "Sample ID                                            ...                      \n",
       "TCGA-28-5213-01A                   IDHwt   TCGA-GBM  ...  Missense_Mutation   \n",
       "TCGA-19-5959-01A                   IDHwt   TCGA-GBM  ...        No_mutation   \n",
       "TCGA-28-5209-01A                   IDHwt   TCGA-GBM  ...  Missense_Mutation   \n",
       "TCGA-19-4065-01A                     NaN   TCGA-GBM  ...        No_mutation   \n",
       "TCGA-4W-AA9T-01A                   IDHwt   TCGA-GBM  ...  Missense_Mutation   \n",
       "...                                  ...        ...  ...                ...   \n",
       "TCGA-19-5958-01A                   IDHwt   TCGA-GBM  ...        No_mutation   \n",
       "TCGA-19-5954-01A                   IDHwt   TCGA-GBM  ...        No_mutation   \n",
       "TCGA-06-6694-01A                   IDHwt   TCGA-GBM  ...        No_mutation   \n",
       "TCGA-12-5299-01A                   IDHwt   TCGA-GBM  ...        Unavailable   \n",
       "TCGA-06-5859-01A                   IDHwt   TCGA-GBM  ...        Unavailable   \n",
       "\n",
       "                          RB1               TP53          NF1  \\\n",
       "Sample ID                                                       \n",
       "TCGA-28-5213-01A  No_mutation        No_mutation  No_mutation   \n",
       "TCGA-19-5959-01A  No_mutation        No_mutation  No_mutation   \n",
       "TCGA-28-5209-01A  No_mutation        No_mutation  No_mutation   \n",
       "TCGA-19-4065-01A  No_mutation  Missense_Mutation  No_mutation   \n",
       "TCGA-4W-AA9T-01A  No_mutation        No_mutation  No_mutation   \n",
       "...                       ...                ...          ...   \n",
       "TCGA-19-5958-01A  No_mutation        No_mutation  No_mutation   \n",
       "TCGA-19-5954-01A  No_mutation        No_mutation  No_mutation   \n",
       "TCGA-06-6694-01A  No_mutation  Missense_Mutation  No_mutation   \n",
       "TCGA-12-5299-01A  Unavailable        Unavailable  Unavailable   \n",
       "TCGA-06-5859-01A  Unavailable        Unavailable  Unavailable   \n",
       "\n",
       "                             PIK3R1               ATRX       PIK3CA  \\\n",
       "Sample ID                                                             \n",
       "TCGA-28-5213-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-19-5959-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-28-5209-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-19-4065-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-4W-AA9T-01A        No_mutation  Missense_Mutation  No_mutation   \n",
       "...                             ...                ...          ...   \n",
       "TCGA-19-5958-01A  Missense_Mutation        No_mutation  No_mutation   \n",
       "TCGA-19-5954-01A        No_mutation        No_mutation     Multiple   \n",
       "TCGA-06-6694-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-12-5299-01A        Unavailable        Unavailable  Unavailable   \n",
       "TCGA-06-5859-01A        Unavailable        Unavailable  Unavailable   \n",
       "\n",
       "                         TERT          CIC        FUBP1  \n",
       "Sample ID                                                \n",
       "TCGA-28-5213-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-19-5959-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-28-5209-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-19-4065-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-4W-AA9T-01A  No_mutation  No_mutation  No_mutation  \n",
       "...                       ...          ...          ...  \n",
       "TCGA-19-5958-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-19-5954-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-06-6694-01A  No_mutation  No_mutation  No_mutation  \n",
       "TCGA-12-5299-01A  Unavailable  Unavailable  Unavailable  \n",
       "TCGA-06-5859-01A  Unavailable  Unavailable  Unavailable  \n",
       "\n",
       "[121 rows x 35 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "34c41b52-d903-4a99-8ae5-a4af64730bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>paper_Supervised.DNA.Methylation.Cluster</th>\n",
       "      <th>Classic-like</th>\n",
       "      <th>LGm6-GBM</th>\n",
       "      <th>Mesenchymal-like</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paper_Original.Subtype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Classical</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mesenchymal</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proneural</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "paper_Supervised.DNA.Methylation.Cluster  Classic-like  LGm6-GBM  \\\n",
       "paper_Original.Subtype                                             \n",
       "Classical                                           12         0   \n",
       "Mesenchymal                                          3         3   \n",
       "Neural                                               6         0   \n",
       "Proneural                                            6         0   \n",
       "\n",
       "paper_Supervised.DNA.Methylation.Cluster  Mesenchymal-like  \n",
       "paper_Original.Subtype                                      \n",
       "Classical                                               12  \n",
       "Mesenchymal                                             11  \n",
       "Neural                                                   2  \n",
       "Proneural                                                7  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(df[\"paper_Original.Subtype\"], df[\"paper_Supervised.DNA.Methylation.Cluster\"])\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6a229bc-4c0d-4ba8-a0a7-bbadb9f6abee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "878b6ed8-f76c-4ab5-acd1-793306f13e19",
   "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>paper_Transcriptome.Subtype</th>\n",
       "      <th>CL</th>\n",
       "      <th>ME</th>\n",
       "      <th>NE</th>\n",
       "      <th>PN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural subgroup (0=low, 1=high)</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",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "paper_Transcriptome.Subtype      CL  ME  NE  PN\n",
       "Neural subgroup (0=low, 1=high)                \n",
       "0                                19  18   2   1\n",
       "1                                12   5   1   9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(df[\"Neural subgroup (0=low, 1=high)\"], df[\"paper_Transcriptome.Subtype\"])\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2ac03aeb-a87a-4fa3-be34-1fe1d165b838",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>paper_Transcriptome.Subtype</th>\n",
       "      <th>CL</th>\n",
       "      <th>ME</th>\n",
       "      <th>NE</th>\n",
       "      <th>PN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paper_Original.Subtype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Classical</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mesenchymal</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proneural</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "paper_Transcriptome.Subtype  CL  ME  NE  PN\n",
       "paper_Original.Subtype                     \n",
       "Classical                    21   2   1   0\n",
       "Mesenchymal                   4  16   0   1\n",
       "Neural                        2   3   2   1\n",
       "Proneural                     4   1   0   8"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(df[\"paper_Original.Subtype\"], df[\"paper_Transcriptome.Subtype\"])\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "92775d6a-2060-4d83-bfb1-435d6edcf825",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>paper_Transcriptome.Subtype</th>\n",
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       "paper_Transcriptome.Subtype      CL  ME  NE  PN\n",
       "Neural subgroup (0=low, 1=high)                \n",
       "0                                19  18   2   1\n",
       "1                                12   5   1   9"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(df[\"Neural subgroup (0=low, 1=high)\"], df[\"paper_Transcriptome.Subtype\"])\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a8923dac-da08-4e46-b412-7075f5faca4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>paper_Original.Subtype</th>\n",
       "      <th>Classical</th>\n",
       "      <th>Mesenchymal</th>\n",
       "      <th>Neural</th>\n",
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      "text/plain": [
       "paper_Original.Subtype           Classical  Mesenchymal  Neural  Proneural\n",
       "Neural subgroup (0=low, 1=high)                                           \n",
       "0                                       16           16       7          3\n",
       "1                                       10            5       2         11"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(df[\"Neural subgroup (0=low, 1=high)\"], df[\"paper_Original.Subtype\"])\n",
    "crosstab"
   ]
  }
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