jupyter-notebooks/Supplemental_PAO1_FastICA_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Example: applying the PathCORE-T software to a FastICA model of the normalized _P. aeruginosa_ gene compendium."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example is run after the normalized _P. aeruginosa_ compendium has been downloaded. You can download the compendium from\n",
    "the Zenodo record [here](https://zenodo.org/record/583694) or use the [shell script in the `../data` directory](https://github.com/greenelab/PathCORE-T-analysis/blob/master/data/download_data.sh) (keep in mind this \n",
    "script will download more than just the compendium). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "\n",
    "from functools import partial\n",
    "import os\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pathcore import CoNetwork\n",
    "from pathcore import aggregate_permuted_network, \\\n",
    "        network_edges_permutation_test\n",
    "from pathcore import pathway_enrichment_with_overlap_correction\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import FastICA, PCA\n",
    "\n",
    "\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specify data file paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = os.path.join(\"..\", \"data\")\n",
    "pao1_data_dir = os.path.join(data_dir, \"pao1_data\")\n",
    "gene_compendium = os.path.join(\n",
    "    pao1_data_dir, \"all-pseudomonas-gene-normalized.pcl\")\n",
    "pathways = os.path.join(pao1_data_dir, \"pseudomonas_KEGG_terms.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_features = 300\n",
    "\n",
    "gene_signature_std = 2.5\n",
    "\n",
    "n_permutations = 1000\n",
    "permutation_test_alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>05_PA14000-4-2_5-10-07_S2.CEL</th>\n",
       "      <th>120330-10978D_22_(Pae_G1a).CEL</th>\n",
       "      <th>120330-10978D_23_(Pae_G1a).CEL</th>\n",
       "      <th>120330-10978D_24_(Pae_G1a).CEL</th>\n",
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       "      <th>120330-10978D_M3_(Pae_G1a).CEL</th>\n",
       "      <th>120330-10978D_M4_(Pae_G1a).CEL</th>\n",
       "      <th>54375-4-05.CEL</th>\n",
       "      <th>AKGlu_plus_nt_7-8-09_s1.CEL</th>\n",
       "      <th>anaerobic_NO3_1.CEL</th>\n",
       "      <th>...</th>\n",
       "      <th>Van_Delden_Kohler_0311_BAL6_2.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_BAL6_3.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_BAL6+_1.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_BAL6+_2.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_BAL6+_3.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_PT5_1.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_PT5_2.CEL</th>\n",
       "      <th>Van_Delden_Kohler_0311_PT5_3.CEL</th>\n",
       "      <th>WT12935-18-05.CEL</th>\n",
       "      <th>WT12935-4-05.CEL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PA0001</th>\n",
       "      <td>0.858277</td>\n",
       "      <td>0.607559</td>\n",
       "      <td>0.663639</td>\n",
       "      <td>0.678894</td>\n",
       "      <td>0.756949</td>\n",
       "      <td>0.692534</td>\n",
       "      <td>0.659413</td>\n",
       "      <td>0.786106</td>\n",
       "      <td>0.795706</td>\n",
       "      <td>0.724939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.513387</td>\n",
       "      <td>0.397381</td>\n",
       "      <td>0.483213</td>\n",
       "      <td>0.410025</td>\n",
       "      <td>0.441816</td>\n",
       "      <td>0.391640</td>\n",
       "      <td>0.562041</td>\n",
       "      <td>0.538511</td>\n",
       "      <td>0.758123</td>\n",
       "      <td>0.771287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0002</th>\n",
       "      <td>0.726212</td>\n",
       "      <td>0.571911</td>\n",
       "      <td>0.591654</td>\n",
       "      <td>0.607486</td>\n",
       "      <td>0.723000</td>\n",
       "      <td>0.657796</td>\n",
       "      <td>0.762965</td>\n",
       "      <td>0.766958</td>\n",
       "      <td>0.732701</td>\n",
       "      <td>0.586621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.648814</td>\n",
       "      <td>0.636713</td>\n",
       "      <td>0.667586</td>\n",
       "      <td>0.651159</td>\n",
       "      <td>0.659428</td>\n",
       "      <td>0.681952</td>\n",
       "      <td>0.732554</td>\n",
       "      <td>0.751807</td>\n",
       "      <td>0.712018</td>\n",
       "      <td>0.756116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0003</th>\n",
       "      <td>0.642580</td>\n",
       "      <td>0.352962</td>\n",
       "      <td>0.356011</td>\n",
       "      <td>0.409253</td>\n",
       "      <td>0.423757</td>\n",
       "      <td>0.407996</td>\n",
       "      <td>0.388565</td>\n",
       "      <td>0.615313</td>\n",
       "      <td>0.722675</td>\n",
       "      <td>0.391616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466754</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.427094</td>\n",
       "      <td>0.412956</td>\n",
       "      <td>0.397766</td>\n",
       "      <td>0.500354</td>\n",
       "      <td>0.468660</td>\n",
       "      <td>0.488663</td>\n",
       "      <td>0.528437</td>\n",
       "      <td>0.577933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0004</th>\n",
       "      <td>0.810364</td>\n",
       "      <td>0.366267</td>\n",
       "      <td>0.454157</td>\n",
       "      <td>0.382114</td>\n",
       "      <td>0.511829</td>\n",
       "      <td>0.512675</td>\n",
       "      <td>0.594287</td>\n",
       "      <td>0.911489</td>\n",
       "      <td>0.715682</td>\n",
       "      <td>0.197238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.530828</td>\n",
       "      <td>0.552241</td>\n",
       "      <td>0.697902</td>\n",
       "      <td>0.648859</td>\n",
       "      <td>0.637043</td>\n",
       "      <td>0.613609</td>\n",
       "      <td>0.621583</td>\n",
       "      <td>0.678549</td>\n",
       "      <td>0.805659</td>\n",
       "      <td>0.948845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0005</th>\n",
       "      <td>0.716328</td>\n",
       "      <td>0.531158</td>\n",
       "      <td>0.475474</td>\n",
       "      <td>0.414180</td>\n",
       "      <td>0.475173</td>\n",
       "      <td>0.507516</td>\n",
       "      <td>0.430163</td>\n",
       "      <td>0.426478</td>\n",
       "      <td>0.541767</td>\n",
       "      <td>0.305761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396448</td>\n",
       "      <td>0.260365</td>\n",
       "      <td>0.261694</td>\n",
       "      <td>0.262412</td>\n",
       "      <td>0.241445</td>\n",
       "      <td>0.350347</td>\n",
       "      <td>0.359055</td>\n",
       "      <td>0.509703</td>\n",
       "      <td>0.480827</td>\n",
       "      <td>0.438330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1051 columns</p>\n",
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      "text/plain": [
       "             05_PA14000-4-2_5-10-07_S2.CEL  120330-10978D_22_(Pae_G1a).CEL  \\\n",
       "Gene_symbol                                                                  \n",
       "PA0001                            0.858277                        0.607559   \n",
       "PA0002                            0.726212                        0.571911   \n",
       "PA0003                            0.642580                        0.352962   \n",
       "PA0004                            0.810364                        0.366267   \n",
       "PA0005                            0.716328                        0.531158   \n",
       "\n",
       "             120330-10978D_23_(Pae_G1a).CEL  120330-10978D_24_(Pae_G1a).CEL  \\\n",
       "Gene_symbol                                                                   \n",
       "PA0001                             0.663639                        0.678894   \n",
       "PA0002                             0.591654                        0.607486   \n",
       "PA0003                             0.356011                        0.409253   \n",
       "PA0004                             0.454157                        0.382114   \n",
       "PA0005                             0.475474                        0.414180   \n",
       "\n",
       "             120330-10978D_M1_(Pae_G1a).CEL  120330-10978D_M3_(Pae_G1a).CEL  \\\n",
       "Gene_symbol                                                                   \n",
       "PA0001                             0.756949                        0.692534   \n",
       "PA0002                             0.723000                        0.657796   \n",
       "PA0003                             0.423757                        0.407996   \n",
       "PA0004                             0.511829                        0.512675   \n",
       "PA0005                             0.475173                        0.507516   \n",
       "\n",
       "             120330-10978D_M4_(Pae_G1a).CEL  54375-4-05.CEL  \\\n",
       "Gene_symbol                                                   \n",
       "PA0001                             0.659413        0.786106   \n",
       "PA0002                             0.762965        0.766958   \n",
       "PA0003                             0.388565        0.615313   \n",
       "PA0004                             0.594287        0.911489   \n",
       "PA0005                             0.430163        0.426478   \n",
       "\n",
       "             AKGlu_plus_nt_7-8-09_s1.CEL  anaerobic_NO3_1.CEL  \\\n",
       "Gene_symbol                                                     \n",
       "PA0001                          0.795706             0.724939   \n",
       "PA0002                          0.732701             0.586621   \n",
       "PA0003                          0.722675             0.391616   \n",
       "PA0004                          0.715682             0.197238   \n",
       "PA0005                          0.541767             0.305761   \n",
       "\n",
       "                   ...         Van_Delden_Kohler_0311_BAL6_2.CEL  \\\n",
       "Gene_symbol        ...                                             \n",
       "PA0001             ...                                  0.513387   \n",
       "PA0002             ...                                  0.648814   \n",
       "PA0003             ...                                  0.466754   \n",
       "PA0004             ...                                  0.530828   \n",
       "PA0005             ...                                  0.396448   \n",
       "\n",
       "             Van_Delden_Kohler_0311_BAL6_3.CEL  \\\n",
       "Gene_symbol                                      \n",
       "PA0001                                0.397381   \n",
       "PA0002                                0.636713   \n",
       "PA0003                                0.390528   \n",
       "PA0004                                0.552241   \n",
       "PA0005                                0.260365   \n",
       "\n",
       "             Van_Delden_Kohler_0311_BAL6+_1.CEL  \\\n",
       "Gene_symbol                                       \n",
       "PA0001                                 0.483213   \n",
       "PA0002                                 0.667586   \n",
       "PA0003                                 0.427094   \n",
       "PA0004                                 0.697902   \n",
       "PA0005                                 0.261694   \n",
       "\n",
       "             Van_Delden_Kohler_0311_BAL6+_2.CEL  \\\n",
       "Gene_symbol                                       \n",
       "PA0001                                 0.410025   \n",
       "PA0002                                 0.651159   \n",
       "PA0003                                 0.412956   \n",
       "PA0004                                 0.648859   \n",
       "PA0005                                 0.262412   \n",
       "\n",
       "             Van_Delden_Kohler_0311_BAL6+_3.CEL  \\\n",
       "Gene_symbol                                       \n",
       "PA0001                                 0.441816   \n",
       "PA0002                                 0.659428   \n",
       "PA0003                                 0.397766   \n",
       "PA0004                                 0.637043   \n",
       "PA0005                                 0.241445   \n",
       "\n",
       "             Van_Delden_Kohler_0311_PT5_1.CEL  \\\n",
       "Gene_symbol                                     \n",
       "PA0001                               0.391640   \n",
       "PA0002                               0.681952   \n",
       "PA0003                               0.500354   \n",
       "PA0004                               0.613609   \n",
       "PA0005                               0.350347   \n",
       "\n",
       "             Van_Delden_Kohler_0311_PT5_2.CEL  \\\n",
       "Gene_symbol                                     \n",
       "PA0001                               0.562041   \n",
       "PA0002                               0.732554   \n",
       "PA0003                               0.468660   \n",
       "PA0004                               0.621583   \n",
       "PA0005                               0.359055   \n",
       "\n",
       "             Van_Delden_Kohler_0311_PT5_3.CEL  WT12935-18-05.CEL  \\\n",
       "Gene_symbol                                                        \n",
       "PA0001                               0.538511           0.758123   \n",
       "PA0002                               0.751807           0.712018   \n",
       "PA0003                               0.488663           0.528437   \n",
       "PA0004                               0.678549           0.805659   \n",
       "PA0005                               0.509703           0.480827   \n",
       "\n",
       "             WT12935-4-05.CEL  \n",
       "Gene_symbol                    \n",
       "PA0001               0.771287  \n",
       "PA0002               0.756116  \n",
       "PA0003               0.577933  \n",
       "PA0004               0.948845  \n",
       "PA0005               0.438330  \n",
       "\n",
       "[5 rows x 1051 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compendium_df = pd.read_table(gene_compendium)\n",
    "compendium_df.set_index(\"Gene_symbol\", inplace=True)\n",
    "compendium_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.47 s, sys: 173 ms, total: 8.65 s\n",
      "Wall time: 4.62 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Compute ICA\n",
    "ica = FastICA(n_components=n_features, whiten=True, random_state=42)\n",
    "_ = ica.fit_transform(compendium_df.T)  # Reconstruct signals\n",
    "weight_matrix = ica.mixing_  # Get estimated mixing matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>Gene_symbol</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PA0001</th>\n",
       "      <td>-0.022617</td>\n",
       "      <td>-0.354677</td>\n",
       "      <td>0.422531</td>\n",
       "      <td>-0.832499</td>\n",
       "      <td>-0.155436</td>\n",
       "      <td>-0.009683</td>\n",
       "      <td>-0.034776</td>\n",
       "      <td>0.416779</td>\n",
       "      <td>0.764146</td>\n",
       "      <td>-0.531564</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012300</td>\n",
       "      <td>0.291144</td>\n",
       "      <td>-0.299322</td>\n",
       "      <td>-0.062180</td>\n",
       "      <td>0.078580</td>\n",
       "      <td>-0.112295</td>\n",
       "      <td>-0.990067</td>\n",
       "      <td>-0.323175</td>\n",
       "      <td>-0.180216</td>\n",
       "      <td>-0.025341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0002</th>\n",
       "      <td>0.135186</td>\n",
       "      <td>-0.226415</td>\n",
       "      <td>0.368578</td>\n",
       "      <td>-0.526275</td>\n",
       "      <td>-0.308264</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>-0.112484</td>\n",
       "      <td>0.301713</td>\n",
       "      <td>0.695212</td>\n",
       "      <td>-0.564204</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035438</td>\n",
       "      <td>0.466807</td>\n",
       "      <td>0.026008</td>\n",
       "      <td>-0.056073</td>\n",
       "      <td>-0.020810</td>\n",
       "      <td>0.005765</td>\n",
       "      <td>-0.429283</td>\n",
       "      <td>0.148320</td>\n",
       "      <td>-0.136729</td>\n",
       "      <td>-0.441995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0003</th>\n",
       "      <td>-0.055635</td>\n",
       "      <td>-0.113121</td>\n",
       "      <td>0.557643</td>\n",
       "      <td>-0.484803</td>\n",
       "      <td>0.209620</td>\n",
       "      <td>0.032549</td>\n",
       "      <td>-0.035405</td>\n",
       "      <td>0.298571</td>\n",
       "      <td>0.413186</td>\n",
       "      <td>-0.113402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108401</td>\n",
       "      <td>0.051195</td>\n",
       "      <td>0.024856</td>\n",
       "      <td>-0.189388</td>\n",
       "      <td>-0.011452</td>\n",
       "      <td>-0.024755</td>\n",
       "      <td>-0.559822</td>\n",
       "      <td>-0.097349</td>\n",
       "      <td>-0.319874</td>\n",
       "      <td>-0.117244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0004</th>\n",
       "      <td>0.059451</td>\n",
       "      <td>-0.450329</td>\n",
       "      <td>0.465481</td>\n",
       "      <td>-0.756150</td>\n",
       "      <td>-0.191004</td>\n",
       "      <td>0.478480</td>\n",
       "      <td>0.099132</td>\n",
       "      <td>0.333053</td>\n",
       "      <td>0.077110</td>\n",
       "      <td>-0.365062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171980</td>\n",
       "      <td>0.369267</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>-0.266537</td>\n",
       "      <td>-0.134995</td>\n",
       "      <td>0.166958</td>\n",
       "      <td>-0.671536</td>\n",
       "      <td>0.153835</td>\n",
       "      <td>-0.236358</td>\n",
       "      <td>-0.250676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA0005</th>\n",
       "      <td>-0.201599</td>\n",
       "      <td>-0.067056</td>\n",
       "      <td>0.238852</td>\n",
       "      <td>-0.422357</td>\n",
       "      <td>0.132034</td>\n",
       "      <td>-0.401187</td>\n",
       "      <td>-0.049908</td>\n",
       "      <td>0.052483</td>\n",
       "      <td>0.093296</td>\n",
       "      <td>-0.031448</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102319</td>\n",
       "      <td>-0.029179</td>\n",
       "      <td>-0.086267</td>\n",
       "      <td>-0.263259</td>\n",
       "      <td>-0.267474</td>\n",
       "      <td>0.322360</td>\n",
       "      <td>-0.791905</td>\n",
       "      <td>-0.293258</td>\n",
       "      <td>-0.122822</td>\n",
       "      <td>-0.009253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3         4         5    \\\n",
       "Gene_symbol                                                               \n",
       "PA0001      -0.022617 -0.354677  0.422531 -0.832499 -0.155436 -0.009683   \n",
       "PA0002       0.135186 -0.226415  0.368578 -0.526275 -0.308264 -0.000198   \n",
       "PA0003      -0.055635 -0.113121  0.557643 -0.484803  0.209620  0.032549   \n",
       "PA0004       0.059451 -0.450329  0.465481 -0.756150 -0.191004  0.478480   \n",
       "PA0005      -0.201599 -0.067056  0.238852 -0.422357  0.132034 -0.401187   \n",
       "\n",
       "                  6         7         8         9      ...          290  \\\n",
       "Gene_symbol                                            ...                \n",
       "PA0001      -0.034776  0.416779  0.764146 -0.531564    ...    -0.012300   \n",
       "PA0002      -0.112484  0.301713  0.695212 -0.564204    ...    -0.035438   \n",
       "PA0003      -0.035405  0.298571  0.413186 -0.113402    ...     0.108401   \n",
       "PA0004       0.099132  0.333053  0.077110 -0.365062    ...     0.171980   \n",
       "PA0005      -0.049908  0.052483  0.093296 -0.031448    ...    -0.102319   \n",
       "\n",
       "                  291       292       293       294       295       296  \\\n",
       "Gene_symbol                                                               \n",
       "PA0001       0.291144 -0.299322 -0.062180  0.078580 -0.112295 -0.990067   \n",
       "PA0002       0.466807  0.026008 -0.056073 -0.020810  0.005765 -0.429283   \n",
       "PA0003       0.051195  0.024856 -0.189388 -0.011452 -0.024755 -0.559822   \n",
       "PA0004       0.369267  0.005031 -0.266537 -0.134995  0.166958 -0.671536   \n",
       "PA0005      -0.029179 -0.086267 -0.263259 -0.267474  0.322360 -0.791905   \n",
       "\n",
       "                  297       298       299  \n",
       "Gene_symbol                                \n",
       "PA0001      -0.323175 -0.180216 -0.025341  \n",
       "PA0002       0.148320 -0.136729 -0.441995  \n",
       "PA0003      -0.097349 -0.319874 -0.117244  \n",
       "PA0004       0.153835 -0.236358 -0.250676  \n",
       "PA0005      -0.293258 -0.122822 -0.009253  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_matrix_df = pd.DataFrame(\n",
    "    weight_matrix, index=compendium_df.index)\n",
    "weight_matrix_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine our gene signature rule(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean centered at 0: -0.00048455555045104256\n",
      "Mean standard deviation of each feature: 0.2826567791664206\n"
     ]
    }
   ],
   "source": [
    "col_averages = np.mean(weight_matrix, axis=0)\n",
    "print(\"Mean centered at 0: {0}\".format(np.mean(col_averages)))\n",
    "print(\"Mean standard deviation of each feature: {0}\".format(\n",
    "    np.mean(np.std(weight_matrix, axis=0))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to define an appropriate gene signature rule for ICA features, we want to visualize their weight distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NmeahHX//9iKNKF1m6mBApDmgaZR9G0e56KqTwImoE092c3SF2/08Ja88Po0Q\nQrxuTcP7q1/9KrVajaeffhrDMNi0aRNf+tKXxqI2IcQrVKo+OnF4KwAj3ozFNz3QYM5Wl7od98Yr\nYYKE5qKb8XrvfEJHKXB0QFNsLG4elzYIIV6/puH99NNP89nPfhbTNEkmk6xatYpnn312LGoTQrxC\nueZjEA+bl8wUthn3pn0rXiI2Z5tHzYxPGavikNB9DLsR3lkTL9BJmCEq0tlY2iwHlAjRopqGt6Zp\neJ6HpsXf8IeGhkZ+FkKMrUq9Ed6hy6CVwzbiYfTArmKHGr1DAXXbAqCmpXBMj46d8e/5rIHv6zhm\nSJjvoeiV2VrePl5NEUK8Dk3D+5xzzuEf//Ef6evr42tf+xrLly/nE5/4xFjUJoR4hUo9wCAiGboM\n7RHevuEzuaqjAUY63m2tZqbRC9C5qfHFO2sQ+BqOGRAWJgHwQn7duLRDCPH6NJ1tvmzZMo477jjW\nrFlDGIbceOONHHPMMWNRmxDiFap1H9uId1cbsHPYZhzenlJMKcchbWVMqEDNzmA/W8PyLIg0BnMW\nga+RM0OiWhJArnsL0aKa9ryDIGDLli2k02lyuRzPPfcc995771jUJoR4hZobYGhxz7vP7hzpeXsK\npvXHO6+ZGRM08GoJ3IJNfVIS3XUopXVCT6FrYEcGujLZUNg0vg0SQhySpj3vz33uc2zbto25c+fu\nda172bJlo1qYEOLVal6Iru2+5j3X6AfA8w0yecVfpp5M3845HK0H+Ns2A91UpqawfBs3VycqNk4W\nsyPMsIP++gBlr0LGTo9jq4QQB6tpeD///PP853/+p0xSE+IwUHdDFBp25FE2UzhOHQCtlKSfyfRl\n5gDQGZn4O1NEWkS9x8He7uACQWOPhoQRgp8Dc4CNpc28uUcuhQnRSpoOm8+dO5e+vr6xqEUI0YTr\nh0QKAs1AaTpOIg5vu5RklzUHK6ixdNH/R2jXsIIEtUwBZeo4Wrz2O1DxMLtjRuhuDoBNxa3j0xgh\nxCFr2vOu1+u8973vHdkeddhtt902qoUJIV4tDm+Nmh6HsWPHW55mSl1EusXswlM4yRA9l4f+JKYZ\n328ZWQA8FQI6SR18N94X/amBZ8jaaf52xklj3yAhxCFpGt7nnXfeIT2x7/tcfvnlbN26Fc/zOP/8\n8znllFNG7n/ooYf47ne/i2maLF++nDPOOOOQXkeIicTzQ6JIUTZTANhWfLSnUesgArr9bUAWGw0f\n0OsplApZPCE3AAAgAElEQVTAinvZrhYPm2c0yNeT6JpO0SuNQ0uEEK9H02HzBQsWYBgG69at44QT\nTkDTNBYsWND0ie+77z46Ozv5yU9+wve//32uvvrqkft832flypXccsst3H777dx555309/e/vpYI\nMQH4QYQZ+uTtLKCwzQClwPc7SHl5NKrxH5ZzgMKsZcCrEdldANQas9NTOoSBTtbKUHTLstOaEC2m\naXj/8Ic/5Jvf/Ca33norlUqFK6+8kh/84AdNn/i9730vn/nMZwBQSmEYxsh969atY9asWXR0dGDb\nNieeeCJr1659Hc0QYmLwfBcrdBmysmhOFVuLDyUBg57qViqGRrlu4tdTaJpCQyORd/GdbgCqRmO2\nuRER+JCzswQqoBbUx7FVQoiD1XTY/Be/+AV33XUXZ5xxBl1dXdxzzz185CMf4dxzz33Nx6XT8dKT\ncrnMpz/9aS666KKR+8rlMtlsdq+/LZdf+4Sjrq4Upmm85t/sT29vtvkftamJ3HZov/ZrkcIeDu9k\nGVvTCIL4c9FZ20W+QyPqi4M6VAodSAz5FKZMQqsYlBubuthmSOjCpGwnm8tbCUy3rd6rdmrLoZjI\n7Z8obW8a3rqu7zVRzXGcvXrRr2X79u1ccMEFnHXWWbz//e8fuT2TyVCpVEZ+r1Qqe4X5vgwNVQ/o\nNV+ptzdLX9/EvKY3kdsO7dn+pOFihR4Fpxcz0Y+lQRjEH+OO+i42TbfxBuLwnjLnZXZtPBKnED/W\n9EyKybjnbZohtq+RIN5pbWd+oG3eq3b8dz8YE7n97dj2/X0ZOaBr3qtWraJWq/Hggw9y/vnnc9JJ\nzWel9vf388lPfpIvfOELfPjDH97rvrlz57Jx40by+Tye5/HYY4/xtre97QCbIsTElTJCdKVA09ES\nFWxNIwoMbL2KE9YI9Q4q5caGK5156skSdgWIFLarUW58XbfNACfSyFrxjPOinO0tREtp2vP+4he/\nyF133cX8+fO59957Wbx4MR/96EebPvH3vvc9isUiN9xwAzfccAMAH/nIR6jVaqxYsYJLL72Uc889\nF6UUy5cvZ8qUKa+/NUK0mT9sfXTk57+dcRK2FkFjcpmeLGFrGqXQJKXF3etQ5Xh69hMElssOQ+Gk\niiSrHZjVAMvTqDQea5shKTQSWuPyll9BCNE6mob3jh07WLRoEYsWLQLiI0KLxSLd3d2v+bgrrriC\nK664Yr/3L1myhCVLlhxkuUJMXEEYYRsRNT0eMHOSVcAhCAySURzeuyYPUs0EmKFJvxHQnS7S1Q9W\nycciyaCKz/22zYAkoAILW7ckvIVoMU3D+4ILLuCFF15g/vz5KKV48cUX6e3txTAMrr76ahYuXDgW\ndQox4cWHkigCzQTDx7ECwCEMDdLeEACFzjpdu97EX4dd/L73OWqpApGp4WctatOWY9Qewg93krQD\nHBT1miJtpSl6RZRSsg2yEC2i6TXvKVOmcOedd/Lzn/+cX/ziF/zsZz/juOOO4/bbb+ff//3fx6JG\nIQSNQ0mUhqtbaHYdq5GzQWiQcQcBqDo6k7ceTUe6TqKeop4qMXhsJ0HGAiDhnEQpsEmYAREa1SFF\nxkoTqkg2axGihTQN761bt3LccceN/D5//nw2bdrEtGnTCMNwVIsTQuxWqwcoNKpGAsOOl4kBBIFG\nol7G18F0u7H8BJlMBU8PUZpBbVICs+LR0fcSup7iSY4laQWEQL0Qkbbi3doG6oPj2DohxMFoOmw+\nc+ZMrr32Wj74wQ8SRRG/+tWvmD17Nn/605/Q9abZL4R4g9RcnyjUqJhJDGcnThQv2QwjxVp7Pn+a\nMx/tBYXt5rEsFy/wcPQ3ga6T2lUn6Q+Rn+SzWTuCk60/E6LwS2rkOND+2iBHdswZxxYKIQ5U0/T9\n+te/ThiGfO5zn+PSSy9FKcU111zD5s2bueqqq8aiRiEmtJfyL/NS/mX6K0XKShFqBppTJ+vFobsu\n38mjueNxdYd6lGBrWEY9tJXQqGNZcwBI9tXxSBIEW6noOQoqg24FhBXIWPHzDNSk5y1Eq2ja885k\nMlxyySWvuv0DH/jAqBQkhHi1SCl+1fdTtPDNACjHJeUniVSJJwc7MaKQf9jyK/6r9yQ2pKaT32zw\n109VeOZvpmEELlbJp9zpE3ibsaw5bFbT0E0fVbdHwrtfwluIliHj3kK0gIH6IKVoCC2Mr3NrTo2U\nn2BdfxfV0OS40np6/AJZqwOAxzqPZe72JJpukfVLaABegFbZDsBGNR3LCtBcSJrJkdcQQrSG/YZ3\ntXpo25EKId5YSim2V3agKQ0VxpusaLZLKrR4fEu8udHbis/Tn82QMpOYhsazuTnku6cCMNgXP8aq\nW9jVCrbKs131YlgBmoKoppM0EgzV8+PTQCHEQdtveJ999tkAfOUrXxmrWoQQ+1ANarihR87tJgg1\n0CI008OMTF7q76JbFZnqDrKjewoaGmlLJ1QG67pnAZDfGRLYAU4tgxMGONEuAixUY/mYW4hIWUny\nboFIRePZVCHEAdrvNe9qtcrnP/95Hn74YVzXfdX9K1euHNXChBCx4d3Psm6GXUpDs13sepoBzyKI\nDI52twJQd3ohhI56QAGNTYleLKWYVtxOkOskUUyioaNHfWDMI0g7mIBbVKSySQbqQ5T9Cjl7YpzK\nJEQr229433LLLaxZs4bHH3+cBQsWjGVNQog9lBqHhnR6SbYqHcOokKhl2V6Nr1XPrcXhrdWyRLai\nG51NKEpVmzfl+5kbbuVFazIJAJWBcBdY4KZTmIT0D/qkuuO13kP1vIS3EC1gv+E9bdo0li1bxjHH\nHMPcuXN5+eWXCcOQo48+GtNsOkldCPEGUEpR9iuYmklWGbiaiWVVcWoZtlWS2EbAtOpOAFyyaICF\nxjwtYkPBp7O/wKyhLTw59SQ6cVEqixftwMSnlsiQpkBUgVRj0tqQW2A2M8exxUKIA9E0hX3f5z3v\neQ+dnZ1EUUR/fz/f/e53eetb3zoW9QkxoVWCKn7kk6aLUA8INRPHcrHKOfKuw5E9Q3ibFb6VxbV2\n95g7lMExKKKKT2d+AFPVAB2nlqKqIqZqg2yzJhMYBbQyZKxGeMukNSFaQtPw/trXvsb1118/EtZP\nPPEEV199Nffcc8+oFyfERDdYjw8cUdVOXrKGgDehbI+wmAPgiFwRX4ONkxaApqGny6hKmsGUTncV\nVL6LUDOZXt6GzxEcsd3hySMVvQyyTZuCl7Uwix5JY7jnLeEtRCtous67Wq3u1cs+4YQT9jmBTQjx\nxhu+3l0r2vQNzwS3Aup+AoBZ6TyekaY/dUT8+4wdBEZAdVqGnSi00OSFSe9gzuBLaISoKENdQQ/x\nl4IgY0Kk4QRxeOfrhTFuoRDiUDQN746ODh588MGR3x988EE6OztHtSghRKzYCG9fq+Ek5pGcmcHG\nJJ5/rpjhFMjbc6FxSMmRU/sIbQ+9J8FmFL4esq1jPpl8nbSbp2blUBFkVBzephN/ER/a6KOhsbG4\neRxaKYQ4WE2Hza+++mq+8IUv8KUvfQmIDyr5xje+MeqFCSGg3Ajv7MyjsZ0jiTpDeMliAOhJ1dC1\nkMHkbDQVEhgKT2mYvkOYtjCTBi/XfeYBL3edQMorUnZ6sLwEFkV0FRGlDSCKN2pJJagGtfFsrhDi\nADUN7zlz5nD33XdTrVaJoohMJjMWdQkhgKJfxjGOw3aOJCyXKW9y6fZShMDUXIVC3aHidJF2B9nV\nA888N5cwkQBdI2EbFGohtlMmz1SmlF4CwK6n8FSNTq1AobOTXLSNsKaRslIM1AaJVISuyc7JQhzO\nDvgTmkqlJLiFGEMVv4oXelj2MSgV4j63ndqOMtTiWeXTsmX6Ct0AJP0SoeEz1NeL25h0nmx8uj0j\nHhqvOF0A2F6aqlL0aHlCyyalD8XhbSZRKApucWwbKoQ4aPL1WojD0B93Ffj91g3oeg+G2UUQbMKv\nGNh6maAeb6gyNVchX4oD2Yw8LC+edFbpDgDIBvF18C3VLCkvT8VqhHc9Ta0R3gCGUyeqa3ut9RZC\nHN6ahvdPf/rTsahDCPEKRXcQ25of/+JuwAsNbLOGG8VXu6ZmK5QrXVhBjUgzSNQy1JJF/IwBQLYe\nAIoiJpNLG1CNoXCnEd7dxCEdJTXCKqSt3busCSEOb03D+8c//vFY1CGEeIWiN4BlziJSddL1QUKl\nk9QVNSCBjxHpBH6CjnofnplCQyffu4UokYbQw/QVCUNRBQLTaDxrhF1PUYsUnVo8PF7PpbFcl4SK\nl5/JWm8hDn9NJ6xNnTqVc845h7e+9a04jjNy+4UXXjiqhQkx0RW8Orqexfc30BHGwWpqOi4wXasy\nlI83aumo72J77ihAke/ZSdrIEPl9aEB3VGUbGV7OzKW73k/J6RkJ7wxVtCik2NHDEe4m8HsAWest\nRCto2vM+4YQTWLBgwV7BLYQYfZUg/m4dhDvINDZR0aM4xHu1AoNDcXiHaXCNJNlcGVPrQdM0gqgA\nKGZU+gHIG0ky4UC8CxsGnm+ha4qkV6XQ0U3WHSB6OQ5t6XkLcfhr2vO+8MILqVarbNq0iXnz5lGv\n10mlUmNRmxATmqfSaEDg78L241nlKrABmGLmGcofBbpi5+wjCHfaJDJDpOuzCAEVFgitFEcVNrA2\nNYcKCmWokecO6knAIxVU6M9MJUGZQm02epfGkPS8hTjsNe15P/LII3zwgx/kU5/6FP39/SxZsoQ/\n/OEPY1GbEBOWUoqIbpTyieounmcBEDbCe5Jdo1hKo7KwfeaRoGlEWRtDi0M+CofQLJ9ZlS3oKqSm\nRXgqhR7FM9HDWvwFPB3Fm8BESUW1CEks6XkL0QKahvd1113HT37yE3K5HJMnT+ZHP/oRX//618ei\nNiEmrLJfxTC6CMNd6GGScs3BBNzGR9YxDUDDNmuUOrtQSahGDpETh3JAH7lgAJOIbr1MVWnk7cnk\n3HgYPSrEi8FzlACoZbNYJY8kFkWvRNAIeSHE4alpeEdRRG9v78jvRx111KgWJISAvlocqmHUT0JP\nUKwnSAIuYKqQuh5f785V++IHzIBKv0aYcoiiKpFWoaMe71PebflEaJQMh2QQD4mbpS5Cpeg24hnn\nhc5u0uUSicaVtKJXGrvGCiEOWtPwnjp1KqtXr0bTNIrFIjfeeCPTp08fi9qEmHD+uKvAH3cV2FSO\njx4Jw0HStk3NN3FQ1IGOoEoxiDdcmbpzHQB+j41fNVBOgiiKh717Bl8i1HQCPz5IqGootMbJZE49\nRUUpeqw4zIsdPaTcMo6Kh+cLroS3EIezpuH91a9+lfvvv5/t27ezdOlSnn32Wb761a8e0JP/+c9/\n5uyzz37V7bfeeiunn346Z599NmeffTbr168/+MqFaGP1MJ5cFkaDdDo6tdDAJkQBXX6JkpvFNH0m\n79qEXauTT+U45i07G48ZwvYiJuVdik4vjop7077t4+kpUApdWVQiRZddRQtCytkO0u4Qqb54K9Wi\nJ1ukCnE4azrbvKenh+uuu45yuYxpmiQSiQN64v/4j//gvvvuI5lMvuq+p556ilWrVnHccccdfMVC\nTAC+MlAqIvRLpLQe3Egnp4WgoMctUHOPoKtzCB3I9hcYmDmFemcSFGjRIDP6TTRgINNNQvfRIoOq\niigkJmNGLoGRoOoZWCmFVQ4oZztJe0NY3kygJPubC3GYa9rzfv755/nQhz7EKaecwuLFiznzzDPZ\ntGlT0yeeNWsW3/nOd/Z539NPP83NN9/MmWeeyU033XTwVQvRxpRShKox/O07mIUsXqSjGh/XDuUD\nkE7HAZsZiP93fW0mAG8zKxzRF++otmWyjRk5pE2fobpD2U7iBFUAio114o5bx7cdTM3HrsTf5wty\nzVuIw1rTnve//Mu/cNFFF7F48WIAHnjgAS6//HJ+9KMfvebj3vOe97Bly5Z93nf66adz1llnkclk\nuPDCC1m9ejXvete7XvP5urpSmCNbPB6c3t7sIT2uHUzktkPrtT9bcym5PmgGYTSIFqbwGjPDAxX/\n/9/S4+VimUQc2rnGJLQhOgBY4JTp31Um1GDnZMWkLZBIDVAuTmcoYzC5HA+N1wc7YUaRpFehRAeV\nbA6zHG/G5Gq1lnvv9tTKtb8RJnL7J0rbm4a367ojwQ3w7ne/m+9+97uH/IJKKT7xiU+QzcZv8OLF\ni3nmmWeahvfQUPWQXq+3N0tf38TsRUzktkNrtr9UqrOtGodrFA2iWynqJQuLeKY5QGSnAcjqcXi7\niUG0ICC0DbSogq1cuvsL7Og2Ce0BAFS2CMXplIxo5LXcQtzzTgfx5LhytoNsIX6VncWBlnvvhrXi\nv/sbaSK3vx3bvr8vI/sN723btgFwzDHHcPPNN/PhD38YwzC4//77efvb337IhZTLZd73vvfxm9/8\nhlQqxZo1a1i+fPkhP58QrSr/379/1W1VI8WQ7oCVIozymE4n1ZqJQxzelgooO92Ypk8yrKKAFzoK\n2EUXrzuNHxQpbKyQUNDX49DfHdL7ckRS0zDwqNZsKok4tL16/CUgreKNWsrZDjqGCjiGTVFmmwtx\nWNtveH/84x9H0zSUUqxZs4Y77rhj5D5N07jiiisO6oXuv/9+qtUqK1as4OKLL+acc87Btm0WLly4\nV89eiImurMXncEdRkVR4ND4aDooKMCmoUbc66MkNYdQCfCNBNeGSrii8blD4vDAQEs1Lsn2Og9Ij\nauk8iXoaMzOAW55GX28XXf0ufmgThhrZxkYt5WwnU/2t5OxOmbAmxGFuv+H90EMPve4nP+KII7jr\nrrsAeP/73z9y+7Jly1i2bNnrfn4h2okKApxtGyhPnga5ZBzelQ5cKpiAAiYFLljQkStjDPlUjAQJ\nN4UWDg+HG6w+1gTioTalYLB3E939s4l6tkB5GkMpk56oQmRYlCspskYc3qVsF0mvRMY8gv7aAGEU\nYuiHNs9ECDG6ml7zXr9+PXfddReFwt6HFaxcuXLUihJiool8n6Hf/oae/BDe2f9MFJUBRaKkqGgK\nQ8W98ZwKiICOjhL6dp+6naTbT9E/eTsmOXQtw6wNGhv8vyJMeJjT11Ho2U7nwEz03CAAFTdEJyTC\noljKkLYH0cKIUraTZFCGQYWyFCW/TKfTMX5vihBivw7oVLHTTjuN+fPnj0U9Qkw4SinKf3yUMD9E\n6ch5uKkcUbANjQSJkk+oR+hh3AO2NYM6sM4e4r/n2Mx2FZu7t5Jw4j0TDD3HS4PvgTDixJk7+N8N\nb8aa+xe2znoKTQfNdnEH9ZETxorFNNOn9WPUQirZHBqKzgEPpkLBLUp4C3GYahreuVyOCy+8cCxq\nEWJCcjdtpL5+HWbPJLb/7VIgvt6taymMckBIhE4c3oGdJjR8/ocCdBhseocCItJGfAKZr1mYnQ7/\nz6zNBN2TeUsx4qm+6dC7DbM4Gb9jgLBvOkNpk1wFioU0c+ZEmIWQeiaBZztk+92R8BZCHJ6ahveH\nPvQhrr/+ek466SRMc/efv+Md7xjVwoSYKGrPPgNA7m8X0WfG66xDVcRQWbRQIzTi690GEBgZaul+\n/k/CYuYjQ/z6LSkCS6MW1TFdBSmLjllpnuk6MX5QFsztFkptJ0xWMHI7CfumM2hb5CpQKmdwbIVZ\n84EE5UwHmcZysYJskSrEYatpeP/xj3/kL3/5C//7v/87cpumadx2222jWpgQE0GQH8Lv24U9fTpm\nLscOI96AJYoKOH4OhUKFJnUg5xTB7SRKFVmatHG3esx6U4L1XTp1dy0M/A2ppELrTHEsL3Ks9wIk\nFM/OfBN/2DENc9I20pNOwFsPRVcDFH5oEQQ2judSJhvvcV6Kl47J4SRCHL6ahvdTTz3Ff/3Xf41F\nLUJMOLUXXwAgcfR8AqCsxcPjUVTEqU8nAGw0ysDk3BD0dTItV0PTNKiHLEzbbA+7qLEJ/Nlo4VFo\nus789X9h8nwXvx7yf9JP8Jw7hzzb8NVOrFwPbsEjVGBoOsVimoxXYoBJlDKdzNjVD1i8MPQScOo4\nvTNCiNfSdG/zefPm8dxzz41FLUJMKCoMqa9fh55I4Bwxky26g9pjjbdTsTEcFxPQnAqJMB5Sn91Z\nRUUKfMV652+IWACAkXuB+c//GaXrPKGOwVUKZets9EP+dlKZyE0SRi9j98S9+wrxpLVSOU1HY4vV\nQkcPabcCSlEL6mP8jgghDlTTnvfmzZv50Ic+RG9vL5ZloZRC0zR+97vfjUV9QrQtv28XyvNIHHMs\nmq6z3ojDOYoqQECiaJLKFdD6ejB6tpMamIHCJ5euEbkRvmXwpH4UugrR9W4iaweTdtR5/s0nUjW6\nWO+GHJswmWToGN0D8OJUmP4yTm4TFboZMnVyIZTLKbqDfgBKuW6syCPlatQSEt5CHK6ahvfr2cdc\nCLF/3vbtANjTpgOw3kiAUkRREdBIlAzsWVWivm7sjj7sbfMIVJGEoaASUs50Un+hj+RfTcEOZ1HX\nB9mRi/cpL+Y6mfW7QaK/m0yPrvOQ6zJLc9gCONY6NGsSQ6FiNopSOc008wWIFOVcvDSspxDRn5bw\nFuJw1TS8165du8/bZ8yY8YYXI8RE4m3fBpqGNWUqBc1gULcAiFQRTVlYkUakhwTJEukovq9mVUjo\noNUjyk6GSlGRsQ3MUoa6UqybaaDVa7gdDqUdCaxNNew5SQIFb+6qstlzKJoF7E4bt6+OpivKlRTO\npDpGPaCWzgAwaUixZUqdSEXoWtOra0KIMdY0vNesWTPys+/7PP7447z97W+X7U2FeB3CSoVgoB9r\n8hR0y2Kj7ozcF0VFjDBFV1eB/noCr2cH2UrcI/YnDWJoGmE9pBymMNNxqE8aKpF1fTZP1ch6G9ET\nx7D1+OPofnINzElyQsJiq7aLaEsv2uQtGDkP+sADrNAgMm3MWojbk8A3LboKEQqDklemw8mNx1sk\nhHgNTcP7ldug5vN5Lr744lErSIiJoPpcvLZ7eMh8W2OJGEAYFbC8FJMnDbG+fxLhtPUkth4NgNfb\nD2jgRvSFGXonhUTAzC07iVyPzVNt6upFUhzDup65nDD5OWr1kKmOzkNhjWkqwS6AxDZgKuVIowuo\nk8Wuerg9CcrZDjqLHmBRcIsS3kIchg56PCyVSrF169bRqEWICaP6zHB4TwNgm26jq3j2d7xMLEnv\npEGqrg6pIqlyJ2ZYRc/UAFD1iIKVIZWMe+wztu/CiboBCNUQAJVUlsHcVKo6GJrGNFPnTWkPFRoo\n+yUMHYZPLChHOdLlxozz3CQ6yl78s2zUIsRhqWnP++yzz47XlBLvwbxlyxY5wlOI16n20otgmJg9\nk3DR6NdMEijqaERRkYw7mWy2Si1RwPIdrMDBVltJDn8W3ZAoYxNYSVCKjqF+nn7zu+gsPUk+40G9\njJ9L8tSf38RJf11ge9SDbU+n2LkJ1deD1rULKwfFfFxP2c/SUetnBzPJd0xi1qbn0SMl53oLcZhq\nGt7//P+zd2dBdh3ngef/eba7r7WvQKGwEBsXUBSpjZRka+mW7Fa03CFZYdrh8at7Ihx+sSMcfrAf\nJD1OeCJmbHd7ukMzatNuuy3RliVLlCiKOwkuAAp7VaH2qrtv596z5zycIkhahBYKBcJk/iIQLFTd\nqpuZrIvvZuaXX/7n/3z9YyEEpVKJgwcP7mmjFOXdLHIcvM0NzJFRhKaxrVkgBFJKosgBfMaFRErw\nsw1Su/vdmC3Su8GbQUihpNPJmKTtFloUsL7vOPNnnuf0sQSyv0xUPsl6VmO5Oc3T+fsJRAIjdQdl\n/4e0qEC+i9vKgYjoORlKnWUAOoUhNCnJ90JaauatKLelGwbvzc1NIL6T+62+Njk5uXetUpR3MWd1\nBaTEGB7mFT0dHxED3N1ZN8BkxsMNdIJCnVRlBoB+tklKi/fGPVdDJoYILYOhjQqbo2lkpHFw1eb0\nsQSBv47JSbxilmes/QQiwX3aq6wGJnqmRAvQUivACUItotfPMN9bA6CbLwJQ6oZ01OUkinJbumHw\n/o3f+A3E7mzgNUIIKpUKQRBw4cKFW9JARXm3cZYWATCHRwBo7ZZERQiiqA0SJgo9NgcGMtkm2RkG\noFnscECMATaDyKCvxzPyUrPG0r4xrG7ISCMk6UgcawdDSoLyKDJhkXGWuDN1gWN6xDeLp4haeURu\nA00epycFhUgjGYUQRdj5OEGt0A1pe2rZXFFuRzcM3t///vff9HfbtvnqV7/Kk08+yZ/+6Z/uecMU\n5d3KWV4CwBwaRgJtzcCUIb7QiWQbI7QolPo8vZWGJKQHWZJ+l2YxJOfokIIgNPAz8cs336xw5n0H\nKG4H9BJlDmy4nJ8X0NtAZqfAdxlcaXL1gMHRgk/AAjk3j53pkNB9OpFFAbDTZSzHw0mlASh1IxbV\nzFtRbks/U7b5M888w6/+6q8C8M1vfpMPfehDe9ooRXm3av3wcfoXLyCSSbRMBheBLzQSb8g0T0cm\nQsBGFGE5GQypk3Nr9JMaOTcAwHUzkAoB6Ott/PQ4VntAOznGgY24MloQrsV76W4dOUiz4MSPv9/q\nU7DiAG1mOwx222YniiQHPSLTINANSu2Qjpp5K8pt6ScmrPX7fb7yla9cn22roK0ov5hw0Ceybayp\naYQQdLV4yXx34ZwoalPEAEK6xoBMOz7+ZcoaQhRICx+AhhxFpiQiilgbsdFEAUdfp5Ua5Y7tC4gI\nXH0TExASovIWy1ofz8syZ0q+n2kjA5NoaBNvd1m+pxfJ9Vp0hoZpFkYp9Cp0vK6qsqYot6EbviKf\neeYZfuVXfgWARx99VAVuRbkJglodAHM4DphdEb9/fi2zxJIdPlqM8KTEt2zSzVEAIrMB7ghpK0C6\nIV2jRD+fJdtpsjyZRNNyhImAWq6EFUiG2iYhDaKgi0gO0y9uEhkBaztldCEYtepYgyJheZsQSYSk\nJ/KUGzsANItjZPsBBCE93761g6Qoyk91w5n3b//2b2MYBk8++SRPPfXU9c+rW8UU5e0LmnHwNspD\nAOy5rBoAACAASURBVHR2k9V8BEQe/1veIKtJrnkRCEjbRYhCnLRN2CqRmNlCOhGeKfETSbKtVXwy\nNF6qEyaLmNk+fTNHRo5RYx3ZW0IW70KmcghbcqWRYX4GTiUMKlqapl4lp4f0Qw0tyDLbuApAuziE\nBhR6IT9ce4pSssiHpx54R8ZMUZQfd8PgrYKzotx8QaMBgFGOl8O7mo4pAxyhUaRLVtNohxGX2wJd\nWiSDBEmvhjFksc8LMJKCsCbB6gHQM1tIO4ff8ai1DQZWgkpqnEH5TmCdyF1mcrPI0Y0Qs9Gnnq/i\neRrTpo6XiiuxJZAMgCwaZj9elm8X4vYVeyGDwKF0a4dJUZSf4obBW90apig3n99oIBJJtFQaF8FA\n6JRlj4bIMiK6nPd8pg2dTTzS3Xh2Xh5s4xdM/tP8IkIX9D2DbCoCoJVsk06NU8gWaS8NsFvw+Mw9\nkJsiv5XkwdNXmd9YeEMLarjbOczPlplNeFzpZtG0kEEoAYGIq6LSLu4G724cvBVFub2oLBRFuUXC\nvk1k9zDKZYQQVHevAC3IeE/Zkl2+a7tkgboWkmnH58BH++s03ElMI94ZD33o7QbXSLZIDgxSgxal\ne0ZIZKA7MUKq3+PXvrfN/IbL9vAw3//kr/GtD8+yWspjVrp4f7vBXZ5BRqSRpoOnxW8G/CiJiELs\nfA6Ig3c/GKAoyu1FBW9FuUXctbiCmbm7ZL6zG7yLWryUvu41sdBpBBqBJkl1hpBSkvGrnNNzrKzG\nyWuZUY1WeQRkSBS1Ga71MZsNBJLiiSFyBY1P/uPXyfU9Xjya5lv3z7E6d4S1Q3fxD6f2s1g+gewG\njP3LFkKz8ZMDipn4DUQrOUy22yG0dt9YdAM2e1u3dJwURfnpVPBWlFvEXV0BwCjFwbuqmZRpEuzW\nK9/wG5R0OL06hAh10m4aXwb0smC38jiL8R61mYHW8Aih7AEa81dXEVGI0W4iMik+cuYHlJpVLmVm\nePZEnn56E7wQ0ziIlu6zMDtHeLgMdY9PPr+FbwwYyfQJkHSSZQqtOkITtNPFeNk8dN+R8VIU5cZU\n8FaUW8RdWwVeT1arC4P7tHN0yAJxgZYCBhu+IN0rIhDofo9WzkDaOcaoAVBzCwSaSVbWMaMM09X4\nHgKzUePAlXPMXzlHNTfMN8YfJFMvEJo+Zm0DTUsjikUG6S4bsycQE0kmVhwONiqMpnwGQKQlybbj\nNwkrs3eQ60cEngreinK72dPg/eqrr/Lwww//2Oe///3v8/nPf54vfOEL/M3f/M1eNkFRbhvu2iro\nBnoujwRczWG/WKct8xAFSFySlWn6qS7pXpzfnXcqtDImqcAnY8bV1a5uxJcCHTO6HPQh5/RJeg7J\ndpP3P/1dfMPk4tRhQNC09wEQunE9dcs8Sj/RZ6MyjvbxMTAED15ZIh/0cUWEQJBqx3vcm7MH0CRk\nu96tHShFUX6qPQvef/mXf8kf/dEf4bpvftfu+z5f/vKX+au/+iu+9rWv8cgjj1Cr1faqGYpyW4h8\nH3dzE6NURGgaXaFzVL+MRNCRWaLIBgnuxgyDTJtMK05WG7PXaKYSTDlVRDJ+uTpe/N8h0ebTwxGi\nbDLcaXJy5RJJd8Ar73uQlfc/QHImj9cYAWnQy21gdD0MYwYnJ+j3UwS5FMYHyiTDgPzFFVKpOKtc\n9uPktebuxSnFbkgQBrd6yBRF+Qn2LHjPzs7yZ3/2Zz/2+cXFRWZnZykUCliWxb333ssLL7ywV81Q\nlNuCt7EBYYi5W5ylLiKOiGVqskwkNKKoR7Y9Qp+ISAtJ9/P0kYy4dVpWhn1eFVJxQZd6Lg6qlctl\nLAOs/zDBXH+TA5V1GuVRzp94H0EqxWSmhRYJqA/jJwZYO2sIITDKd+ObLt12Ev1EnmYmQ3Frixkz\nXn7vhvEyvptJE2oaxW5IZaDeYCvK7eQn1jb/RXzqU59ifX39xz7f6/XI5XLX/57JZOj1ej/155VK\naQxD/6mPeysjI7mf/qB3qfdy3+H26f/2y3HZ0ezkGLlcAiHXMETIlfAIADJoU2xMMMi0SfXzaFLH\nlhGZcEDDyvGhwQ4iGf/+twpDSBlSXS1wRu7n5OFlxntNJPDMR/4dhVaD1tAoQ82XyXbynGlMkhje\nxmUZEc5hWEXs3DUq2yOUSjabd+6n9MwCdyyf4anSQQZGAaQEIaiNTlHs1djxqrfNWP4s/i21dS+8\nl/v/Xun7ngXvG8lms9j267WSbdt+UzC/kWaz/7aeb2QkR7X63rwZ6b3cd7i9+l9buARAkM7T6Tqk\ncyt40mAnHAUdZNQk35yiNnOJdDdOaPOlR6hDjwxjgzoiNUYYCYKkSRQ1aA+ts7Y6y+zlV0m2fDhW\nRC/p/Lv//l+4evgkr9z9AT75/COcKfwHZGDQKW0yut3GniphjxjU1zNUZYfsdMBqcYbZxhoUPNBS\nZLot+pk82xOzlLa2eam9c9uM5U9zO/1/fye8l/v/buz7jd6M3PJs8/n5eVZWVmi1Wniex4svvsg9\n99xzq5uhKLeUu7oCuo5RLBEYDZJiwGK0D6nFe8lWL0IPTZxsnUw3TlZLeG1aWZ3CIESXESR1Gn4e\nhCCM6iT0BSy/j77ZITQNEh8o8OnUUzhjeQ5fOsOHnnockdA53l4mbIwTWC604tUwrzhBz07RjPLk\nkh5Pj9wDhmB0sIEuBNntJlLX2ZzcR7GrLidRlNvNLQvejz76KI888gimafIHf/AH/M7v/A5f/OIX\n+fznP8/Y2Nitaoai3HIyinDX10hMTiJ0Hc+Mi55sRNO4u4tfmVoaicRO2GS6Q7hIRgZVWjmd2W47\n/kEpnWoY75mHYZPhdo/jO09gRj4LB+7nh+YHSGo+6c8Ms3YgxdT6EtI0+WDzLGElLnfcy1zD6vbR\nMyN4KUnDyZG1PDbJY9xbpDSoxM9VizPMa2NTZO0IgkAFcEW5jezpsvn09PT1o2CvXS8K8PGPf5yP\nf/zje/nUinLb8La3kJ5HYmYfkXAIjAYVOYQnUzgYEAXkamXcfB3Dy6CHJi0ks16D9qjBwXoFCYiE\nTs2JZ+VR1GS64lF0d3CMNBUOsOlMMLF9kSPzHQqfGKX2922Gq/EtZvsaAzb7WTqlHY6sXGP7xDHa\n+3Ry7QS98TyeFETHymTOt+JG7+5ShaZFuzxCvhey0l7j+PAd78AIKoryr6kiLYqyx16rrJbYtw/P\n2kIIOB8dJIeDJwx0x8UMLdyhbVJ2AYAekrLXoZXVGW93EQkNoUFdi4N3GNXIdw00KTl3xwcQoc7E\n2S3WF03OdiNGDR3x4Bi9dA4JfLR2mrA6BZrECVchCHFGirgNkzWm0LUIVybIHRcIGZJ+7WSYlOyM\nzVDshCw0Lr0Do6coyltRwVtR9pi7shu8Z2bwzW18abIoZ0nIAITAHMS1EJxs63rw7gMlv0MrY5By\nJUEhAUBLyxNFNmObAfmBQ7M4zOXjd+CnDbS2hhYFfDvos+1KpsZ96g/MgRCMOU1GdnLISLAxUWd4\n7RpYafp+ihU5RUKPGHgm6TuTpLw2oZEmXatjBD6ViWlK3YirraV3YvgURXkLKngryh5z1lZBQF+/\niNR81qJJAgw04lvCzH6IJMIxbdJ2AYnEkz6pyKNrJRGAOz1CXyZxtQRhVOODZ20EkpXUXWihQfNI\nARDY+iGEb/Ls1SS+Lzh4uMf64XkAPrn1PGF9giDVJ9m8HLetnKAh81hGwMA30U2BbflIoXPsuedB\nSipjMxR6ATt2BSnlOzSKiqK8kQreirKHpJS4qyvoQ0U8dwOAhegghgzxRHxu22xr2NkGthaS7Odx\ngFxoE2gQSgMJNIZnqMp4yXxk8woztRa10iQ7xSmcYobIFOxrvIKI0sxeOYVZKXLhUh5dh/I9OoGm\nM2HXGNoaBmBx2CYKfPpjSczAR9Oh58U3iXWLcYAudGwyvTa9fJGUnySQIaubT9zaAVQU5S2p4K0o\neyio1Yj6fcwDwwRuHRGm2GSEPA494qVwsx/SG97CdDNokYENjDhN2lmdbF9iJ0w8LUWNMkjJ/S+e\nA+Cl+z5MUG6DLkjVXBrZDO3SNpneEHjzbGyNU6mmGRry2XngLs6Of5TjDQOrW8IpNQmqa0jTRPMl\ngZm8Hryz6bi2eTc5wslXnwPAT08AcK2vKq0pyu1ABW9F2UPO6jUAtANpAAZhGSkEeenQDpMAGIMA\nq9gkubvfbSMZdVu0cjp5O2B9uExC+FRlmdlrl5mo2qzsPwKtDv3R+I7vVLVD1zpEt1BBmMuAThSN\ncXbhJH6gM3uiS3togk56nLHt/QDo0RUAwkjDS6Tp+PHhk4IpCZE0kqMcuHKWbKdJtzSFHkqudNXd\n3opyO1DBW1H2kLsaXwMqSz6gUY3imgZ5HLoige4GSOmRSDk/lqzWzumUex7nZw5hJTx2wjKnnn+M\nSAgu7T+GZdv0h4Yw+30sZ5lQ95haOcFMbQtDX0bXtvC8HpcvD2OaIR888BQPLf6/HFqsoQ0y9LJr\nSK+D1DX0tHl95p0TBq7p4pg5IgxOvvQUlfEZCr2QlX79nRhGRVH+FRW8FWUPuasriCELqbmYqRGq\nIi51mJUenmFi2CGDXJOODK8nq/UhPiaW08m6HgNrP37SZObiRYqtJpcOzWPW6tjDw0hdJ7uzQ7vc\nYPXgywgp2EnfCTJA07roep2NLWg0TdKzBtaBBOPBJsmtA6BJPPtlwpTBaMenvxu8UxqQ6iKEYDs/\nw/zVc/RTWfI9jabXp1V5nl7t9I/9URTl1lHBW1H2kLO6gnlXfAtYIj1FTaQA0KL42k2jH5Ao16mH\nkmQ/jwdEQMnv0soZOMkILcjSFUlOvfBDPEPn4vRx9CCgNzoOQKayQ22kjZ2vU3AXcc0sWlB6Qys0\nzl8oEoagf2SY4cE285Ui0k3i6FeJoi5GQqfvx8HbsnxkIq4Pfbl8B0YYcnThRfKMccLS2e5Xb8nY\nKYpyYyp4K8oeCdotwnYbbS6JEAZmapSaSJOQAZ2BBYDZDxgdbpBwXk9WS4QelgxoZXR2ChmyXkju\nzApJp8/pEzMEjkdgmgxKJRKdDp7RZJB2KbQzpMMqGbeJp40QRenrbekPDJZXsugZHfP+MiP2DoX1\nwyAiHPcFBpNp7N2Zt2X6eEZ89rypDzFIZbhj4UUiMcy/zyRJNl9i0FlUx8YU5R2kgrei7BFnaREx\nbEFSIvwU7SvLdESCPAMabjwD110HkepfT1brIin5HXxdYBsJAmkyVK8wcf4ynXyR9UKOsUab3vgE\nCEG2ssPOWBOA0Z0irUyBo5UnQUaE0RhSaoAEJNdWMth9Hf1knhlrhen6JKKXxQ8WcdJtBl6csGZZ\nPmFoEYmIJBrnjr4Py/cYq3f5Tm+AJ2HQvki3+ixR6L4TQ6so73kqeCvKHhksLqLPZwDQwwz13SXz\ngnToyDjTPK032A6jNyWrjXpNGnkdwzNJ20nuqD6HJiUXPniKYjOJRNCZnEKEIUZrlU7BJtNLkrVT\nBLqBb0r2N88AJmE0cr09USS4cDGP0ATDD0h0GTG0cihuq/skvhAEocCyfCwvS6BFJBEsjBwl0HTm\nlq5wxo34uu1jpsYI3Abd6vNEkX/rBlVRFEAFb0XZM87SItqBDEgNLcpQYzd4Nys4lgmRZCZbZTOI\n3pSsNuS1aRZ0RKAxUYso97ZZn5nHntJJeCb94SGCZJLc9ja14Xj/eXTn9T3uncIIc40zJIIOUhaI\notfvA240E2xtJzFGLQ6PX2WsN47RHieK6hjTy9ieiWX5JAZZfBEXOI86kpWZg2TsLkdWTKq+g148\nSSIzQ+h36NVeREbhrRtYRVFU8FaUvSCDALe1hla2MIISAu31ZLVORJjSMZyQoXKdXhQnqwXEyWpl\nr0OjYJByQg41Ngh1nec/+AlSdnxdZ3tqGoBEfZlGuUtyYFFoZwABCBrZMqGucXLrceC15XPretsu\nXcnh+4J9x2skEz76tXmEyKKNLLLoRVimh+WmSU/Gx9wSbY9Ld74fCdx7vgNSsjZokC6dxEpNELgN\n7ObZWzW0iqKggrei7Inmue8iZuKAKWS8l1wTaZCSoOYTJgzMgUcv1buerObsfm/Z71DP6XxsoYoV\n+Zx+/0fRizpm3cfNZnGKJVKNBrXiGgiY2BxCIK4/txQaldwIBbdBwVsENIJwHCnjx3ieztWlLKYZ\ncfTIEgd1g6R2HwLBD8I2NQJ0LUKmOwAkvYid8Rkq2VmG2h3uuFTmhZUthBBkhu5Ct4p4/Q3sxplb\nNr6K8l6ngrei7AF/dQt9Ng0StDCDBOoiRQEXox0nmOXCHlth+KbKakJGFP0uxV7IbG1APTfG+ZP3\nMyu2EO0UjbkDACSrV2mVeqTtxO6s+802S+NI4HDlVYRoA0miN+x/r62nabcNpiYrTOT6pPUBon0/\nAZJHegPcYhXfSYAekAb8rsfZifi5T13q016PM9OF0MkO3QPCoLH2LXy3sWdjqijK61TwVpQ94G5u\nIcYTCN9CoGNj4giD+avn6eXjYD1qNFgPQlJ2HoAWUAhsQPL+hT6hgGfe/ykQgv3aOm1jmEF5iGSz\nQaVwEYDJjeE3zbpfM0ikqWfL5J0eQ85VwCWSRcLotb1xweUrOaSEI4euoW3b6OzjiDOGJ+HygZdp\nEWKk+1gIZN2hN5llJzPCsL3G2FKZxaseALqRJlM6gYw86tf+lzpCpii3gAreinKTSSkJ/AZCE2hk\nAaiJFIbvceSZJ6gPxZd8TKS22QojUnYRidy9kKRBqAsSvsQ2UtT3T5CkTzmss7MvzgzX62fpZ1yK\nzSy5XvpGzWB1KN4b31dfxdA2AJ8oGiGMioBkZqbPYJAgmxlwlzZAMzXy9hD/KZtEkxrV0U16hXif\n3ag5aPksz+TuQADT7XM8+yOHRj1OVEtkpkgXj+H1N1S1NUW5BVTwVpSbzK9VESPxS0uP4uBdFylO\nvPos1sCmUyoDkExsEklI9fNExMlqJ7tLWKHk2miKa3NHkYbBHGvYYQI3XyBV22Fn6CoiEkytD//E\ndvRSORqZIsV+m2G7gqGvAwFRNMr0ZMT4mMvAkTiOxd2zO1imR8+z2G8a3F+bw3RTbGfiZfBEz8XO\nj3IpM0snlWaidwXN83js2wMunne5fNGjNP1phJ6gtfkYod/dq+FVFAUVvBXlputfuoA2m4ZAR+xm\neXfskBOvPkM7OYKXtUh4Lud993qymgcU/S7z/XUCDX54xxib+48AcEhfo2sUEUFA33+a0IiY2hjG\n2i1n+pMsjs4RIZivLKNLB0NfJ59rc+RIDc/TObtQ4KVL+9B1yeHEGj03bu+o7jK/8EHCZA+AbKpL\nx9co5Ju8PHoSM5SM+C/gDCQbq/Hse9C+RCp/CBm5VBf/WtU8V5Q9pIK3otxk9vpZRErHNF/PAh8+\n/TJGEHBh/AEiSycfdbngB9eLswxkxGd2nkJHcuZQChFYVKdmEJHNODWaoohZPUMnWyPXSTNcLfxM\nbRkk0myUJ0n6LrO1dQzD4dTdC+ia5JWzR+kPpljcztBo5nl/epGuEwfvtNnHCBKUdmYIEn1SbhrH\nfYbh/ICXjAN4hsa+2hK64bO5HmL34lrticwshlXEG2zhO+rub0XZKyp4K8pN1Hz8B3jO7jWgTvzy\n8ls9pq5coFUcpjocXyaS0+sMJKS6cQLZqL3CjFMhRPDykRR5Rgktkzw1NCFp9TWq2dOYnsG+lbG3\nTFK7kdWhaQZmgpnGBncdrJNJByxfy1KrpZAyT44UC5fmyJsOPT8O3ikrrppmOBnMbBcjtLD0Hdyi\njStMFmbmyDgRM9ErAKwsBUgpEUKQLp0AoN86j5TRTRhVRVH+NRW8FeUmino9xKSJjCR6FB/h6r28\njCYlC4ceIMjGwbEWrQOQ7g6BlLx/5zkczcQ3oJfRoTQDwKzeB2DVuYQWasxfncT0jZ+rTaFucHHy\nMPrRHMPTIe2mwdWlLLq+gRBtTEza7Txb1WEiNCIJekYHwPATdLLxvneyM0Q9vYZItzldOkUkYGL1\nIsUidDuStZW4IpthFXarr3Vxe6u/4IgqivJWVPBWlJvIra+jjSQQPQ2BRlDvIJY3qY5MsKVNEubi\nfeoNfxst1Ek6GdJ+m5R0OZM7SLVkIWWCzsQsUkqm2QKgHfSZvzpJykm8rXZFwymMB4eRToj73QpE\nEiEkulbB1yr4wOVL+8klXGzXwkjrhLqH4afoGHH5mMT2fqQISc6fpW6nWJzOknZCjtSeRwg4/ZxL\nGMbHxFKFIwhhMOhcJgz6v/jAKoryJip4K8pN5Ec7AGjE9cSd01cAeOn9HyVqeXg5EyFDItknZRcQ\nCIbtNdaTI7SNDFsjBqY8QqTHs+sSNbqOydyVKbJ26m21SdMkd51ooRmC+jMO+WqLg9uLICVCgCW6\nnEXS7qfQdI2ua5I0fEKzhemlkEF8M1naySIHo5DqoRW2eHn2LgAy184zVnTpdSUXF+Lldk1PkCwc\nQkY+7a3Hf4ERVRTlrajgrSg3iQxDokJ8RaZuFgl2mgTrNRpjE6yPzJIRPn7GJAobSAmZVnxkLO/U\n+M7IAwz5HTZHDUwrrmQWuk+S10NqvRSW99Mzy2/kjsNtstmA1bU0rwRzdBMZJtoVDlSugZTouwfV\nqoQM/Aw918IUIVp6gECQHOQIkzYZwLt8FKTAmLnEFvPUi2kEcGDhWxiG5NWXHK5dbbO51qbZKqEZ\nGXq103iDnV9scBVFeRMVvBXlJhksX0WbSoANmrQY7M66n7/3IZxrHfRUEjSBG24hBExsxxnj22aB\naqLEkN9meySHmRomYbcZlfH3Vzpvb8YNMDHeZ3pqQKdrcPlqnkjTOTdzHNtKMd3cZF9tFSEgQUAV\nieELurvHxcRIvO9tOVk62SYagoSbwhzMoyUcfGuRC/vjPviBzlS4QhgIlq6+lkynkS4eAyTN9W+r\nymuKchOp4K0oN0n36vMIS0Mf5Ag264TbTbqTJTYn5zC3enjZeL86DNcptQJEWCDh2zxfOg5AljYy\nfQQhNHKVC0wY8cuz1k6+rfYUCx7Hj7bxfcGZsyWiKA6qvmFyduY4AzPJvvo6M/V1LBHgohO60fXg\nrRXj5zUDi36mBUAGcCt3QKRjjC5ydvY4tpkj5zWoOQU0LWJjHXrx8XCs1CjJ/CHc3gqD1oW31Q9F\nUX6cCt6KcpO4/WUADGPy+l73t+4ZZbBpcyDdx9tNVgucDh98SSfQ4+DY1XTyvk2tLLDMIxCGOOEF\nJomDaKv98yeppZIBd98ZX4Dy6rkS/cGbM9Q9M8GZ2eM4hsVcdYWy247bIiXt3aS4dEYjEiF6YBCY\n8XZAUUS4tRAzGkPoEZ5W49r+EgK4o/I0UaQBgpdeEES7p8RKU58EodHc/C5R5P/cfVEU5cftWfCO\noog//uM/5gtf+AIPP/wwKysrb/r6f/tv/43PfOYzPPzwwzz88MMsLS3tVVMUZc+F3S5R0UMGEveF\nq4S1DpdnE7RKc/RXO+QQeHmTKAq582oN0xkFwDZSeEJj1Gtw4cg+dL1EcX2dZt5lwtRoDRKEP2e8\nS1ghp+5pYFkRFy/naTTeOvi7ZpIzsydwDIvJXlzD3EfSHMRvKkY0Byfdw/TTiEgjEhFFIogkWvMg\n0SAL6U2uTsL2+AxFt8p9q99AyBDfEzz3tKDbiTCTQ+RG7if02nR3nn77g6woynV7Fry/973v4Xke\njzzyCL//+7/PV77ylTd9/dy5c3z1q1/la1/7Gl/72tc4cODAXjVFUfZc++wzaCUL0TGx11eJBLx4\nPI++uY+ZRItuL4OXM0k3zvHhM22qmfjSkNcOUY24DWr7TgEwe2mBbMYgbUiuNQpYhD9zOywr5H2n\n6mTSIUvLWdY3fvy60DdyrBRnZk+SiuIbwswooNWPg/eovY2b9RBSI9sewUm3EdJEAwaNHCKKM+rX\nxjSuzR0hEho5r8nd6/8S980WfOsbNrWdLoXxB9GMDJ2dpwi89s/cH0VR3tqeBe/Tp0/zkY98BIC7\n776bc+fOvenrCwsL/MVf/AW//uu/zp//+Z/vVTMU5ZaorD0DQK3uovddzh9Ikt95H51OyD0TPbyc\nBULw4A+fQIt02qkxAAYyDsxWOsKw5vAHbYaay8yZ8TL3Yr1IQgQ/UxvSqYD7TtXJZEKWr2W4upT9\nmb7PsZJUyvFd34nQo+MmiCIwPIcgGyeZZcI0dq4JCEbMCLcdggZBfZzQ6NMPF1i4834EUHZ3GOsu\nAuC58I2vv0Jl26E4+UtIGdDa+N7P1C5FUW7s5yvV9HPo9Xpks6//46HrOkEQYBjxU37mM5/hS1/6\nEtlslt/93d/lBz/4AR/72Mdu+PNKpTSGob+ttoyM5N7W970bvJf7Drem/5HnkUg5QArjpS18HSqH\n76Fl3cGhsQHRBRMvb3Hs7PNM1jqcmd3Pa++bvcgFLc3WiWMAOGub2AWPOb0IwFK9yDE2fmobSiWX\nu082MU3J0rUMVxdz8HOUUNVMDULQkERS0HYSJDMBQ9UaMEpkmTiZeMY8E9rskCNq7ENq28hI5+K+\niJo4ytziApleh4O1F6lm9qMZcQD/5tdf5Yu/cx/p/Az91gJJ40Fypb1bbVO/9+/d/r9X+r5nwTub\nzWLb9vW/R1F0PXBLKfmt3/otcrl4kB966CHOnz//E4N3s/n2qjSNjOSoVt+b1xO+l/sOt6b/vdpp\nBpcW0SaTOJ0AmzJP/MdP0C7PAvC+8Dmu1OZIj1a597kfMEgIrk4dplAFpGSAxJQB7dl5wmCL7Eqb\n7rDGXZbGVjtD3zMw9BvXBxdCMj/XZW6/jZRw7nyBza0b3/F9Iwni2b1r7CbJDZLMDbWZPLfOZuEI\nwjbJjFtIJCkfRFLD3ylgnTxNsDWLmFombH6HZz/8aX7523+DFblMtxZYLd/JcN6n3jX56//6HL/8\n6SkM1lh85f9j8vj/jhA3f/FP/d6/d/v/buz7jd6M7Nmy+alTp3jiiScAeOWVVzh8+PD1r/V6+4Qp\n9wAAIABJREFUPT772c9i2zZSSp577jlOnDixV01RlD3lbC0hdMHGYJxHP/87tMuzmHWH9OXLFPp9\ncEI++OI/o0Uh3/5AnlQnTlaLkHT0NFohCYT0u88y3u2TnU6jC1jYHiJBiLjBBLpccnngvhoH5mwc\nR+fFl4beVuAG0IXEIiAQ8erWa0lrqUQCP9XF6idI548xyLRxtBzFkoYMBFFnGGH4EBSolHwaOY9r\nc3egyYi55qsYoUOzBXMHIIrgB9+zwJgk9DvY9ZffVlsVRdnD4P2JT3wCy7L44he/yJe//GX+8A//\nkEcffZRHHnmEXC7H7/3e7/Gbv/mbfOlLX+LgwYM89NBDe9UURdkzMozopOJkr5eG3kdq0GLy4hr9\nVyr80tQldnbKHKv8iKzd5YU7R9kpDmO58QqUZzhIIdDzSQbOkwRdjTG3wfhQnB1+bnsEi3+93y0p\nlVxO3V3nfaca5HIBaxtpnn5umFbb+oX6ksQn3P0nobGbtCbKCRJhHU1q1G0dCi4IjYNOfFmJ3D6E\nNrJOWJtDSKjzDM994KMMUhl0Io5UnyPUTOzLmxw9HuG58MQTM0ipU197jCgY/EJtVpT3qj1bNtc0\njT/5kz950+fm5+evf/y5z32Oz33uc3v19Iqy5yIpefyaw9EJg45MMHbmHGMHJec35piaqDCZ79H7\nTsiwvcHa7AGePdZlZHsOgCChsT49DIs9JpuX2Zi8QmQfYsK6zGQyy2ozR8tJMSy6gCSbCRgZdpmY\n6JPNxElutbrFlcU83e7bL536Rknh0ZFxNbdqL57BGwWN4nKdVmIes11BDE/BZkCp2SUxPoZbBa1X\nQuQWSQSHcMwrRM7zPPnRz/KJf36Ekf4qabdJxRpnZucKc5MTLG/mWbo2w/zcNdrbT1Ca/tRNab+i\nvJeoIi2K8nN6vtLmqe0mf7awypousUSAuNLmWHaVhBPgFtr88uEVuucC9lUX6KZzPPaRk0hNkG+O\nIYHKPcM4gziTuxCeB0D2cpSORAgh2OrER7z2jXV56MMVPvhAjUMHu6RTIZtbSZ57cYiXXhm6aYEb\nICleP1BetePgncxJtF7czmQroFYsI4XECzLkpnePoW0eQct0cf00uZ6gklynlWxy4di96FHIyZ3H\nkUJjZTXNdLnN7H7J5SvTDJwU3eoL+E71pvVBUd4rVPBWlJ+DlBF+f4M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CG+Huw1uI4RtI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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116317320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_weight_distributions(plot_n_features, weight_matrix):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    plot_n_features : int\n",
    "        The first `plot_n_features` distributions are displayed\n",
    "    weight_matrix : pandas.DataFrame\n",
    "        The weight matrix dataframe (rows are genes,\n",
    "        cols are components)\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    None\n",
    "        Displays the weight distributions of the first\n",
    "        `plot_n_features` features.\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots()\n",
    "    for i in range(0, plot_n_features):\n",
    "        sns.distplot(weight_matrix[:,i])\n",
    "    ax.set_xlabel(\"Weight\")\n",
    "    ax.set_ylabel(\"Number of genes at weight\")\n",
    "    plt.show()\n",
    "\n",
    "plot_feature_weight_distributions(100, weight_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the weight distribution of an ADAGE/eADAGE feature, an ICA feature is approximately normally distributed.\n",
    "Because of this, the gene signature rule we use can be the same as the one used in the ADAGE/eADAGE case.\n",
    "\n",
    "(see: [`constants/gene_signature_definitions.py`](https://github.com/kathyxchen/PathCORE-T-analysis/blob/master/constants/gene_signature_definitions.py))\n",
    "\n",
    "This gene signature must be defined as a partial function and passed in as a parameter to the `pathcore.pathway_enrichment_with_overlap_correction` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def define_FastICA_gene_signature(std=2.5):\n",
    "    \"\"\"This method returns a partial function\n",
    "    that, when applied to a feature's gene weights,\n",
    "    outputs the feature's gene signature.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    std : int\n",
    "        Based on how the weights are distributed in\n",
    "        the model's features, specify a standard\n",
    "        deviation for the weight cutoff. \n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    functools.partial\n",
    "        The partial function should have a return type\n",
    "        of tup(set, set). One of the sets can be None\n",
    "        if only one gene signature needs to be specified\n",
    "        for a feature.\n",
    "    \"\"\"\n",
    "    def _gene_signature(feature_weight_vector, std):\n",
    "        mean = feature_weight_vector.mean()\n",
    "        cutoff = std * feature_weight_vector.std()\n",
    "        positive_gene_signature = set(\n",
    "            feature_weight_vector[(feature_weight_vector >=\n",
    "                                   mean + cutoff)].index)\n",
    "        negative_gene_signature = set(\n",
    "                feature_weight_vector[(feature_weight_vector <=\n",
    "                                       mean - cutoff)].index)\n",
    "        return positive_gene_signature, negative_gene_signature\n",
    "\n",
    "    return partial(_gene_signature, std=std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example of a key-value pair in the `pathway_definitions_map` dict:\n",
      "('KEGG-Pathway-pae00072: Synthesis and degradation of ketone bodies - Pseudomonas aeruginosa PAO1', {'PA1999', 'PA3925', 'PA2553', 'PA3589', 'PA1736', 'PA2001', 'PA2000', 'PA4785', 'PA2011', 'PA2003'})\n"
     ]
    }
   ],
   "source": [
    "# load the pathway definitions file as a pandas.DataFrame\n",
    "pathway_definitions_df = pd.read_table(\n",
    "    pathways, header=None, names=[\"pw\", \"size\", \"genes\"])\n",
    "pathway_definitions_df[\"genes\"] = pathway_definitions_df[\"genes\"].map(\n",
    "    lambda x: x.split(\";\"))\n",
    "pathway_definitions_df.set_index(\"pw\", inplace=True)\n",
    "\n",
    "# create a pathway definitions map\n",
    "pathway_definitions_map = {}\n",
    "for index, row in pathway_definitions_df.iterrows():\n",
    "    pathway_definitions_map[index] = set(row[\"genes\"])\n",
    "\n",
    "print(\"Example of a key-value pair in the `pathway_definitions_map` dict:\")\n",
    "print(list(pathway_definitions_map.items())[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a helper function for running the PathCORE-T analysis workflow\n",
    "The method `get_pathways_overrepresented_in_features` is a helper function\n",
    "that calls the [`pathway_enrichment_with_overlap_correction`](https://github.com/greenelab/PathCORE-T/blob/master/pathcore/feature_pathway_overrepresentation.py#L15)\n",
    "method provided in the [pathcore](https://github.com/greenelab/PathCORE-T) package.\n",
    "\n",
    "It applies [`pathway_enrichment_with_overlap_correction`](https://github.com/greenelab/PathCORE-T/blob/master/pathcore/feature_pathway_overrepresentation.py#L15) to every feature in the weight matrix\n",
    "and creates a pandas.DataFrame of the pathways overrepresented across the entire model\n",
    "(weight matrix of features) constructed by FastICA on the compendium. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_pathways_overrepresented_in_features(current_weight_matrix,\n",
    "                                             pathway_definitions,\n",
    "                                             partial_function_signature,\n",
    "                                             alpha=0.05,\n",
    "                                             correct_all_genes=True,\n",
    "                                             metadata=False):\n",
    "    \"\"\"\n",
    "    This method is a simplification of the method already provided in the\n",
    "    PathCORE-T-analysis repository.\n",
    "    See documentation in `run_network_creation.py`, line 155\n",
    "    \n",
    "    Modifications:\n",
    "    * Removed the `overlap_correction` parameter for this example\n",
    "      - overlap correction will be applied by default.\n",
    "    * `current_weight_matrix` is just a pandas.DataFrame rather\n",
    "      than a tuple of a filename and a pandas.DataFrame\n",
    "\n",
    "    We kept some of the parameters (`alpha`, `correct_all_genes`, `metadata`)\n",
    "    so that users interested in running the notebook themselves\n",
    "    could examine differences in results that occurs with parameter\n",
    "    changes and/or view the metadata that accompanies this analysis.\n",
    "    \"\"\"\n",
    "    n_genes, _ = current_weight_matrix.shape\n",
    "    significant_pathways_df = pd.DataFrame()\n",
    "    feature_metadata = {}\n",
    "    for feature in current_weight_matrix:\n",
    "        feature_df, additional = \\\n",
    "            pathway_enrichment_with_overlap_correction(\n",
    "                current_weight_matrix[feature],\n",
    "                pathway_definitions,\n",
    "                partial_function_signature,\n",
    "                alpha=alpha,\n",
    "                correct_all_genes=correct_all_genes,\n",
    "                metadata=metadata)\n",
    "        if feature_df is not None:\n",
    "            feature_df.loc[:, \"feature\"] = pd.Series(\n",
    "                [feature] * len(feature_df.index), index=feature_df.index)\n",
    "            significant_pathways_df = pd.concat(\n",
    "                [significant_pathways_df, feature_df], axis=0)\n",
    "            if additional:\n",
    "                feature_metadata[feature] = additional\n",
    "    significant_pathways_df.reset_index(drop=True, inplace=True)\n",
    "    return {\"significant_pathways\": significant_pathways_df,\n",
    "            \"feature_metadata\": feature_metadata}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PathCORE-T network creation steps (based on [this script](https://github.com/greenelab/PathCORE-T-analysis/blob/master/run_network_creation.py))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: We have not implemented logging for the overlap correction step. \n",
    "\n",
    "The print output from calling `get_pathways_overrepresented_in_features` reports that\n",
    "an approximation step is taking place in overlap correction\n",
    "because numpy is reporting a FloatingPointError when dividing by near-zero probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "118 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "129 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "129 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "105 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "107 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "129 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "132 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "129 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "82 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "131 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "105 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "82 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "128 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "83 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "81 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "82 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "133 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "120 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "129 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "82 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "84 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "132 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "117 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "105 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "106 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "128 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "81 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "131 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "107 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "107 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "119 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "83 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "131 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "132 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "118 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "121 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "83 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "105 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "145 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "128 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "82 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "83 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "81 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "128 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "121 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "85 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "130 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "83 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "131 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "128 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "81 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "132 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "120 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "108 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "128 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "133 adjustments made to a vector of length 186 containing the raw weight values in a call to 'update_probabilities'\n",
      "CPU times: user 1min 9s, sys: 601 ms, total: 1min 9s\n",
      "Wall time: 1min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "feature_significant_pathways_info = get_pathways_overrepresented_in_features(\n",
    "    weight_matrix_df,\n",
    "    pathway_definitions_map,\n",
    "    define_FastICA_gene_signature(gene_signature_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys in output: dict_keys(['significant_pathways', 'feature_metadata'])\n",
      "The value stored in the `feature_metadata` key: {}\n"
     ]
    }
   ],
   "source": [
    "# if the `metadata` parameter in `get_pathways_overrepresented_in_features` were set to True,\n",
    "# the `feature_metadata` key in `feature_significant_pathways_info` would have a\n",
    "# non-empty dict value.\n",
    "print(\"Keys in output: {0}\".format(feature_significant_pathways_info.keys()))\n",
    "print(\"The value stored in the `feature_metadata` key: {0}\".format(\n",
    "    feature_significant_pathways_info[\"feature_metadata\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`significant_pathways` is a DataFrame that reports on the pathways significant in each of the ICA features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p-value</th>\n",
       "      <th>side</th>\n",
       "      <th>q-value</th>\n",
       "      <th>pathway</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.487335e-06</td>\n",
       "      <td>pos</td>\n",
       "      <td>6.486444e-04</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.652635e-20</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.730780e-17</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.259411e-04</td>\n",
       "      <td>pos</td>\n",
       "      <td>3.031252e-02</td>\n",
       "      <td>KEGG-Module-M00334: Type VI secretion system</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.598473e-04</td>\n",
       "      <td>pos</td>\n",
       "      <td>1.982107e-02</td>\n",
       "      <td>KEGG-Pathway-pae00520: Amino sugar and nucleot...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.110037e-13</td>\n",
       "      <td>neg</td>\n",
       "      <td>3.924668e-11</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        p-value side       q-value  \\\n",
       "0  3.487335e-06  pos  6.486444e-04   \n",
       "1  4.652635e-20  pos  1.730780e-17   \n",
       "2  3.259411e-04  pos  3.031252e-02   \n",
       "3  1.598473e-04  pos  1.982107e-02   \n",
       "4  2.110037e-13  neg  3.924668e-11   \n",
       "\n",
       "                                             pathway  feature  \n",
       "0  KEGG-Pathway-pae02024: Quorum sensing - Pseudo...      0.0  \n",
       "1  KEGG-Pathway-pae00280: Valine, leucine and iso...      0.0  \n",
       "2       KEGG-Module-M00334: Type VI secretion system      0.0  \n",
       "3  KEGG-Pathway-pae00520: Amino sugar and nucleot...      0.0  \n",
       "4  KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...      1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significant_pathways = feature_significant_pathways_info[\"significant_pathways\"]\n",
    "significant_pathways.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use this information to construct a co-occurrence network by loading it into the CoNetwork constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_network = CoNetwork(\n",
    "    n_features, significant_pathways=significant_pathways)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pathway co-occurrence network contains 90 pathways.\n",
      "Number of edges in the network: 411\n",
      "Number of edges with a weight value above 1 (i.e. the pathway-pathway relationship appears in more than one feature): 108\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pw0</th>\n",
       "      <th>pw1</th>\n",
       "      <th>weight</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KEGG-Module-M00178: Ribosome, bacteria</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0 49.0 56.0 65.0 66.0 118.0 119.0 120.0 127....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0 47.0 51.0 111.0 112.0 187.0 192.0 196.0 23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KEGG-Module-M00334: Type VI secretion system</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0 30.0 36.0 60.0 101.0 114.0 156.0 187.0 196...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0 49.0 65.0 119.0 127.0 146.0 214.0 243.0 266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00627: Aminobenzoate degradati...</td>\n",
       "      <td>9</td>\n",
       "      <td>51.0 73.0 104.0 112.0 121.0 163.0 232.0 240.0 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   pw0  \\\n",
       "8               KEGG-Module-M00178: Ribosome, bacteria   \n",
       "0    KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "1         KEGG-Module-M00334: Type VI secretion system   \n",
       "9    KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...   \n",
       "123  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "\n",
       "                                                   pw1 weight  \\\n",
       "8    KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...     21   \n",
       "0    KEGG-Pathway-pae02024: Quorum sensing - Pseudo...     10   \n",
       "1    KEGG-Pathway-pae02024: Quorum sensing - Pseudo...     10   \n",
       "9    KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...      9   \n",
       "123  KEGG-Pathway-pae00627: Aminobenzoate degradati...      9   \n",
       "\n",
       "                                              features  \n",
       "8    1.0 49.0 56.0 65.0 66.0 118.0 119.0 120.0 127....  \n",
       "0    0.0 47.0 51.0 111.0 112.0 187.0 192.0 196.0 23...  \n",
       "1    0.0 30.0 36.0 60.0 101.0 114.0 156.0 187.0 196...  \n",
       "9    1.0 49.0 65.0 119.0 127.0 146.0 214.0 243.0 266.0  \n",
       "123  51.0 73.0 104.0 112.0 121.0 163.0 232.0 240.0 ...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_network_df = model_network.to_dataframe()\n",
    "print(\"Number of edges in the network: {0}\".format(\n",
    "    len(model_network_df)))\n",
    "\n",
    "edges_above_one = model_network_df[model_network_df.weight > 1]\n",
    "print(\"Number of edges with a weight value above 1 \"\n",
    "      \"(i.e. the pathway-pathway relationship appears \"\n",
    "      \"in more than one feature): {0}\".format(\n",
    "          len(edges_above_one)))\n",
    "edges_above_one.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PathCORE-T permutation test (based on [this script](https://github.com/greenelab/PathCORE-T-analysis/blob/master/run_permutation_test.py#L146))\n",
    "The permutation test is used to evaluate whether each edge observed in the network (`model_network_df`) at a given weight occurs more than we would expect by chance.\n",
    "By generating `n_permutations` (see cell [4]) of null networks, we can determine the null edge weight distribution of each observed edge.\n",
    "Edges that are insignificant under the null distribution are discarded from the network. \n",
    "\n",
    "Significance is determined by collecting the p-values of every observed edge and applying a false discovery rate correction (Benjamini--Hochberg) to that list of p-values.\n",
    "Edges with a q-value below the specified alpha (`permutation_test_alpha` in cell [4]) are significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 54.5 s, sys: 567 ms, total: 55 s\n",
      "Wall time: 56.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "permutations = []\n",
    "for i in range(n_permutations):\n",
    "    # `aggregate_permutated_network` takes in a list of networks right now.\n",
    "    # adjusting the `random_state` parameter in FastICA would allow us to\n",
    "    # generate multiple networks from different ICA models, adding an extra\n",
    "    # stability assessment for the pathway-pathway relationships identified.\n",
    "    permutations.append(aggregate_permuted_network([model_network]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60 edges are significant under the null distribution, generated from 1000 permutations, for alpha = 0.05.\n"
     ]
    }
   ],
   "source": [
    "final_network = network_edges_permutation_test(\n",
    "    model_network, permutations, permutation_test_alpha,\n",
    "    n_networks=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Edges in the network after the permutation test are weighted by their **odds ratios**.\n",
    "The odds ratio is computed by dividing the observed edge weight by the expected edge weight (the average of its non-zero edge weight distribution).\n",
    "If the observed edge does not show up in any of the permuted networks, divide by 1 (no change)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds ratio</th>\n",
       "      <th>pw0</th>\n",
       "      <th>pw1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.298083</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.622554</td>\n",
       "      <td>KEGG-Module-M00334: Type VI secretion system</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.210784</td>\n",
       "      <td>KEGG-Module-M00334: Type VI secretion system</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.083333</td>\n",
       "      <td>KEGG-Module-M00009: Citrate cycle (TCA cycle, ...</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.937360</td>\n",
       "      <td>KEGG-Module-M00178: Ribosome, bacteria</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.984479</td>\n",
       "      <td>KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.092105</td>\n",
       "      <td>KEGG-Module-M00009: Citrate cycle (TCA cycle, ...</td>\n",
       "      <td>KEGG-Module-M00178: Ribosome, bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.445910</td>\n",
       "      <td>KEGG-Module-M00009: Citrate cycle (TCA cycle, ...</td>\n",
       "      <td>KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.941584</td>\n",
       "      <td>KEGG-Module-M00178: Ribosome, bacteria</td>\n",
       "      <td>KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.858824</td>\n",
       "      <td>KEGG-Module-M00242: Zinc transport system</td>\n",
       "      <td>KEGG-Pathway-pae01053: Biosynthesis of siderop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.742647</td>\n",
       "      <td>KEGG-Pathway-pae00405: Phenazine biosynthesis ...</td>\n",
       "      <td>KEGG-Pathway-pae00910: Nitrogen metabolism - P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.847059</td>\n",
       "      <td>KEGG-Pathway-pae00405: Phenazine biosynthesis ...</td>\n",
       "      <td>KEGG-Pathway-pae00460: Cyanoamino acid metabol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.080439</td>\n",
       "      <td>KEGG-Module-M00644: Vanadium resistance, efflu...</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Module-M00156: Cytochrome c oxidase, cbb3...</td>\n",
       "      <td>KEGG-Pathway-pae00220: Arginine biosynthesis -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.933775</td>\n",
       "      <td>KEGG-Pathway-pae00405: Phenazine biosynthesis ...</td>\n",
       "      <td>KEGG-Pathway-pae03070: Bacterial secretion sys...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.506390</td>\n",
       "      <td>KEGG-Pathway-pae00405: Phenazine biosynthesis ...</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.942839</td>\n",
       "      <td>KEGG-Pathway-pae00460: Cyanoamino acid metabol...</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.977901</td>\n",
       "      <td>KEGG-Pathway-pae00350: Tyrosine metabolism - P...</td>\n",
       "      <td>KEGG-Pathway-pae00630: Glyoxylate and dicarbox...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.931034</td>\n",
       "      <td>KEGG-Module-M00222: Phosphate transport system</td>\n",
       "      <td>KEGG-Module-M00223: Phosphonate transport system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.969925</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00405: Phenazine biosynthesis ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.007212</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00627: Aminobenzoate degradati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.832061</td>\n",
       "      <td>KEGG-Module-M00644: Vanadium resistance, efflu...</td>\n",
       "      <td>KEGG-Pathway-pae00627: Aminobenzoate degradati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.378837</td>\n",
       "      <td>KEGG-Pathway-pae00190: Oxidative phosphorylati...</td>\n",
       "      <td>KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.503772</td>\n",
       "      <td>KEGG-Module-M00178: Ribosome, bacteria</td>\n",
       "      <td>KEGG-Pathway-pae00190: Oxidative phosphorylati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.755319</td>\n",
       "      <td>KEGG-Module-M00328: Hemophore/metalloprotease ...</td>\n",
       "      <td>KEGG-Pathway-pae01053: Biosynthesis of siderop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.851429</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00340: Histidine metabolism - ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.863415</td>\n",
       "      <td>KEGG-Pathway-pae00340: Histidine metabolism - ...</td>\n",
       "      <td>KEGG-Pathway-pae00627: Aminobenzoate degradati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>KEGG-Pathway-pae00340: Histidine metabolism - ...</td>\n",
       "      <td>KEGG-Pathway-pae00362: Benzoate degradation - ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.546279</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00362: Benzoate degradation - ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.793103</td>\n",
       "      <td>KEGG-Pathway-pae00362: Benzoate degradation - ...</td>\n",
       "      <td>KEGG-Pathway-pae00627: Aminobenzoate degradati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.159420</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00640: Propanoate metabolism -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2.752243</td>\n",
       "      <td>KEGG-Pathway-pae02024: Quorum sensing - Pseudo...</td>\n",
       "      <td>KEGG-Pathway-pae02040: Flagellar assembly - Ps...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2.970297</td>\n",
       "      <td>KEGG-Module-M00222: Phosphate transport system</td>\n",
       "      <td>KEGG-Pathway-pae00440: Phosphonate and phosphi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3.450815</td>\n",
       "      <td>KEGG-Pathway-pae00520: Amino sugar and nucleot...</td>\n",
       "      <td>KEGG-Pathway-pae02040: Flagellar assembly - Ps...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>KEGG-Module-M00208: Glycine betaine/proline tr...</td>\n",
       "      <td>KEGG-Pathway-pae00260: Glycine, serine and thr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Module-M00709: Macrolide resistance, MacA...</td>\n",
       "      <td>KEGG-Pathway-pae00260: Glycine, serine and thr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.916667</td>\n",
       "      <td>KEGG-Module-M00242: Zinc transport system</td>\n",
       "      <td>KEGG-Pathway-pae00260: Glycine, serine and thr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Module-M00242: Zinc transport system</td>\n",
       "      <td>KEGG-Module-M00709: Macrolide resistance, MacA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Pathway-pae00910: Nitrogen metabolism - P...</td>\n",
       "      <td>KEGG-Pathway-pae04122: Sulfur relay system - P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.925134</td>\n",
       "      <td>KEGG-Pathway-pae00030: Pentose phosphate pathw...</td>\n",
       "      <td>KEGG-Pathway-pae00920: Sulfur metabolism - Pse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae01501: beta-Lactam resistance ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>KEGG-Pathway-pae00625: Chloroalkane and chloro...</td>\n",
       "      <td>KEGG-Pathway-pae01501: beta-Lactam resistance ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.939024</td>\n",
       "      <td>KEGG-Pathway-pae00630: Glyoxylate and dicarbox...</td>\n",
       "      <td>KEGG-Pathway-pae00640: Propanoate metabolism -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2.580101</td>\n",
       "      <td>KEGG-Module-M00324: Dipeptide transport system</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.968872</td>\n",
       "      <td>KEGG-Pathway-pae00280: Valine, leucine and iso...</td>\n",
       "      <td>KEGG-Pathway-pae00643: Styrene degradation - P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Module-M00324: Dipeptide transport system</td>\n",
       "      <td>KEGG-Pathway-pae00643: Styrene degradation - P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.951807</td>\n",
       "      <td>KEGG-Pathway-pae00260: Glycine, serine and thr...</td>\n",
       "      <td>KEGG-Pathway-pae00680: Methane metabolism - Ps...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2.727862</td>\n",
       "      <td>KEGG-Module-M00324: Dipeptide transport system</td>\n",
       "      <td>KEGG-Module-M00334: Type VI secretion system</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Module-M00188: NitT/TauT family transport...</td>\n",
       "      <td>KEGG-Pathway-pae00920: Sulfur metabolism - Pse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>KEGG-Module-M00188: NitT/TauT family transport...</td>\n",
       "      <td>KEGG-Pathway-pae00550: Peptidoglycan biosynthe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>KEGG-Module-M00223: Phosphonate transport system</td>\n",
       "      <td>KEGG-Pathway-pae00564: Glycerophospholipid met...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.965517</td>\n",
       "      <td>KEGG-Module-M00331: Type II general secretion ...</td>\n",
       "      <td>KEGG-Pathway-pae00564: Glycerophospholipid met...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1.956522</td>\n",
       "      <td>KEGG-Pathway-pae00440: Phosphonate and phosphi...</td>\n",
       "      <td>KEGG-Pathway-pae00564: Glycerophospholipid met...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1.928571</td>\n",
       "      <td>KEGG-Module-M00223: Phosphonate transport system</td>\n",
       "      <td>KEGG-Module-M00331: Type II general secretion ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1.981982</td>\n",
       "      <td>KEGG-Module-M00223: Phosphonate transport system</td>\n",
       "      <td>KEGG-Pathway-pae00440: Phosphonate and phosphi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2.976562</td>\n",
       "      <td>KEGG-Module-M00331: Type II general secretion ...</td>\n",
       "      <td>KEGG-Pathway-pae00440: Phosphonate and phosphi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.971429</td>\n",
       "      <td>KEGG-Pathway-pae00030: Pentose phosphate pathw...</td>\n",
       "      <td>KEGG-Pathway-pae01210: 2-Oxocarboxylic acid me...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1.975309</td>\n",
       "      <td>KEGG-Module-M00324: Dipeptide transport system</td>\n",
       "      <td>KEGG-Pathway-pae00640: Propanoate metabolism -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>3.963636</td>\n",
       "      <td>KEGG-Module-M00236: Putative polar amino acid ...</td>\n",
       "      <td>KEGG-Pathway-pae00920: Sulfur metabolism - Pse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>KEGG-Pathway-pae00780: Biotin metabolism - Pse...</td>\n",
       "      <td>KEGG-Pathway-pae01212: Fatty acid metabolism -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    odds ratio                                                pw0  \\\n",
       "0     3.298083  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "1     4.622554       KEGG-Module-M00334: Type VI secretion system   \n",
       "2     3.210784       KEGG-Module-M00334: Type VI secretion system   \n",
       "3     6.083333  KEGG-Module-M00009: Citrate cycle (TCA cycle, ...   \n",
       "4    13.937360             KEGG-Module-M00178: Ribosome, bacteria   \n",
       "5     6.984479  KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...   \n",
       "6     7.092105  KEGG-Module-M00009: Citrate cycle (TCA cycle, ...   \n",
       "7     6.445910  KEGG-Module-M00009: Citrate cycle (TCA cycle, ...   \n",
       "8     6.941584             KEGG-Module-M00178: Ribosome, bacteria   \n",
       "9     2.858824          KEGG-Module-M00242: Zinc transport system   \n",
       "10    2.742647  KEGG-Pathway-pae00405: Phenazine biosynthesis ...   \n",
       "11    2.847059  KEGG-Pathway-pae00405: Phenazine biosynthesis ...   \n",
       "12    5.080439  KEGG-Module-M00644: Vanadium resistance, efflu...   \n",
       "13    2.000000  KEGG-Module-M00156: Cytochrome c oxidase, cbb3...   \n",
       "14    1.933775  KEGG-Pathway-pae00405: Phenazine biosynthesis ...   \n",
       "15    3.506390  KEGG-Pathway-pae00405: Phenazine biosynthesis ...   \n",
       "16    3.942839  KEGG-Pathway-pae00460: Cyanoamino acid metabol...   \n",
       "17    1.977901  KEGG-Pathway-pae00350: Tyrosine metabolism - P...   \n",
       "18    2.931034     KEGG-Module-M00222: Phosphate transport system   \n",
       "19    4.969925  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "20    6.007212  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "21    2.832061  KEGG-Module-M00644: Vanadium resistance, efflu...   \n",
       "22    5.378837  KEGG-Pathway-pae00190: Oxidative phosphorylati...   \n",
       "23    4.503772             KEGG-Module-M00178: Ribosome, bacteria   \n",
       "24    3.755319  KEGG-Module-M00328: Hemophore/metalloprotease ...   \n",
       "25    3.851429  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "26    3.863415  KEGG-Pathway-pae00340: Histidine metabolism - ...   \n",
       "27    3.000000  KEGG-Pathway-pae00340: Histidine metabolism - ...   \n",
       "28    4.546279  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "29    5.793103  KEGG-Pathway-pae00362: Benzoate degradation - ...   \n",
       "30    4.159420  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "31    2.752243  KEGG-Pathway-pae02024: Quorum sensing - Pseudo...   \n",
       "32    2.970297     KEGG-Module-M00222: Phosphate transport system   \n",
       "33    3.450815  KEGG-Pathway-pae00520: Amino sugar and nucleot...   \n",
       "34    3.000000  KEGG-Module-M00208: Glycine betaine/proline tr...   \n",
       "35    2.000000  KEGG-Module-M00709: Macrolide resistance, MacA...   \n",
       "36    1.916667          KEGG-Module-M00242: Zinc transport system   \n",
       "37    2.000000          KEGG-Module-M00242: Zinc transport system   \n",
       "38    2.000000  KEGG-Pathway-pae00910: Nitrogen metabolism - P...   \n",
       "39    1.925134  KEGG-Pathway-pae00030: Pentose phosphate pathw...   \n",
       "40    2.000000  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "41    1.000000  KEGG-Pathway-pae00625: Chloroalkane and chloro...   \n",
       "42    1.939024  KEGG-Pathway-pae00630: Glyoxylate and dicarbox...   \n",
       "43    2.580101     KEGG-Module-M00324: Dipeptide transport system   \n",
       "44    1.968872  KEGG-Pathway-pae00280: Valine, leucine and iso...   \n",
       "45    2.000000     KEGG-Module-M00324: Dipeptide transport system   \n",
       "46    1.951807  KEGG-Pathway-pae00260: Glycine, serine and thr...   \n",
       "47    2.727862     KEGG-Module-M00324: Dipeptide transport system   \n",
       "48    2.000000  KEGG-Module-M00188: NitT/TauT family transport...   \n",
       "49    1.000000  KEGG-Module-M00188: NitT/TauT family transport...   \n",
       "50    2.000000   KEGG-Module-M00223: Phosphonate transport system   \n",
       "51    1.965517  KEGG-Module-M00331: Type II general secretion ...   \n",
       "52    1.956522  KEGG-Pathway-pae00440: Phosphonate and phosphi...   \n",
       "53    1.928571   KEGG-Module-M00223: Phosphonate transport system   \n",
       "54    1.981982   KEGG-Module-M00223: Phosphonate transport system   \n",
       "55    2.976562  KEGG-Module-M00331: Type II general secretion ...   \n",
       "56    1.971429  KEGG-Pathway-pae00030: Pentose phosphate pathw...   \n",
       "57    1.975309     KEGG-Module-M00324: Dipeptide transport system   \n",
       "58    3.963636  KEGG-Module-M00236: Putative polar amino acid ...   \n",
       "59    1.000000  KEGG-Pathway-pae00780: Biotin metabolism - Pse...   \n",
       "\n",
       "                                                  pw1  \n",
       "0   KEGG-Pathway-pae02024: Quorum sensing - Pseudo...  \n",
       "1   KEGG-Pathway-pae02024: Quorum sensing - Pseudo...  \n",
       "2   KEGG-Pathway-pae00280: Valine, leucine and iso...  \n",
       "3   KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...  \n",
       "4   KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...  \n",
       "5   KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...  \n",
       "6              KEGG-Module-M00178: Ribosome, bacteria  \n",
       "7   KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...  \n",
       "8   KEGG-Pathway-pae00020: Citrate cycle (TCA cycl...  \n",
       "9   KEGG-Pathway-pae01053: Biosynthesis of siderop...  \n",
       "10  KEGG-Pathway-pae00910: Nitrogen metabolism - P...  \n",
       "11  KEGG-Pathway-pae00460: Cyanoamino acid metabol...  \n",
       "12  KEGG-Pathway-pae02024: Quorum sensing - Pseudo...  \n",
       "13  KEGG-Pathway-pae00220: Arginine biosynthesis -...  \n",
       "14  KEGG-Pathway-pae03070: Bacterial secretion sys...  \n",
       "15  KEGG-Pathway-pae02024: Quorum sensing - Pseudo...  \n",
       "16  KEGG-Pathway-pae02024: Quorum sensing - Pseudo...  \n",
       "17  KEGG-Pathway-pae00630: Glyoxylate and dicarbox...  \n",
       "18   KEGG-Module-M00223: Phosphonate transport system  \n",
       "19  KEGG-Pathway-pae00405: Phenazine biosynthesis ...  \n",
       "20  KEGG-Pathway-pae00627: Aminobenzoate degradati...  \n",
       "21  KEGG-Pathway-pae00627: Aminobenzoate degradati...  \n",
       "22  KEGG-Pathway-pae03010: Ribosome - Pseudomonas ...  \n",
       "23  KEGG-Pathway-pae00190: Oxidative phosphorylati...  \n",
       "24  KEGG-Pathway-pae01053: Biosynthesis of siderop...  \n",
       "25  KEGG-Pathway-pae00340: Histidine metabolism - ...  \n",
       "26  KEGG-Pathway-pae00627: Aminobenzoate degradati...  \n",
       "27  KEGG-Pathway-pae00362: Benzoate degradation - ...  \n",
       "28  KEGG-Pathway-pae00362: Benzoate degradation - ...  \n",
       "29  KEGG-Pathway-pae00627: Aminobenzoate degradati...  \n",
       "30  KEGG-Pathway-pae00640: Propanoate metabolism -...  \n",
       "31  KEGG-Pathway-pae02040: Flagellar assembly - Ps...  \n",
       "32  KEGG-Pathway-pae00440: Phosphonate and phosphi...  \n",
       "33  KEGG-Pathway-pae02040: Flagellar assembly - Ps...  \n",
       "34  KEGG-Pathway-pae00260: Glycine, serine and thr...  \n",
       "35  KEGG-Pathway-pae00260: Glycine, serine and thr...  \n",
       "36  KEGG-Pathway-pae00260: Glycine, serine and thr...  \n",
       "37  KEGG-Module-M00709: Macrolide resistance, MacA...  \n",
       "38  KEGG-Pathway-pae04122: Sulfur relay system - P...  \n",
       "39  KEGG-Pathway-pae00920: Sulfur metabolism - Pse...  \n",
       "40  KEGG-Pathway-pae01501: beta-Lactam resistance ...  \n",
       "41  KEGG-Pathway-pae01501: beta-Lactam resistance ...  \n",
       "42  KEGG-Pathway-pae00640: Propanoate metabolism -...  \n",
       "43  KEGG-Pathway-pae00280: Valine, leucine and iso...  \n",
       "44  KEGG-Pathway-pae00643: Styrene degradation - P...  \n",
       "45  KEGG-Pathway-pae00643: Styrene degradation - P...  \n",
       "46  KEGG-Pathway-pae00680: Methane metabolism - Ps...  \n",
       "47       KEGG-Module-M00334: Type VI secretion system  \n",
       "48  KEGG-Pathway-pae00920: Sulfur metabolism - Pse...  \n",
       "49  KEGG-Pathway-pae00550: Peptidoglycan biosynthe...  \n",
       "50  KEGG-Pathway-pae00564: Glycerophospholipid met...  \n",
       "51  KEGG-Pathway-pae00564: Glycerophospholipid met...  \n",
       "52  KEGG-Pathway-pae00564: Glycerophospholipid met...  \n",
       "53  KEGG-Module-M00331: Type II general secretion ...  \n",
       "54  KEGG-Pathway-pae00440: Phosphonate and phosphi...  \n",
       "55  KEGG-Pathway-pae00440: Phosphonate and phosphi...  \n",
       "56  KEGG-Pathway-pae01210: 2-Oxocarboxylic acid me...  \n",
       "57  KEGG-Pathway-pae00640: Propanoate metabolism -...  \n",
       "58  KEGG-Pathway-pae00920: Sulfur metabolism - Pse...  \n",
       "59  KEGG-Pathway-pae01212: Fatty acid metabolism -...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significant_edges = []\n",
    "for edge_id, edge_obj in final_network.edges.items():\n",
    "    if edge_obj.significant:\n",
    "        pw0, pw1 = final_network.get_edge_pathways(edge_id)\n",
    "        significant_edges.append({\"pw0\": pw0,\n",
    "                                  \"pw1\": pw1,\n",
    "                                  \"odds ratio\": edge_obj.weight})\n",
    "significant_edges_df = pd.DataFrame(significant_edges)\n",
    "significant_edges_df.sort_values(\"odds ratio\", ascending=False)\n",
    "significant_edges_df"
   ]
  }
 ],
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