mds_risk_feature_importances.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import polars as pl\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from src.base_classes.omic_data_loader import OmicDataLoader\n",
    "from src.data_managers.concat import CatOmicDataManager\n",
    "from src.data_managers.sample_graph import SampleGraphDataManager\n",
    "\n",
    "from src.gnn_utils.interactions import (\n",
    "    get_mirna_genes_circrna_interactions,\n",
    "    get_mirna_gene_interactions,\n",
    "    gg_interactions,\n",
    "    pp_interactions,\n",
    "    tf_links\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrna_loader = OmicDataLoader(\n",
    "    data_dir=\"mds_data/splits_74_risk/mrna\",\n",
    ")\n",
    "mirna_loader = OmicDataLoader(\n",
    "    data_dir=\"mds_data/splits_74_risk/mirna_genes\",\n",
    ")\n",
    "circrna_loader = OmicDataLoader(\n",
    "    data_dir=\"mds_data/splits_74_risk/circrna\",\n",
    ")\n",
    "# pirna_loader = OmicDataLoader(\n",
    "#     data_dir=\"mds_data/splits_74/pirna\",\n",
    "# )\n",
    "te_loader = OmicDataLoader(\n",
    "    data_dir=\"mds_data/splits_74_risk/te_counts\",\n",
    ")\n",
    "\n",
    "omic_data_loaders = {\n",
    "    \"mrna\": mrna_loader,\n",
    "    # \"mirna\": mirna_loader,\n",
    "    # \"circrna\": circrna_loader,\n",
    "    # # \"pirna\": pirna_loader,\n",
    "    # \"te\": te_loader,\n",
    "}\n",
    "odm = CatOmicDataManager(omic_data_loaders, n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-12-16 16:27:48,484] A new study created in memory with name: no-name-e24b02fd-2df9-4d74-9c81-7491f7ec467d\n",
      "[I 2024-12-16 16:27:48,547] Trial 0 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.08816818936671046, 'alpha': 8.708475261918125e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:48,611] Trial 1 finished with value: 0.454051219036375 and parameters: {'booster': 'gblinear', 'lambda': 1.1945522536154498e-05, 'alpha': 1.4276665102341759e-05}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:48,741] Trial 2 finished with value: 0.21534604040404037 and parameters: {'booster': 'dart', 'lambda': 0.41312494896934576, 'alpha': 0.09601118966708615, 'max_depth': 6, 'eta': 0.0019777739132768794, 'gamma': 0.22722917465695128, 'grow_policy': 'depthwise', 'sample_type': 'weighted', 'normalize_type': 'tree', 'rate_drop': 0.00019021038826133902, 'skip_drop': 0.006300767171403498}. Best is trial 0 with value: 0.49764519230326676.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New best score: 0.498\n",
      "Best model performance:\n",
      "Accuracy: 0.795 ± 0.064\n",
      "F1 Macro: 0.791 ± 0.061\n",
      "F1 Weighted: 0.792 ± 0.063\n",
      "[{'acc': 0.7272727272727273, 'f1_macro': np.float64(0.7272727272727273), 'f1_weighted': np.float64(0.7272727272727273)}, {'acc': 0.8181818181818182, 'f1_macro': np.float64(0.8166666666666667), 'f1_weighted': np.float64(0.8181818181818182)}, {'acc': 0.7272727272727273, 'f1_macro': np.float64(0.7272727272727273), 'f1_weighted': np.float64(0.7272727272727273)}, {'acc': 0.9, 'f1_macro': np.float64(0.8901098901098901), 'f1_weighted': np.float64(0.8967032967032967)}, {'acc': 0.8, 'f1_macro': np.float64(0.7916666666666667), 'f1_weighted': np.float64(0.7916666666666667)}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-12-16 16:27:48,838] Trial 3 finished with value: 0.21534604040404037 and parameters: {'booster': 'gbtree', 'lambda': 0.11497459024365182, 'alpha': 0.0001902412347384584, 'max_depth': 4, 'eta': 0.019134470502336753, 'gamma': 0.005779377993448926, 'grow_policy': 'lossguide'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:48,940] Trial 4 finished with value: 0.21534604040404037 and parameters: {'booster': 'gbtree', 'lambda': 2.5451701354784127e-06, 'alpha': 0.002316161045415025, 'max_depth': 2, 'eta': 0.0007505718969214737, 'gamma': 2.196972621341745e-08, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,008] Trial 5 finished with value: 0.4102701493067927 and parameters: {'booster': 'gbtree', 'lambda': 0.0001133881007512582, 'alpha': 2.2425819013481e-05, 'max_depth': 1, 'eta': 0.20853625270345477, 'gamma': 1.462509717533428e-07, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,128] Trial 6 finished with value: 0.21534604040404037 and parameters: {'booster': 'gbtree', 'lambda': 0.001461696622038509, 'alpha': 0.486496246786584, 'max_depth': 8, 'eta': 0.052788077817491466, 'gamma': 0.002946134839382222, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,228] Trial 7 finished with value: 0.21534604040404037 and parameters: {'booster': 'gbtree', 'lambda': 5.246386732775593e-07, 'alpha': 6.340547995830311e-07, 'max_depth': 5, 'eta': 0.0006506314945280246, 'gamma': 6.607574515381371e-05, 'grow_policy': 'lossguide'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,320] Trial 8 finished with value: 0.2149085674931129 and parameters: {'booster': 'gbtree', 'lambda': 0.006575196516684399, 'alpha': 0.0007317942052676928, 'max_depth': 7, 'eta': 3.3071964274008504e-06, 'gamma': 1.7363563564069384e-06, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,427] Trial 9 finished with value: 0.2149085674931129 and parameters: {'booster': 'gbtree', 'lambda': 8.146233086945354e-06, 'alpha': 0.001154475150347085, 'max_depth': 2, 'eta': 3.6630423270500065e-07, 'gamma': 0.00042867521852928863, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,492] Trial 10 finished with value: 0.4080174439853076 and parameters: {'booster': 'gblinear', 'lambda': 1.2801019748107345e-08, 'alpha': 7.78271909714901e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,593] Trial 11 finished with value: 0.4630809911198852 and parameters: {'booster': 'gblinear', 'lambda': 0.0001518256147920626, 'alpha': 2.5310615931779706e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,660] Trial 12 finished with value: 0.4643536045844739 and parameters: {'booster': 'gblinear', 'lambda': 0.007639392680044817, 'alpha': 1.54239838893255e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,730] Trial 13 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.017796406017075713, 'alpha': 2.8379854742225362e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,831] Trial 14 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.035914974972488044, 'alpha': 1.1878219816708563e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:49,945] Trial 15 finished with value: 0.2149085674931129 and parameters: {'booster': 'dart', 'lambda': 0.4745908623959181, 'alpha': 3.258143088501576e-07, 'max_depth': 9, 'eta': 4.812333468837323e-08, 'gamma': 0.7304715189395551, 'grow_policy': 'lossguide', 'sample_type': 'uniform', 'normalize_type': 'forest', 'rate_drop': 1.633867902273347e-08, 'skip_drop': 2.0560553238952677e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,032] Trial 16 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.001675417995766264, 'alpha': 1.1799299977996034e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,095] Trial 17 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.04134582259449052, 'alpha': 2.4785684301560327e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,190] Trial 18 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.9297442879310581, 'alpha': 5.2928341069791586e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,306] Trial 19 finished with value: 0.2149085674931129 and parameters: {'booster': 'dart', 'lambda': 0.0007889875805733036, 'alpha': 1.6153559713910023e-06, 'max_depth': 4, 'eta': 8.257752246140315e-06, 'gamma': 2.283291048515745e-05, 'grow_policy': 'lossguide', 'sample_type': 'weighted', 'normalize_type': 'tree', 'rate_drop': 0.8211186667000889, 'skip_drop': 0.8381256452552212}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,368] Trial 20 finished with value: 0.4634441457301969 and parameters: {'booster': 'gblinear', 'lambda': 0.012392867034753383, 'alpha': 0.013626267150432768}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,448] Trial 21 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.03645705079312357, 'alpha': 1.0706205894979233e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,524] Trial 22 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.09601185831721132, 'alpha': 1.0397131430461989e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,610] Trial 23 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.1799088372182612, 'alpha': 1.1376248014735711e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,702] Trial 24 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.02961220021533292, 'alpha': 4.332714137653484e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,766] Trial 25 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.002775679930167038, 'alpha': 3.5951354226345047e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,906] Trial 26 finished with value: 0.23578778812922616 and parameters: {'booster': 'dart', 'lambda': 0.0003874673372922796, 'alpha': 1.667430134706626e-05, 'max_depth': 9, 'eta': 2.192791374729872e-08, 'gamma': 4.642521682373344e-06, 'grow_policy': 'lossguide', 'sample_type': 'uniform', 'normalize_type': 'forest', 'rate_drop': 1.6749507607898784e-08, 'skip_drop': 1.2667107288599745e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:50,965] Trial 27 finished with value: 0.4643536045844739 and parameters: {'booster': 'gblinear', 'lambda': 0.016085408356618466, 'alpha': 3.968809167422193e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,030] Trial 28 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.251815003458489, 'alpha': 5.479722380452175e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,119] Trial 29 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.0037393512594151926, 'alpha': 5.536889782248012e-05}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,191] Trial 30 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.00040079097262042917, 'alpha': 2.3066145346222162e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,256] Trial 31 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.05793669830885808, 'alpha': 1.3648328554864846e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,348] Trial 32 finished with value: 0.4643536045844739 and parameters: {'booster': 'gblinear', 'lambda': 0.0014361189081892624, 'alpha': 1.522145633102396e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,411] Trial 33 finished with value: 0.42309798923626546 and parameters: {'booster': 'gblinear', 'lambda': 3.5497955481289245e-05, 'alpha': 6.781829419499154e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,550] Trial 34 finished with value: 0.2149085674931129 and parameters: {'booster': 'dart', 'lambda': 0.01514706753891755, 'alpha': 9.751268131015636e-07, 'max_depth': 3, 'eta': 2.5115280950373185e-05, 'gamma': 0.03245650844430665, 'grow_policy': 'lossguide', 'sample_type': 'weighted', 'normalize_type': 'tree', 'rate_drop': 0.7475184456667022, 'skip_drop': 1.6120702633759106e-05}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,611] Trial 35 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.07848975938822485, 'alpha': 2.842520465564464e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,677] Trial 36 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.0032190688766026453, 'alpha': 2.0404443965486253e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,768] Trial 37 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.43062953858481273, 'alpha': 5.306541415411199e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,907] Trial 38 finished with value: 0.2837037355016248 and parameters: {'booster': 'dart', 'lambda': 3.5741652760267384e-05, 'alpha': 0.00026893309404468486, 'max_depth': 6, 'eta': 0.48465435646008037, 'gamma': 3.6709470027134763e-07, 'grow_policy': 'depthwise', 'sample_type': 'uniform', 'normalize_type': 'forest', 'rate_drop': 9.380301669795938e-05, 'skip_drop': 4.8098545889726284e-05}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:51,987] Trial 39 finished with value: 0.2523998390074793 and parameters: {'booster': 'gbtree', 'lambda': 0.9354825887500454, 'alpha': 3.545405949306964e-08, 'max_depth': 1, 'eta': 3.6572559177659006e-07, 'gamma': 2.1393295656817693e-08, 'grow_policy': 'lossguide'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,049] Trial 40 finished with value: 0.3617996954931034 and parameters: {'booster': 'gblinear', 'lambda': 0.0007934319034249979, 'alpha': 0.017020443852249108}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,145] Trial 41 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.023318344108853615, 'alpha': 1.1472055986837163e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,209] Trial 42 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.08851876729627346, 'alpha': 4.446937335449533e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,272] Trial 43 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.006600415896012346, 'alpha': 7.208748449485208e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,395] Trial 44 finished with value: 0.2149085674931129 and parameters: {'booster': 'gbtree', 'lambda': 0.2101464052958386, 'alpha': 2.197120523442538e-07, 'max_depth': 8, 'eta': 9.506652143949605e-05, 'gamma': 0.0004533623784470329, 'grow_policy': 'lossguide'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,457] Trial 45 finished with value: 0.07498688884758245 and parameters: {'booster': 'gblinear', 'lambda': 1.901117608872562e-08, 'alpha': 0.9019768229031041}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,523] Trial 46 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.04398679804892232, 'alpha': 5.828760596138974e-05}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,615] Trial 47 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.008237567722610766, 'alpha': 5.836822066832559e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,733] Trial 48 finished with value: 0.21534604040404037 and parameters: {'booster': 'gbtree', 'lambda': 0.002807196645462596, 'alpha': 2.479660345310914e-06, 'max_depth': 5, 'eta': 0.008298423583234113, 'gamma': 0.049781685823653575, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,811] Trial 49 finished with value: 0.4080174439853076 and parameters: {'booster': 'gblinear', 'lambda': 1.6121626158079722e-06, 'alpha': 2.1058347453582077e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:52,945] Trial 50 finished with value: 0.2149085674931129 and parameters: {'booster': 'dart', 'lambda': 0.15959692512010107, 'alpha': 5.6811276502724166e-08, 'max_depth': 7, 'eta': 2.282924753671021e-06, 'gamma': 1.648716223503447e-05, 'grow_policy': 'depthwise', 'sample_type': 'uniform', 'normalize_type': 'tree', 'rate_drop': 2.98565115394429e-05, 'skip_drop': 0.3554159401730199}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,010] Trial 51 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.053759054300205555, 'alpha': 1.6564435077662067e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,079] Trial 52 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.028580933339596052, 'alpha': 1.5039519935259753e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,146] Trial 53 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.010266792413680155, 'alpha': 1.09378734665397e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,244] Trial 54 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.030736115385210944, 'alpha': 4.258935871694896e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,310] Trial 55 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.38038885084293206, 'alpha': 1.0110965889385267e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,373] Trial 56 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.11479400734156867, 'alpha': 9.127869382131399e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,464] Trial 57 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.005039704662483633, 'alpha': 3.8785400682664947e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,586] Trial 58 finished with value: 0.21534604040404037 and parameters: {'booster': 'gbtree', 'lambda': 0.0011751622772004557, 'alpha': 1.6009138347367148e-07, 'max_depth': 3, 'eta': 0.00010102003415882691, 'gamma': 0.0004799720344239435, 'grow_policy': 'lossguide'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,649] Trial 59 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.016695133275896345, 'alpha': 1.0456248891482871e-05}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,719] Trial 60 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.04275427050389467, 'alpha': 3.40934519904254e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,811] Trial 61 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.11753208479351128, 'alpha': 1.0795570002202613e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,873] Trial 62 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.06766417057356318, 'alpha': 2.740276273955033e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:53,939] Trial 63 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.23672640092284508, 'alpha': 7.256044187873021e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,039] Trial 64 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.534758224579539, 'alpha': 1.8439182795788313e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,110] Trial 65 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.017188353600350717, 'alpha': 2.548183681759617e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,177] Trial 66 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.010667830835636953, 'alpha': 1.039145514672964e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,328] Trial 67 finished with value: 0.2149085674931129 and parameters: {'booster': 'dart', 'lambda': 0.029434046460768155, 'alpha': 7.175075464840935e-07, 'max_depth': 6, 'eta': 2.325756307306448e-07, 'gamma': 3.5303629780643175e-07, 'grow_policy': 'depthwise', 'sample_type': 'weighted', 'normalize_type': 'forest', 'rate_drop': 0.0038252608803575733, 'skip_drop': 1.4022601310461262e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,391] Trial 68 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.0019103481255092196, 'alpha': 3.776359979798647e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,456] Trial 69 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.10495281637659234, 'alpha': 1.684870605406508e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,558] Trial 70 finished with value: 0.45566872660318813 and parameters: {'booster': 'gblinear', 'lambda': 0.00024597160274606746, 'alpha': 1.1937587079529731e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,621] Trial 71 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.18493301963949416, 'alpha': 5.605644096583982e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,686] Trial 72 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.611485896599741, 'alpha': 2.32853514811179e-08}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:54,976] Trial 73 finished with value: 0.46267497291593757 and parameters: {'booster': 'gblinear', 'lambda': 0.3348460920217444, 'alpha': 2.8539589252056927e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:55,039] Trial 74 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.054863100912585935, 'alpha': 1.5931804023865288e-06}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:55,105] Trial 75 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.1572688527481356, 'alpha': 1.032422807318474e-07}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:55,249] Trial 76 finished with value: 0.2149085674931129 and parameters: {'booster': 'dart', 'lambda': 0.08124444391274899, 'alpha': 4.4944869954838534e-08, 'max_depth': 3, 'eta': 0.09779098806791546, 'gamma': 0.005632835380006051, 'grow_policy': 'lossguide', 'sample_type': 'weighted', 'normalize_type': 'forest', 'rate_drop': 1.081754646106435e-06, 'skip_drop': 0.007346892723442058}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:55,340] Trial 77 finished with value: 0.27221242406183604 and parameters: {'booster': 'gbtree', 'lambda': 0.004249488820859017, 'alpha': 0.0023527922013090825, 'max_depth': 4, 'eta': 0.6858280751694212, 'gamma': 1.9836670878006984e-06, 'grow_policy': 'lossguide'}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:55,402] Trial 78 finished with value: 0.34458582973869295 and parameters: {'booster': 'gblinear', 'lambda': 9.761797449911232e-08, 'alpha': 0.10995176900894106}. Best is trial 0 with value: 0.49764519230326676.\n",
      "[I 2024-12-16 16:27:55,490] Trial 79 finished with value: 0.49764519230326676 and parameters: {'booster': 'gblinear', 'lambda': 0.02440819768575284, 'alpha': 1.8277288379266475e-07}. Best is trial 0 with value: 0.49764519230326676.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model performance:\n",
      "Accuracy: 0.795 ± 0.064\n",
      "F1 Macro: 0.791 ± 0.061\n",
      "F1 Weighted: 0.792 ± 0.063\n",
      "Best hyperparameters:\n",
      "{'booster': 'gblinear', 'lambda': 0.08816818936671046, 'alpha': 8.708475261918125e-08}\n"
     ]
    }
   ],
   "source": [
    "from src.evals.xgboost import XGBoostEvaluator\n",
    "\n",
    "xgb_eval = XGBoostEvaluator(\n",
    "    data_manager=odm,\n",
    "    n_trials=80,\n",
    "    verbose=True,\n",
    ")\n",
    "_ = xgb_eval.evaluate()\n",
    "xgb_eval.print_best_results()\n",
    "xgb_eval.print_best_parameters()\n",
    "# xgb_eval.save_results(results_file=save_folder, row_name=\"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-12-18 23:20:14,269] A new study created in memory with name: no-name-567c9a0d-3bd0-44f4-9326-af38e93cba99\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using: attention integrator\n",
      "metrics (0.6226415094339622, np.float64(0.5146520146520146), np.float64(0.5362499136084042))\n",
      "score 0.4200657390087427\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "metrics (0.6981132075471698, np.float64(0.6418918918918919), np.float64(0.6552779194288627))\n",
      "score 0.7028104137921743\n",
      "tensor([0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0])\n",
      "metrics (0.7924528301886793, np.float64(0.7847914359542266), np.float64(0.788622133071453))\n",
      "score 0.7272727273727273\n",
      "tensor([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])\n",
      "metrics (0.8867924528301887, np.float64(0.8847826086956522), np.float64(0.8862182116488926))\n",
      "score 0.8166657297516677\n",
      "tensor([1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0])\n",
      "Using: attention integrator\n",
      "metrics (0.5660377358490566, np.float64(0.39802469135802465), np.float64(0.42802702073142324))\n",
      "score 0.4200657390087427\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "metrics (0.6037735849056604, np.float64(0.5015673981191222), np.float64(0.522860353699651))\n",
      "score 0.5781383137274039\n",
      "tensor([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "metrics (0.6981132075471698, np.float64(0.6718266253869969), np.float64(0.6805888194403878))\n",
      "score 0.6200114499842091\n",
      "tensor([0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0])\n",
      "metrics (0.7735849056603774, np.float64(0.7633928571428572), np.float64(0.7680256064690026))\n",
      "score 0.7226006967551408\n",
      "tensor([0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0])\n",
      "metrics (0.8113207547169812, np.float64(0.8096264367816092), np.float64(0.8113207547169812))\n",
      "score 0.7272727273727273\n",
      "tensor([1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0])\n",
      "metrics (0.8867924528301887, np.float64(0.8867521367521367), np.float64(0.8869537171423963))\n",
      "score 0.81767645569606\n",
      "tensor([1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0])\n",
      "Using: attention integrator\n",
      "metrics (0.5471698113207547, np.float64(0.35365853658536583), np.float64(0.38702254947077774))\n",
      "score 0.4200657390087427\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "metrics (0.6037735849056604, np.float64(0.4781997187060478), np.float64(0.5023485391290502))\n",
      "score 0.7028104137921743\n",
      "tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1])\n",
      "metrics (0.6792452830188679, np.float64(0.6124731182795699), np.float64(0.627648610265774))\n",
      "score 0.81767645569606\n",
      "tensor([1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1])\n",
      "Using: attention integrator\n",
      "metrics (0.5660377358490566, np.float64(0.39802469135802465), np.float64(0.42802702073142324))\n",
      "score 0.46608487599492704\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "metrics (0.5471698113207547, np.float64(0.3884615384615384), np.float64(0.4178519593613933))\n",
      "score 0.6453714585100166\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n",
      "metrics (0.6981132075471698, np.float64(0.6418918918918919), np.float64(0.6552779194288627))\n",
      "score 0.7807974916431516\n",
      "tensor([0, 0, 0, 0, 1, 0, 0, 0, 0, 1])\n",
      "metrics (0.8490566037735849, np.float64(0.846376811594203), np.float64(0.8482909488651901))\n",
      "score 0.895594946160942\n",
      "tensor([0, 1, 0, 0, 1, 0, 0, 0, 0, 1])\n",
      "Using: attention integrator\n",
      "metrics (0.5660377358490566, np.float64(0.39802469135802465), np.float64(0.42802702073142324))\n",
      "score 0.38157141428619634\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "metrics (0.660377358490566, np.float64(0.6102941176470589), np.float64(0.6234739178690345))\n",
      "score 0.5480657681539445\n",
      "tensor([0, 0, 0, 0, 1, 0, 0, 0, 0, 0])\n",
      "metrics (0.7924528301886793, np.float64(0.7897583844212045), np.float64(0.7920037558941002))\n",
      "score 0.6800773462955018\n",
      "tensor([0, 0, 0, 0, 1, 0, 0, 1, 0, 0])\n",
      "metrics (0.8490566037735849, np.float64(0.8485714285714285), np.float64(0.8493800539083558))\n",
      "score 0.7944347545564259\n",
      "tensor([0, 1, 0, 0, 1, 0, 0, 1, 0, 0])\n",
      "metrics (0.8867924528301887, np.float64(0.8834310850439883), np.float64(0.8852985115918774))\n",
      "score 0.8000000001\n",
      "tensor([0, 1, 0, 0, 1, 1, 1, 1, 0, 0])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-12-18 23:20:22,544] Trial 0 finished with value: 0.5708039754384809 and parameters: {}. Best is trial 0 with value: 0.5708039754384809.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New best score: 0.571\n",
      "Best model performance:\n",
      "Accuracy: 0.831 ± 0.035\n",
      "F1 Macro: 0.828 ± 0.032\n",
      "F1 Weighted: 0.830 ± 0.034\n",
      "[{'acc': 0.8181818181818182, 'f1_macro': np.float64(0.8166666666666667), 'f1_weighted': np.float64(0.8151515151515153)}, {'acc': 0.8181818181818182, 'f1_macro': np.float64(0.8166666666666667), 'f1_weighted': np.float64(0.8181818181818182)}, {'acc': 0.8181818181818182, 'f1_macro': np.float64(0.8166666666666667), 'f1_weighted': np.float64(0.8181818181818182)}, {'acc': 0.9, 'f1_macro': np.float64(0.8901098901098901), 'f1_weighted': np.float64(0.8967032967032967)}, {'acc': 0.8, 'f1_macro': np.float64(0.8), 'f1_weighted': np.float64(0.8)}]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'acc': np.float64(0.8309090909090908),\n",
       " 'f1_macro': np.float64(0.8280219780219781),\n",
       " 'f1_weighted': np.float64(0.8296436896436896),\n",
       " 'acc_std': np.float64(0.035255853508482385),\n",
       " 'f1_macro_std': np.float64(0.031707946535935436),\n",
       " 'f1_weighted_std': np.float64(0.03420091037095021)}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.evals.mogonet import MOGONETEvaluator\n",
    "\n",
    "mogonet_eval = MOGONETEvaluator(\n",
    "    data_manager=SampleGraphDataManager(\n",
    "        omic_data_loaders=omic_data_loaders,\n",
    "        n_splits=5,\n",
    "        params={\n",
    "            \"graph_style\": \"threshold\",\n",
    "            \"self_connections\": True,\n",
    "            \"avg_degree\": 8,\n",
    "        },\n",
    "    ),\n",
    "    n_trials=1,\n",
    "    params={\n",
    "        \"encoder_hidden_channels\": {\n",
    "            \"mrna\": 32,\n",
    "            \"mirna\": 32,\n",
    "            \"circrna\": 32,\n",
    "            \"te\": 32,\n",
    "        },\n",
    "        \"encoder_type\": \"gat\",\n",
    "        \"dropout\": 0.1,\n",
    "        \"integrator_type\": \"attention\", # vcdn seems to work better on mds disease\n",
    "        \"integration_in_dim\": 16,\n",
    "        \"vcdn_hidden_channels\": 16,\n",
    "        \"epochs\": 250,\n",
    "        \"log_interval\": 251,\n",
    "    },\n",
    "    save_model_path=\"mogonet_risk_best_models/model\",\n",
    ")\n",
    "mogonet_eval.in_channels, mogonet_eval.omic_names\n",
    "mogonet_eval.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0: Acc: 0.8679, F1 Macro: 0.8672, F1 Weighted: 0.8681\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:01<00:05,  1.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1: Acc: 0.9434, F1 Macro: 0.9431, F1 Weighted: 0.9435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:02<00:04,  1.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 2: Acc: 0.7547, F1 Macro: 0.7301, F1 Weighted: 0.7378\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:04<00:02,  1.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 3: Acc: 0.8868, F1 Macro: 0.8858, F1 Weighted: 0.8868\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [00:05<00:01,  1.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4: Acc: 0.9434, F1 Macro: 0.9427, F1 Weighted: 0.9433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:07<00:00,  1.44s/it]\n"
     ]
    }
   ],
   "source": [
    "feat_importances = mogonet_eval.feature_importance(\"mogonet_risk_best_models\")\n",
    "\n",
    "fi_sorted = dict(sorted(feat_importances.items(), key=lambda x: abs(x[1]), reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.7272727272727273, 'f1_macro': np.float64(0.7272727272727273), 'f1_weighted': np.float64(0.7272727272727273)}\n",
      "{'ENSG00000239839': [0.0660216361284256, -0.06561287492513657], 'ENSG00000064687': [0.007436187006533146, -0.007854610681533813], 'ENSG00000254995': [-0.04646565392613411, 0.04636714980006218], 'ENSG00000240247': [0.06736958771944046, -0.06594625115394592], 'ENSG00000163221': [0.0728432834148407, -0.07219428569078445], 'ENSG00000206047': [0.06769867986440659, -0.06522583216428757], 'ENSG00000122224': [0.05424657464027405, -0.054517246782779694], 'ENSG00000188153': [-0.08864794671535492, 0.08876623213291168], 'ENSG00000233822': [0.13270708918571472, -0.1314355731010437], 'ENSG00000091181': [0.0439787432551384, -0.041507989168167114], 'ENSG00000138131': [0.0012328404700383544, -0.001692092278972268], 'ENSG00000075275': [0.08584799617528915, -0.08378273248672485], 'ENSG00000106976': [-0.0625431090593338, 0.058794327080249786], 'ENSG00000111249': [0.05534717068076134, -0.05629640072584152], 'ENSG00000197405': [0.05106375738978386, -0.049325209110975266], 'ENSG00000169946': 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-0.020707158371806145], 'ENSG00000141753': [-0.001291305641643703, 0.00221391674131155], 'ENSG00000106976': [-0.023759767413139343, 0.023916486650705338], 'ENSG00000137267': [-0.018815739080309868, 0.019374845549464226], 'ENSG00000161544': [0.009425734169781208, -0.010581115260720253], 'ENSG00000099804': [0.011185789480805397, -0.011036477982997894], 'ENSG00000275183': [-0.02601248398423195, 0.027045609429478645], 'ENSG00000179403': [-0.025403734296560287, 0.025402963161468506], 'ENSG00000184357': [-0.008509058505296707, 0.009228319860994816], 'ENSG00000168685': [0.026579754427075386, -0.026416752487421036], 'ENSG00000189266': [0.0001701174769550562, 0.0011276999721303582], 'ENSG00000266302': [0.062000785022974014, -0.061197202652692795], 'ENSG00000143409': [-0.013599609956145287, 0.013331579975783825], 'ENSG00000103196': [0.03927498310804367, -0.03884062170982361], 'ENSG00000110777': [-0.009167514741420746, 0.008519148454070091], 'ENSG00000163421': [-0.05426674336194992, 0.05300961434841156], 'ENSG00000091879': [0.02411492168903351, -0.023759236559271812], 'ENSG00000168298': [0.00350396241992712, -0.004130471032112837], 'ENSG00000275379': [-0.010017966851592064, 0.00966457836329937], 'ENSG00000129757': [-0.011482618749141693, 0.0110995564609766], 'ENSG00000270882': [0.010514086112380028, -0.009703109040856361], 'ENSG00000006534': [-0.03225782886147499, 0.03165721893310547], 'ENSG00000143153': [-0.0062368400394916534, 0.006043272092938423], 'ENSG00000179593': [0.023508870974183083, -0.023673195391893387], 'ENSG00000175097': [0.012456148862838745, -0.013178796507418156], 'ENSG00000167815': [0.015179803594946861, -0.01543533243238926], 'ENSG00000163687': [-0.004348374903202057, 0.004152228124439716], 'ENSG00000008516': [0.03469254821538925, -0.034265242516994476], 'ENSG00000159374': [-0.012479341588914394, 0.01143506821244955], 'ENSG00000064393': [-0.024602845311164856, 0.02402665466070175], 'ENSG00000185829': [-0.019792746752500534, 0.020390672609210014], 'ENSG00000160588': [-0.008159961551427841, 0.008184968493878841], 'ENSG00000109089': [0.005598000716418028, -0.005473942495882511], 'ENSG00000055208': [-0.020198030397295952, 0.020529741421341896], 'ENSG00000132386': [0.02388705313205719, -0.021835695952177048], 'ENSG00000112667': [0.00796280987560749, -0.007807220332324505], 'ENSG00000205730': [-0.03383464738726616, 0.03286712244153023], 'ENSG00000100003': [0.021329224109649658, -0.020962130278348923], 'ENSG00000284691': [0.061568308621644974, -0.06253743916749954], 'ENSG00000278463': [-0.006196949630975723, 0.00721411406993866], 'ENSG00000135074': [-0.00391420628875494, 0.005435726139694452], 'ENSG00000177191': [0.037776343524456024, -0.037677180022001266], 'ENSG00000126803': [-0.03978371620178223, 0.03923086076974869], 'ENSG00000270800': [-0.03268898278474808, 0.032486237585544586], 'ENSG00000117245': [0.037319060415029526, -0.03532798960804939], 'ENSG00000070371': [0.05219454690814018, -0.0553177148103714], 'ENSG00000137959': [-0.06958627700805664, 0.06838853657245636], 'ENSG00000204482': [-0.012704185210168362, 0.012619095854461193]}\n"
     ]
    }
   ],
   "source": [
    "feat_importances = xgb_eval.get_feature_importances(xgb_eval.best_params)\n",
    "\n",
    "fi_sorted = dict(sorted(feat_importances.items(), key=lambda x: abs(x[1]), reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrna = pl.read_csv(\"mds_data/preprocessed_74/mrna.csv\")\n",
    "mirna = pl.read_csv(\"mds_data/preprocessed_74/mirna_genes.csv\")\n",
    "\n",
    "mrna_gene = mrna.select(\"GENE_NAME\", \"GENE_ID\")\n",
    "mirna_gene = mirna.select(\"GENE_NAME\", \"GENE_ID\")\n",
    "\n",
    "# create one big dict to go from id to name\n",
    "mrna_dict = dict(zip(mrna_gene[\"GENE_ID\"], mrna_gene[\"GENE_NAME\"]))\n",
    "mirna_dict = dict(zip(mirna_gene[\"GENE_ID\"], mirna_gene[\"GENE_NAME\"]))\n",
    "\n",
    "# merge the dicts\n",
    "mrna_dict.update(mirna_dict)\n",
    "\n",
    "# rename the keys from fi_sorted to the name of the gene if it exists in mrna_dict\n",
    "fi_nn = {mrna_dict.get(k, k): v for k, v in fi_sorted.items()}\n",
    "\n",
    "te = pl.read_csv(\"mds_data/preprocessed_74/te_counts.csv\")\n",
    "te_names = te['TE'].to_list()\n",
    "\n",
    "# add \"TE: \" to all te features\n",
    "te_names = {x : f\"TE: {x}\" for x in te_names}\n",
    "\n",
    "# Create a new dictionary with the updated keys\n",
    "fi_nn = {te_names.get(k, k): v for k, v in fi_nn.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.plotting.feature_importance_plot import plot_top_genes\n",
    "\n",
    "_ = plot_top_genes(\n",
    "    fi_nn,\n",
    "    n_genes=30, \n",
    "    title=\"Top Genes\",\n",
    "    save_file=\"plots/risk/mds_risk.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Arrayed molecular barcoding identifies TNFSF13 as a positive regulator of acute myeloid leukemia-initiating cells](https://pmc.ncbi.nlm.nih.gov/articles/PMC6886409/)\n",
    "- [PI3K-dependent Reprogramming of Hexokinase Isoforms Regulates B Lymphocyte Metabolism, i.e. HK3 and HK2](https://journals.aai.org/jimmunol/article/204/1_Supplement/151.30/64960/PI3K-dependent-Reprogramming-of-Hexokinase) and [PI3-kinase deletion promotes myelodysplasia by dysregulating autophagy in hematopoietic stem cells, i.e. PI3-kinase is directly linked to myelodysplastic syndrome](https://www.science.org/doi/10.1126/sciadv.ade8222?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed)\n",
    "- [COL24A1 is enhanced in leukemia and myeloma as per protein atlas](https://www.proteinatlas.org/ENSG00000171502-COL24A1/cell+line)\n",
    "- [GOLGA8S is enchanced in myeloma and also slightly in leukemia as per protein atlas](https://www.proteinatlas.org/ENSG00000261739-GOLGA8S/cell+line#leukemia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.plotting.feature_importance_plot import plot_top_genes\n",
    "\n",
    "_ = plot_top_genes(\n",
    "    fi_nn,\n",
    "    n_genes=30, \n",
    "    title=\"Top Genes\",\n",
    "    save_file=\"plots/risk/mds_risk_fi_mrna_only.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mrna_dict = {}\n",
    "mirna_dict = {}\n",
    "circrna_dict = {}\n",
    "\n",
    "# go trough the fi_nn dict and sort it into three lists of mrna, mirna and circrna\n",
    "for k, v in fi_nn.items():\n",
    "    if k.startswith(\"MIR\"):\n",
    "        mirna_dict[k] = v\n",
    "    elif k.startswith(\"hsa_circ\"):\n",
    "        circrna_dict[k] = v\n",
    "    else:\n",
    "        mrna_dict[k] = v\n",
    "\n",
    "# keep only top 30 out of each list\n",
    "n_top_genes = 60\n",
    "mrna_dict = {k: v for k, v in sorted(mrna_dict.items(), key=lambda item: item[1], reverse=True)[:n_top_genes]}\n",
    "mirna_dict = {k: v for k, v in sorted(mirna_dict.items(), key=lambda item: item[1], reverse=True)[:n_top_genes]}\n",
    "circrna_dict = {k: v for k, v in sorted(circrna_dict.items(), key=lambda item: item[1], reverse=True)[:n_top_genes]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: No interactions found, are all inputs correct?\n",
      "WARNING: No interactions found, are all inputs correct?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor(20), tensor(0.), tensor(0.))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrna_genes = list(mrna_dict.keys())\n",
    "mirna_genes = list(mirna_dict.keys())\n",
    "circrna_names = list(circrna_dict.keys())\n",
    "\n",
    "gg_A = gg_interactions(mrna_genes, mrna_genes)\n",
    "pp_A = pp_interactions(mrna_genes, mrna_genes)\n",
    "tf_links_A = tf_links(\n",
    "    mrna_genes,\n",
    "    mrna_genes,\n",
    "    tflinksdb=\"interaction_data/TFLink_Homo_sapiens_interactions_SS_simpleFormat_v1.0.tsv\",\n",
    "    # tflinksdb=\"interaction_data/TFLink_Homo_sapiens_interactions_LS_simpleFormat_v1.0.tsv\",\n",
    ")\n",
    "mrna_A = torch.logical_or(gg_A, pp_A).logical_or(tf_links_A).int()\n",
    "\n",
    "mirna_gene_A = get_mirna_gene_interactions(mirna_genes, mrna_genes, mirna_mrna_db=\"interaction_data/mirna_genes_mrna.csv\")\n",
    "\n",
    "# interactions are in a shape of (mirnas, circrnas)\n",
    "circrna_mirna_A = get_mirna_genes_circrna_interactions(\n",
    "    mirna_names=mirna_genes,\n",
    "    circrna_names=circrna_names,\n",
    "    mirna_circrna_interactions=\"interaction_data/circrna_mirna_interactions_mirbase.csv\",\n",
    ")\n",
    "\n",
    "mrna_A.sum(), mirna_gene_A.sum(), circrna_mirna_A.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4169\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.plotting.graph_plotting import create_multi_omic_network, plot_multi_omic_network\n",
    "\n",
    "# Example usage:\n",
    "G = create_multi_omic_network(mrna_dict, mirna_dict, circrna_dict, \n",
    "                             mrna_A, mirna_gene_A, circrna_mirna_A)\n",
    "\n",
    "# generate a random number\n",
    "# seed = 8894 # 7306 - disease\n",
    "# seed = 4580 # - risk\n",
    "seed = np.random.randint(0, 10000)\n",
    "print(seed)\n",
    "fig = plot_multi_omic_network(\n",
    "    G,\n",
    "    seed=seed, \n",
    "    title=\"Multi-omic Interaction Network for MDS Risk\",\n",
    "    figsize=(20,20),\n",
    "    max_iter=500,\n",
    "    gravity=1.0,\n",
    "    jitter_tolerance=2.0,\n",
    ")\n",
    "fig.savefig('plots/risk/multi_omic_network_genes_small.png', dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- the DEFA gene family which has high scores here has been associated with different types of cancer but not with MDS it seems\n",
    "- [Whole blood defensin mRNA expression is a predictive biomarker of docetaxel response in castration-resistant prostate cancer](https://pmc.ncbi.nlm.nih.gov/articles/PMC4527520/) \n",
    "- [S100A12 is a promising biomarker in papillary thyroid cancer](https://www.nature.com/articles/s41598-020-58534-1)\n",
    "- [S100A8, S100A9 and S100A12 are strongly correlated with the occurrence and development of cancer](https://onlinelibrary.wiley.com/doi/10.1111/imm.13722)\n",
    "- [S100A9 induces differentiation of acute myeloid leukemia cells through TLR4](https://pubmed.ncbi.nlm.nih.gov/28137827/)\n",
    "- [S100 Proteins in Acute Myeloid Leukemia](https://pubmed.ncbi.nlm.nih.gov/30366122/)\n",
    "- [Roles of Histone Deacetylases in Acute Myeloid Leukemia With Fusion Proteins](https://pmc.ncbi.nlm.nih.gov/articles/PMC8440836/) and [https://eurjmedres.biomedcentral.com/articles/10.1186/s40001-024-02108-8](https://eurjmedres.biomedcentral.com/articles/10.1186/s40001-024-02108-8)\n",
    "- we have three histone genes here as important features\n",
    "    - HIST1H2BN 4.245093017816544\n",
    "    - HIST1H2AC 1.6199357211589813\n",
    "    - HIST1H1A 1.2683670080441516"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig.savefig('plots/risk/multi_omic_network.png', dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[MiR-570 inhibits cell proliferation and glucose metabolism by targeting IRS1 and IRS2 in human chronic myelogenous leukemia](https://pmc.ncbi.nlm.nih.gov/articles/PMC5478775/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "shape: (20, 4)\n",
      "+-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------+\n",
      "| Gene_set                    | Term                                                       | Adjusted P-value | Genes                                                                                  |\n",
      "| ---                         | ---                                                        | ---              | ---                                                                                    |\n",
      "| str                         | str                                                        | f64              | str                                                                                    |\n",
      "+======================================================================================================================================================================================================+\n",
      "| Reactome_Pathways_2024      | Metal Sequestration by Antimicrobial Proteins              | 0.000003         | LCN2;S100A9;S100A8;LTF                                                                 |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Chronic Hyperglycemia Impairment Of Neuron Function WP5283 | 0.000721         | MMP25;BDNF;PRKCA;MMP8;MMP9                                                             |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Neutrophil Degranulation                                   | 0.000838         | CTSA;MMP25;TUBB;C5AR1;LCN2;S100A12;DEFA1;MMP8;S100A9;MMP9;S100A8;LTF                   |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | Proteins Involved in Chronic Obstructive Pulmonary Disease | 0.000852         | BDNF;DEFA3;PRKCA;DEFA1;MMP8;S100A9;MMP9;S100A8                                         |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Antimicrobial Peptides                                     | 0.000961         | LCN2;DEFA3;DEFA1;S100A9;S100A8;LTF                                                     |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Fragile X Syndrome WP4549                                  | 0.003418         | HOMER1;BDNF;CLTCL1;PRKCA;MMP9;DNM1                                                     |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Neural Crest Cell Migration During Development WP4564      | 0.003418         | BDNF;MMP8;MMP9;EPHB4                                                                   |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Neural Crest Cell Migration In Cancer WP4565               | 0.003422         | BDNF;MMP8;MMP9;EPHB4                                                                   |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Degradation of the Extracellular Matrix                    | 0.006497         | MMP25;COL2A1;COL4A5;LAMC1;MMP8;MMP9                                                    |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | EPH-ephrin Mediated Repulsion of Cells                     | 0.008692         | CLTCL1;MMP9;EPHB4;DNM1                                                                 |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Non-integrin membrane-ECM Interactions                     | 0.011502         | COL2A1;COL4A5;PRKCA;LAMC1                                                              |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | RHO GTPases Activate NADPH Oxidases                        | 0.011502         | PRKCA;S100A9;S100A8                                                                    |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Collagen Degradation                                       | 0.013142         | COL2A1;COL4A5;MMP8;MMP9                                                                |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Matrix Metalloproteinases WP129                            | 0.019552         | MMP25;MMP8;MMP9                                                                        |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | AHR in Intestinal Cell Antimicrobial Barrier Maintenance   | 0.021345         | LCN2;S100A9;S100A8                                                                     |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Activation of Matrix Metalloproteinases                    | 0.022375         | MMP25;MMP8;MMP9                                                                        |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Interferon Alpha Beta Signaling                            | 0.022375         | IFITM1;MX2;MX1;IFI6                                                                    |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Extracellular Matrix Organization                          | 0.023875         | MMP25;COL2A1;COL4A5;PRKCA;LAMC1;MMP8;MMP9                                              |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Immune System                                              | 0.023875         | CTSA;IFITM1;MX2;TUBB;MX1;C5AR1;IFI6;DEFA3;IL5RA;DEFA1;HSPA2;MMP8;MMP9;DNM1;MMP25;COL2A |\n",
      "|                             |                                                            |                  | 1;LCN2;S100A12;S100A9;CLEC4G;S100A8;LTF                                                |\n",
      "|-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Glycosphingolipid Catabolism                               | 0.026485         | SMPD3;CTSA;GLB1L                                                                       |\n",
      "+-----------------------------+------------------------------------------------------------+------------------+----------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "import gseapy as gp\n",
    "\n",
    "mrna_dict = {}\n",
    "mirna_dict = {}\n",
    "circrna_dict = {}\n",
    "\n",
    "# go trough the fi_nn dict and sort it into three lists of mrna, mirna and circrna\n",
    "for k, v in fi_nn.items():\n",
    "    if k.startswith(\"MIR\"):\n",
    "        mirna_dict[k] = v\n",
    "    elif k.startswith(\"hsa_circ\"):\n",
    "        circrna_dict[k] = v\n",
    "    else:\n",
    "        mrna_dict[k] = v\n",
    "\n",
    "n_top_genes = 100\n",
    "mrna_dict_enrich = {k: v for k, v in sorted(mrna_dict.items(), key=lambda item: item[1], reverse=True)}\n",
    "mrna_genes_enrich = list(mrna_dict.keys())[:n_top_genes]\n",
    "print(len(mrna_genes_enrich))\n",
    "\n",
    "# if you are only intrested in dataframe that enrichr returned, please set outdir=None\n",
    "enr = gp.enrichr(\n",
    "    gene_list=mrna_genes_enrich, # or \"./tests/data/gene_list.txt\",\n",
    "    gene_sets=['BioPlanet_2019','KEGG_2021_Human', 'Elsevier_Pathway_Collection', 'WikiPathways_2024_Human', 'Reactome_Pathways_2024'],\n",
    "    organism='human',\n",
    "    outdir=None,\n",
    ")\n",
    "# select by Adjusted P-value < 0.05\n",
    "results = pl.DataFrame(enr.results)\n",
    "significant_results = (\n",
    "    results\n",
    "    .filter(pl.col(\"Adjusted P-value\") < 0.05)\n",
    "    .select([\"Gene_set\", \"Term\", \"Adjusted P-value\", \"Genes\"])\n",
    "    .sort(\"Adjusted P-value\")\n",
    ")\n",
    "significant_results = significant_results\n",
    "\n",
    "with pl.Config(tbl_rows=-1, tbl_cols=-1, tbl_width_chars=200, fmt_str_lengths=1000, set_tbl_formatting='ASCII_FULL'):\n",
    "    print(significant_results.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (42, 4)\n",
      "+-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------+\n",
      "| Gene_set                    | Term                                                               | Adjusted P-value | Genes                                                                          |\n",
      "| ---                         | ---                                                                | ---              | ---                                                                            |\n",
      "| str                         | str                                                                | f64              | str                                                                            |\n",
      "+======================================================================================================================================================================================================+\n",
      "| Reactome_Pathways_2024      | Metal Sequestration by Antimicrobial Proteins                      | 0.000003         | LCN2;S100A9;S100A8;LTF                                                         |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Chronic Hyperglycemia Impairment Of Neuron Function WP5283         | 0.000721         | MMP25;BDNF;PRKCA;MMP8;MMP9                                                     |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Neutrophil Degranulation                                           | 0.000838         | CTSA;MMP25;TUBB;C5AR1;LCN2;S100A12;DEFA1;MMP8;S100A9;MMP9;S100A8;LTF           |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | Proteins Involved in Chronic Obstructive Pulmonary Disease         | 0.000852         | BDNF;DEFA3;PRKCA;DEFA1;MMP8;S100A9;MMP9;S100A8                                 |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Antimicrobial Peptides                                             | 0.000961         | LCN2;DEFA3;DEFA1;S100A9;S100A8;LTF                                             |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Fragile X Syndrome WP4549                                          | 0.003418         | HOMER1;BDNF;CLTCL1;PRKCA;MMP9;DNM1                                             |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Neural Crest Cell Migration During Development WP4564              | 0.003418         | BDNF;MMP8;MMP9;EPHB4                                                           |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Neural Crest Cell Migration In Cancer WP4565                       | 0.003422         | BDNF;MMP8;MMP9;EPHB4                                                           |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Degradation of the Extracellular Matrix                            | 0.006497         | MMP25;COL2A1;COL4A5;LAMC1;MMP8;MMP9                                            |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | EPH-ephrin Mediated Repulsion of Cells                             | 0.008692         | CLTCL1;MMP9;EPHB4;DNM1                                                         |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Non-integrin membrane-ECM Interactions                             | 0.011502         | COL2A1;COL4A5;PRKCA;LAMC1                                                      |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | RHO GTPases Activate NADPH Oxidases                                | 0.011502         | PRKCA;S100A9;S100A8                                                            |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Collagen Degradation                                               | 0.013142         | COL2A1;COL4A5;MMP8;MMP9                                                        |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| WikiPathways_2024_Human     | Matrix Metalloproteinases WP129                                    | 0.019552         | MMP25;MMP8;MMP9                                                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | AHR in Intestinal Cell Antimicrobial Barrier Maintenance           | 0.021345         | LCN2;S100A9;S100A8                                                             |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Activation of Matrix Metalloproteinases                            | 0.022375         | MMP25;MMP8;MMP9                                                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Interferon Alpha Beta Signaling                                    | 0.022375         | IFITM1;MX2;MX1;IFI6                                                            |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Extracellular Matrix Organization                                  | 0.023875         | MMP25;COL2A1;COL4A5;PRKCA;LAMC1;MMP8;MMP9                                      |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Immune System                                                      | 0.023875         | CTSA;IFITM1;MX2;TUBB;MX1;C5AR1;IFI6;DEFA3;IL5RA;DEFA1;HSPA2;MMP8;MMP9;DNM1;MMP |\n",
      "|                             |                                                                    |                  | 25;COL2A1;LCN2;S100A12;S100A9;CLEC4G;S100A8;LTF                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Glycosphingolipid Catabolism                                       | 0.026485         | SMPD3;CTSA;GLB1L                                                               |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Alpha-defensins                                                    | 0.027436         | DEFA3;DEFA1                                                                    |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | EPH-Ephrin Signaling                                               | 0.027703         | CLTCL1;MMP9;EPHB4;DNM1                                                         |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Formation of Annular Gap Junctions                                 | 0.029246         | CLTCL1;DNM1                                                                    |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Signaling by Receptor Tyrosine Kinases                             | 0.030443         | COL2A1;BDNF;SH2D2A;PDE3B;COL4A5;PRKCA;LAMC1;MMP9;DNM1                          |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Gap Junction Degradation                                           | 0.031094         | CLTCL1;DNM1                                                                    |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Innate Immune System                                               | 0.031749         | CTSA;TUBB;C5AR1;DEFA3;DEFA1;MMP8;MMP9;DNM1;MMP25;LCN2;S100A12;S100A9;S100A8;LT |\n",
      "|                             |                                                                    |                  | F                                                                              |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024      | Transcriptional Regulation of Testis Differentiation               | 0.032965         | WT1;ZFPM2                                                                      |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Extracellular matrix organization                                  | 0.035623         | MMP25;COL2A1;COL4A5;MMP8;MMP9                                                  |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Interferon alpha/beta signaling                                    | 0.035623         | IFITM1;MX2;MX1;IFI6                                                            |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Interleukin-2 signaling pathway                                    | 0.035623         | HIST1H2BN;TSC22D3;SH2D2A;MX1;PDE3B;IL5RA;PRKCA;AQP3;LY9;PRSS23;TPST2;FAS;HIST1 |\n",
      "|                             |                                                                    |                  | H2AC                                                                           |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Matrix metalloproteinases                                          | 0.035623         | MMP25;MMP8;MMP9                                                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Activation of matrix metalloproteinases                            | 0.035623         | MMP25;MMP8;MMP9                                                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | Proteins with Altered Expression in Psoriasis                      | 0.038869         | MMP9;S100A9;S100A8                                                             |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | RAGE/AGER and S100 Proteins in Cardiovascular Injury               | 0.038869         | S100A12;S100A9;S100A8                                                          |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | Proteins Involved in Atherosclerosis                               | 0.038869         | S100A12;ALOX12;MMP8;S100A9;MMP9;S100A8                                         |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | Neutrophil Associated Lung Damage in Chronic Obstructive Pulmonary | 0.038869         | MMP8;MMP9                                                                      |\n",
      "|                             | Disease                                                            |                  |                                                                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | IL22 Induces Keratinocyte Proliferation in Psoriasis               | 0.038869         | MMP9;S100A9;S100A8                                                             |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Systemic lupus erythematosus                                       | 0.04358          | HIST1H2AM;HIST1H2BN;HIST1H2AC;HIST1H4F;HIST1H3E                                |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Alpha defensins                                                    | 0.04358          | DEFA3;DEFA1                                                                    |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | T cell receptor regulation of apoptosis                            | 0.04358          | IFITM1;TUBB;TSC22D3;MX1;PDE3B;FAS;HSPA2;PRODH;HIST1H2AC;LTF                    |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| Elsevier_Pathway_Collection | Proteins Involved in Osteoarthritis                                | 0.043925         | COL2A1;FAS;MMP8;MMP9                                                           |\n",
      "|-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------|\n",
      "| BioPlanet_2019              | Gap junction degradation                                           | 0.048103         | CLTCL1;DNM1                                                                    |\n",
      "+-----------------------------+--------------------------------------------------------------------+------------------+--------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# XGBOOST RESULTS\n",
    "\n",
    "with pl.Config(tbl_rows=-1, tbl_cols=-1, tbl_width_chars=200, fmt_str_lengths=1000, set_tbl_formatting='ASCII_FULL'):\n",
    "    print(significant_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (29, 4)\n",
      "+------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------+\n",
      "| Gene_set               | Term                                                                        | Adjusted P-value | Genes                                                                      |\n",
      "| ---                    | ---                                                                         | ---              | ---                                                                        |\n",
      "| str                    | str                                                                         | f64              | str                                                                        |\n",
      "+======================================================================================================================================================================================================+\n",
      "| BioPlanet_2019         | Systemic lupus erythematosus                                                | 0.00014          | HIST1H2BM;HIST1H2AI;HIST1H4L;HIST1H2BE;HIST1H4I;HIST1H4D;HIST1H2AC;HIST1H2 |\n",
      "|                        |                                                                             |                  | BD                                                                         |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Interleukin-2 signaling pathway                                             | 0.000484         | HIST1H2BM;SMARCD3;HIST1H2AI;CITED2;TSC22D3;SH2D2A;IL1RAP;PRSS23;FOSL2;TPST |\n",
      "|                        |                                                                             |                  | 2;CCND1;IRF4;BLVRB;HIST1H2BE;HIST1H2AC;HIST1H2BD                           |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Type II interferon signaling (interferon-gamma)                             | 0.000484         | IRF4;HIST1H4L;IFI6;HIST1H4I;HIST1H4D                                       |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Meiotic synapsis                                                            | 0.000584         | HIST1H2BM;STAG3;H2BFS;HIST1H2AC;HIST1H2BD                                  |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | RNA polymerase I promoter opening                                           | 0.000787         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Interferon alpha/beta signaling                                             | 0.000787         | IFITM1;IRF4;IFI6;HLA-C;HLA-G                                               |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Packaging of telomere ends                                                  | 0.000787         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Meiosis                                                                     | 0.002184         | HIST1H2BM;STAG3;H2BFS;HIST1H2AC;HIST1H2BD                                  |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Deposition of new CENP-A-containing nucleosomes at the centromere           | 0.002413         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Amyloids                                                                    | 0.00356          | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Meiotic recombination                                                       | 0.003744         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | RNA polymerase I transcription                                              | 0.003744         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Chromosome maintenance                                                      | 0.003744         | HIST1H2BM;STAG3;H2BFS;HIST1H2AC;HIST1H2BD                                  |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | T cell receptor regulation of apoptosis                                     | 0.004192         | IFITM1;TUBB2A;CCND1;IRF4;TSC22D3;GTF2IRD1;IL1RAP;HLA-G;HIST1H2AC;HSPA1A;MC |\n",
      "|                        |                                                                             |                  | L1                                                                         |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Telomere maintenance                                                        | 0.004192         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Viral myocarditis                                                           | 0.008015         | CXADR;CCND1;HLA-C;HLA-G                                                    |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024 | Interferon Alpha Beta Signaling                                             | 0.01207          | IFITM1;IRF4;IFI6;HLA-C;HLA-G                                               |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024 | Interferon Signaling                                                        | 0.01207          | IFITM1;TUBB2A;IRF4;IFI6;HLA-C;HLA-G;HSPA1A;SMAD7                           |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024 | Other Semaphorin Interactions                                               | 0.01207          | CD72;SEMA6A;PLXNA4                                                         |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| Reactome_Pathways_2024 | Cytokine Signaling in Immune System                                         | 0.01207          | IFITM1;IFI6;HLA-C;IL1RAP;HLA-G;SMAD7;TNFSF13B;TUBB2A;CCND1;IRF4;RHOU;HSPA1 |\n",
      "|                        |                                                                             |                  | A;MCL1                                                                     |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Endosomal/vacuolar pathway                                                  | 0.014856         | HLA-C;HLA-G                                                                |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | RNA polymerase I, RNA polymerase III, and mitochondrial transcription       | 0.021303         | HIST1H2BM;H2BFS;HIST1H2AC;HIST1H2BD                                        |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Interferon-gamma signaling pathway                                          | 0.021798         | IRF4;HLA-C;HLA-G;SMAD7                                                     |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Interferon signaling                                                        | 0.022622         | IFITM1;IRF4;IFI6;HLA-C;HLA-G                                               |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| KEGG_2021_Human        | Viral myocarditis                                                           | 0.031014         | CXADR;CCND1;HLA-C;HLA-G                                                    |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Immune system signaling by interferons, interleukins, prolactin, and growth | 0.035776         | IFITM1;IRF4;IFI6;HLA-C;IL1RAP;HLA-G                                        |\n",
      "|                        | hormones                                                                    |                  |                                                                            |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Amine-derived hormones                                                      | 0.035776         | PNMT;SLC5A5                                                                |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Other semaphorin interactions                                               | 0.035776         | SEMA6A;PLXNA4                                                              |\n",
      "|------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------|\n",
      "| BioPlanet_2019         | Immunoregulatory interactions between a lymphoid and a non-lymphoid cell    | 0.03638          | IFITM1;CXADR;HLA-C;HLA-G                                                   |\n",
      "+------------------------+-----------------------------------------------------------------------------+------------------+----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# MDS RESULTS\n",
    "\n",
    "# with pl.Config(tbl_rows=-1, tbl_cols=-1, tbl_width_chars=200, fmt_str_lengths=1000, set_tbl_formatting='ASCII_FULL'):\n",
    "#     print(significant_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "import gseapy as gp\n",
    "\n",
    "mrna_dict = {}\n",
    "mirna_dict = {}\n",
    "circrna_dict = {}\n",
    "\n",
    "# go trough the fi_nn dict and sort it into three lists of mrna, mirna and circrna\n",
    "for k, v in fi_nn.items():\n",
    "    if k.startswith(\"MIR\"):\n",
    "        mirna_dict[k] = v\n",
    "    elif k.startswith(\"hsa_circ\"):\n",
    "        circrna_dict[k] = v\n",
    "    else:\n",
    "        mrna_dict[k] = v\n",
    "\n",
    "n_top_genes = 100\n",
    "mrna_dict_enrich = {k: v for k, v in sorted(mrna_dict.items(), key=lambda item: item[1], reverse=True)}\n",
    "mrna_genes_enrich = list(mrna_dict.keys())[:n_top_genes]\n",
    "print(len(mrna_genes_enrich))\n",
    "# if you are only intrested in dataframe that enrichr returned, please set outdir=None\n",
    "enr = gp.enrichr(\n",
    "    gene_list=mrna_genes_enrich, # or \"./tests/data/gene_list.txt\",\n",
    "    gene_sets=[\n",
    "        \"GO_Biological_Process_2023\",\n",
    "        \"GO_Molecular_Function_2023\",\n",
    "        \"GO_Cellular_Component_2023\",\n",
    "        # \"Jensen_DISEASES\",\n",
    "        # \"Human_Phenotype_Ontology\"\n",
    "    ],\n",
    "    organism='human', # don't forget to set organism to the one you desired! e.g. Yeast\n",
    "    outdir=None, # don't write to disk\n",
    ")\n",
    "\n",
    "# select by Adjusted P-value < 0.05\n",
    "results = pl.DataFrame(enr.results)\n",
    "significant_results = results.filter(pl.col(\"Adjusted P-value\") < 0.05)\n",
    "\n",
    "significant_results = significant_results.select([\"Gene_set\", \"Term\", \"Adjusted P-value\", \"Genes\"]).sort(\"Adjusted P-value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (36, 4)\n",
      "+----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------+\n",
      "| Gene_set                   | Term                                                                      | Adjusted P-value | Genes                                                                    |\n",
      "| ---                        | ---                                                                       | ---              | ---                                                                      |\n",
      "| str                        | str                                                                       | f64              | str                                                                      |\n",
      "+======================================================================================================================================================================================================+\n",
      "| GO_Cellular_Component_2023 | Collagen-Containing Extracellular Matrix (GO:0062023)                     | 0.000182         | COL2A1;ADAMTSL4;VWA1;COL4A5;DEFA1;LAMC1;MMP8;S100A9;MMP9;S100A8;PF4      |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Secretory Granule Lumen (GO:0034774)                                      | 0.000182         | CTSA;LCN2;DEFA3;S100A12;DEFA1;MMP8;S100A9;S100A8;PF4;LTF                 |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Antimicrobial Humoral Immune Response Mediated By Antimicrobial Peptide   | 0.0007           | S100A12;DEFA3;DEFA1;S100A9;LTF;PF4                                       |\n",
      "|                            | (GO:0061844)                                                              |                  |                                                                          |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | RAGE Receptor Binding (GO:0050786)                                        | 0.000907         | S100A12;S100A9;S100A8                                                    |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Antimicrobial Humoral Response (GO:0019730)                               | 0.003389         | S100A12;DEFA3;DEFA1;S100A9;LTF;PF4                                       |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Defense Response To Fungus (GO:0050832)                                   | 0.003389         | S100A12;S100A9;S100A8;LTF                                                |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Polymeric Cytoskeletal Fiber (GO:0099513)                                 | 0.010901         | SYNM;MX2;MX1;COBL;EVPL;KIF17;DNM1                                        |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Arachidonic Acid Binding (GO:0050544)                                     | 0.013908         | S100A9;S100A8                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Icosatetraenoic Acid Binding (GO:0050543)                                 | 0.013908         | S100A9;S100A8                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Transition Metal Ion Binding (GO:0046914)                                 | 0.013908         | WT1;LCN2;S100A12;ZFPM2;MMP8;S100A9;MMP9;S100A8;LTF                       |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Icosanoid Binding (GO:0050542)                                            | 0.013908         | S100A9;S100A8                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Defense Response To Bacterium (GO:0042742)                                | 0.014902         | LCN2;DEFA3;S100A12;DEFA1;S100A9;S100A8;LTF                               |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Innate Immune Response In Mucosa (GO:0002227)                             | 0.024204         | DEFA3;DEFA1;LTF                                                          |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Intracellular Organelle Lumen (GO:0070013)                                | 0.025433         | COL2A1;ADAMTSL4;DEFA3;VWA1;COL4A5;DEFA1;LAMC1;MMP8;MMP9;PRSS23;HS3ST1;LT |\n",
      "|                            |                                                                           |                  | F                                                                        |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Microtubule Binding (GO:0008017)                                          | 0.025681         | MX2;MX1;S100A9;KIF17;S100A8;DNM1                                         |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Toll-like Receptor Binding (GO:0035325)                                   | 0.025681         | S100A9;S100A8                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Molecular_Function_2023 | Zinc Ion Binding (GO:0008270)                                             | 0.027635         | WT1;S100A12;ZFPM2;MMP8;S100A9;MMP9;S100A8                                |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Response To Gonadotropin (GO:0034698)                                     | 0.02796          | WT1;SLC5A5                                                               |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Extracellular Matrix Organization (GO:0030198)                            | 0.02796          | COL2A1;ADAMTSL4;COL4A5;LAMC1;MMP8;MMP9                                   |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Wnt Signaling Pathway, Planar Cell Polarity Pathway (GO:0060071)          | 0.02796          | FZD2;PRICKLE2;CELSR1                                                     |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Mucosal Immune Response (GO:0002385)                                      | 0.02796          | DEFA3;DEFA1;LTF                                                          |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Cellular Response To Gonadotropin Stimulus (GO:0071371)                   | 0.02796          | WT1;SLC5A5                                                               |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Regulation Of Establishment Of Planar Polarity (GO:0090175)               | 0.02796          | FZD2;PRICKLE2;CELSR1                                                     |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Neutrophil Chemotaxis (GO:0030593)                                        | 0.02796          | S100A12;S100A9;S100A8;PF4                                                |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Granulocyte Chemotaxis (GO:0071621)                                       | 0.029256         | S100A12;S100A9;S100A8;PF4                                                |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Leukocyte Aggregation (GO:0070486)                                        | 0.029256         | S100A9;S100A8                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Neutrophil Migration (GO:1990266)                                         | 0.032144         | S100A12;S100A9;S100A8;PF4                                                |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Sarcolemma (GO:0042383)                                                   | 0.034763         | SYNM;SSPN;ALOX12                                                         |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Tertiary Granule Lumen (GO:1904724)                                       | 0.034763         | MMP8;MMP9;LTF                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Endoplasmic Reticulum Lumen (GO:0005788)                                  | 0.038229         | COL2A1;ADAMTSL4;VWA1;COL4A5;LAMC1;PRSS23                                 |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Cellular_Component_2023 | Specific Granule Lumen (GO:0035580)                                       | 0.038917         | LCN2;MMP8;LTF                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Peptidyl-Cysteine Modification (GO:0018198)                               | 0.043598         | S100A9;S100A8                                                            |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Regulation Of Cysteine-Type Endopeptidase Activity Involved In Apoptotic  | 0.047484         | FAS;MMP9                                                                 |\n",
      "|                            | Signaling Pathway (GO:2001267)                                            |                  |                                                                          |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Positive Regulation Of Macrophage Differentiation (GO:0045651)            | 0.047484         | PRKCA;PF4                                                                |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Non-Canonical Wnt Signaling Pathway (GO:0035567)                          | 0.047484         | FZD2;PRICKLE2;CELSR1                                                     |\n",
      "|----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------|\n",
      "| GO_Biological_Process_2023 | Response To Lipopolysaccharide (GO:0032496)                               | 0.04898          | DEFA3;DEFA1;S100A9;S100A8;PF4                                            |\n",
      "+----------------------------+---------------------------------------------------------------------------+------------------+--------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "with pl.Config(tbl_rows=-1, tbl_cols=-1, tbl_width_chars=200, fmt_str_lengths=200, set_tbl_formatting='ASCII_FULL'):\n",
    "    print(significant_results)"
   ]
  }
 ],
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