GO_in_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "308c6e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this part is not important, only a simple example of scanpy processing to get a list of genes\n",
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "from matplotlib.pyplot import rc_context\n",
    "\n",
    "my_data_path = 'path/to/data/outs/filtered_feature_bc_matrix'\n",
    "\n",
    "def pp(path):\n",
    "    adata = sc.read_10x_mtx(path)\n",
    "    sc.pp.filter_cells(adata, min_genes=200) #get rid of cells with fewer than 200 genes\n",
    "    sc.pp.filter_genes(adata, min_cells=3) #get rid of genes that are found in fewer than 3 cells\n",
    "    adata.var['mt'] = adata.var_names.str.startswith('mt-')  # annotate the group of mitochondrial genes as 'mt'\n",
    "    sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)\n",
    "    upper_lim = np.quantile(adata.obs.n_genes_by_counts.values, .98)\n",
    "    lower_lim = np.quantile(adata.obs.n_genes_by_counts.values, .02)\n",
    "    adata = adata[(adata.obs.n_genes_by_counts < upper_lim) & (adata.obs.n_genes_by_counts > lower_lim)]\n",
    "    adata = adata[adata.obs.pct_counts_mt < 20]\n",
    "    sc.pp.normalize_total(adata, target_sum=1e4) #normalize every cell to 10,000 UMI\n",
    "    sc.pp.log1p(adata) #change to log counts\n",
    "    sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5) #these are default values\n",
    "    adata.raw = adata #save raw data before processing values and further filtering\n",
    "    adata = adata[:, adata.var.highly_variable] #filter highly variable\n",
    "    sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt']) #Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed\n",
    "    sc.pp.scale(adata, max_value=10) #scale each gene to unit variance\n",
    "    sc.tl.pca(adata, svd_solver='arpack')\n",
    "    sc.pp.neighbors(adata, n_neighbors=10, n_pcs=20)\n",
    "    sc.tl.leiden(adata, resolution = 0.25)\n",
    "    sc.tl.umap(adata)\n",
    "    sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')\n",
    "    \n",
    "    \n",
    "    #find markers\n",
    "    results = adata.uns['rank_genes_groups']\n",
    "    out = np.array([[0,0,0,0,0]])\n",
    "    for group in results['names'].dtype.names:\n",
    "        out = np.vstack((out, np.vstack((results['names'][group],\n",
    "                                         results['scores'][group],\n",
    "                                         results['pvals_adj'][group],\n",
    "                                         results['logfoldchanges'][group],\n",
    "                                         np.array([group] * len(results['names'][group])).astype('object'))).T))\n",
    "    markers = pd.DataFrame(out[1:], columns = ['Gene', 'scores', 'pval_adj', 'lfc', 'cluster'])\n",
    "    adata.uns['markers'] = markers #save marker df to uns\n",
    "\n",
    "    \n",
    "    return adata\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a9bba87",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = pp(my_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "106aaa43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with rc_context({'figure.figsize': (6, 6)}):\n",
    "    sc.pl.umap(adata, color='Cdh5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "930c9f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with rc_context({'figure.figsize': (6, 6)}):\n",
    "    sc.pl.umap(adata, color = 'leiden')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c17750a3",
   "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>Gene</th>\n",
       "      <th>scores</th>\n",
       "      <th>pval_adj</th>\n",
       "      <th>lfc</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19566</th>\n",
       "      <td>Fabp4</td>\n",
       "      <td>38.404152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.357558</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19567</th>\n",
       "      <td>Cdh5</td>\n",
       "      <td>38.241219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.285158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19568</th>\n",
       "      <td>Cav1</td>\n",
       "      <td>38.071117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.605687</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19569</th>\n",
       "      <td>Flt1</td>\n",
       "      <td>37.972519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.320522</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19570</th>\n",
       "      <td>Egfl7</td>\n",
       "      <td>37.810642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.874594</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20925</th>\n",
       "      <td>P2ry2</td>\n",
       "      <td>2.511215</td>\n",
       "      <td>0.045055</td>\n",
       "      <td>2.368968</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20928</th>\n",
       "      <td>Vegfc</td>\n",
       "      <td>2.500475</td>\n",
       "      <td>0.046302</td>\n",
       "      <td>1.625132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20929</th>\n",
       "      <td>Gbp3</td>\n",
       "      <td>2.496509</td>\n",
       "      <td>0.046769</td>\n",
       "      <td>1.562324</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20931</th>\n",
       "      <td>Hoxd8</td>\n",
       "      <td>2.492298</td>\n",
       "      <td>0.047242</td>\n",
       "      <td>1.570327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20937</th>\n",
       "      <td>Nr5a2</td>\n",
       "      <td>2.472649</td>\n",
       "      <td>0.049633</td>\n",
       "      <td>3.966382</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>767 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene     scores  pval_adj       lfc cluster\n",
       "19566  Fabp4  38.404152       0.0  7.357558       1\n",
       "19567   Cdh5  38.241219       0.0  6.285158       1\n",
       "19568   Cav1  38.071117       0.0  4.605687       1\n",
       "19569   Flt1  37.972519       0.0  6.320522       1\n",
       "19570  Egfl7  37.810642       0.0  5.874594       1\n",
       "...      ...        ...       ...       ...     ...\n",
       "20925  P2ry2   2.511215  0.045055  2.368968       1\n",
       "20928  Vegfc   2.500475  0.046302  1.625132       1\n",
       "20929   Gbp3   2.496509  0.046769  1.562324       1\n",
       "20931  Hoxd8   2.492298  0.047242  1.570327       1\n",
       "20937  Nr5a2   2.472649  0.049633  3.966382       1\n",
       "\n",
       "[767 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_markers = adata.uns['markers']\n",
    "ec_markers = ec_markers[(ec_markers.cluster == '1') & (ec_markers.pval_adj < 0.05) & (ec_markers.lfc > 1.5)]\n",
    "ec_markers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209156d1",
   "metadata": {},
   "source": [
    "# START GO example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6ee7c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install goatools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73390b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#first need to make background gene set\n",
    "\n",
    "#https://www.ncbi.nlm.nih.gov/gene\n",
    "#\"10090\"[Taxonomy ID] AND alive[property] AND genetype protein coding[Properties]\n",
    "#human is 9606\n",
    "\n",
    "#send to file\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7abc909d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      26,385 lines READ:  gene_result.txt\n",
      "      26,371 geneids WROTE: genes_ncbi_mus_musculus_proteincoding.py\n"
     ]
    }
   ],
   "source": [
    "!mv path/to/Downloads/gene_result.txt .\n",
    "!python path/to/miniconda3/envs/sc/bin/ncbi_gene_results_to_python.py -o genes_ncbi_mus_musculus_proteincoding.py gene_result.txt\n",
    "#move created file to default import location so you dont have to make every time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7bd2c50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from genes_ncbi_mus_musculus_proteincoding import GENEID2NT as GeneID2nt_mus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0eb33092",
   "metadata": {},
   "outputs": [],
   "source": [
    "from goatools.base import download_go_basic_obo\n",
    "from goatools.base import download_ncbi_associations\n",
    "from goatools.obo_parser import GODag\n",
    "from goatools.anno.genetogo_reader import Gene2GoReader\n",
    "from goatools.goea.go_enrichment_ns import GOEnrichmentStudyNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "86c66151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$ get http://purl.obolibrary.org/obo/go/go-basic.obo\n",
      "requests.get(http://purl.obolibrary.org/obo/go/go-basic.obo, stream=True)\n",
      "  WROTE: go-basic.obo\n",
      "\n",
      "$ get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz\n",
      "FTP RETR ftp.ncbi.nlm.nih.gov gene/DATA gene2go.gz -> gene2go.gz\n",
      "$ gunzip gene2go.gz\n",
      "go-basic.obo: fmt(1.2) rel(2022-03-22) 47,103 Terms\n"
     ]
    }
   ],
   "source": [
    "#run one time to initialize\n",
    "obo_fname = download_go_basic_obo()\n",
    "fin_gene2go = download_ncbi_associations()\n",
    "obodag = GODag(\"go-basic.obo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4fc69ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#run one time to initialize\n",
    "mapper = {}\n",
    "\n",
    "for key in GeneID2nt_mus:\n",
    "    mapper[GeneID2nt_mus[key].Symbol] = GeneID2nt_mus[key].GeneID\n",
    "    \n",
    "inv_map = {v: k for k, v in mapper.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d8004ec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HMS:0:00:03.978075 427,869 annotations, 29,800 genes, 18,995 GOs, 1 taxids READ: gene2go \n"
     ]
    }
   ],
   "source": [
    "#run one time to initialize\n",
    "\n",
    "# Read NCBI's gene2go. Store annotations in a list of namedtuples\n",
    "objanno = Gene2GoReader(fin_gene2go, taxids=[10090])\n",
    "# Get namespace2association where:\n",
    "#    namespace is:\n",
    "#        BP: biological_process               \n",
    "#        MF: molecular_function\n",
    "#        CC: cellular_component\n",
    "#    assocation is a dict:\n",
    "#        key: NCBI GeneID\n",
    "#        value: A set of GO IDs associated with that gene\n",
    "ns2assoc = objanno.get_ns2assc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "99b31196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Load BP Ontology Enrichment Analysis ...\n",
      " 67% 17,753 of 26,371 population items found in association\n",
      "\n",
      "Load CC Ontology Enrichment Analysis ...\n",
      " 71% 18,759 of 26,371 population items found in association\n",
      "\n",
      "Load MF Ontology Enrichment Analysis ...\n",
      " 65% 17,084 of 26,371 population items found in association\n"
     ]
    }
   ],
   "source": [
    "#run one time to initialize\n",
    "goeaobj = GOEnrichmentStudyNS(\n",
    "        GeneID2nt_mus.keys(), # List of mouse protein-coding genes\n",
    "        ns2assoc, # geneid/GO associations\n",
    "        obodag, # Ontologies\n",
    "        propagate_counts = False,\n",
    "        alpha = 0.05, # default significance cut-off\n",
    "        methods = ['fdr_bh']) # defult multipletest correction method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ad2b0cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#run one time to initialize\n",
    "GO_items = []\n",
    "\n",
    "temp = goeaobj.ns2objgoea['BP'].assoc\n",
    "for item in temp:\n",
    "    GO_items += temp[item]\n",
    "    \n",
    "\n",
    "temp = goeaobj.ns2objgoea['CC'].assoc\n",
    "for item in temp:\n",
    "    GO_items += temp[item]\n",
    "    \n",
    "\n",
    "temp = goeaobj.ns2objgoea['MF'].assoc\n",
    "for item in temp:\n",
    "    GO_items += temp[item]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c3fd991e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pass list of gene symbols\n",
    "def go_it(test_genes):\n",
    "    print(f'input genes: {len(test_genes)}')\n",
    "    \n",
    "    mapped_genes = []\n",
    "    for gene in test_genes:\n",
    "        try:\n",
    "            mapped_genes.append(mapper[gene])\n",
    "        except:\n",
    "            pass\n",
    "    print(f'mapped genes: {len(mapped_genes)}')\n",
    "    \n",
    "    goea_results_all = goeaobj.run_study(mapped_genes)\n",
    "    goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]\n",
    "    GO = pd.DataFrame(list(map(lambda x: [x.GO, x.goterm.name, x.goterm.namespace, x.p_uncorrected, x.p_fdr_bh,\\\n",
    "                   x.ratio_in_study[0], x.ratio_in_study[1], GO_items.count(x.GO), list(map(lambda y: inv_map[y], x.study_items)),\\\n",
    "                   ], goea_results_sig)), columns = ['GO', 'term', 'class', 'p', 'p_corr', 'n_genes',\\\n",
    "                                                    'n_study', 'n_go', 'study_genes'])\n",
    "\n",
    "    GO = GO[GO.n_genes > 1]\n",
    "    return GO\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "003dfa0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input genes: 767\n",
      "mapped genes: 735\n",
      "\n",
      "Runing BP Ontology Analysis: current study set of 735 IDs.\n",
      " 90%    664 of    735 study items found in association\n",
      "100%    735 of    735 study items found in population(26371)\n",
      "Calculating 12,632 uncorrected p-values using fisher_scipy_stats\n",
      "  12,632 terms are associated with 17,753 of 26,371 population items\n",
      "   3,516 terms are associated with    664 of    735 study items\n",
      "  METHOD fdr_bh:\n",
      "     299 GO terms found significant (< 0.05=alpha) (297 enriched +   2 purified): statsmodels fdr_bh\n",
      "     551 study items associated with significant GO IDs (enriched)\n",
      "      22 study items associated with significant GO IDs (purified)\n",
      "\n",
      "Runing CC Ontology Analysis: current study set of 735 IDs.\n",
      " 95%    699 of    735 study items found in association\n",
      "100%    735 of    735 study items found in population(26371)\n",
      "Calculating 1,836 uncorrected p-values using fisher_scipy_stats\n",
      "   1,836 terms are associated with 18,759 of 26,371 population items\n",
      "     471 terms are associated with    699 of    735 study items\n",
      "  METHOD fdr_bh:\n",
      "      92 GO terms found significant (< 0.05=alpha) ( 91 enriched +   1 purified): statsmodels fdr_bh\n",
      "     688 study items associated with significant GO IDs (enriched)\n",
      "       2 study items associated with significant GO IDs (purified)\n",
      "\n",
      "Runing MF Ontology Analysis: current study set of 735 IDs.\n",
      " 88%    649 of    735 study items found in association\n",
      "100%    735 of    735 study items found in population(26371)\n",
      "Calculating 4,471 uncorrected p-values using fisher_scipy_stats\n",
      "   4,471 terms are associated with 17,084 of 26,371 population items\n",
      "     902 terms are associated with    649 of    735 study items\n",
      "  METHOD fdr_bh:\n",
      "      72 GO terms found significant (< 0.05=alpha) ( 70 enriched +   2 purified): statsmodels fdr_bh\n",
      "     573 study items associated with significant GO IDs (enriched)\n",
      "       0 study items associated with significant GO IDs (purified)\n"
     ]
    }
   ],
   "source": [
    "df = go_it(ec_markers.Gene.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9fefa00a",
   "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>GO</th>\n",
       "      <th>term</th>\n",
       "      <th>class</th>\n",
       "      <th>p</th>\n",
       "      <th>p_corr</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>n_study</th>\n",
       "      <th>n_go</th>\n",
       "      <th>study_genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0001525</td>\n",
       "      <td>angiogenesis</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>1.919384e-39</td>\n",
       "      <td>2.424566e-35</td>\n",
       "      <td>62</td>\n",
       "      <td>735</td>\n",
       "      <td>263</td>\n",
       "      <td>[Ptk2, Egfl7, Pik3r6, Meox2, Tspan12, Ramp2, N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0007155</td>\n",
       "      <td>cell adhesion</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>6.007381e-21</td>\n",
       "      <td>3.794262e-17</td>\n",
       "      <td>64</td>\n",
       "      <td>735</td>\n",
       "      <td>581</td>\n",
       "      <td>[Egfl7, Itga1, Lama4, Lama5, Cdh13, Epha2, Eph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GO:0030335</td>\n",
       "      <td>positive regulation of cell migration</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>2.237660e-20</td>\n",
       "      <td>9.422042e-17</td>\n",
       "      <td>42</td>\n",
       "      <td>735</td>\n",
       "      <td>258</td>\n",
       "      <td>[Mpzl1, Sema4c, Ptk2, Sema6a, Sema6b, Sema7a, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO:0016477</td>\n",
       "      <td>cell migration</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>5.943413e-20</td>\n",
       "      <td>1.872937e-16</td>\n",
       "      <td>43</td>\n",
       "      <td>735</td>\n",
       "      <td>282</td>\n",
       "      <td>[Coro6, Ptk2, Spata13, Wwc2, Fscn1, Lama5, Rnd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO:0007275</td>\n",
       "      <td>multicellular organism development</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>7.413460e-20</td>\n",
       "      <td>1.872937e-16</td>\n",
       "      <td>89</td>\n",
       "      <td>735</td>\n",
       "      <td>1089</td>\n",
       "      <td>[Epha2, Epha4, Ephb4, Spred2, Id1, Fzd4, Id3, ...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>GO:0035005</td>\n",
       "      <td>1-phosphatidylinositol-4-phosphate 3-kinase ac...</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>6.939866e-04</td>\n",
       "      <td>4.496832e-02</td>\n",
       "      <td>3</td>\n",
       "      <td>735</td>\n",
       "      <td>7</td>\n",
       "      <td>[Pik3c2a, Pik3cb, Pik3c2b]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>GO:0042392</td>\n",
       "      <td>sphingosine-1-phosphate phosphatase activity</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>6.939866e-04</td>\n",
       "      <td>4.496832e-02</td>\n",
       "      <td>3</td>\n",
       "      <td>735</td>\n",
       "      <td>7</td>\n",
       "      <td>[Plpp6, Plpp3, Plpp1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>GO:0030971</td>\n",
       "      <td>receptor tyrosine kinase binding</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>7.215019e-04</td>\n",
       "      <td>4.608336e-02</td>\n",
       "      <td>8</td>\n",
       "      <td>735</td>\n",
       "      <td>70</td>\n",
       "      <td>[Hyal2, Angpt2, Dock4, Plcg1, Nck1, Nck2, Dusp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>GO:0086075</td>\n",
       "      <td>gap junction channel activity involved in card...</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>7.757945e-04</td>\n",
       "      <td>4.878913e-02</td>\n",
       "      <td>2</td>\n",
       "      <td>735</td>\n",
       "      <td>2</td>\n",
       "      <td>[Gja1, Gja5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>GO:0003774</td>\n",
       "      <td>cytoskeletal motor activity</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>7.856893e-04</td>\n",
       "      <td>4.878913e-02</td>\n",
       "      <td>6</td>\n",
       "      <td>735</td>\n",
       "      <td>40</td>\n",
       "      <td>[Myo6, Myo18a, Myo1e, Myo10, Myo1b, Myo1d]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             GO                                               term  \\\n",
       "0    GO:0001525                                       angiogenesis   \n",
       "1    GO:0007155                                      cell adhesion   \n",
       "2    GO:0030335              positive regulation of cell migration   \n",
       "3    GO:0016477                                     cell migration   \n",
       "4    GO:0007275                 multicellular organism development   \n",
       "..          ...                                                ...   \n",
       "456  GO:0035005  1-phosphatidylinositol-4-phosphate 3-kinase ac...   \n",
       "457  GO:0042392       sphingosine-1-phosphate phosphatase activity   \n",
       "458  GO:0030971                   receptor tyrosine kinase binding   \n",
       "459  GO:0086075  gap junction channel activity involved in card...   \n",
       "460  GO:0003774                        cytoskeletal motor activity   \n",
       "\n",
       "                  class             p        p_corr  n_genes  n_study  n_go  \\\n",
       "0    biological_process  1.919384e-39  2.424566e-35       62      735   263   \n",
       "1    biological_process  6.007381e-21  3.794262e-17       64      735   581   \n",
       "2    biological_process  2.237660e-20  9.422042e-17       42      735   258   \n",
       "3    biological_process  5.943413e-20  1.872937e-16       43      735   282   \n",
       "4    biological_process  7.413460e-20  1.872937e-16       89      735  1089   \n",
       "..                  ...           ...           ...      ...      ...   ...   \n",
       "456  molecular_function  6.939866e-04  4.496832e-02        3      735     7   \n",
       "457  molecular_function  6.939866e-04  4.496832e-02        3      735     7   \n",
       "458  molecular_function  7.215019e-04  4.608336e-02        8      735    70   \n",
       "459  molecular_function  7.757945e-04  4.878913e-02        2      735     2   \n",
       "460  molecular_function  7.856893e-04  4.878913e-02        6      735    40   \n",
       "\n",
       "                                           study_genes  \n",
       "0    [Ptk2, Egfl7, Pik3r6, Meox2, Tspan12, Ramp2, N...  \n",
       "1    [Egfl7, Itga1, Lama4, Lama5, Cdh13, Epha2, Eph...  \n",
       "2    [Mpzl1, Sema4c, Ptk2, Sema6a, Sema6b, Sema7a, ...  \n",
       "3    [Coro6, Ptk2, Spata13, Wwc2, Fscn1, Lama5, Rnd...  \n",
       "4    [Epha2, Epha4, Ephb4, Spred2, Id1, Fzd4, Id3, ...  \n",
       "..                                                 ...  \n",
       "456                         [Pik3c2a, Pik3cb, Pik3c2b]  \n",
       "457                              [Plpp6, Plpp3, Plpp1]  \n",
       "458  [Hyal2, Angpt2, Dock4, Plcg1, Nck1, Nck2, Dusp...  \n",
       "459                                       [Gja1, Gja5]  \n",
       "460         [Myo6, Myo18a, Myo1e, Myo10, Myo1b, Myo1d]  \n",
       "\n",
       "[460 rows x 9 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d62d46e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['per'] = df.n_genes/df.n_go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5d8b4bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f91a3fe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "18b109ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "import textwrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c4b52376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 36x198 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (0.5, 2.75))\n",
    "\n",
    "cmap = mpl.cm.bwr_r\n",
    "norm = mpl.colors.Normalize(vmin = df.p_corr.min(), vmax = df.p_corr.max())\n",
    "\n",
    "mapper = cm.ScalarMappable(norm = norm, cmap = cm.bwr_r)\n",
    "\n",
    "cbl = mpl.colorbar.ColorbarBase(ax, cmap = cmap, norm = norm, orientation = 'vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8502eb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29994dde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58b9ac91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d7e5c412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (2,4))\n",
    "\n",
    "\n",
    "\n",
    "ax = sns.barplot(data = df, x = 'per', y = 'term', palette = mapper.to_rgba(df.p_corr.values))\n",
    "\n",
    "ax.set_yticklabels([textwrap.fill(e, 22) for e in df['term']])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30e5c63b",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}