tutorials/functional_enrichment_analysis.ipynb

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    "# Functional enrichment analysis using PyWGCNA\n",
    "\n",
    "After finding coexpression modules, you can investigate each module seperatly using functional enrichment analysis. PyWGCNA supports this directly from the GO, KEGG, and REACTOME databases using the `functional_enrichment_analysis()` function.\n",
    "\n",
    "In this notebook, we will show you how you can do that!\n",
    "\n",
    "First read in the PyWGCNA object. \n",
    "Make sure that the gene name, which is parsed by the above databases, is present by checking if `gene_name` is one of the columns of `datExpr.var`."
   ]
  },
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     "text": [
      "\u001b[1m\u001b[94mReading 5xFAD WGCNA done!\u001b[0m\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dynamicColors</th>\n",
       "      <th>moduleColors</th>\n",
       "      <th>moduleLabels</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_biotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000000028</th>\n",
       "      <td>darkred</td>\n",
       "      <td>darkred</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cdc45</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000000049</th>\n",
       "      <td>darkred</td>\n",
       "      <td>darkred</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Apoh</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000000056</th>\n",
       "      <td>darkgrey</td>\n",
       "      <td>darkgrey</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Narf</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000000058</th>\n",
       "      <td>coral</td>\n",
       "      <td>coral</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cav2</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000000078</th>\n",
       "      <td>gainsboro</td>\n",
       "      <td>gainsboro</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Klf6</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                   dynamicColors moduleColors  moduleLabels gene_name  \\\n",
       "ENSMUSG00000000028       darkred      darkred           4.0     Cdc45   \n",
       "ENSMUSG00000000049       darkred      darkred           4.0      Apoh   \n",
       "ENSMUSG00000000056      darkgrey     darkgrey           3.0      Narf   \n",
       "ENSMUSG00000000058         coral        coral           2.0      Cav2   \n",
       "ENSMUSG00000000078     gainsboro    gainsboro           7.0      Klf6   \n",
       "\n",
       "                      gene_biotype  \n",
       "ENSMUSG00000000028  protein_coding  \n",
       "ENSMUSG00000000049  protein_coding  \n",
       "ENSMUSG00000000056  protein_coding  \n",
       "ENSMUSG00000000058  protein_coding  \n",
       "ENSMUSG00000000078  protein_coding  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PyWGCNA\n",
    "pyWGCNA_5xFAD = PyWGCNA.readWGCNA(\"5xFAD.p\")\n",
    "pyWGCNA_5xFAD.datExpr.var.head(5)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene ontology (GO) analysis\n",
    "\n",
    "Say we want to investigate darkgray module.\n",
    "After defining which gene set libraries we want to use, (pick from [here](https://maayanlab.cloud/Enrichr/#libraries)), we make the following call: "
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "pyWGCNA_5xFAD.figureType = \"png\"\n",
    "gene_set_library = [\"GO_Biological_Process_2021\", \"GO_Cellular_Component_2021\", \"GO_Molecular_Function_2021\"]\n",
    "pyWGCNA_5xFAD.functional_enrichment_analysis(type=\"GO\",\n",
    "                                             moduleName=\"coral\",\n",
    "                                             sets=gene_set_library,\n",
    "                                             p_value=0.05,\n",
    "                                             file_name=\"GO_coral_2021\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![GO](figures/GO/GO_coral_2021.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[93mKEGG directory does not exist!\n",
      "Creating KEGG directory!\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "pyWGCNA_5xFAD.figureType = \"png\"\n",
    "gene_set_library = [\"KEGG_2019_Mouse\"]\n",
    "pyWGCNA_5xFAD.functional_enrichment_analysis(type=\"KEGG\",\n",
    "                                             moduleName=\"coral\",\n",
    "                                             sets=gene_set_library,\n",
    "                                             p_value=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![GO](figures/KEGG/coral.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[93mREACTOME directory does not exist!\n",
      "Creating REACTOME directory!\u001b[0m\n",
      "827 out of 1335 genes (identifiers) in the sample were found in Reactome.\n",
      "7748 pathways were hit by at least one of them, which 0 of them have p-value less than 0.05.\n",
      "Report was saved figures/REACTOME/coral.pdf!\n",
      "For more information please visit https://reactome.org/PathwayBrowser/#/DTAB=AN&ANALYSIS=MjAyMzAzMjIwODA5MTVfMTA3NzE%3D\n"
     ]
    }
   ],
   "source": [
    "pyWGCNA_5xFAD.figureType = \"pdf\"\n",
    "pyWGCNA_5xFAD.functional_enrichment_analysis(type=\"REACTOME\",\n",
    "                                             moduleName=\"coral\",\n",
    "                                             p_value=0.05)"
   ]
  }
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