{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55e261bb",
   "metadata": {},
   "source": [
    "## Example usage with PyDESeq2\n",
    "\n",
    "The codes are derived from [Step-by-step PyDESeq2 workflow](https://pydeseq2.readthedocs.io/en/latest/auto_examples/plot_step_by_step.html).\n",
    "The dataset used was from the paper investigating BK polyomavirus infection in urothelial cells ([Baker et al. Oncogene. 2022](https://www.nature.com/articles/s41388-022-02235-8))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2743dfe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle as pkl\n",
    "\n",
    "from pydeseq2.dds import DeseqDataSet\n",
    "from pydeseq2.ds import DeseqStats\n",
    "from pydeseq2.utils import load_example_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61388295",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1BG</th>\n",
       "      <th>A1BG-AS1</th>\n",
       "      <th>A1CF</th>\n",
       "      <th>A2M</th>\n",
       "      <th>A2M-AS1</th>\n",
       "      <th>A2ML1</th>\n",
       "      <th>A2MP1</th>\n",
       "      <th>A3GALT2</th>\n",
       "      <th>A4GALT</th>\n",
       "      <th>A4GNT</th>\n",
       "      <th>...</th>\n",
       "      <th>ZWILCH</th>\n",
       "      <th>ZWINT</th>\n",
       "      <th>ZXDA</th>\n",
       "      <th>ZXDB</th>\n",
       "      <th>ZXDC</th>\n",
       "      <th>ZYG11A</th>\n",
       "      <th>ZYG11B</th>\n",
       "      <th>ZYX</th>\n",
       "      <th>ZZEF1</th>\n",
       "      <th>ZZZ3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR14509882</th>\n",
       "      <td>49</td>\n",
       "      <td>57</td>\n",
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       "      <td>...</td>\n",
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       "      <td>793</td>\n",
       "      <td>223</td>\n",
       "      <td>1320</td>\n",
       "      <td>882</td>\n",
       "      <td>1</td>\n",
       "      <td>1791</td>\n",
       "      <td>2787</td>\n",
       "      <td>2276</td>\n",
       "      <td>1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509883</th>\n",
       "      <td>106</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
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       "      <td>1715</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>731</td>\n",
       "      <td>1266</td>\n",
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       "      <td>5</td>\n",
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       "      <td>2127</td>\n",
       "      <td>871</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509884</th>\n",
       "      <td>67</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1172</td>\n",
       "      <td>2256</td>\n",
       "      <td>122</td>\n",
       "      <td>741</td>\n",
       "      <td>732</td>\n",
       "      <td>14</td>\n",
       "      <td>915</td>\n",
       "      <td>3463</td>\n",
       "      <td>1419</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509885</th>\n",
       "      <td>85</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2155</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1348</td>\n",
       "      <td>3155</td>\n",
       "      <td>187</td>\n",
       "      <td>1566</td>\n",
       "      <td>738</td>\n",
       "      <td>26</td>\n",
       "      <td>1198</td>\n",
       "      <td>3738</td>\n",
       "      <td>2044</td>\n",
       "      <td>1051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509886</th>\n",
       "      <td>29</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2834</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>137</td>\n",
       "      <td>205</td>\n",
       "      <td>743</td>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>1548</td>\n",
       "      <td>2878</td>\n",
       "      <td>1952</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29744 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             A1BG  A1BG-AS1  A1CF  A2M  A2M-AS1  A2ML1  A2MP1  A3GALT2  \\\n",
       "SRR14509882    49        57     0    0        0    718      1        0   \n",
       "SRR14509883   106        82     0   12        6     83      0        0   \n",
       "SRR14509884    67        36     2   25        5    499      0        0   \n",
       "SRR14509885    85        67     0    2        3    393      0        0   \n",
       "SRR14509886    29        42     0    2        2     76      0        0   \n",
       "\n",
       "             A4GALT  A4GNT  ...  ZWILCH  ZWINT  ZXDA  ZXDB  ZXDC  ZYG11A  \\\n",
       "SRR14509882    3682      0  ...     574    793   223  1320   882       1   \n",
       "SRR14509883    1715      1  ...     731   1266    44   471   389       5   \n",
       "SRR14509884    2188      0  ...    1172   2256   122   741   732      14   \n",
       "SRR14509885    2155      2  ...    1348   3155   187  1566   738      26   \n",
       "SRR14509886    2834      0  ...     298    137   205   743   852       0   \n",
       "\n",
       "             ZYG11B   ZYX  ZZEF1  ZZZ3  \n",
       "SRR14509882    1791  2787   2276  1576  \n",
       "SRR14509883     632  2127    871   676  \n",
       "SRR14509884     915  3463   1419  1009  \n",
       "SRR14509885    1198  3738   2044  1051  \n",
       "SRR14509886    1548  2878   1952  1297  \n",
       "\n",
       "[5 rows x 29744 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "count_df = pd.read_csv(\"../PRJNA728925_count.txt\", sep=\"\\t\").T\n",
    "count_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2eb2223c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run</th>\n",
       "      <th>Assay Type</th>\n",
       "      <th>AvgSpotLen</th>\n",
       "      <th>Bases</th>\n",
       "      <th>BioProject</th>\n",
       "      <th>BioSample</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>Center Name</th>\n",
       "      <th>Consent</th>\n",
       "      <th>DATASTORE filetype</th>\n",
       "      <th>...</th>\n",
       "      <th>LibrarySelection</th>\n",
       "      <th>LibrarySource</th>\n",
       "      <th>Organism</th>\n",
       "      <th>Platform</th>\n",
       "      <th>ReleaseDate</th>\n",
       "      <th>Sample Name</th>\n",
       "      <th>source_name</th>\n",
       "      <th>SRA Study</th>\n",
       "      <th>Tissue</th>\n",
       "      <th>viral_infection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR14509882</th>\n",
       "      <td>SRR14509882</td>\n",
       "      <td>RNA-Seq</td>\n",
       "      <td>300</td>\n",
       "      <td>11066499600</td>\n",
       "      <td>PRJNA728925</td>\n",
       "      <td>SAMN19107552</td>\n",
       "      <td>3333844788</td>\n",
       "      <td>GEO</td>\n",
       "      <td>public</td>\n",
       "      <td>sra,fastq</td>\n",
       "      <td>...</td>\n",
       "      <td>cDNA</td>\n",
       "      <td>TRANSCRIPTOMIC</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>ILLUMINA</td>\n",
       "      <td>2022-02-23T00:00:00Z</td>\n",
       "      <td>GSM5289794</td>\n",
       "      <td>Normal human urothelial cells</td>\n",
       "      <td>SRP319465</td>\n",
       "      <td>Ureter</td>\n",
       "      <td>BKPyV (Dunlop) MOI=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509883</th>\n",
       "      <td>SRR14509883</td>\n",
       "      <td>RNA-Seq</td>\n",
       "      <td>302</td>\n",
       "      <td>8436386308</td>\n",
       "      <td>PRJNA728925</td>\n",
       "      <td>SAMN19107551</td>\n",
       "      <td>2801216097</td>\n",
       "      <td>GEO</td>\n",
       "      <td>public</td>\n",
       "      <td>fastq,sra</td>\n",
       "      <td>...</td>\n",
       "      <td>cDNA</td>\n",
       "      <td>TRANSCRIPTOMIC</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>ILLUMINA</td>\n",
       "      <td>2022-02-23T00:00:00Z</td>\n",
       "      <td>GSM5289795</td>\n",
       "      <td>Normal human urothelial cells</td>\n",
       "      <td>SRP319465</td>\n",
       "      <td>Ureter</td>\n",
       "      <td>BKPyV (Dunlop) MOI=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509884</th>\n",
       "      <td>SRR14509884</td>\n",
       "      <td>RNA-Seq</td>\n",
       "      <td>300</td>\n",
       "      <td>9742943700</td>\n",
       "      <td>PRJNA728925</td>\n",
       "      <td>SAMN19107550</td>\n",
       "      <td>3188119940</td>\n",
       "      <td>GEO</td>\n",
       "      <td>public</td>\n",
       "      <td>fastq,sra</td>\n",
       "      <td>...</td>\n",
       "      <td>cDNA</td>\n",
       "      <td>TRANSCRIPTOMIC</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>ILLUMINA</td>\n",
       "      <td>2022-02-23T00:00:00Z</td>\n",
       "      <td>GSM5289796</td>\n",
       "      <td>Normal human urothelial cells</td>\n",
       "      <td>SRP319465</td>\n",
       "      <td>Ureter</td>\n",
       "      <td>BKPyV (Dunlop) MOI=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509885</th>\n",
       "      <td>SRR14509885</td>\n",
       "      <td>RNA-Seq</td>\n",
       "      <td>300</td>\n",
       "      <td>11410353600</td>\n",
       "      <td>PRJNA728925</td>\n",
       "      <td>SAMN19107549</td>\n",
       "      <td>3722953816</td>\n",
       "      <td>GEO</td>\n",
       "      <td>public</td>\n",
       "      <td>sra,fastq</td>\n",
       "      <td>...</td>\n",
       "      <td>cDNA</td>\n",
       "      <td>TRANSCRIPTOMIC</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>ILLUMINA</td>\n",
       "      <td>2022-02-23T00:00:00Z</td>\n",
       "      <td>GSM5289797</td>\n",
       "      <td>Normal human urothelial cells</td>\n",
       "      <td>SRP319465</td>\n",
       "      <td>Ureter</td>\n",
       "      <td>BKPyV (Dunlop) MOI=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR14509886</th>\n",
       "      <td>SRR14509886</td>\n",
       "      <td>RNA-Seq</td>\n",
       "      <td>300</td>\n",
       "      <td>9985769400</td>\n",
       "      <td>PRJNA728925</td>\n",
       "      <td>SAMN19107548</td>\n",
       "      <td>3153799143</td>\n",
       "      <td>GEO</td>\n",
       "      <td>public</td>\n",
       "      <td>sra,fastq</td>\n",
       "      <td>...</td>\n",
       "      <td>cDNA</td>\n",
       "      <td>TRANSCRIPTOMIC</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>ILLUMINA</td>\n",
       "      <td>2022-02-23T00:00:00Z</td>\n",
       "      <td>GSM5289798</td>\n",
       "      <td>Normal human urothelial cells</td>\n",
       "      <td>SRP319465</td>\n",
       "      <td>Ureter</td>\n",
       "      <td>No infection</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Run Assay Type  AvgSpotLen        Bases   BioProject  \\\n",
       "SRR14509882  SRR14509882    RNA-Seq         300  11066499600  PRJNA728925   \n",
       "SRR14509883  SRR14509883    RNA-Seq         302   8436386308  PRJNA728925   \n",
       "SRR14509884  SRR14509884    RNA-Seq         300   9742943700  PRJNA728925   \n",
       "SRR14509885  SRR14509885    RNA-Seq         300  11410353600  PRJNA728925   \n",
       "SRR14509886  SRR14509886    RNA-Seq         300   9985769400  PRJNA728925   \n",
       "\n",
       "                BioSample       Bytes Center Name Consent DATASTORE filetype  \\\n",
       "SRR14509882  SAMN19107552  3333844788         GEO  public          sra,fastq   \n",
       "SRR14509883  SAMN19107551  2801216097         GEO  public          fastq,sra   \n",
       "SRR14509884  SAMN19107550  3188119940         GEO  public          fastq,sra   \n",
       "SRR14509885  SAMN19107549  3722953816         GEO  public          sra,fastq   \n",
       "SRR14509886  SAMN19107548  3153799143         GEO  public          sra,fastq   \n",
       "\n",
       "             ... LibrarySelection   LibrarySource      Organism  Platform  \\\n",
       "SRR14509882  ...             cDNA  TRANSCRIPTOMIC  Homo sapiens  ILLUMINA   \n",
       "SRR14509883  ...             cDNA  TRANSCRIPTOMIC  Homo sapiens  ILLUMINA   \n",
       "SRR14509884  ...             cDNA  TRANSCRIPTOMIC  Homo sapiens  ILLUMINA   \n",
       "SRR14509885  ...             cDNA  TRANSCRIPTOMIC  Homo sapiens  ILLUMINA   \n",
       "SRR14509886  ...             cDNA  TRANSCRIPTOMIC  Homo sapiens  ILLUMINA   \n",
       "\n",
       "                      ReleaseDate Sample Name                    source_name  \\\n",
       "SRR14509882  2022-02-23T00:00:00Z  GSM5289794  Normal human urothelial cells   \n",
       "SRR14509883  2022-02-23T00:00:00Z  GSM5289795  Normal human urothelial cells   \n",
       "SRR14509884  2022-02-23T00:00:00Z  GSM5289796  Normal human urothelial cells   \n",
       "SRR14509885  2022-02-23T00:00:00Z  GSM5289797  Normal human urothelial cells   \n",
       "SRR14509886  2022-02-23T00:00:00Z  GSM5289798  Normal human urothelial cells   \n",
       "\n",
       "             SRA Study  Tissue       viral_infection  \n",
       "SRR14509882  SRP319465  Ureter  BKPyV (Dunlop) MOI=1  \n",
       "SRR14509883  SRP319465  Ureter  BKPyV (Dunlop) MOI=1  \n",
       "SRR14509884  SRP319465  Ureter  BKPyV (Dunlop) MOI=1  \n",
       "SRR14509885  SRP319465  Ureter  BKPyV (Dunlop) MOI=1  \n",
       "SRR14509886  SRP319465  Ureter          No infection  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_df = pd.read_csv(\"../SraRunTable_PRJNA728925.txt\", sep=\",\")\n",
    "clinical_df.index = clinical_df.Run\n",
    "clinical_df.index.name = None\n",
    "clinical_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a39342b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dds = DeseqDataSet(\n",
    "    counts=count_df,\n",
    "    clinical=clinical_df,\n",
    "    design_factors=\"viral_infection\",  # compare samples based on the \"condition\"\n",
    "    refit_cooks=True,\n",
    "    n_cpus=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d18e2fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting size factors...\n",
      "... done in 0.05 seconds.\n",
      "\n",
      "Fitting dispersions...\n",
      "... done in 3.05 seconds.\n",
      "\n",
      "Fitting dispersion trend curve...\n",
      "... done in 6.95 seconds.\n",
      "\n",
      "Fitting MAP dispersions...\n",
      "... done in 3.70 seconds.\n",
      "\n",
      "Fitting LFCs...\n",
      "... done in 1.91 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dds.fit_size_factors()\n",
    "dds.fit_genewise_dispersions()\n",
    "dds.fit_dispersion_trend()\n",
    "dds.fit_dispersion_prior()\n",
    "dds.fit_MAP_dispersions()\n",
    "dds.fit_LFC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d152892b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refitting 357 outliers.\n",
      "\n",
      "Fitting dispersions...\n",
      "... done in 0.08 seconds.\n",
      "\n",
      "Fitting MAP dispersions...\n",
      "... done in 0.08 seconds.\n",
      "\n",
      "Fitting LFCs...\n",
      "... done in 0.08 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dds.calculate_cooks()\n",
    "if dds.refit_cooks:\n",
    "    # Replace outlier counts\n",
    "    dds.refit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fac004ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Wald tests...\n",
      "... done in 3.29 seconds.\n",
      "\n",
      "Log2 fold change & Wald test p-value: viral_infection BKPyV (Dunlop) MOI=1 vs No infection\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG</th>\n",
       "      <td>65.042617</td>\n",
       "      <td>-0.174495</td>\n",
       "      <td>0.274393</td>\n",
       "      <td>-0.635932</td>\n",
       "      <td>0.524821</td>\n",
       "      <td>0.998845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1BG-AS1</th>\n",
       "      <td>60.814662</td>\n",
       "      <td>-0.097512</td>\n",
       "      <td>0.260997</td>\n",
       "      <td>-0.373613</td>\n",
       "      <td>0.708692</td>\n",
       "      <td>0.998845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>0.234785</td>\n",
       "      <td>-0.438579</td>\n",
       "      <td>2.539843</td>\n",
       "      <td>-0.172680</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>3.841866</td>\n",
       "      <td>0.917285</td>\n",
       "      <td>0.822768</td>\n",
       "      <td>1.114876</td>\n",
       "      <td>0.264904</td>\n",
       "      <td>0.965674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M-AS1</th>\n",
       "      <td>3.520622</td>\n",
       "      <td>0.412318</td>\n",
       "      <td>0.492133</td>\n",
       "      <td>0.837817</td>\n",
       "      <td>0.402133</td>\n",
       "      <td>0.994437</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYG11A</th>\n",
       "      <td>4.020418</td>\n",
       "      <td>0.989967</td>\n",
       "      <td>0.716537</td>\n",
       "      <td>1.381599</td>\n",
       "      <td>0.167095</td>\n",
       "      <td>0.906121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYG11B</th>\n",
       "      <td>1387.374886</td>\n",
       "      <td>-0.189671</td>\n",
       "      <td>0.126651</td>\n",
       "      <td>-1.497585</td>\n",
       "      <td>0.134241</td>\n",
       "      <td>0.873862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>2956.789644</td>\n",
       "      <td>-0.056822</td>\n",
       "      <td>0.104073</td>\n",
       "      <td>-0.545980</td>\n",
       "      <td>0.585079</td>\n",
       "      <td>0.998845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>1916.557304</td>\n",
       "      <td>-0.129015</td>\n",
       "      <td>0.110549</td>\n",
       "      <td>-1.167041</td>\n",
       "      <td>0.243194</td>\n",
       "      <td>0.963318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>1102.880698</td>\n",
       "      <td>-0.019320</td>\n",
       "      <td>0.093984</td>\n",
       "      <td>-0.205573</td>\n",
       "      <td>0.837124</td>\n",
       "      <td>0.998845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29744 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             baseMean  log2FoldChange     lfcSE      stat    pvalue      padj\n",
       "A1BG        65.042617       -0.174495  0.274393 -0.635932  0.524821  0.998845\n",
       "A1BG-AS1    60.814662       -0.097512  0.260997 -0.373613  0.708692  0.998845\n",
       "A1CF         0.234785       -0.438579  2.539843 -0.172680  0.862903       NaN\n",
       "A2M          3.841866        0.917285  0.822768  1.114876  0.264904  0.965674\n",
       "A2M-AS1      3.520622        0.412318  0.492133  0.837817  0.402133  0.994437\n",
       "...               ...             ...       ...       ...       ...       ...\n",
       "ZYG11A       4.020418        0.989967  0.716537  1.381599  0.167095  0.906121\n",
       "ZYG11B    1387.374886       -0.189671  0.126651 -1.497585  0.134241  0.873862\n",
       "ZYX       2956.789644       -0.056822  0.104073 -0.545980  0.585079  0.998845\n",
       "ZZEF1     1916.557304       -0.129015  0.110549 -1.167041  0.243194  0.963318\n",
       "ZZZ3      1102.880698       -0.019320  0.093984 -0.205573  0.837124  0.998845\n",
       "\n",
       "[29744 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stat_res = DeseqStats(dds, alpha=0.05,contrast=[\"viral_infection\",\"BKPyV (Dunlop) MOI=1\",\"No infection\"])\n",
    "stat_res.run_wald_test()\n",
    "if stat_res.cooks_filter:\n",
    "    stat_res._cooks_filtering()\n",
    "stat_res.p_values\n",
    "if stat_res.independent_filter:\n",
    "    stat_res._independent_filtering()\n",
    "else:\n",
    "    stat_res._p_value_adjustment()\n",
    "stat_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9297514d",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_res_summary = stat_res.results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c360aa66",
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_genes = stat_res_summary[stat_res_summary.padj<0.05].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "015d32dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lfc_key = stat_res_summary.log2FoldChange.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa9a61a",
   "metadata": {},
   "source": [
    "### Visualize the DEG information\n",
    "Visualize the log2 fold changes of the genes in the certain pathway, and highlight DEGs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9bcd9b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pykegg\n",
    "import requests_cache\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "## Cache all the downloaded\n",
    "requests_cache.install_cache('pykegg_cache')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0acd4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = pykegg.KGML_graph(pid=\"hsa04110\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed6de6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "nds = graph.get_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b65082f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "highlight_value = []\n",
    "## If one of the symbols in identifiers in the nodes is in DEGs\n",
    "for node in nds.graphics_name:\n",
    "    in_node = [i.replace(\"...\",\"\") for i in node.split(\",\")]\n",
    "    intersect = set(in_node) & set(sig_genes)\n",
    "    if len(intersect) > 0:\n",
    "        highlight_value.append(True)\n",
    "    else:\n",
    "        highlight_value.append(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d987ab64",
   "metadata": {},
   "outputs": [],
   "source": [
    "nds = pykegg.append_colors_continuous_values(nds, lfc_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ab61fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nds[\"highlight\"] = highlight_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cacfe061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=1105x877>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray(pykegg.overlay_opencv_image(nds,\n",
    "                                            pid=\"hsa04110\",\n",
    "                                            highlight_nodes=\"highlight\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145bfbd7",
   "metadata": {},
   "source": [
    "### deseq2_raw_map() function\n",
    "deseq2_raw_map serves as a wrapper function, consolidating the aforementioned example into a single function, with appending the colormap. It necessitates extracting PyDESeq2's results_df as a DataFrame for input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0a400d8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=568x745>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pykegg.deseq2_raw_map(stat_res_summary, pid=\"hsa03460\", legend_width=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "365c9a64",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}