9HM_GO_main_populations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7266ca3d-d3c4-4eab-880b-57be81184907",
   "metadata": {},
   "source": [
    "# GO markers of human and mouse populations\n",
    "\n",
    "**TO RUN THIS NOTEBOOK YOU HAVE TO RUN 4H and 4M NOTEBOOKS FULLY!!!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82603b55-ff88-4087-bdda-8a07d367de56",
   "metadata": {},
   "source": [
    "## imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75c8de56-6f94-4765-b2df-b08cd5acbc10",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e88ed44-3558-4c3a-a03e-1cb9b556f5da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import scanpy.external as sce\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import triku as tk\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from tqdm.notebook import tqdm\n",
    "import scipy.sparse as spr\n",
    "import networkx as nx\n",
    "import gseapy as gp\n",
    "from matplotlib import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc6c348d-52bd-4968-98d7-6c82c0543d73",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install cellassign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48dc9733-5960-488c-b8e7-954243796fa9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# local imports and imports from other notebooks\n",
    "from cellassign import assign_cats\n",
    "from fb_functions import make_gene_scoring_with_expr, plot_score_graph, plot_UMAPS_gene, plot_adata_cluster_properties, make_dicts_fraction_mean, plot_dotplot_gene\n",
    "%store -r dict_colors_human\n",
    "%store -r seed\n",
    "%store -r magma\n",
    "%store -r data_dir\n",
    "%store -r dict_cats_fb_human"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a966e7-679d-42d9-945e-e17e2da476e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store -r plot_params\n",
    "\n",
    "pylab.rcParams.update(plot_params)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.options.display.float_format = \"{:,.2f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cced1da2-73d1-4b8a-9c91-682e51720d3b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def retrieve_enrichr(list_genes, org='Human', name='sample', pval=0.05):\n",
    "    enr = gp.enrichr(gene_list=list_genes,\n",
    "                 gene_sets=['GO_Biological_Process_2021','GO_Cellular_Component_2021', 'GO_Molecular_Function_2021', \n",
    "                            'Reactome 2022', 'WikiPathway_2021_Human', 'KEGG_2021_Human'],\n",
    "                 organism=org, # don't forget to set organism to the one you desired! e.g. Yeast\n",
    "                 description='test_name',\n",
    "                 outdir='test/enrichr/{sample}',\n",
    "                 # no_plot=True,\n",
    "                 cutoff=0.5 # test dataset, use lower value from range(0,1)\n",
    "                )\n",
    "    \n",
    "    res = enr.results.sort_values(by='Adjusted P-value')\n",
    "    res = res[res['Adjusted P-value'] < pval][['Gene_set', 'Term', 'Adjusted P-value', 'Combined Score', 'Genes']]\n",
    "    res['Population'] = name\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf216d01-5d54-4139-8cfd-3ff003019db1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "N = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205532c5-2333-4956-b34e-9a09c9b9e211",
   "metadata": {},
   "source": [
    "## Creation of list of genes\n",
    "To do that, we use a mixture of the list of genes obtained in Notebook 4H and 4M, and in the Gene Info table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3cc4c31-7712-44a9-a9f1-2ac613106872",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_GOs = pd.DataFrame(columns=['Population', 'Gene_set', 'Term', 'Adjusted P-value', 'Combined Score', 'Genes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5c0c816-7883-4955-8951-d871d6dbeeb4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_A1 = ['AGTR1', 'ANGPTL5', 'ARFGEF3', 'CA12', 'CADM3', 'CD24', 'CD55', 'CD70', 'CDA', 'CGREF1', 'CLEC3B', 'CPVL', \n",
    "            'CYBRD1', 'DKK1', 'DPP4', 'EYA3', 'FZD6', 'GALNT15', 'GDF15', 'GLRB', 'LGR5', 'LIMS2', 'MTCL1', 'NPR1', 'RAB32', 'TUBB4A', \n",
    "            'ABCB5', 'ABCC9', 'ACKR3', 'ACKR4', 'ADA', 'ADGRD1', 'AEBP1', 'AOX1', 'CHRDL1', 'CPZ', 'CTHRC1', 'CYP4B1', 'DCN', \n",
    "            'ENTPD1', 'FAP', 'FBLN1', 'FBLN2', 'GPNMB', 'HPGD', 'ISLR', 'LOX', 'LOXL1', 'MEDAG', 'MGST1', 'MMP27', 'MTSS1', 'NTN1',\n",
    "            'OMD', 'PCOLCE', 'PI16', 'PIEZO2', 'PODN', 'PRKG2', 'QPCT', 'SCARA5', 'SEMA3B', 'SERPINF1', 'SGCA', 'SGCG', 'SLPI', 'SMOC2', \n",
    "            'THBS3', 'TNXB', 'TSPAN8', 'WISP1', 'CCN4', 'WISP2', 'CCN5']\n",
    "\n",
    "res_A1 = retrieve_enrichr(human_A1, org='Human', name='A1')\n",
    "df_GOs = pd.concat([df_GOs, res_A1.iloc[:N]])\n",
    "res_A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fc77527-8699-4b57-b7db-d2aa96b58b14",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_A2 = ['AHRR2', 'AKAP6', 'ANTXR1', 'APELA', 'C1orf98', 'CLEC2A', 'CMKLR1', 'COL13A1', 'COL18A1', 'COL23A1', 'EDN3', 'EFNA5',\n",
    "            'F13A1', 'F5', 'FGFR2', 'HSPB3', 'KCNQ3', 'LINC00327', 'MAP2', 'MAP7', 'MOXD1', 'MYO1D', 'NCKAP5', 'NTM', 'OSBP2', 'PCSK2', \n",
    "            'PHACTR3', 'PREX1', 'PTGS1', 'COX1', 'PTPRE', 'RAMP3', 'RGS2', 'RSPO3', 'SCARF2', 'SHISAL1', 'STC1', 'STC2', 'THBD', 'TMEM52', 'TMTC2', \n",
    "            'TNFRSF19', 'TTR', 'APCDD1', 'AXIN2', 'CD9', 'DCD42EP3', 'COL6A1', 'COL6A2', 'COL6A3', 'COL6A5', 'COMP', 'CYP26B1', 'EPHB6', 'GREM2', 'ISM1',\n",
    "            'KCNK2', 'LEPR', 'LSAMP', 'NKD2', 'NPTX2', 'PHLDA1', 'PTK7', 'RSPO1', 'SMIM3', 'STMN1', 'THSD1', 'THSD4', 'TNC', 'TNFRSF21', 'WNT11']\n",
    "\n",
    "res_A2 = retrieve_enrichr(human_A2, org='Human', name='A2')\n",
    "df_GOs = pd.concat([df_GOs, res_A2.iloc[:N]])\n",
    "res_A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5892159c-fdbc-46d1-9678-299570656d9b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_A3 = ['ANGPTL5', 'ARFGEF3', 'CES1', 'COL14A1', 'CORIN', 'CPZ', 'ELN', 'ENPP1', 'SGC1', 'SGCG1', 'SLC47A2', 'SOSTDC1', 'WIF1', \n",
    "            'ADGRD1', 'ANGTL1', 'CP', 'ENTPD1', 'EPHB6', 'ITIH5', 'LOXL1', 'MMP27', 'PCOLCE', 'PDPN', 'QPCT', 'SERPINF1', 'SFRP2', 'SVEP1', 'THSB2', \n",
    "            'THBS3', 'THBS4', 'XG']\n",
    "\n",
    "res_A3 = retrieve_enrichr(human_A3, org='Human', name='A3')\n",
    "df_GOs = pd.concat([df_GOs, res_A3.iloc[:N]])\n",
    "res_A3.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2363f160-5227-4bd0-8a8c-9c23974440e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_A4 = ['ACE', 'AIF1L', 'APCDD1L', 'C1QTNF3', 'CD248', 'CD34', 'CILP', 'DBN1', 'EMILIN2', 'EMP3', 'FABP3', 'FAM180B', 'FBN1', 'HSD3B7', \n",
    "            'LINC01133', 'MATN4', 'PAMR1', 'PCOLCE2', 'PRG4', 'PTGIS', 'SEMA3C', 'SEMA3E', 'SH3BGRL3', 'TPPP3', 'TRAC', 'WNT10B', 'YWHAH', \n",
    "            'ABCC9', 'ACKR3', 'AGTR1', 'AQP1', 'CD151', 'CERCAM', 'CLEC3B', 'CLU', 'CRIP1', 'DKK1', 'DPP4', 'EBF3', 'FNDC1', 'GALNT15', \n",
    "            'GPX3', 'GSN', 'IGFBP6', 'NPR1', 'PIEZO2', 'PPIC', 'SCARA5', 'SEMA3B', 'SLC29A1', 'SLPI', 'THY1', 'TIMP2', 'TNXB', \n",
    "            'TPM1', 'WNT2', 'XG']\n",
    "\n",
    "res_A4 = retrieve_enrichr(human_A4, org='Human', name='A4')\n",
    "df_GOs = pd.concat([df_GOs, res_A4.iloc[:N]])\n",
    "res_A4.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6444ca-562a-4893-ad1f-e4cf4939d70b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_B1 = ['ADAMTS4', 'APLNR', 'BAZ1A', 'C11orf96', 'CDKN1A', 'CHI3L1', 'CXCL1', 'CXCL2', 'CXCL3', 'DYRK3', 'ELL2', 'ERRFI1', 'FOSL1', \n",
    "            'GCH1', 'GEM', 'HMGA1', 'IER3', 'IL6', 'KDM6B', 'KPNA2', 'MMP1', 'MMP3', 'NFIL3', 'NFKB1', 'NR4A3', 'PPP1R15B', 'REL', 'TIPARP', \n",
    "            'TNFAIP6', 'TNFSF14', 'ZC3H12A', 'ZNF331', 'ARID5B', 'AVPR1A', 'CEBPB', 'CEBPD', 'CHD1', 'DNAJA1', 'HLA-DRB1', 'ICAM1', 'IRF1', \n",
    "            'JUNB', 'MAFF', 'NFKBIA', 'PIM3', 'PLAUR', 'PNRC1', 'PPP1R15A', 'SOD2', 'TNFAIP3', 'TNFRSF10B', 'TSHZ2', 'UAP1', 'WTAP1',]\n",
    "\n",
    "res_B1 = retrieve_enrichr(human_B1, org='Human', name='B1')\n",
    "df_GOs = pd.concat([df_GOs, res_B1.iloc[:N]])\n",
    "res_B1.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beacf98e-cdf6-45f7-a0b1-a77418ffee50",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_B2 = ['ADRA2A', 'APOC1', 'APOE', 'CLSTN3', 'CXCL12', 'CYP7B1', 'HLA-B', 'ICAM2', 'IGFBP7', 'IL33', 'IL34', 'JAK3', 'LPAR4', 'MSC', \n",
    "            'MUSK', 'PCDH17', 'PPDPFL', 'RBP5', 'SCN4B', 'SDK1', 'SLC9A3R2', 'SLCO2B1', 'ST8SIA1', 'TMEM150C', 'TNFSF13B', 'TYMP', 'VCAM1', \n",
    "            'ANKRD29', 'APLNR', 'C3', 'C6', 'C7', 'CCDC146', 'COX4I2', 'CYGB', 'EPHX1', 'FXYD6', 'GGT5', 'HMGCLL1', 'NFIB', 'NLGN4X', \n",
    "            'NOTCH3', 'RARRES2', 'SLIT2', 'TMEM176A', 'TMEM176B', 'TSHZ2']\n",
    "\n",
    "res_B2 = retrieve_enrichr(human_B2, org='Human', name='B2')\n",
    "df_GOs = pd.concat([df_GOs, res_B2.iloc[:N]])\n",
    "res_B2.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceda96a0-aa11-4e52-b356-cb9c34e16041",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_B3 = ['ACHE', 'BIRC3', 'CCL19', 'CD74', 'CH25H', 'CSF1', 'CRB2', 'CTSH', 'CX3CL1', 'EGR1', 'EXOC3L4', 'HAS2', 'HLA-B', 'HLA-F', 'IL15', \n",
    "            'IL4I1', 'IRF8', 'OLFM2', 'POPDC2', 'PTPRT', 'RGS16', 'TYMP', 'UBD', 'ADRA2A', 'ANKRD29', 'CCDC146', 'CCL2', 'CD82', 'CLSTN3', \n",
    "            'CYP7B1', 'IL32', 'IL34', 'IRF1', 'JAK3', 'LPAR4', 'PIM3', 'RBP5', 'SDK1', 'SLC2A3', 'SOCS3', 'TMEM150C', 'TNC', 'TNFSF13B', \n",
    "            'VCAM1', 'ZFP36']\n",
    "\n",
    "res_B3 = retrieve_enrichr(human_B3, org='Human', name='B3')\n",
    "df_GOs = pd.concat([df_GOs, res_B3.iloc[:N]])\n",
    "res_B3.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47dbed0c-7d57-4e03-b67d-5937c3101df4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_B4 = ['AADAC', 'ADA', 'ATP8B4', 'C6', 'CD36', 'EFEMP1', 'FGF10', 'FZD4', 'GDF10', 'GPX3', 'HHIP', 'HSPB6', 'IGF2', 'MGP', 'MYOC', \n",
    "            'PHLDA3', 'PLA2G2A', 'PLA2G5', 'PLBD1', 'PPARG', 'PPP1R1A', 'PQLC2L', 'SERPINA5', 'SIX1', 'SULT1A1', 'TNNT3', 'ZFHX4', \n",
    "            'ABCA10', 'APOC1', 'C7', 'CFH', 'EBF3', 'FMO1', 'GGT5', 'GSN', 'HMGCLL1', 'IGF1', 'ITM2A', 'LSP1', 'MGST1', 'NTRK2', \n",
    "            'PODN', 'RARRES2', 'STMN2', 'TXNIP', 'WNT11', ]\n",
    "\n",
    "res_B4 = retrieve_enrichr(human_B4, org='Human', name='B4')\n",
    "df_GOs = pd.concat([df_GOs, res_B4.iloc[:N]])\n",
    "res_B4.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "208eb019-1900-44cd-b994-d679e23ee5cb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_C1 = ['ACAN1', 'ACTA2', 'ADAMTS18', 'ANOs1', 'ARHGAP44', 'BCL11B', 'CALD1', 'CFHR1', 'CNN2', \n",
    "            'COL11A1', 'COL21A1', 'DOK6', 'DPEP1', 'EGFL6', 'EVA1A', 'F2RL2', 'GNAO1', 'HS6ST2', \n",
    "            'LGR4', 'LRRN3', 'MAGI1', 'MEF2C', 'MME', 'PAFAH1B3', 'PTCH1', 'RANBP3L', 'RBFOX1', \n",
    "            'RBP4', 'SLC26A7', 'TMEM171', 'UGT3A2', 'VIPR2', 'WDR86', 'WFDC1', 'ALX4', 'C1QTNF6', \n",
    "            'CPXM2', 'CYYR1', 'DKK3', 'F2R', 'HAPLN1', 'KIF26B', 'LAMC3', 'MFAP2', 'PCDH19', 'PDE1A', \n",
    "            'PPP1R14A', 'RFLNB', 'SGIP1', 'THSD4', 'TENM3', 'TRIL', 'TSPAN13']\n",
    "\n",
    "res_C1 = retrieve_enrichr(human_C1, org='Human', name='C1')\n",
    "df_GOs = pd.concat([df_GOs, res_C1.iloc[:N]])\n",
    "res_C1.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15389875-07db-4356-b13b-a108593de125",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_C2 = ['ARHGAP15', 'ATP1B1', 'BTBD11', 'CADM2', 'CCDC68', 'CCK', 'CHADL', 'CHST15', 'COCH', \n",
    "            'COL24A1', 'CPNE5', 'CRABP1', 'EMB', 'FGF13', 'FIBIN', 'GAP43', 'GRP', 'HSPA2', 'KCNAB1', \n",
    "            'LRIG1', 'MAB21L2', 'MKX', 'NDNF', 'P3H2', 'PCSK9', 'PLPP5', 'PLXC1', 'TRIB2', 'TRPM3', \n",
    "            'VEGFD', 'WNT10A', 'ZC2HC1C', 'CADM1', 'F13A1', 'IGFBP5', 'MEOX2', 'NECAB1', \n",
    "            'NOTUM', 'NFATC2', 'PTGFR', 'PTH1R', 'PTPRK', 'TCF4', 'TNMD', 'TNN', 'TRPS1',]\n",
    "\n",
    "res_C2 = retrieve_enrichr(human_C2, org='Human', name='C2')\n",
    "df_GOs = pd.concat([df_GOs, res_C2.iloc[:N]])\n",
    "res_C2.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b79220d9-ac87-4097-a07f-78ae8da4a8a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_C3 = ['ADAM12', 'BGN', 'C1QTNF6', 'EDIL3', 'HAPLN', 'KLK4', 'MMP11', 'P4HA3', \n",
    "            'POSTN', 'RAS11LB', 'RUNX2', 'SEMA5A', 'SGIP1', 'SLC5A3', 'TAC1', 'TENM3', \n",
    "            'TSPAN13', 'ACAN', 'ASPN', 'COL5A2', 'COL8A2', 'ELN', 'F2R', 'LOXL2', 'LRRC15', \n",
    "            'MDK', 'PMEPA1', 'SDC1', 'TRIL']\n",
    "\n",
    "res_C3 = retrieve_enrichr(human_C3, org='Human', name='C3')\n",
    "df_GOs = pd.concat([df_GOs, res_C3.iloc[:N]])\n",
    "res_C3.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2512f1e3-700f-436b-b8e7-90a441cfc53c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_C5 = ['AOPEP', 'BMP7', 'CDH11', 'CENPW', 'CNTN4', 'CXCR4', 'FBXO32', 'GRIK1', 'IGFBP3', \n",
    "            'IQGAP2', 'KRT9', 'LMO3', 'LUZP2', 'MAGI1', 'MARCKSL1', 'PDE3A', 'PGM2L1', 'PKP4', \n",
    "            'PRDM1', 'PREX2', 'PRLR', 'SLC5A3', 'SOX18', 'TFAP2A', 'TFAP2C', 'TPD52', 'WNT5A', \n",
    "            'ARHGAP44', 'CRABP1', 'DCXR', 'DIO2', 'HEY2', 'HMGB3', 'HPSE2', 'INHBA', 'JAG1', \n",
    "            'KRT17', 'LMO4', 'MRPS6', 'NKD1', 'PAFAH1B3', 'ROBO1', 'ROBO2', 'RSPO3', 'SEMA3G', \n",
    "            'SNAI2', 'SPON2', 'TBX3', 'TRPS1']\n",
    "\n",
    "res_C5 = retrieve_enrichr(human_C5, org='Human', name='C5')\n",
    "df_GOs = pd.concat([df_GOs, res_C5.iloc[:N]])\n",
    "res_C5.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "becaaac4-ea24-47e3-824a-e95011a638b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_D1 = ['AFAP1L2', 'ALDH1A2', 'ANGPTL7', 'ATP1A2', 'BAMBI', 'C2orf40', 'ECRG4', 'CDH19', \n",
    "            'CHN1', 'COL28A1', 'COL9A3', 'CRISPLD1', 'CYP1B1', 'DUSP5', 'EGR3', 'ENTPD2', 'ETV1', \n",
    "            'ETV4', 'FMO2', 'FMO3', 'FOXC2', 'LZTS1', 'MCTP1', 'MRAS', 'MYBPC1', 'PODNL1', \n",
    "            'PSAT1', 'SCRG1', 'SOX8', 'SOX9', 'TIAM1', 'CD200', 'CLDN1', 'COL8A1', 'EBF2', \n",
    "            'EFNA1', 'EFNB1', 'FGFBP2', 'FOXS2', 'KLK1', 'LDLR', 'LPCAT2', 'MARCKSL1', 'MTUS1', \n",
    "            'NRP2', 'P2RY14', 'PLK2', 'PTGDS', 'SCN7A', 'SFRP4', 'SPRY2', 'TENM2', 'TM4SF1']\n",
    "\n",
    "res_D1 = retrieve_enrichr(human_D1, org='Human', name='D1')\n",
    "df_GOs = pd.concat([df_GOs, res_D1.iloc[:N]])\n",
    "res_D1.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d6a346d-fd9e-4cae-b478-07573b92e4c1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_D2 = ['ADAMTSL3', 'ADAMTSL4', 'ADAMTSL5', 'AQP3', 'BNC2', 'C19orf33', 'CAV2', 'CAVIN2', \n",
    "            'CCL13', 'CTTNBP2', 'DACT1', 'DOCK9', 'EGFLAM', 'GFRA2', 'GPC6', 'HRH1', 'IGF2', \n",
    "            'INMT', 'ISYNA1', 'ITGA6', 'ITGB4', 'KLF5', 'KRT19', 'NDUFA4L2', 'NGFR', 'PALMD', \n",
    "            'PDZRN4', 'PEAR1', 'SBSPON', 'SDPR', 'CAVIN2', 'SLC22A3', 'SHISA3', 'SLC2A1', \n",
    "            'TNNC1', 'ECRG4', 'C2orf40', 'CSRP1', 'DAAM1', 'DDIT4L', 'DUSP5', 'EBF2', 'GAB1', \n",
    "            'LMO7', 'MATN2', 'NDRG2', 'PLEKHA4', 'S100B', 'SFRP4', 'SORBS1', 'TAGLN', 'TJP1', \n",
    "            'TM4SF1', 'TXNIP', 'UACA', 'VIT']\n",
    "res_D2 = retrieve_enrichr(human_D2, org='Human', name='D2')\n",
    "df_GOs = pd.concat([df_GOs, res_D2.iloc[:N]])\n",
    "res_D2.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b325b5ec-54dd-47c7-af0a-0e4be4f1e7ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_E1 = ['CMKLR1', 'COL26A1', 'EGR2', 'FHAD1', 'FZD7', 'GMFG', 'HOPX', 'IGFBP2', 'LFNG', \n",
    "            'MAP2', 'NECAB1', 'NTRK3', 'PLA2G5', 'PLEKHA6', 'PRDM8', 'RAMP1', 'SAMD5', 'SIX1', \n",
    "            'SLC22A3', 'THSD1', 'A2M', 'ANTXR2', 'CPE', 'IGF1', 'ITM2A', 'MEOX1', 'MGP', 'MMP16', \n",
    "            'OLFML2A', 'PDGFD', 'PTN', 'RGCC', 'RGMA', 'SFRP1', 'SPON2', 'SPRY1', 'SPRY2', \n",
    "            'STMN2', 'TCF7L2', 'TIMP3', 'TMPO', 'TNFRSF21', 'UNC5B', 'WDR86']\n",
    "\n",
    "res_E1 = retrieve_enrichr(human_E1, org='Human', name='E1')\n",
    "df_GOs = pd.concat([df_GOs, res_E1.iloc[:N]])\n",
    "res_E1.iloc[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bef5398-7f7e-4892-8278-dcbfd7450e2f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_GOs.to_csv('results/GOEA.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5f0a09-c5cc-4e03-8cc6-fa05baf0248d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_GOs_mod = df_GOs.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0394331-69d3-4d61-82b7-213ede7a8cbc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_GOs_mod = df_GOs_mod[['Population', 'Term', 'Adjusted P-value', 'Genes']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77e3dae9-cabf-4333-978b-2ee5c95a553b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_GOs_mod.to_csv('results/GOEA_mod.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d39ee289-fcb1-4ec0-8f49-56527d49555b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "os.system('jupyter nbconvert --to html 9HM_GO_main_populations.ipynb')"
   ]
  }
 ],
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