PyDeseq2_DE_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ff8dc3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install pydeseq2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8508227b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydeseq2.dds import DeseqDataSet\n",
    "from pydeseq2.ds import DeseqStats\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a51169a4",
   "metadata": {},
   "outputs": [
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       "                Geneid  Ctr_s1  Ctr_s2  Ctr_s7  Ctr_s13  RS_s6  RS_s9  RS_s12   \n",
       "0      ENSG00000284662       0       0       0        0      0      0       0  \\\n",
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       "\n",
       "       RS_s16  \n",
       "0           0  \n",
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       "3         202  \n",
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       "...       ...  \n",
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       "\n",
       "[60663 rows x 9 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = pd.read_csv('count_table.csv')\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cebc3ee5",
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   "outputs": [
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       "                 Ctr_s1  Ctr_s2  Ctr_s7  Ctr_s13  RS_s6  RS_s9  RS_s12  RS_s16\n",
       "Geneid                                                                        \n",
       "ENSG00000284662       0       0       0        0      0      0       0       0\n",
       "ENSG00000186827       0       0       1        1      4      0       0       3\n",
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       "ENSG00000160072     361     427     416      549    328    174     246     202\n",
       "ENSG00000041988     118     156     124      133    101     56      85      89\n",
       "...                 ...     ...     ...      ...    ...    ...     ...     ...\n",
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       "\n",
       "[60663 rows x 8 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = counts.set_index('Geneid')\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9437cd34",
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       "  </tbody>\n",
       "</table>\n",
       "<p>31378 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Ctr_s1  Ctr_s2  Ctr_s7  Ctr_s13  RS_s6  RS_s9  RS_s12  RS_s16\n",
       "Geneid                                                                        \n",
       "ENSG00000186827       0       0       1        1      4      0       0       3\n",
       "ENSG00000186891       1       0       0        0      0      2       2       2\n",
       "ENSG00000160072     361     427     416      549    328    174     246     202\n",
       "ENSG00000041988     118     156     124      133    101     56      85      89\n",
       "ENSG00000260179       2       0       1        2      0      1       0       0\n",
       "...                 ...     ...     ...      ...    ...    ...     ...     ...\n",
       "ENSG00000278817       0       0       1        0      1      0       1       0\n",
       "ENSG00000278384       2       7       6        5      8      2       2       1\n",
       "ENSG00000276345      20       0       0        0      0     12       0       0\n",
       "ENSG00000271254      56      55      54       76     47     67      49      53\n",
       "ENSG00000275987       0       0       1        0      0      0       0       0\n",
       "\n",
       "[31378 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = counts[counts.sum(axis = 1) > 0]\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2537fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = counts.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7422d03b",
   "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>Geneid</th>\n",
       "      <th>ENSG00000186827</th>\n",
       "      <th>ENSG00000186891</th>\n",
       "      <th>ENSG00000160072</th>\n",
       "      <th>ENSG00000041988</th>\n",
       "      <th>ENSG00000260179</th>\n",
       "      <th>ENSG00000225972</th>\n",
       "      <th>ENSG00000224315</th>\n",
       "      <th>ENSG00000198744</th>\n",
       "      <th>ENSG00000228037</th>\n",
       "      <th>ENSG00000142611</th>\n",
       "      <th>...</th>\n",
       "      <th>ENSG00000278673</th>\n",
       "      <th>ENSG00000278704</th>\n",
       "      <th>ENSG00000277400</th>\n",
       "      <th>ENSG00000276256</th>\n",
       "      <th>ENSG00000273748</th>\n",
       "      <th>ENSG00000278817</th>\n",
       "      <th>ENSG00000278384</th>\n",
       "      <th>ENSG00000276345</th>\n",
       "      <th>ENSG00000271254</th>\n",
       "      <th>ENSG00000275987</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ctr_s1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>361</td>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Ctr_s2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>427</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ctr_s7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>416</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ctr_s13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>549</td>\n",
       "      <td>133</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s6</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>624</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s12</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>246</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s16</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>202</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Geneid   ENSG00000186827  ENSG00000186891  ENSG00000160072  ENSG00000041988   \n",
       "Ctr_s1                 0                1              361              118  \\\n",
       "Ctr_s2                 0                0              427              156   \n",
       "Ctr_s7                 1                0              416              124   \n",
       "Ctr_s13                1                0              549              133   \n",
       "RS_s6                  4                0              328              101   \n",
       "RS_s9                  0                2              174               56   \n",
       "RS_s12                 0                2              246               85   \n",
       "RS_s16                 3                2              202               89   \n",
       "\n",
       "Geneid   ENSG00000260179  ENSG00000225972  ENSG00000224315  ENSG00000198744   \n",
       "Ctr_s1                 2                6                0                1  \\\n",
       "Ctr_s2                 0                2                0              494   \n",
       "Ctr_s7                 1                3                0                6   \n",
       "Ctr_s13                2                4                0                4   \n",
       "RS_s6                  0                8                1              624   \n",
       "RS_s9                  1                1                0                0   \n",
       "RS_s12                 0                3                0                4   \n",
       "RS_s16                 0                5                0                3   \n",
       "\n",
       "Geneid   ENSG00000228037  ENSG00000142611  ...  ENSG00000278673   \n",
       "Ctr_s1                 0               73  ...                1  \\\n",
       "Ctr_s2                 0               24  ...                1   \n",
       "Ctr_s7                 0               54  ...                0   \n",
       "Ctr_s13                0               14  ...                0   \n",
       "RS_s6                  0                5  ...                1   \n",
       "RS_s9                  1               11  ...                0   \n",
       "RS_s12                 1               17  ...                0   \n",
       "RS_s16                 0                1  ...                0   \n",
       "\n",
       "Geneid   ENSG00000278704  ENSG00000277400  ENSG00000276256  ENSG00000273748   \n",
       "Ctr_s1                 0                4               74               26  \\\n",
       "Ctr_s2                 0                0               16               50   \n",
       "Ctr_s7                 0                1               68               37   \n",
       "Ctr_s13                0                3               28               15   \n",
       "RS_s6                  0                1               15               40   \n",
       "RS_s9                  0                0               21               20   \n",
       "RS_s12                 2                3               28               30   \n",
       "RS_s16                 0                0               16               13   \n",
       "\n",
       "Geneid   ENSG00000278817  ENSG00000278384  ENSG00000276345  ENSG00000271254   \n",
       "Ctr_s1                 0                2               20               56  \\\n",
       "Ctr_s2                 0                7                0               55   \n",
       "Ctr_s7                 1                6                0               54   \n",
       "Ctr_s13                0                5                0               76   \n",
       "RS_s6                  1                8                0               47   \n",
       "RS_s9                  0                2               12               67   \n",
       "RS_s12                 1                2                0               49   \n",
       "RS_s16                 0                1                0               53   \n",
       "\n",
       "Geneid   ENSG00000275987  \n",
       "Ctr_s1                 0  \n",
       "Ctr_s2                 0  \n",
       "Ctr_s7                 1  \n",
       "Ctr_s13                0  \n",
       "RS_s6                  0  \n",
       "RS_s9                  0  \n",
       "RS_s12                 0  \n",
       "RS_s16                 0  \n",
       "\n",
       "[8 rows x 31378 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3c8eba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.DataFrame(zip(counts.index, ['C','C','C','C', 'RS', 'RS', 'RS', 'RS']),\n",
    "                        columns = ['Sample', 'Condition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "555282e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>Condition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ctr_s1</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ctr_s2</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ctr_s7</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ctr_s13</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s6</th>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s9</th>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s12</th>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS_s16</th>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Condition\n",
       "Sample           \n",
       "Ctr_s1          C\n",
       "Ctr_s2          C\n",
       "Ctr_s7          C\n",
       "Ctr_s13         C\n",
       "RS_s6          RS\n",
       "RS_s9          RS\n",
       "RS_s12         RS\n",
       "RS_s16         RS"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = metadata.set_index('Sample')\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38d221ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "dds = DeseqDataSet(counts=counts,\n",
    "            clinical=metadata,\n",
    "            design_factors=\"Condition\")\n",
    "\n",
    "\n",
    "#design_factors=[\"batch\", \"condition\"] = ~ batch + condtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c52b3680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting size factors...\n",
      "... done in 0.01 seconds.\n",
      "\n",
      "Fitting dispersions...\n",
      "... done in 1.95 seconds.\n",
      "\n",
      "Fitting dispersion trend curve...\n",
      "... done in 8.54 seconds.\n",
      "\n",
      "Fitting MAP dispersions...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/dds.py:521: RuntimeWarning: invalid value encountered in log\n",
      "  ) - np.log(self[:, self.non_zero_genes].varm[\"fitted_dispersions\"])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... done in 3.36 seconds.\n",
      "\n",
      "Fitting LFCs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/dds.py:586: RuntimeWarning: invalid value encountered in log\n",
      "  self.varm[\"_outlier_genes\"] = np.log(self.varm[\"genewise_dispersions\"]) > np.log(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... done in 2.02 seconds.\n",
      "\n",
      "Refitting 0 outliers.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dds.deseq2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cc91e5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8 × 31378\n",
       "    obs: 'Condition'\n",
       "    uns: 'trend_coeffs', '_squared_logres', 'prior_disp_var'\n",
       "    obsm: 'design_matrix', 'size_factors'\n",
       "    varm: 'non_zero', '_rough_dispersions', 'genewise_dispersions', '_genewise_converged', '_normed_means', 'fitted_dispersions', 'MAP_dispersions', '_MAP_converged', 'dispersions', '_outlier_genes', 'LFC', '_LFC_converged', 'replaced'\n",
       "    layers: 'normed_counts', '_mu_hat', '_mu_LFC', '_hat_diagonals', 'cooks'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5d90ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_res = DeseqStats(dds, n_cpus=8, contrast = ('Condition','RS','C'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a080ec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Wald tests...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:607: RuntimeWarning: invalid value encountered in log\n",
      "  log_alpha_hat = np.log(alpha_hat)\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/pydeseq2/utils.py:648: RuntimeWarning: invalid value encountered in log\n",
      "  x0=np.log(alpha_hat),\n",
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/numpy/linalg/linalg.py:2079: RuntimeWarning: invalid value encountered in slogdet\n",
      "  sign, logdet = _umath_linalg.slogdet(a, signature=signature)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... done in 3.56 seconds.\n",
      "\n",
      "Log2 fold change & Wald test p-value: Condition RS vs C\n"
     ]
    },
    {
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      "text/plain": [
       "                   baseMean  log2FoldChange     lfcSE      stat    pvalue   \n",
       "Geneid                                                                      \n",
       "ENSG00000186827    1.107782        2.119166  1.486697  1.425419  0.154036  \\\n",
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       "ENSG00000260179    0.758697       -1.637766  1.558991 -1.050529  0.293475   \n",
       "...                     ...             ...       ...       ...       ...   \n",
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       "\n",
       "                     padj  \n",
       "Geneid                     \n",
       "ENSG00000186827       NaN  \n",
       "ENSG00000186891       NaN  \n",
       "ENSG00000160072  0.013103  \n",
       "ENSG00000041988  0.173132  \n",
       "ENSG00000260179       NaN  \n",
       "...                   ...  \n",
       "ENSG00000278817       NaN  \n",
       "ENSG00000278384  0.894836  \n",
       "ENSG00000276345  0.993656  \n",
       "ENSG00000271254  0.655572  \n",
       "ENSG00000275987       NaN  \n",
       "\n",
       "[31378 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stat_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0460fc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = stat_res.results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c173104d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000278384</th>\n",
       "      <td>3.791022</td>\n",
       "      <td>-0.243724</td>\n",
       "      <td>0.662745</td>\n",
       "      <td>-0.367750</td>\n",
       "      <td>0.713060</td>\n",
       "      <td>0.894836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000276345</th>\n",
       "      <td>4.833929</td>\n",
       "      <td>-0.092388</td>\n",
       "      <td>3.260101</td>\n",
       "      <td>-0.028339</td>\n",
       "      <td>0.977392</td>\n",
       "      <td>0.993656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000271254</th>\n",
       "      <td>58.543851</td>\n",
       "      <td>0.316375</td>\n",
       "      <td>0.333484</td>\n",
       "      <td>0.948695</td>\n",
       "      <td>0.342776</td>\n",
       "      <td>0.655572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000275987</th>\n",
       "      <td>0.108747</td>\n",
       "      <td>-0.300576</td>\n",
       "      <td>3.869272</td>\n",
       "      <td>-0.077683</td>\n",
       "      <td>0.938080</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31378 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   baseMean  log2FoldChange     lfcSE      stat    pvalue   \n",
       "Geneid                                                                      \n",
       "ENSG00000186827    1.107782        2.119166  1.486697  1.425419  0.154036  \\\n",
       "ENSG00000186891    1.071147        2.761974  1.566738  1.762882  0.077920   \n",
       "ENSG00000160072  320.481168       -0.492175  0.147030 -3.347457  0.000816   \n",
       "ENSG00000041988  103.354977       -0.282138  0.130485 -2.162226  0.030601   \n",
       "ENSG00000260179    0.758697       -1.637766  1.558991 -1.050529  0.293475   \n",
       "...                     ...             ...       ...       ...       ...   \n",
       "ENSG00000278817    0.352622        1.119898  1.744744  0.641870  0.520958   \n",
       "ENSG00000278384    3.791022       -0.243724  0.662745 -0.367750  0.713060   \n",
       "ENSG00000276345    4.833929       -0.092388  3.260101 -0.028339  0.977392   \n",
       "ENSG00000271254   58.543851        0.316375  0.333484  0.948695  0.342776   \n",
       "ENSG00000275987    0.108747       -0.300576  3.869272 -0.077683  0.938080   \n",
       "\n",
       "                     padj  \n",
       "Geneid                     \n",
       "ENSG00000186827       NaN  \n",
       "ENSG00000186891       NaN  \n",
       "ENSG00000160072  0.013103  \n",
       "ENSG00000041988  0.173132  \n",
       "ENSG00000260179       NaN  \n",
       "...                   ...  \n",
       "ENSG00000278817       NaN  \n",
       "ENSG00000278384  0.894836  \n",
       "ENSG00000276345  0.993656  \n",
       "ENSG00000271254  0.655572  \n",
       "ENSG00000275987       NaN  \n",
       "\n",
       "[31378 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11fcefd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install sanbomics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a8592aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sanbomics.tools import id_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06f71b0a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mapper = id_map(species = 'human')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "83b38de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "res['Symbol'] = res.index.map(mapper.mapper) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fb6ad16c",
   "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></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000186827</th>\n",
       "      <td>1.107782</td>\n",
       "      <td>2.119166</td>\n",
       "      <td>1.486697</td>\n",
       "      <td>1.425419</td>\n",
       "      <td>0.154036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TNFRSF4</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000186891</th>\n",
       "      <td>1.071147</td>\n",
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       "      <td>1.566738</td>\n",
       "      <td>1.762882</td>\n",
       "      <td>0.077920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TNFRSF18</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000160072</th>\n",
       "      <td>320.481168</td>\n",
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       "      <td>-3.347457</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.013103</td>\n",
       "      <td>ATAD3B</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000041988</th>\n",
       "      <td>103.354977</td>\n",
       "      <td>-0.282138</td>\n",
       "      <td>0.130485</td>\n",
       "      <td>-2.162226</td>\n",
       "      <td>0.030601</td>\n",
       "      <td>0.173132</td>\n",
       "      <td>THAP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000260179</th>\n",
       "      <td>0.758697</td>\n",
       "      <td>-1.637766</td>\n",
       "      <td>1.558991</td>\n",
       "      <td>-1.050529</td>\n",
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       "      <td>AL162741.1</td>\n",
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       "      <th>ENSG00000278817</th>\n",
       "      <td>0.352622</td>\n",
       "      <td>1.119898</td>\n",
       "      <td>1.744744</td>\n",
       "      <td>0.641870</td>\n",
       "      <td>0.520958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC007325.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000278384</th>\n",
       "      <td>3.791022</td>\n",
       "      <td>-0.243724</td>\n",
       "      <td>0.662745</td>\n",
       "      <td>-0.367750</td>\n",
       "      <td>0.713060</td>\n",
       "      <td>0.894836</td>\n",
       "      <td>AL354822.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000276345</th>\n",
       "      <td>4.833929</td>\n",
       "      <td>-0.092388</td>\n",
       "      <td>3.260101</td>\n",
       "      <td>-0.028339</td>\n",
       "      <td>0.977392</td>\n",
       "      <td>0.993656</td>\n",
       "      <td>AC004556.3</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000271254</th>\n",
       "      <td>58.543851</td>\n",
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       "      <td>0.333484</td>\n",
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       "      <td>0.655572</td>\n",
       "      <td>AC240274.1</td>\n",
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       "      <th>ENSG00000275987</th>\n",
       "      <td>0.108747</td>\n",
       "      <td>-0.300576</td>\n",
       "      <td>3.869272</td>\n",
       "      <td>-0.077683</td>\n",
       "      <td>0.938080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31378 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   baseMean  log2FoldChange     lfcSE      stat    pvalue   \n",
       "Geneid                                                                      \n",
       "ENSG00000186827    1.107782        2.119166  1.486697  1.425419  0.154036  \\\n",
       "ENSG00000186891    1.071147        2.761974  1.566738  1.762882  0.077920   \n",
       "ENSG00000160072  320.481168       -0.492175  0.147030 -3.347457  0.000816   \n",
       "ENSG00000041988  103.354977       -0.282138  0.130485 -2.162226  0.030601   \n",
       "ENSG00000260179    0.758697       -1.637766  1.558991 -1.050529  0.293475   \n",
       "...                     ...             ...       ...       ...       ...   \n",
       "ENSG00000278817    0.352622        1.119898  1.744744  0.641870  0.520958   \n",
       "ENSG00000278384    3.791022       -0.243724  0.662745 -0.367750  0.713060   \n",
       "ENSG00000276345    4.833929       -0.092388  3.260101 -0.028339  0.977392   \n",
       "ENSG00000271254   58.543851        0.316375  0.333484  0.948695  0.342776   \n",
       "ENSG00000275987    0.108747       -0.300576  3.869272 -0.077683  0.938080   \n",
       "\n",
       "                     padj      Symbol  \n",
       "Geneid                                 \n",
       "ENSG00000186827       NaN     TNFRSF4  \n",
       "ENSG00000186891       NaN    TNFRSF18  \n",
       "ENSG00000160072  0.013103      ATAD3B  \n",
       "ENSG00000041988  0.173132       THAP3  \n",
       "ENSG00000260179       NaN  AL162741.1  \n",
       "...                   ...         ...  \n",
       "ENSG00000278817       NaN  AC007325.4  \n",
       "ENSG00000278384  0.894836  AL354822.1  \n",
       "ENSG00000276345  0.993656  AC004556.3  \n",
       "ENSG00000271254  0.655572  AC240274.1  \n",
       "ENSG00000275987       NaN         NaN  \n",
       "\n",
       "[31378 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3de38251",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = res[res.baseMean >= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a1f7dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.173132</td>\n",
       "      <td>THAP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198744</th>\n",
       "      <td>120.137318</td>\n",
       "      <td>0.569431</td>\n",
       "      <td>2.236123</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000142611</th>\n",
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       "      <td>0.883023</td>\n",
       "      <td>-2.139474</td>\n",
       "      <td>0.032397</td>\n",
       "      <td>0.179729</td>\n",
       "      <td>PRDM16</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000225630</th>\n",
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       "      <td>0.268363</td>\n",
       "      <td>1.009947</td>\n",
       "      <td>0.312521</td>\n",
       "      <td>0.628233</td>\n",
       "      <td>MT-CYB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210196</th>\n",
       "      <td>77.259551</td>\n",
       "      <td>0.457372</td>\n",
       "      <td>0.223366</td>\n",
       "      <td>2.047635</td>\n",
       "      <td>0.040596</td>\n",
       "      <td>0.206184</td>\n",
       "      <td>MT-TP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000276256</th>\n",
       "      <td>32.825088</td>\n",
       "      <td>-0.834185</td>\n",
       "      <td>0.632601</td>\n",
       "      <td>-1.318659</td>\n",
       "      <td>0.187283</td>\n",
       "      <td>0.487127</td>\n",
       "      <td>AC011043.1</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000273748</th>\n",
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       "      <td>58.543851</td>\n",
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       "      <td>0.948695</td>\n",
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       "      <td>0.655572</td>\n",
       "      <td>AC240274.1</td>\n",
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       "  </tbody>\n",
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       "<p>13799 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     baseMean  log2FoldChange     lfcSE      stat    pvalue   \n",
       "Geneid                                                                        \n",
       "ENSG00000160072    320.481168       -0.492175  0.147030 -3.347457  0.000816  \\\n",
       "ENSG00000041988    103.354977       -0.282138  0.130485 -2.162226  0.030601   \n",
       "ENSG00000198744    120.137318        0.569431  2.236123  0.254651       NaN   \n",
       "ENSG00000142611     23.766575       -1.889206  0.883023 -2.139474  0.032397   \n",
       "ENSG00000225630    922.811465        0.732964  0.408622  1.793748  0.072853   \n",
       "...                       ...             ...       ...       ...       ...   \n",
       "ENSG00000198727  46929.471443        0.271032  0.268363  1.009947  0.312521   \n",
       "ENSG00000210196     77.259551        0.457372  0.223366  2.047635  0.040596   \n",
       "ENSG00000276256     32.825088       -0.834185  0.632601 -1.318659  0.187283   \n",
       "ENSG00000273748     27.853823        0.074828  0.437564  0.171010  0.864216   \n",
       "ENSG00000271254     58.543851        0.316375  0.333484  0.948695  0.342776   \n",
       "\n",
       "                     padj      Symbol  \n",
       "Geneid                                 \n",
       "ENSG00000160072  0.013103      ATAD3B  \n",
       "ENSG00000041988  0.173132       THAP3  \n",
       "ENSG00000198744       NaN    MTCO3P12  \n",
       "ENSG00000142611  0.179729      PRDM16  \n",
       "ENSG00000225630  0.292902    MTND2P28  \n",
       "...                   ...         ...  \n",
       "ENSG00000198727  0.628233      MT-CYB  \n",
       "ENSG00000210196  0.206184       MT-TP  \n",
       "ENSG00000276256  0.487127  AC011043.1  \n",
       "ENSG00000273748  0.955017  AL592183.1  \n",
       "ENSG00000271254  0.655572  AC240274.1  \n",
       "\n",
       "[13799 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c261f6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigs = res[(res.padj < 0.05) & (abs(res.log2FoldChange) > 0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8e3844fb",
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</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>ENSG00000069424</th>\n",
       "      <td>231.438079</td>\n",
       "      <td>1.135128</td>\n",
       "      <td>0.276396</td>\n",
       "      <td>4.106888</td>\n",
       "      <td>4.010261e-05</td>\n",
       "      <td>1.342057e-03</td>\n",
       "      <td>KCNAB2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000116254</th>\n",
       "      <td>16.685968</td>\n",
       "      <td>2.713808</td>\n",
       "      <td>0.659952</td>\n",
       "      <td>4.112128</td>\n",
       "      <td>3.920294e-05</td>\n",
       "      <td>1.322754e-03</td>\n",
       "      <td>CHD5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000204138</th>\n",
       "      <td>315.739587</td>\n",
       "      <td>-0.543588</td>\n",
       "      <td>0.131546</td>\n",
       "      <td>-4.132290</td>\n",
       "      <td>3.591673e-05</td>\n",
       "      <td>1.239615e-03</td>\n",
       "      <td>PHACTR4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198830</th>\n",
       "      <td>1111.179223</td>\n",
       "      <td>-1.207821</td>\n",
       "      <td>0.352071</td>\n",
       "      <td>-3.430615</td>\n",
       "      <td>6.022151e-04</td>\n",
       "      <td>1.041796e-02</td>\n",
       "      <td>HMGN2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000060688</th>\n",
       "      <td>363.794327</td>\n",
       "      <td>-0.623651</td>\n",
       "      <td>0.093196</td>\n",
       "      <td>-6.691831</td>\n",
       "      <td>2.203959e-11</td>\n",
       "      <td>1.138620e-08</td>\n",
       "      <td>SNRNP40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000160298</th>\n",
       "      <td>76.683762</td>\n",
       "      <td>-1.196214</td>\n",
       "      <td>0.364446</td>\n",
       "      <td>-3.282282</td>\n",
       "      <td>1.029704e-03</td>\n",
       "      <td>1.558889e-02</td>\n",
       "      <td>C21orf58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000159055</th>\n",
       "      <td>123.457054</td>\n",
       "      <td>-0.951653</td>\n",
       "      <td>0.321382</td>\n",
       "      <td>-2.961125</td>\n",
       "      <td>3.065173e-03</td>\n",
       "      <td>3.518989e-02</td>\n",
       "      <td>MIS18A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000160193</th>\n",
       "      <td>135.509940</td>\n",
       "      <td>-0.712941</td>\n",
       "      <td>0.252288</td>\n",
       "      <td>-2.825895</td>\n",
       "      <td>4.714877e-03</td>\n",
       "      <td>4.787860e-02</td>\n",
       "      <td>WDR4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000215424</th>\n",
       "      <td>29.067412</td>\n",
       "      <td>-0.865389</td>\n",
       "      <td>0.270897</td>\n",
       "      <td>-3.194527</td>\n",
       "      <td>1.400604e-03</td>\n",
       "      <td>1.959456e-02</td>\n",
       "      <td>MCM3AP-AS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000159200</th>\n",
       "      <td>1180.226926</td>\n",
       "      <td>1.546833</td>\n",
       "      <td>0.529144</td>\n",
       "      <td>2.923272</td>\n",
       "      <td>3.463741e-03</td>\n",
       "      <td>3.827712e-02</td>\n",
       "      <td>RCAN1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1162 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    baseMean  log2FoldChange     lfcSE      stat   \n",
       "Geneid                                                             \n",
       "ENSG00000069424   231.438079        1.135128  0.276396  4.106888  \\\n",
       "ENSG00000116254    16.685968        2.713808  0.659952  4.112128   \n",
       "ENSG00000204138   315.739587       -0.543588  0.131546 -4.132290   \n",
       "ENSG00000198830  1111.179223       -1.207821  0.352071 -3.430615   \n",
       "ENSG00000060688   363.794327       -0.623651  0.093196 -6.691831   \n",
       "...                      ...             ...       ...       ...   \n",
       "ENSG00000160298    76.683762       -1.196214  0.364446 -3.282282   \n",
       "ENSG00000159055   123.457054       -0.951653  0.321382 -2.961125   \n",
       "ENSG00000160193   135.509940       -0.712941  0.252288 -2.825895   \n",
       "ENSG00000215424    29.067412       -0.865389  0.270897 -3.194527   \n",
       "ENSG00000159200  1180.226926        1.546833  0.529144  2.923272   \n",
       "\n",
       "                       pvalue          padj      Symbol  \n",
       "Geneid                                                   \n",
       "ENSG00000069424  4.010261e-05  1.342057e-03      KCNAB2  \n",
       "ENSG00000116254  3.920294e-05  1.322754e-03        CHD5  \n",
       "ENSG00000204138  3.591673e-05  1.239615e-03     PHACTR4  \n",
       "ENSG00000198830  6.022151e-04  1.041796e-02       HMGN2  \n",
       "ENSG00000060688  2.203959e-11  1.138620e-08     SNRNP40  \n",
       "...                       ...           ...         ...  \n",
       "ENSG00000160298  1.029704e-03  1.558889e-02    C21orf58  \n",
       "ENSG00000159055  3.065173e-03  3.518989e-02      MIS18A  \n",
       "ENSG00000160193  4.714877e-03  4.787860e-02        WDR4  \n",
       "ENSG00000215424  1.400604e-03  1.959456e-02  MCM3AP-AS1  \n",
       "ENSG00000159200  3.463741e-03  3.827712e-02       RCAN1  \n",
       "\n",
       "[1162 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "325e7e58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d831f8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PCA\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5d9844f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8 × 31378\n",
       "    obs: 'Condition'\n",
       "    uns: 'trend_coeffs', '_squared_logres', 'prior_disp_var'\n",
       "    obsm: 'design_matrix', 'size_factors'\n",
       "    varm: 'non_zero', '_rough_dispersions', 'genewise_dispersions', '_genewise_converged', '_normed_means', 'fitted_dispersions', 'MAP_dispersions', '_MAP_converged', 'dispersions', '_outlier_genes', 'LFC', '_LFC_converged', 'replaced'\n",
       "    layers: 'normed_counts', '_mu_hat', '_mu_LFC', '_hat_diagonals', 'cooks'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d904d96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.pca(dds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ed0b7bb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jrlab2019/miniconda3/envs/pydeseq2/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.pca(dds, color = 'Condition', size = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "22804801",
   "metadata": {},
   "outputs": [],
   "source": [
    "#GSEA\n",
    "\n",
    "import gseapy as gp\n",
    "from gseapy.plot import gseaplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c6bc4216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</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>ENSG00000160072</th>\n",
       "      <td>320.481168</td>\n",
       "      <td>-0.492175</td>\n",
       "      <td>0.147030</td>\n",
       "      <td>-3.347457</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.013103</td>\n",
       "      <td>ATAD3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000041988</th>\n",
       "      <td>103.354977</td>\n",
       "      <td>-0.282138</td>\n",
       "      <td>0.130485</td>\n",
       "      <td>-2.162226</td>\n",
       "      <td>0.030601</td>\n",
       "      <td>0.173132</td>\n",
       "      <td>THAP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198744</th>\n",
       "      <td>120.137318</td>\n",
       "      <td>0.569431</td>\n",
       "      <td>2.236123</td>\n",
       "      <td>0.254651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTCO3P12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000142611</th>\n",
       "      <td>23.766575</td>\n",
       "      <td>-1.889206</td>\n",
       "      <td>0.883023</td>\n",
       "      <td>-2.139474</td>\n",
       "      <td>0.032397</td>\n",
       "      <td>0.179729</td>\n",
       "      <td>PRDM16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000225630</th>\n",
       "      <td>922.811465</td>\n",
       "      <td>0.732964</td>\n",
       "      <td>0.408622</td>\n",
       "      <td>1.793748</td>\n",
       "      <td>0.072853</td>\n",
       "      <td>0.292902</td>\n",
       "      <td>MTND2P28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>46929.471443</td>\n",
       "      <td>0.271032</td>\n",
       "      <td>0.268363</td>\n",
       "      <td>1.009947</td>\n",
       "      <td>0.312521</td>\n",
       "      <td>0.628233</td>\n",
       "      <td>MT-CYB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210196</th>\n",
       "      <td>77.259551</td>\n",
       "      <td>0.457372</td>\n",
       "      <td>0.223366</td>\n",
       "      <td>2.047635</td>\n",
       "      <td>0.040596</td>\n",
       "      <td>0.206184</td>\n",
       "      <td>MT-TP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000276256</th>\n",
       "      <td>32.825088</td>\n",
       "      <td>-0.834185</td>\n",
       "      <td>0.632601</td>\n",
       "      <td>-1.318659</td>\n",
       "      <td>0.187283</td>\n",
       "      <td>0.487127</td>\n",
       "      <td>AC011043.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000273748</th>\n",
       "      <td>27.853823</td>\n",
       "      <td>0.074828</td>\n",
       "      <td>0.437564</td>\n",
       "      <td>0.171010</td>\n",
       "      <td>0.864216</td>\n",
       "      <td>0.955017</td>\n",
       "      <td>AL592183.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000271254</th>\n",
       "      <td>58.543851</td>\n",
       "      <td>0.316375</td>\n",
       "      <td>0.333484</td>\n",
       "      <td>0.948695</td>\n",
       "      <td>0.342776</td>\n",
       "      <td>0.655572</td>\n",
       "      <td>AC240274.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13799 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     baseMean  log2FoldChange     lfcSE      stat    pvalue   \n",
       "Geneid                                                                        \n",
       "ENSG00000160072    320.481168       -0.492175  0.147030 -3.347457  0.000816  \\\n",
       "ENSG00000041988    103.354977       -0.282138  0.130485 -2.162226  0.030601   \n",
       "ENSG00000198744    120.137318        0.569431  2.236123  0.254651       NaN   \n",
       "ENSG00000142611     23.766575       -1.889206  0.883023 -2.139474  0.032397   \n",
       "ENSG00000225630    922.811465        0.732964  0.408622  1.793748  0.072853   \n",
       "...                       ...             ...       ...       ...       ...   \n",
       "ENSG00000198727  46929.471443        0.271032  0.268363  1.009947  0.312521   \n",
       "ENSG00000210196     77.259551        0.457372  0.223366  2.047635  0.040596   \n",
       "ENSG00000276256     32.825088       -0.834185  0.632601 -1.318659  0.187283   \n",
       "ENSG00000273748     27.853823        0.074828  0.437564  0.171010  0.864216   \n",
       "ENSG00000271254     58.543851        0.316375  0.333484  0.948695  0.342776   \n",
       "\n",
       "                     padj      Symbol  \n",
       "Geneid                                 \n",
       "ENSG00000160072  0.013103      ATAD3B  \n",
       "ENSG00000041988  0.173132       THAP3  \n",
       "ENSG00000198744       NaN    MTCO3P12  \n",
       "ENSG00000142611  0.179729      PRDM16  \n",
       "ENSG00000225630  0.292902    MTND2P28  \n",
       "...                   ...         ...  \n",
       "ENSG00000198727  0.628233      MT-CYB  \n",
       "ENSG00000210196  0.206184       MT-TP  \n",
       "ENSG00000276256  0.487127  AC011043.1  \n",
       "ENSG00000273748  0.955017  AL592183.1  \n",
       "ENSG00000271254  0.655572  AC240274.1  \n",
       "\n",
       "[13799 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "21f44480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>stat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000040341</th>\n",
       "      <td>STAU2</td>\n",
       "      <td>10.143220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000145390</th>\n",
       "      <td>USP53</td>\n",
       "      <td>8.254562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000106366</th>\n",
       "      <td>SERPINE1</td>\n",
       "      <td>7.809237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000261115</th>\n",
       "      <td>TMEM178B</td>\n",
       "      <td>7.735058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000197746</th>\n",
       "      <td>PSAP</td>\n",
       "      <td>7.382811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000164163</th>\n",
       "      <td>ABCE1</td>\n",
       "      <td>-9.725978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000143549</th>\n",
       "      <td>TPM3</td>\n",
       "      <td>-9.793049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000071626</th>\n",
       "      <td>DAZAP1</td>\n",
       "      <td>-9.828079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000213281</th>\n",
       "      <td>NRAS</td>\n",
       "      <td>-11.311653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000165119</th>\n",
       "      <td>HNRNPK</td>\n",
       "      <td>-12.482970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13663 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Symbol       stat\n",
       "Geneid                              \n",
       "ENSG00000040341     STAU2  10.143220\n",
       "ENSG00000145390     USP53   8.254562\n",
       "ENSG00000106366  SERPINE1   7.809237\n",
       "ENSG00000261115  TMEM178B   7.735058\n",
       "ENSG00000197746      PSAP   7.382811\n",
       "...                   ...        ...\n",
       "ENSG00000164163     ABCE1  -9.725978\n",
       "ENSG00000143549      TPM3  -9.793049\n",
       "ENSG00000071626    DAZAP1  -9.828079\n",
       "ENSG00000213281      NRAS -11.311653\n",
       "ENSG00000165119    HNRNPK -12.482970\n",
       "\n",
       "[13663 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking = res[['Symbol', 'stat']].dropna().sort_values('stat', ascending = False)\n",
    "ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8a69d668",
   "metadata": {},
   "outputs": [],
   "source": [
    "ranking = ranking.drop_duplicates('Symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aacd293c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>stat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000040341</th>\n",
       "      <td>STAU2</td>\n",
       "      <td>10.143220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000145390</th>\n",
       "      <td>USP53</td>\n",
       "      <td>8.254562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000106366</th>\n",
       "      <td>SERPINE1</td>\n",
       "      <td>7.809237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000261115</th>\n",
       "      <td>TMEM178B</td>\n",
       "      <td>7.735058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000197746</th>\n",
       "      <td>PSAP</td>\n",
       "      <td>7.382811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000164163</th>\n",
       "      <td>ABCE1</td>\n",
       "      <td>-9.725978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000143549</th>\n",
       "      <td>TPM3</td>\n",
       "      <td>-9.793049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000071626</th>\n",
       "      <td>DAZAP1</td>\n",
       "      <td>-9.828079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000213281</th>\n",
       "      <td>NRAS</td>\n",
       "      <td>-11.311653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000165119</th>\n",
       "      <td>HNRNPK</td>\n",
       "      <td>-12.482970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13662 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Symbol       stat\n",
       "Geneid                              \n",
       "ENSG00000040341     STAU2  10.143220\n",
       "ENSG00000145390     USP53   8.254562\n",
       "ENSG00000106366  SERPINE1   7.809237\n",
       "ENSG00000261115  TMEM178B   7.735058\n",
       "ENSG00000197746      PSAP   7.382811\n",
       "...                   ...        ...\n",
       "ENSG00000164163     ABCE1  -9.725978\n",
       "ENSG00000143549      TPM3  -9.793049\n",
       "ENSG00000071626    DAZAP1  -9.828079\n",
       "ENSG00000213281      NRAS -11.311653\n",
       "ENSG00000165119    HNRNPK -12.482970\n",
       "\n",
       "[13662 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e7ad1042",
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_set = {'things':['STAU2', 'USP53', 'SERPINE1', 'TMEM178B', 'PSAP']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ac9b8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gp.get_library_name() #to look at available libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "12d52a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-11 12:33:35,516 [WARNING] Duplicated values found in preranked stats: 0.01% of genes\n",
      "The order of those genes will be arbitrary, which may produce unexpected results.\n"
     ]
    }
   ],
   "source": [
    "pre_res = gp.prerank(rnk = ranking,\n",
    "                     gene_sets = ['GO_Biological_Process_2021', manual_set],\n",
    "                     seed = 6, permutation_num = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4b1fbf30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Term</th>\n",
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       "      <th>es</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO_Biological_Process_2021__regulation of telo...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.667799</td>\n",
       "      <td>-2.409877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO_Biological_Process_2021__ncRNA processing (...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.644970</td>\n",
       "      <td>-2.736208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GO_Biological_Process_2021__mRNA processing (G...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.660822</td>\n",
       "      <td>-3.034242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO_Biological_Process_2021__mRNA transport (GO...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.697400</td>\n",
       "      <td>-2.776052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO_Biological_Process_2021__protein sumoylatio...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.699002</td>\n",
       "      <td>-2.347639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>GO_Biological_Process_2021__regulation of lipi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.187820</td>\n",
       "      <td>0.536522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>GO_Biological_Process_2021__tight junction ass...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.176722</td>\n",
       "      <td>-0.592341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2242</th>\n",
       "      <td>GO_Biological_Process_2021__regulation of phos...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.207557</td>\n",
       "      <td>0.624156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>GO_Biological_Process_2021__blood vessel morph...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.133931</td>\n",
       "      <td>0.485470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>GO_Biological_Process_2021__negative regulatio...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.163146</td>\n",
       "      <td>-0.665214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2245 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Term  fdr        es   \n",
       "0     GO_Biological_Process_2021__regulation of telo...  0.0 -0.667799  \\\n",
       "1     GO_Biological_Process_2021__ncRNA processing (...  0.0 -0.644970   \n",
       "2     GO_Biological_Process_2021__mRNA processing (G...  0.0 -0.660822   \n",
       "3     GO_Biological_Process_2021__mRNA transport (GO...  0.0 -0.697400   \n",
       "4     GO_Biological_Process_2021__protein sumoylatio...  0.0 -0.699002   \n",
       "...                                                 ...  ...       ...   \n",
       "2240  GO_Biological_Process_2021__regulation of lipi...  1.0  0.187820   \n",
       "2241  GO_Biological_Process_2021__tight junction ass...  1.0 -0.176722   \n",
       "2242  GO_Biological_Process_2021__regulation of phos...  1.0  0.207557   \n",
       "2243  GO_Biological_Process_2021__blood vessel morph...  1.0  0.133931   \n",
       "2244  GO_Biological_Process_2021__negative regulatio...  1.0 -0.163146   \n",
       "\n",
       "           nes  \n",
       "0    -2.409877  \n",
       "1    -2.736208  \n",
       "2    -3.034242  \n",
       "3    -2.776052  \n",
       "4    -2.347639  \n",
       "...        ...  \n",
       "2240  0.536522  \n",
       "2241 -0.592341  \n",
       "2242  0.624156  \n",
       "2243  0.485470  \n",
       "2244 -0.665214  \n",
       "\n",
       "[2245 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = []\n",
    "\n",
    "for term in list(pre_res.results):\n",
    "    out.append([term,\n",
    "               pre_res.results[term]['fdr'],\n",
    "               pre_res.results[term]['es'],\n",
    "               pre_res.results[term]['nes']])\n",
    "\n",
    "out_df = pd.DataFrame(out, columns = ['Term','fdr', 'es', 'nes']).sort_values('fdr').reset_index(drop = True)\n",
    "out_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7548fe45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GO_Biological_Process_2021__mitotic spindle organization (GO:0007052)'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df.sort_values('nes').iloc[0].Term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "50f90cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gseaplot(pre_res.ranking,\n",
    "         term = 'GO_Biological_Process_2021__mitotic spindle organization (GO:0007052)',\n",
    "         **pre_res.results['GO_Biological_Process_2021__mitotic spindle organization (GO:0007052)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d8d749",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e767fc60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4c99b4cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b244198e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.        ,   0.99504649, 359.21178396, ...,  19.90092986,\n",
       "         55.72260361,   0.        ],\n",
       "       [  0.        ,   0.        , 326.26705735, ...,   0.        ,\n",
       "         42.02503081,   0.        ],\n",
       "       [  0.86997733,   0.        , 361.91056914, ...,   0.        ,\n",
       "         46.9787758 ,   0.86997733],\n",
       "       ...,\n",
       "       [  0.        ,   3.12841738, 272.17231221, ...,  18.77050429,\n",
       "        104.80198229,   0.        ],\n",
       "       [  0.        ,   2.08655037, 256.64569522, ...,   0.        ,\n",
       "         51.12048401,   0.        ],\n",
       "       [  3.53873778,   2.35915852, 238.27501079, ...,   0.        ,\n",
       "         62.51770085,   0.        ]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds.layers['normed_counts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f9df58cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dds.layers['log1p'] = np.log1p(dds.layers['normed_counts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f390705e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.69066735, 5.88669215, ..., 3.03979365, 4.03817278,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 5.79077653, ..., 0.        , 3.76178206,\n",
       "        0.        ],\n",
       "       [0.62592631, 0.        , 5.89415644, ..., 0.        , 3.87075874,\n",
       "        0.62592631],\n",
       "       ...,\n",
       "       [0.        , 1.41789413, 5.61010278, ..., 2.98419114, 4.66156926,\n",
       "        0.        ],\n",
       "       [0.        , 1.12705408, 5.55158537, ..., 0.        , 3.95355804,\n",
       "        0.        ],\n",
       "       [1.51264895, 1.2116905 , 5.47761356, ..., 0.        , 4.15131862,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds.layers['log1p']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "70d46ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000069424</th>\n",
       "      <td>231.438079</td>\n",
       "      <td>1.135128</td>\n",
       "      <td>0.276396</td>\n",
       "      <td>4.106888</td>\n",
       "      <td>4.010261e-05</td>\n",
       "      <td>1.342057e-03</td>\n",
       "      <td>KCNAB2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000116254</th>\n",
       "      <td>16.685968</td>\n",
       "      <td>2.713808</td>\n",
       "      <td>0.659952</td>\n",
       "      <td>4.112128</td>\n",
       "      <td>3.920294e-05</td>\n",
       "      <td>1.322754e-03</td>\n",
       "      <td>CHD5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000204138</th>\n",
       "      <td>315.739587</td>\n",
       "      <td>-0.543588</td>\n",
       "      <td>0.131546</td>\n",
       "      <td>-4.132290</td>\n",
       "      <td>3.591673e-05</td>\n",
       "      <td>1.239615e-03</td>\n",
       "      <td>PHACTR4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198830</th>\n",
       "      <td>1111.179223</td>\n",
       "      <td>-1.207821</td>\n",
       "      <td>0.352071</td>\n",
       "      <td>-3.430615</td>\n",
       "      <td>6.022151e-04</td>\n",
       "      <td>1.041796e-02</td>\n",
       "      <td>HMGN2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000060688</th>\n",
       "      <td>363.794327</td>\n",
       "      <td>-0.623651</td>\n",
       "      <td>0.093196</td>\n",
       "      <td>-6.691831</td>\n",
       "      <td>2.203959e-11</td>\n",
       "      <td>1.138620e-08</td>\n",
       "      <td>SNRNP40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000160298</th>\n",
       "      <td>76.683762</td>\n",
       "      <td>-1.196214</td>\n",
       "      <td>0.364446</td>\n",
       "      <td>-3.282282</td>\n",
       "      <td>1.029704e-03</td>\n",
       "      <td>1.558889e-02</td>\n",
       "      <td>C21orf58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000159055</th>\n",
       "      <td>123.457054</td>\n",
       "      <td>-0.951653</td>\n",
       "      <td>0.321382</td>\n",
       "      <td>-2.961125</td>\n",
       "      <td>3.065173e-03</td>\n",
       "      <td>3.518989e-02</td>\n",
       "      <td>MIS18A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000160193</th>\n",
       "      <td>135.509940</td>\n",
       "      <td>-0.712941</td>\n",
       "      <td>0.252288</td>\n",
       "      <td>-2.825895</td>\n",
       "      <td>4.714877e-03</td>\n",
       "      <td>4.787860e-02</td>\n",
       "      <td>WDR4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000215424</th>\n",
       "      <td>29.067412</td>\n",
       "      <td>-0.865389</td>\n",
       "      <td>0.270897</td>\n",
       "      <td>-3.194527</td>\n",
       "      <td>1.400604e-03</td>\n",
       "      <td>1.959456e-02</td>\n",
       "      <td>MCM3AP-AS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000159200</th>\n",
       "      <td>1180.226926</td>\n",
       "      <td>1.546833</td>\n",
       "      <td>0.529144</td>\n",
       "      <td>2.923272</td>\n",
       "      <td>3.463741e-03</td>\n",
       "      <td>3.827712e-02</td>\n",
       "      <td>RCAN1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1162 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    baseMean  log2FoldChange     lfcSE      stat   \n",
       "Geneid                                                             \n",
       "ENSG00000069424   231.438079        1.135128  0.276396  4.106888  \\\n",
       "ENSG00000116254    16.685968        2.713808  0.659952  4.112128   \n",
       "ENSG00000204138   315.739587       -0.543588  0.131546 -4.132290   \n",
       "ENSG00000198830  1111.179223       -1.207821  0.352071 -3.430615   \n",
       "ENSG00000060688   363.794327       -0.623651  0.093196 -6.691831   \n",
       "...                      ...             ...       ...       ...   \n",
       "ENSG00000160298    76.683762       -1.196214  0.364446 -3.282282   \n",
       "ENSG00000159055   123.457054       -0.951653  0.321382 -2.961125   \n",
       "ENSG00000160193   135.509940       -0.712941  0.252288 -2.825895   \n",
       "ENSG00000215424    29.067412       -0.865389  0.270897 -3.194527   \n",
       "ENSG00000159200  1180.226926        1.546833  0.529144  2.923272   \n",
       "\n",
       "                       pvalue          padj      Symbol  \n",
       "Geneid                                                   \n",
       "ENSG00000069424  4.010261e-05  1.342057e-03      KCNAB2  \n",
       "ENSG00000116254  3.920294e-05  1.322754e-03        CHD5  \n",
       "ENSG00000204138  3.591673e-05  1.239615e-03     PHACTR4  \n",
       "ENSG00000198830  6.022151e-04  1.041796e-02       HMGN2  \n",
       "ENSG00000060688  2.203959e-11  1.138620e-08     SNRNP40  \n",
       "...                       ...           ...         ...  \n",
       "ENSG00000160298  1.029704e-03  1.558889e-02    C21orf58  \n",
       "ENSG00000159055  3.065173e-03  3.518989e-02      MIS18A  \n",
       "ENSG00000160193  4.714877e-03  4.787860e-02        WDR4  \n",
       "ENSG00000215424  1.400604e-03  1.959456e-02  MCM3AP-AS1  \n",
       "ENSG00000159200  3.463741e-03  3.827712e-02       RCAN1  \n",
       "\n",
       "[1162 rows x 7 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3d1e1c06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 8 × 1162\n",
       "    obs: 'Condition'\n",
       "    uns: 'trend_coeffs', '_squared_logres', 'prior_disp_var', 'pca', 'Condition_colors'\n",
       "    obsm: 'design_matrix', 'size_factors', 'X_pca'\n",
       "    varm: 'non_zero', '_rough_dispersions', 'genewise_dispersions', '_genewise_converged', '_normed_means', 'fitted_dispersions', 'MAP_dispersions', '_MAP_converged', 'dispersions', '_outlier_genes', 'LFC', '_LFC_converged', 'replaced', 'PCs'\n",
       "    layers: 'normed_counts', '_mu_hat', '_mu_LFC', '_hat_diagonals', 'cooks', 'log1p'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds_sigs = dds[:, sigs.index]\n",
    "dds_sigs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c30c7377",
   "metadata": {},
   "outputs": [],
   "source": [
    "grapher = pd.DataFrame(dds_sigs.layers['log1p'].T,\n",
    "                       index=dds_sigs.var_names, columns=dds_sigs.obs_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "227daaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f6480d982e0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(grapher, z_score=0, cmap = 'RdYlBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07a2999d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "aca093cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('senGPT.txt') as f:\n",
    "    senGPT = [x.strip() for x in list(f)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "53d69714",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CDKN2A',\n",
       " 'CDKN1A',\n",
       " 'CDKN2B',\n",
       " 'IL6',\n",
       " 'IL8',\n",
       " 'CXCL1',\n",
       " 'CXCL2',\n",
       " 'CXCL3',\n",
       " 'CXCL5',\n",
       " 'CXCL6',\n",
       " 'MMP1',\n",
       " 'MMP2',\n",
       " 'MMP3',\n",
       " 'MMP9',\n",
       " 'MMP10',\n",
       " 'MMP12',\n",
       " 'MMP13',\n",
       " 'MMP14',\n",
       " 'SERPINE1',\n",
       " 'IGFBP7',\n",
       " 'SASP',\n",
       " 'CCND1',\n",
       " 'CCNE1',\n",
       " 'BCL2',\n",
       " 'BAX',\n",
       " 'IL1A',\n",
       " 'IL1B',\n",
       " 'IL10',\n",
       " 'TNF',\n",
       " 'TGFB1',\n",
       " 'CCL2',\n",
       " 'CCL5',\n",
       " 'CCL20',\n",
       " 'ICAM1',\n",
       " 'VCAM1',\n",
       " 'RELA',\n",
       " 'STAT1',\n",
       " 'STAT3',\n",
       " 'HIF1A',\n",
       " 'PTGS2',\n",
       " 'NOX4',\n",
       " 'EDN1',\n",
       " 'COL1A1',\n",
       " 'COL1A2',\n",
       " 'COL3A1',\n",
       " 'TIMP1',\n",
       " 'TIMP2',\n",
       " 'SOD2',\n",
       " 'NRF2',\n",
       " 'LMNB1',\n",
       " 'GDF15',\n",
       " 'BMP2',\n",
       " 'BMP4',\n",
       " 'BMP6',\n",
       " 'CTGF',\n",
       " 'FGF2',\n",
       " 'FGF7',\n",
       " 'IGFBP2',\n",
       " 'IGFBP3',\n",
       " 'IGFBP4',\n",
       " 'IGFBP5',\n",
       " 'IGFBP6',\n",
       " 'LOX',\n",
       " 'LOXL2',\n",
       " 'MMP11',\n",
       " 'MMP15',\n",
       " 'MMP16',\n",
       " 'MMP17',\n",
       " 'MMP19',\n",
       " 'MMP20',\n",
       " 'MMP24',\n",
       " 'MMP25',\n",
       " 'MMP27',\n",
       " 'MMP28',\n",
       " 'ANXA1',\n",
       " 'TP53',\n",
       " 'CDKN1B',\n",
       " 'ATM',\n",
       " 'ATR',\n",
       " 'CHEK1',\n",
       " 'CHEK2',\n",
       " 'SIRT1',\n",
       " 'SIRT6',\n",
       " 'TERT',\n",
       " 'LMNA',\n",
       " 'RB1',\n",
       " 'E2F1',\n",
       " 'E2F2',\n",
       " 'E2F3',\n",
       " 'CCND2',\n",
       " 'CCND3',\n",
       " 'FOXO1',\n",
       " 'FOXO3',\n",
       " 'FOXO4',\n",
       " 'HSPB1',\n",
       " 'PML',\n",
       " 'SAHF',\n",
       " 'DDIT3',\n",
       " 'XBP1',\n",
       " 'ATF6']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senGPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "88872838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ENSG00000113368', 'ENSG00000106366', 'ENSG00000146674',\n",
       "       'ENSG00000147889', 'ENSG00000035862', 'ENSG00000271503',\n",
       "       'ENSG00000125966', 'ENSG00000101412', 'ENSG00000130513'],\n",
       "      dtype='object', name='Geneid')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigs[sigs.Symbol.isin(senGPT)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "295a8390",
   "metadata": {},
   "outputs": [],
   "source": [
    "dds_sub = dds[:, sigs[sigs.Symbol.isin(senGPT)].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9f036ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 8 × 9\n",
       "    obs: 'Condition'\n",
       "    uns: 'trend_coeffs', '_squared_logres', 'prior_disp_var', 'pca', 'Condition_colors'\n",
       "    obsm: 'design_matrix', 'size_factors', 'X_pca'\n",
       "    varm: 'non_zero', '_rough_dispersions', 'genewise_dispersions', '_genewise_converged', '_normed_means', 'fitted_dispersions', 'MAP_dispersions', '_MAP_converged', 'dispersions', '_outlier_genes', 'LFC', '_LFC_converged', 'replaced', 'PCs'\n",
       "    layers: 'normed_counts', '_mu_hat', '_mu_LFC', '_hat_diagonals', 'cooks', 'log1p'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dds_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b379b3c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "grapher = pd.DataFrame(dds_sub.layers['log1p'].T,\n",
    "                       index=dds_sub.var_names, columns=dds_sub.obs_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2e6f19c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "grapher.index = grapher.index.map(mapper.mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8cb78642",
   "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>Sample</th>\n",
       "      <th>Ctr_s1</th>\n",
       "      <th>Ctr_s2</th>\n",
       "      <th>Ctr_s7</th>\n",
       "      <th>Ctr_s13</th>\n",
       "      <th>RS_s6</th>\n",
       "      <th>RS_s9</th>\n",
       "      <th>RS_s12</th>\n",
       "      <th>RS_s16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</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>LMNB1</th>\n",
       "      <td>6.693308</td>\n",
       "      <td>6.041412</td>\n",
       "      <td>7.246656</td>\n",
       "      <td>6.717729</td>\n",
       "      <td>5.140168</td>\n",
       "      <td>5.643882</td>\n",
       "      <td>4.917246</td>\n",
       "      <td>3.527190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERPINE1</th>\n",
       "      <td>10.940599</td>\n",
       "      <td>11.100268</td>\n",
       "      <td>10.955758</td>\n",
       "      <td>11.153568</td>\n",
       "      <td>11.651848</td>\n",
       "      <td>12.029931</td>\n",
       "      <td>11.861163</td>\n",
       "      <td>11.947979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IGFBP3</th>\n",
       "      <td>9.154503</td>\n",
       "      <td>8.898189</td>\n",
       "      <td>9.218882</td>\n",
       "      <td>9.205999</td>\n",
       "      <td>9.303741</td>\n",
       "      <td>10.551874</td>\n",
       "      <td>9.699758</td>\n",
       "      <td>9.855071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>5.551881</td>\n",
       "      <td>5.069954</td>\n",
       "      <td>5.113767</td>\n",
       "      <td>5.551664</td>\n",
       "      <td>5.702586</td>\n",
       "      <td>5.892561</td>\n",
       "      <td>6.610385</td>\n",
       "      <td>7.043327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMP2</th>\n",
       "      <td>9.216315</td>\n",
       "      <td>9.370149</td>\n",
       "      <td>9.320031</td>\n",
       "      <td>9.190763</td>\n",
       "      <td>9.622696</td>\n",
       "      <td>9.759793</td>\n",
       "      <td>10.101443</td>\n",
       "      <td>9.668156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCL5</th>\n",
       "      <td>0.690667</td>\n",
       "      <td>1.191579</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600290</td>\n",
       "      <td>4.716196</td>\n",
       "      <td>2.603702</td>\n",
       "      <td>1.418235</td>\n",
       "      <td>2.345322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP24</th>\n",
       "      <td>3.632751</td>\n",
       "      <td>3.725617</td>\n",
       "      <td>3.648292</td>\n",
       "      <td>3.816017</td>\n",
       "      <td>4.235256</td>\n",
       "      <td>4.484083</td>\n",
       "      <td>4.626909</td>\n",
       "      <td>4.926030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E2F1</th>\n",
       "      <td>5.322935</td>\n",
       "      <td>5.157241</td>\n",
       "      <td>5.811744</td>\n",
       "      <td>5.789251</td>\n",
       "      <td>5.250777</td>\n",
       "      <td>4.719024</td>\n",
       "      <td>4.751521</td>\n",
       "      <td>4.352503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDF15</th>\n",
       "      <td>5.664932</td>\n",
       "      <td>4.638491</td>\n",
       "      <td>5.354388</td>\n",
       "      <td>5.475396</td>\n",
       "      <td>7.655135</td>\n",
       "      <td>6.890938</td>\n",
       "      <td>5.788436</td>\n",
       "      <td>5.806525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sample       Ctr_s1     Ctr_s2     Ctr_s7    Ctr_s13      RS_s6      RS_s9   \n",
       "Geneid                                                                       \n",
       "LMNB1      6.693308   6.041412   7.246656   6.717729   5.140168   5.643882  \\\n",
       "SERPINE1  10.940599  11.100268  10.955758  11.153568  11.651848  12.029931   \n",
       "IGFBP3     9.154503   8.898189   9.218882   9.205999   9.303741  10.551874   \n",
       "CDKN2A     5.551881   5.069954   5.113767   5.551664   5.702586   5.892561   \n",
       "TIMP2      9.216315   9.370149   9.320031   9.190763   9.622696   9.759793   \n",
       "CCL5       0.690667   1.191579   0.000000   0.600290   4.716196   2.603702   \n",
       "MMP24      3.632751   3.725617   3.648292   3.816017   4.235256   4.484083   \n",
       "E2F1       5.322935   5.157241   5.811744   5.789251   5.250777   4.719024   \n",
       "GDF15      5.664932   4.638491   5.354388   5.475396   7.655135   6.890938   \n",
       "\n",
       "Sample       RS_s12     RS_s16  \n",
       "Geneid                          \n",
       "LMNB1      4.917246   3.527190  \n",
       "SERPINE1  11.861163  11.947979  \n",
       "IGFBP3     9.699758   9.855071  \n",
       "CDKN2A     6.610385   7.043327  \n",
       "TIMP2     10.101443   9.668156  \n",
       "CCL5       1.418235   2.345322  \n",
       "MMP24      4.626909   4.926030  \n",
       "E2F1       4.751521   4.352503  \n",
       "GDF15      5.788436   5.806525  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grapher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0722750c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.clustermap(grapher, z_score=0, cmap = 'RdYlBu_r', figsize=(4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a6c20db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "343046a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#volcano\n",
    "from sanbomics.plots import volcano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2b9a4223",
   "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>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geneid</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>ENSG00000160072</th>\n",
       "      <td>320.481168</td>\n",
       "      <td>-0.492175</td>\n",
       "      <td>0.147030</td>\n",
       "      <td>-3.347457</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.013103</td>\n",
       "      <td>ATAD3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000041988</th>\n",
       "      <td>103.354977</td>\n",
       "      <td>-0.282138</td>\n",
       "      <td>0.130485</td>\n",
       "      <td>-2.162226</td>\n",
       "      <td>0.030601</td>\n",
       "      <td>0.173132</td>\n",
       "      <td>THAP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198744</th>\n",
       "      <td>120.137318</td>\n",
       "      <td>0.569431</td>\n",
       "      <td>2.236123</td>\n",
       "      <td>0.254651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTCO3P12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000142611</th>\n",
       "      <td>23.766575</td>\n",
       "      <td>-1.889206</td>\n",
       "      <td>0.883023</td>\n",
       "      <td>-2.139474</td>\n",
       "      <td>0.032397</td>\n",
       "      <td>0.179729</td>\n",
       "      <td>PRDM16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000225630</th>\n",
       "      <td>922.811465</td>\n",
       "      <td>0.732964</td>\n",
       "      <td>0.408622</td>\n",
       "      <td>1.793748</td>\n",
       "      <td>0.072853</td>\n",
       "      <td>0.292902</td>\n",
       "      <td>MTND2P28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>46929.471443</td>\n",
       "      <td>0.271032</td>\n",
       "      <td>0.268363</td>\n",
       "      <td>1.009947</td>\n",
       "      <td>0.312521</td>\n",
       "      <td>0.628233</td>\n",
       "      <td>MT-CYB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000210196</th>\n",
       "      <td>77.259551</td>\n",
       "      <td>0.457372</td>\n",
       "      <td>0.223366</td>\n",
       "      <td>2.047635</td>\n",
       "      <td>0.040596</td>\n",
       "      <td>0.206184</td>\n",
       "      <td>MT-TP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000276256</th>\n",
       "      <td>32.825088</td>\n",
       "      <td>-0.834185</td>\n",
       "      <td>0.632601</td>\n",
       "      <td>-1.318659</td>\n",
       "      <td>0.187283</td>\n",
       "      <td>0.487127</td>\n",
       "      <td>AC011043.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000273748</th>\n",
       "      <td>27.853823</td>\n",
       "      <td>0.074828</td>\n",
       "      <td>0.437564</td>\n",
       "      <td>0.171010</td>\n",
       "      <td>0.864216</td>\n",
       "      <td>0.955017</td>\n",
       "      <td>AL592183.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000271254</th>\n",
       "      <td>58.543851</td>\n",
       "      <td>0.316375</td>\n",
       "      <td>0.333484</td>\n",
       "      <td>0.948695</td>\n",
       "      <td>0.342776</td>\n",
       "      <td>0.655572</td>\n",
       "      <td>AC240274.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13799 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     baseMean  log2FoldChange     lfcSE      stat    pvalue   \n",
       "Geneid                                                                        \n",
       "ENSG00000160072    320.481168       -0.492175  0.147030 -3.347457  0.000816  \\\n",
       "ENSG00000041988    103.354977       -0.282138  0.130485 -2.162226  0.030601   \n",
       "ENSG00000198744    120.137318        0.569431  2.236123  0.254651       NaN   \n",
       "ENSG00000142611     23.766575       -1.889206  0.883023 -2.139474  0.032397   \n",
       "ENSG00000225630    922.811465        0.732964  0.408622  1.793748  0.072853   \n",
       "...                       ...             ...       ...       ...       ...   \n",
       "ENSG00000198727  46929.471443        0.271032  0.268363  1.009947  0.312521   \n",
       "ENSG00000210196     77.259551        0.457372  0.223366  2.047635  0.040596   \n",
       "ENSG00000276256     32.825088       -0.834185  0.632601 -1.318659  0.187283   \n",
       "ENSG00000273748     27.853823        0.074828  0.437564  0.171010  0.864216   \n",
       "ENSG00000271254     58.543851        0.316375  0.333484  0.948695  0.342776   \n",
       "\n",
       "                     padj      Symbol  \n",
       "Geneid                                 \n",
       "ENSG00000160072  0.013103      ATAD3B  \n",
       "ENSG00000041988  0.173132       THAP3  \n",
       "ENSG00000198744       NaN    MTCO3P12  \n",
       "ENSG00000142611  0.179729      PRDM16  \n",
       "ENSG00000225630  0.292902    MTND2P28  \n",
       "...                   ...         ...  \n",
       "ENSG00000198727  0.628233      MT-CYB  \n",
       "ENSG00000210196  0.206184       MT-TP  \n",
       "ENSG00000276256  0.487127  AC011043.1  \n",
       "ENSG00000273748  0.955017  AL592183.1  \n",
       "ENSG00000271254  0.655572  AC240274.1  \n",
       "\n",
       "[13799 rows x 7 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b3c0a207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volcano(res, symbol='Symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2443abea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LMNB1', 'SERPINE1', 'IGFBP3', 'CDKN2A', 'TIMP2', 'CCL5', 'MMP24',\n",
       "       'E2F1', 'GDF15'],\n",
       "      dtype='object', name='Geneid')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grapher.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8d89aa6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volcano(res, symbol='Symbol', to_label = grapher.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f96a3112",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44576aeb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}