Example_notebooks/Mass2Motifs_2_Network_FeatureBased.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download GNPS data <br>\n",
    "Replace Job ID below with your GNPS job ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 2078k    0 2078k    0     0   716k      0 --:--:--  0:00:02 --:--:--  716k\n",
      "Archive:  GNPS_output_graphML.zip\n",
      "  inflating: GNPS_output_graphML/FEATURE-BASED-MOLECULAR-NETWORKING-b817262c-download_cytoscape_data-main.graphml  \n",
      "  inflating: GNPS_output_graphML/params.xml  \n",
      "  inflating: GNPS_output_graphML/DB_result/199547f600354368817ea480a44d4791.tsv  \n",
      "  inflating: GNPS_output_graphML/networking_pairs_results_file_filtered/0aafc490cc974963a5b02dccc4cd27f6.tsv  \n",
      "  inflating: GNPS_output_graphML/clusterinfo_summary/864b10ab978e40a68ddb30f358079b00.tsv  \n",
      "  inflating: GNPS_output_graphML/gnps_molecular_network_graphml/e359f26d5ed4488eb589126f2fe9abc8.graphml  \n"
     ]
    }
   ],
   "source": [
    "! curl -d \"\" 'https://gnps.ucsd.edu/ProteoSAFe/DownloadResult?task=b817262cb6114e7295fee4f73b22a3ad&view=download_cytoscape_data' -o GNPS_output_graphML.zip\n",
    "! unzip -d GNPS_output_graphML/ GNPS_output_graphML.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from pyMolNetEnhancer import *\n",
    "from networkx import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import MS2LDA data, replace the MS2LDA job ID in the link below with your MS2LDA job ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "motifs = pd.read_csv('http://ms2lda.org/basicviz/get_gnps_summary/907')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scans</th>\n",
       "      <th>precursormass</th>\n",
       "      <th>parentrt</th>\n",
       "      <th>document</th>\n",
       "      <th>motif</th>\n",
       "      <th>probability</th>\n",
       "      <th>overlap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11542</td>\n",
       "      <td>791.6269</td>\n",
       "      <td>549.98</td>\n",
       "      <td>11542</td>\n",
       "      <td>motif_224</td>\n",
       "      <td>0.374831</td>\n",
       "      <td>0.672890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11542</td>\n",
       "      <td>791.6269</td>\n",
       "      <td>549.98</td>\n",
       "      <td>11542</td>\n",
       "      <td>motif_226</td>\n",
       "      <td>0.256762</td>\n",
       "      <td>0.936743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11542</td>\n",
       "      <td>791.6269</td>\n",
       "      <td>549.98</td>\n",
       "      <td>11542</td>\n",
       "      <td>motif_159</td>\n",
       "      <td>0.362378</td>\n",
       "      <td>0.946766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11543</td>\n",
       "      <td>664.5352</td>\n",
       "      <td>504.11</td>\n",
       "      <td>11543</td>\n",
       "      <td>motif_284</td>\n",
       "      <td>0.486913</td>\n",
       "      <td>0.957790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11543</td>\n",
       "      <td>664.5352</td>\n",
       "      <td>504.11</td>\n",
       "      <td>11543</td>\n",
       "      <td>motif_181</td>\n",
       "      <td>0.490921</td>\n",
       "      <td>0.191585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scans  precursormass  parentrt  document      motif  probability   overlap\n",
       "0  11542       791.6269    549.98     11542  motif_224     0.374831  0.672890\n",
       "1  11542       791.6269    549.98     11542  motif_226     0.256762  0.936743\n",
       "2  11542       791.6269    549.98     11542  motif_159     0.362378  0.946766\n",
       "3  11543       664.5352    504.11     11543  motif_284     0.486913  0.957790\n",
       "4  11543       664.5352    504.11     11543  motif_181     0.490921  0.191585"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motifs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = pd.read_csv('GNPS_output_graphML/networking_pairs_results_file_filtered/' + str(os.listdir('GNPS_output_graphML/networking_pairs_results_file_filtered/')[0]), sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLUSTERID1</th>\n",
       "      <th>CLUSTERID2</th>\n",
       "      <th>ComponentIndex</th>\n",
       "      <th>Cosine</th>\n",
       "      <th>DeltaMZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>632</td>\n",
       "      <td>2706</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9542</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>704</td>\n",
       "      <td>4778</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9458</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>704</td>\n",
       "      <td>6480</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9315</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6480</td>\n",
       "      <td>7674</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>704</td>\n",
       "      <td>2760</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000</td>\n",
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      "text/plain": [
       "   CLUSTERID1  CLUSTERID2  ComponentIndex  Cosine  DeltaMZ\n",
       "0         632        2706               1  0.9542    0.000\n",
       "1         704        4778               2  0.9458   -0.000\n",
       "2         704        6480               2  0.9315   -0.001\n",
       "3        6480        7674               2  0.9171   -0.000\n",
       "4         704        2760               2  1.0000    0.000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create network data with mapped motifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "motif_network = Mass2Motif_2_Network(edges,motifs,prob = 0.01,overlap = 0.3, top = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precursormass</th>\n",
       "      <th>parentrt</th>\n",
       "      <th>document</th>\n",
       "      <th>motif</th>\n",
       "      <th>probability</th>\n",
       "      <th>overlap</th>\n",
       "      <th>motif_224</th>\n",
       "      <th>motif_226</th>\n",
       "      <th>motif_159</th>\n",
       "      <th>motif_284</th>\n",
       "      <th>...</th>\n",
       "      <th>motif_279</th>\n",
       "      <th>motif_101</th>\n",
       "      <th>motif_194</th>\n",
       "      <th>motif_21</th>\n",
       "      <th>motif_268</th>\n",
       "      <th>motif_211</th>\n",
       "      <th>motif_59</th>\n",
       "      <th>motif_9</th>\n",
       "      <th>motif_86</th>\n",
       "      <th>motif_11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[108.5179]</td>\n",
       "      <td>[614.7]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[motif_68]</td>\n",
       "      <td>[0.999887349136534]</td>\n",
       "      <td>[0.481143964810191]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[99.51299999999999]</td>\n",
       "      <td>[614.66]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[motif_68]</td>\n",
       "      <td>[0.9179863395371429]</td>\n",
       "      <td>[0.48705219816889295]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[176.972]</td>\n",
       "      <td>[627.39]</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[motif_258]</td>\n",
       "      <td>[0.9999055546033491]</td>\n",
       "      <td>[0.38119890477732504]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[385.3466, 385.3466, 385.3466, 385.3466, 385.3...</td>\n",
       "      <td>[522.15, 522.15, 522.15, 522.15, 522.15, 522.15]</td>\n",
       "      <td>[8, 8, 8, 8, 8, 8]</td>\n",
       "      <td>[motif_150, motif_146, motif_131, motif_104, m...</td>\n",
       "      <td>[0.0417360419404697, 0.31512992919703803, 0.02...</td>\n",
       "      <td>[0.57934706565501, 0.558333509294337, 0.606623...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[204.9669]</td>\n",
       "      <td>[628.42]</td>\n",
       "      <td>[11]</td>\n",
       "      <td>[motif_104]</td>\n",
       "      <td>[0.49988363228626503]</td>\n",
       "      <td>[0.797610043469034]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 273 columns</p>\n",
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      ],
      "text/plain": [
       "                                           precursormass  \\\n",
       "scans                                                      \n",
       "1                                             [108.5179]   \n",
       "3                                    [99.51299999999999]   \n",
       "4                                              [176.972]   \n",
       "8      [385.3466, 385.3466, 385.3466, 385.3466, 385.3...   \n",
       "11                                            [204.9669]   \n",
       "\n",
       "                                               parentrt            document  \\\n",
       "scans                                                                         \n",
       "1                                               [614.7]                 [1]   \n",
       "3                                              [614.66]                 [3]   \n",
       "4                                              [627.39]                 [4]   \n",
       "8      [522.15, 522.15, 522.15, 522.15, 522.15, 522.15]  [8, 8, 8, 8, 8, 8]   \n",
       "11                                             [628.42]                [11]   \n",
       "\n",
       "                                                   motif  \\\n",
       "scans                                                      \n",
       "1                                             [motif_68]   \n",
       "3                                             [motif_68]   \n",
       "4                                            [motif_258]   \n",
       "8      [motif_150, motif_146, motif_131, motif_104, m...   \n",
       "11                                           [motif_104]   \n",
       "\n",
       "                                             probability  \\\n",
       "scans                                                      \n",
       "1                                    [0.999887349136534]   \n",
       "3                                   [0.9179863395371429]   \n",
       "4                                   [0.9999055546033491]   \n",
       "8      [0.0417360419404697, 0.31512992919703803, 0.02...   \n",
       "11                                 [0.49988363228626503]   \n",
       "\n",
       "                                                 overlap  motif_224  \\\n",
       "scans                                                                 \n",
       "1                                    [0.481143964810191]        0.0   \n",
       "3                                  [0.48705219816889295]        0.0   \n",
       "4                                  [0.38119890477732504]        0.0   \n",
       "8      [0.57934706565501, 0.558333509294337, 0.606623...        0.0   \n",
       "11                                   [0.797610043469034]        0.0   \n",
       "\n",
       "       motif_226  motif_159  motif_284    ...     motif_279  motif_101  \\\n",
       "scans                                     ...                            \n",
       "1            0.0        0.0        0.0    ...           0.0        0.0   \n",
       "3            0.0        0.0        0.0    ...           0.0        0.0   \n",
       "4            0.0        0.0        0.0    ...           0.0        0.0   \n",
       "8            0.0        0.0        0.0    ...           0.0        0.0   \n",
       "11           0.0        0.0        0.0    ...           0.0        0.0   \n",
       "\n",
       "       motif_194  motif_21  motif_268  motif_211  motif_59  motif_9  motif_86  \\\n",
       "scans                                                                           \n",
       "1            0.0       0.0        0.0        0.0       0.0      0.0       0.0   \n",
       "3            0.0       0.0        0.0        0.0       0.0      0.0       0.0   \n",
       "4            0.0       0.0        0.0        0.0       0.0      0.0       0.0   \n",
       "8            0.0       0.0        0.0        0.0       0.0      0.0       0.0   \n",
       "11           0.0       0.0        0.0        0.0       0.0      0.0       0.0   \n",
       "\n",
       "       motif_11  \n",
       "scans            \n",
       "1           0.0  \n",
       "3           0.0  \n",
       "4           0.0  \n",
       "8           0.0  \n",
       "11          0.0  \n",
       "\n",
       "[5 rows x 273 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motif_network['nodes'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLUSTERID1</th>\n",
       "      <th>interaction</th>\n",
       "      <th>CLUSTERID2</th>\n",
       "      <th>ComponentIndex</th>\n",
       "      <th>Cosine</th>\n",
       "      <th>DeltaMZ</th>\n",
       "      <th>MEH</th>\n",
       "      <th>OtherScore</th>\n",
       "      <th>shared_motifs</th>\n",
       "      <th>TopSharedMotifs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>632</td>\n",
       "      <td>cosine</td>\n",
       "      <td>2706</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9542</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[motif_54, motif_98]</td>\n",
       "      <td>[motif_54, motif_98]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>704</td>\n",
       "      <td>cosine</td>\n",
       "      <td>4778</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9458</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[motif_94]</td>\n",
       "      <td>[motif_94, motif_178, motif_260, motif_289, mo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>704</td>\n",
       "      <td>cosine</td>\n",
       "      <td>6480</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9315</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[motif_94, motif_178, motif_260]</td>\n",
       "      <td>[motif_94, motif_178, motif_260, motif_289, mo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6480</td>\n",
       "      <td>cosine</td>\n",
       "      <td>7674</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[motif_178, motif_260]</td>\n",
       "      <td>[motif_94, motif_178, motif_260, motif_289, mo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>704</td>\n",
       "      <td>cosine</td>\n",
       "      <td>2760</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[motif_94, motif_178, motif_260, motif_289]</td>\n",
       "      <td>[motif_94, motif_178, motif_260, motif_289, mo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CLUSTERID1 interaction  CLUSTERID2  ComponentIndex  Cosine  DeltaMZ  MEH  \\\n",
       "0         632      cosine        2706             1.0  0.9542    0.000  0.0   \n",
       "1         704      cosine        4778             2.0  0.9458   -0.000  0.0   \n",
       "2         704      cosine        6480             2.0  0.9315   -0.001  0.0   \n",
       "3        6480      cosine        7674             2.0  0.9171   -0.000  0.0   \n",
       "4         704      cosine        2760             2.0  1.0000    0.000  0.0   \n",
       "\n",
       "   OtherScore                                shared_motifs  \\\n",
       "0         0.0                         [motif_54, motif_98]   \n",
       "1         0.0                                   [motif_94]   \n",
       "2         0.0             [motif_94, motif_178, motif_260]   \n",
       "3         0.0                       [motif_178, motif_260]   \n",
       "4         0.0  [motif_94, motif_178, motif_260, motif_289]   \n",
       "\n",
       "                                     TopSharedMotifs  \n",
       "0                               [motif_54, motif_98]  \n",
       "1  [motif_94, motif_178, motif_260, motif_289, mo...  \n",
       "2  [motif_94, motif_178, motif_260, motif_289, mo...  \n",
       "3  [motif_94, motif_178, motif_260, motif_289, mo...  \n",
       "4  [motif_94, motif_178, motif_260, motif_289, mo...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motif_network['edges'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "write network data with mapped motifs to files: \n",
    "The edges file can be importet as network into Cytoscape, whereas the nodes file can be imported as table. Select column 'CLUSTERID1' as Source Node, column 'interact' as Interaction Type and 'CLUSTERID2' as Target Node. When importing the nodes file as table, make sure to select the semicolon delimiter under Advanced Options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "motif_network['edges'].to_csv(\"Mass2Motifs_Edges_FeatureBased.tsv\",sep='\\t',index=False)\n",
    "motif_network['nodes'].to_csv(\"Mass2Motifs_Nodes_FeatureBased.tsv\",sep='\\t',index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create graphML file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "MG = make_motif_graphml(motif_network['nodes'],motif_network['edges'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "write graphML file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.write_graphml(MG, \"Motif_Network_FeatureBased.graphml\", infer_numeric_types = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## map chemical class information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read in chemical class information (to create this file follow descriptions in ChemicalClasses_2_Network_FeatureBased.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.read_csv('ClassyFireResults_Network.txt', sep = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster index</th>\n",
       "      <th>CF_componentindex</th>\n",
       "      <th>CF_NrNodes</th>\n",
       "      <th>CF_kingdom</th>\n",
       "      <th>CF_kingdom_score</th>\n",
       "      <th>CF_superclass</th>\n",
       "      <th>CF_superclass_score</th>\n",
       "      <th>CF_class</th>\n",
       "      <th>CF_class_score</th>\n",
       "      <th>CF_subclass</th>\n",
       "      <th>CF_subclass_score</th>\n",
       "      <th>CF_Dparent</th>\n",
       "      <th>CF_Dparent_score</th>\n",
       "      <th>CF_MFramework</th>\n",
       "      <th>CF_MFramework_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>S1</td>\n",
       "      <td>1</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no matches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster index CF_componentindex  CF_NrNodes  CF_kingdom  CF_kingdom_score  \\\n",
       "0              1               343           4  no matches               NaN   \n",
       "1              3               343           4  no matches               NaN   \n",
       "2             20               343           4  no matches               NaN   \n",
       "3           1959               343           4  no matches               NaN   \n",
       "4              2                S1           1  no matches               NaN   \n",
       "\n",
       "  CF_superclass  CF_superclass_score    CF_class  CF_class_score CF_subclass  \\\n",
       "0    no matches                  NaN  no matches             NaN  no matches   \n",
       "1    no matches                  NaN  no matches             NaN  no matches   \n",
       "2    no matches                  NaN  no matches             NaN  no matches   \n",
       "3    no matches                  NaN  no matches             NaN  no matches   \n",
       "4    no matches                  NaN  no matches             NaN  no matches   \n",
       "\n",
       "   CF_subclass_score  CF_Dparent  CF_Dparent_score CF_MFramework  \\\n",
       "0                NaN  no matches               NaN    no matches   \n",
       "1                NaN  no matches               NaN    no matches   \n",
       "2                NaN  no matches               NaN    no matches   \n",
       "3                NaN  no matches               NaN    no matches   \n",
       "4                NaN  no matches               NaN    no matches   \n",
       "\n",
       "   CF_MFramework_score  \n",
       "0                  NaN  \n",
       "1                  NaN  \n",
       "2                  NaN  \n",
       "3                  NaN  \n",
       "4                  NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphML_classy = make_classyfire_graphml(MG,final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "write graphML file containing motif and chemical class information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.write_graphml(graphML_classy, \"Motif_ChemicalClass_Network_FeatureBased.graphml\", infer_numeric_types = True)"
   ]
  }
 ],
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