PN_SH_Neg.ipynb

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    "## Pinot Noir Metabolomics Data Analysis\n",
    "### Samples collected on orbitrap processed in MZmine. "
   ]
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   "source": [
    "Pinot noir grapes grown in the Okanagan were exposed to smoke and compared to control grapes that were not exposed to smoke. Grapes were frozen until anlaysis by positive and negative mode LCMS on an Orbitrap mass spectrometer. Full scan data was also collected using GCMS. \n",
    "LCMS data was processed in MZmine3, GCMS data was processed in MS-DIAL. "
   ]
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   "metadata": {},
   "source": [
    "Import libraries to be used"
   ]
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# from sklearn.model_selection import train_test_split\n",
    "import cimcb_lite as cb # statitiscal package from the Center for integrative metabolomics and computational biology for non-targeted MS data"
   ]
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   "source": [
    "# Merge peak Data with Metadata to align treatment information\n",
    "# pnMetaData = pd.read_excel(\"C:/Users/7820/Desktop/Zandberg Grape Metab/PinotNoirSH/PinotNoirMetaData.xlsx\")\n",
    "# Datatemp = pd.read_excel(\"C:/Users/7820/Desktop/Zandberg Grape Metab/PinotNoirSH/Negative Mode/PinotNoir_SH_Neg_PyInput.xlsx\", sheet_name=\"Data\")\n",
    "# Datatemp.merge(pnMetaData, how=\"left\", left_on=\"SampleID\", right_on=\"LIMS\").to_excel(\"tempfile.xlsx\")\n",
    "#manually moved sample info columns to the front then resaved the pytoninput file"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data"
   ]
  },
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   "execution_count": 2,
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     "name": "stdout",
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     "text": [
      "Loadings PeakFile: PeakInfo\n",
      "Loadings DataFile: Data\n",
      "Data Table & Peak Table is suitable.\n",
      "TOTAL SAMPLES: 34 TOTAL PEAKS: 2744\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "input = 'C:/Users/7820/Desktop/Zandberg Grape Metab/PinotNoirSH/Negative Mode/PinotNoir_SH_Neg_PyInput.xlsx'\n",
    "dataTable, peakTable = cb.utils.load_dataXL(input, DataSheet='Data', PeakSheet = 'PeakInfo')"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Calculate the RSD of each compound in the QC injections in order to filter dataset by compounds with inconsistent signals in QC samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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     "data": {
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       "<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>Idx</th>\n",
       "      <th>Name</th>\n",
       "      <th>mz</th>\n",
       "      <th>rt</th>\n",
       "      <th>Label</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>mol_formula</th>\n",
       "      <th>neutral_mass</th>\n",
       "      <th>database_match_info</th>\n",
       "      <th>QC_RSD_x</th>\n",
       "      <th>QC_RSD_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>feature_4</td>\n",
       "      <td>179.0552</td>\n",
       "      <td>1.09</td>\n",
       "      <td>D-Glucose</td>\n",
       "      <td>D-Glucose</td>\n",
       "      <td>C6H12O6</td>\n",
       "      <td>180.0634</td>\n",
       "      <td>KEGG C00031</td>\n",
       "      <td>198.347721</td>\n",
       "      <td>198.347721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>feature_7</td>\n",
       "      <td>226.9651</td>\n",
       "      <td>1.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>feature_8</td>\n",
       "      <td>161.0443</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Lichenin</td>\n",
       "      <td>Lichenin</td>\n",
       "      <td>C6H10O5</td>\n",
       "      <td>162.0528</td>\n",
       "      <td>KEGG C00478</td>\n",
       "      <td>15.895904</td>\n",
       "      <td>15.895904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>feature_9</td>\n",
       "      <td>130.9822</td>\n",
       "      <td>1.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.827034</td>\n",
       "      <td>4.827034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>feature_12</td>\n",
       "      <td>159.9781</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3,4-Dichloroaniline</td>\n",
       "      <td>3,4-Dichloroaniline</td>\n",
       "      <td>C6H5Cl2N</td>\n",
       "      <td>160.9799</td>\n",
       "      <td>KEGG C02791</td>\n",
       "      <td>3.328703</td>\n",
       "      <td>3.328703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idx        Name        mz    rt                Label        compound_name  \\\n",
       "0    1   feature_4  179.0552  1.09            D-Glucose            D-Glucose   \n",
       "1    2   feature_7  226.9651  1.11                  NaN                  NaN   \n",
       "2    3   feature_8  161.0443  1.11             Lichenin             Lichenin   \n",
       "3    4   feature_9  130.9822  1.12                  NaN                  NaN   \n",
       "4    5  feature_12  159.9781  1.13  3,4-Dichloroaniline  3,4-Dichloroaniline   \n",
       "\n",
       "  mol_formula  neutral_mass database_match_info    QC_RSD_x    QC_RSD_y  \n",
       "0     C6H12O6      180.0634         KEGG C00031  198.347721  198.347721  \n",
       "1         NaN           NaN                 NaN  100.000000         NaN  \n",
       "2     C6H10O5      162.0528         KEGG C00478   15.895904   15.895904  \n",
       "3         NaN           NaN                 NaN    4.827034    4.827034  \n",
       "4    C6H5Cl2N      160.9799         KEGG C02791    3.328703    3.328703  "
      ]
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     "execution_count": 29,
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   ],
   "source": [
    "QC_means = dataTable[dataTable['SampleType'] == 'qc'].iloc[:, 3:].agg(['mean', 'std']).T\n",
    "QC_means['QC_RSD'] = QC_means['std'] / QC_means['mean'] * 100\n",
    "peakTable = peakTable.merge(QC_means.reset_index().rename(columns={'index': 'Name'}).drop(['mean', 'std'], axis=1), on='Name', how='left').fillna({'QC_RSD': 100})\n",
    "peakTable.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove features wtih a QC_RSD higher than 20%. This number can be adjusted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started with 2744 now have 1200 peaks\n"
     ]
    }
   ],
   "source": [
    "rsd = peakTable['QC_RSD']\n",
    "peakTableClean = peakTable[(rsd < 20)]\n",
    "print('Started with {}'.format(len(peakTable)), 'now have {}'.format(len(peakTableClean)), 'peaks')"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "Create PCA plots of the data. \n"
   ]
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Xknn: 34 rows & 1200 columns\n"
     ]
    },
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\",\"dtype\":\"float64\",\"shape\":[1200]},\"y\":{\"__ndarray__\":\"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      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1698"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "peaklist = peakTableClean['Name']                   # Set peaklist to the metabolite names in the peakTableClean\n",
    "X = dataTable[peaklist].values                      # Extract X matrix from dataTable using peaklist\n",
    "Xlog = np.log10(X)                                  # Log scale (base-10)\n",
    "Xscale = cb.utils.scale(Xlog, method='auto')        # methods include auto, pareto, vast, and level\n",
    "Xknn = cb.utils.knnimpute(Xscale, k=3)              # missing value imputation (knn - 3 nearest neighbors)\n",
    "\n",
    "print(\"Xknn: {} rows & {} columns\".format(*Xknn.shape))\n",
    "\n",
    "cb.plot.pca(Xknn,\n",
    "            pcx=1,                                                  # pc for x-axis\n",
    "            pcy=2,                                                  # pc for y-axis\n",
    "            group_label=dataTable['SampleType'])  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conduct univariate Statistics for each feature to determine which would be of interest for pathway analysis and identification confirmation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>Name</th>\n",
       "      <th>Label</th>\n",
       "      <th>Grp0_Mean</th>\n",
       "      <th>Grp0_Mean-95CI</th>\n",
       "      <th>Grp1_Mean</th>\n",
       "      <th>Grp1_Mean-95CI</th>\n",
       "      <th>Sign</th>\n",
       "      <th>TTestStat</th>\n",
       "      <th>TTestPvalue</th>\n",
       "      <th>bhQvalue</th>\n",
       "      <th>TotalMissing</th>\n",
       "      <th>PercTotalMissing</th>\n",
       "      <th>Grp0_Missing</th>\n",
       "      <th>Grp1_Missing</th>\n",
       "      <th>ShapiroW</th>\n",
       "      <th>ShapiroPvalue</th>\n",
       "      <th>LeveneW</th>\n",
       "      <th>LevenePvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>feature_8</td>\n",
       "      <td>Lichenin</td>\n",
       "      <td>14973.994125</td>\n",
       "      <td>(12586.69, 17361.3)</td>\n",
       "      <td>13981.036925</td>\n",
       "      <td>(10882.75, 17079.32)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.497578</td>\n",
       "      <td>0.626505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>44.828</td>\n",
       "      <td>46.667</td>\n",
       "      <td>42.857</td>\n",
       "      <td>0.925662</td>\n",
       "      <td>0.207939</td>\n",
       "      <td>1.129694</td>\n",
       "      <td>0.305831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>feature_9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21529.507364</td>\n",
       "      <td>(20107.52, 22951.5)</td>\n",
       "      <td>23784.629385</td>\n",
       "      <td>(21960.02, 25609.24)</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.858072</td>\n",
       "      <td>0.076593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>17.241</td>\n",
       "      <td>26.667</td>\n",
       "      <td>7.143</td>\n",
       "      <td>0.945349</td>\n",
       "      <td>0.214378</td>\n",
       "      <td>1.464142</td>\n",
       "      <td>0.239114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>feature_12</td>\n",
       "      <td>3,4-Dichloroaniline</td>\n",
       "      <td>136310.605667</td>\n",
       "      <td>(128486.28, 144134.94)</td>\n",
       "      <td>153927.207143</td>\n",
       "      <td>(143639.89, 164214.53)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.694031</td>\n",
       "      <td>0.011989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.954670</td>\n",
       "      <td>0.241498</td>\n",
       "      <td>0.779778</td>\n",
       "      <td>0.385004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>feature_13</td>\n",
       "      <td>Trichloroethanol glucuronide</td>\n",
       "      <td>7730.000800</td>\n",
       "      <td>(6900.49, 8559.51)</td>\n",
       "      <td>8750.084600</td>\n",
       "      <td>(7832.28, 9667.89)</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.619420</td>\n",
       "      <td>0.133646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>55.172</td>\n",
       "      <td>53.333</td>\n",
       "      <td>57.143</td>\n",
       "      <td>0.964721</td>\n",
       "      <td>0.824360</td>\n",
       "      <td>0.037143</td>\n",
       "      <td>0.850686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>feature_26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33723.963600</td>\n",
       "      <td>(31710.6, 35737.33)</td>\n",
       "      <td>40744.174286</td>\n",
       "      <td>(37344.2, 44144.15)</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.539228</td>\n",
       "      <td>0.001477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.932177</td>\n",
       "      <td>0.062652</td>\n",
       "      <td>1.447666</td>\n",
       "      <td>0.239347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2734</td>\n",
       "      <td>feature_5638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57198.911067</td>\n",
       "      <td>(54107.8, 60290.02)</td>\n",
       "      <td>60200.428286</td>\n",
       "      <td>(56922.91, 63477.94)</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.306935</td>\n",
       "      <td>0.202257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.949656</td>\n",
       "      <td>0.179317</td>\n",
       "      <td>0.093953</td>\n",
       "      <td>0.761562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2736</td>\n",
       "      <td>feature_5642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61490.858733</td>\n",
       "      <td>(57430.62, 65551.09)</td>\n",
       "      <td>70812.527500</td>\n",
       "      <td>(63784.44, 77840.61)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.289437</td>\n",
       "      <td>0.030097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.943505</td>\n",
       "      <td>0.123880</td>\n",
       "      <td>3.909849</td>\n",
       "      <td>0.058296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2740</td>\n",
       "      <td>feature_5646</td>\n",
       "      <td>Ticlopidine</td>\n",
       "      <td>79860.686714</td>\n",
       "      <td>(72401.5, 87319.87)</td>\n",
       "      <td>84481.041143</td>\n",
       "      <td>(74196.3, 94765.78)</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.712786</td>\n",
       "      <td>0.482327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.448</td>\n",
       "      <td>6.667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.977395</td>\n",
       "      <td>0.784098</td>\n",
       "      <td>0.348768</td>\n",
       "      <td>0.559910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>2742</td>\n",
       "      <td>feature_5648</td>\n",
       "      <td>D-Mannitol 1-phosphate</td>\n",
       "      <td>38441.184933</td>\n",
       "      <td>(35177.11, 41705.26)</td>\n",
       "      <td>40396.577286</td>\n",
       "      <td>(36561.45, 44231.71)</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.764456</td>\n",
       "      <td>0.451225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.968790</td>\n",
       "      <td>0.527358</td>\n",
       "      <td>0.287347</td>\n",
       "      <td>0.596315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>2743</td>\n",
       "      <td>feature_5649</td>\n",
       "      <td>dCMP</td>\n",
       "      <td>12762.681889</td>\n",
       "      <td>(11624.8, 13900.57)</td>\n",
       "      <td>13621.843956</td>\n",
       "      <td>(11869.83, 15373.86)</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.806068</td>\n",
       "      <td>0.432021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>37.931</td>\n",
       "      <td>40.000</td>\n",
       "      <td>35.714</td>\n",
       "      <td>0.944928</td>\n",
       "      <td>0.351058</td>\n",
       "      <td>0.595534</td>\n",
       "      <td>0.451535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idx          Name                         Label      Grp0_Mean  \\\n",
       "1        3     feature_8                      Lichenin   14973.994125   \n",
       "2        4     feature_9                           NaN   21529.507364   \n",
       "3        5    feature_12           3,4-Dichloroaniline  136310.605667   \n",
       "4        6    feature_13  Trichloroethanol glucuronide    7730.000800   \n",
       "5        8    feature_26                           NaN   33723.963600   \n",
       "...    ...           ...                           ...            ...   \n",
       "1196  2734  feature_5638                           NaN   57198.911067   \n",
       "1197  2736  feature_5642                           NaN   61490.858733   \n",
       "1198  2740  feature_5646                   Ticlopidine   79860.686714   \n",
       "1199  2742  feature_5648        D-Mannitol 1-phosphate   38441.184933   \n",
       "1200  2743  feature_5649                          dCMP   12762.681889   \n",
       "\n",
       "              Grp0_Mean-95CI      Grp1_Mean          Grp1_Mean-95CI  Sign  \\\n",
       "1        (12586.69, 17361.3)   13981.036925    (10882.75, 17079.32)     0   \n",
       "2        (20107.52, 22951.5)   23784.629385    (21960.02, 25609.24)     1   \n",
       "3     (128486.28, 144134.94)  153927.207143  (143639.89, 164214.53)     1   \n",
       "4         (6900.49, 8559.51)    8750.084600      (7832.28, 9667.89)     1   \n",
       "5        (31710.6, 35737.33)   40744.174286     (37344.2, 44144.15)     1   \n",
       "...                      ...            ...                     ...   ...   \n",
       "1196     (54107.8, 60290.02)   60200.428286    (56922.91, 63477.94)     1   \n",
       "1197    (57430.62, 65551.09)   70812.527500    (63784.44, 77840.61)     1   \n",
       "1198     (72401.5, 87319.87)   84481.041143     (74196.3, 94765.78)     1   \n",
       "1199    (35177.11, 41705.26)   40396.577286    (36561.45, 44231.71)     1   \n",
       "1200     (11624.8, 13900.57)   13621.843956    (11869.83, 15373.86)     1   \n",
       "\n",
       "      TTestStat  TTestPvalue  bhQvalue  TotalMissing  PercTotalMissing  \\\n",
       "1      0.497578     0.626505       NaN            13            44.828   \n",
       "2     -1.858072     0.076593       NaN             5            17.241   \n",
       "3     -2.694031     0.011989       NaN             0             0.000   \n",
       "4     -1.619420     0.133646       NaN            16            55.172   \n",
       "5     -3.539228     0.001477       NaN             0             0.000   \n",
       "...         ...          ...       ...           ...               ...   \n",
       "1196  -1.306935     0.202257       NaN             0             0.000   \n",
       "1197  -2.289437     0.030097       NaN             0             0.000   \n",
       "1198  -0.712786     0.482327       NaN             1             3.448   \n",
       "1199  -0.764456     0.451225       NaN             0             0.000   \n",
       "1200  -0.806068     0.432021       NaN            11            37.931   \n",
       "\n",
       "      Grp0_Missing  Grp1_Missing  ShapiroW  ShapiroPvalue   LeveneW  \\\n",
       "1           46.667        42.857  0.925662       0.207939  1.129694   \n",
       "2           26.667         7.143  0.945349       0.214378  1.464142   \n",
       "3            0.000         0.000  0.954670       0.241498  0.779778   \n",
       "4           53.333        57.143  0.964721       0.824360  0.037143   \n",
       "5            0.000         0.000  0.932177       0.062652  1.447666   \n",
       "...            ...           ...       ...            ...       ...   \n",
       "1196         0.000         0.000  0.949656       0.179317  0.093953   \n",
       "1197         0.000         0.000  0.943505       0.123880  3.909849   \n",
       "1198         6.667         0.000  0.977395       0.784098  0.348768   \n",
       "1199         0.000         0.000  0.968790       0.527358  0.287347   \n",
       "1200        40.000        35.714  0.944928       0.351058  0.595534   \n",
       "\n",
       "      LevenePvalue  \n",
       "1         0.305831  \n",
       "2         0.239114  \n",
       "3         0.385004  \n",
       "4         0.850686  \n",
       "5         0.239347  \n",
       "...            ...  \n",
       "1196      0.761562  \n",
       "1197      0.058296  \n",
       "1198      0.559910  \n",
       "1199      0.596315  \n",
       "1200      0.451535  \n",
       "\n",
       "[1200 rows x 19 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataTable_stats=dataTable[dataTable['Class'].isin(['smoke','control'])] #remove QC samples to do statistics between smoked and non-smoked\n",
    "pos_outcome='smoke'\n",
    "statsTable = cb.utils.univariate_2class(dataTable_stats, peakTableClean, group ='Class', posclass=pos_outcome, parametric=True)\n",
    "statsTable.to_excel('statsCompoundTablePNpos.xlsx')\n",
    "display(statsTable)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the statistics table by pvalue to see how many compounds were significantly different between groups under a ttest with a pvalue cut off of 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 332 compounds significantly different between treatments under ttest.\n",
      "226 of those have putative KEGG annoations\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>Name</th>\n",
       "      <th>Label</th>\n",
       "      <th>Grp0_Mean</th>\n",
       "      <th>Grp0_Mean-95CI</th>\n",
       "      <th>Grp1_Mean</th>\n",
       "      <th>Grp1_Mean-95CI</th>\n",
       "      <th>Sign</th>\n",
       "      <th>TTestStat</th>\n",
       "      <th>TTestPvalue</th>\n",
       "      <th>bhQvalue</th>\n",
       "      <th>TotalMissing</th>\n",
       "      <th>PercTotalMissing</th>\n",
       "      <th>Grp0_Missing</th>\n",
       "      <th>Grp1_Missing</th>\n",
       "      <th>ShapiroW</th>\n",
       "      <th>ShapiroPvalue</th>\n",
       "      <th>LeveneW</th>\n",
       "      <th>LevenePvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>684</td>\n",
       "      <td>feature_1304</td>\n",
       "      <td>Coformycin</td>\n",
       "      <td>44536.883867</td>\n",
       "      <td>(38115.23, 50958.54)</td>\n",
       "      <td>7.148859e+04</td>\n",
       "      <td>(62322.62, 80654.55)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.772834</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.976994</td>\n",
       "      <td>0.757372</td>\n",
       "      <td>1.242547</td>\n",
       "      <td>0.274805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>428</td>\n",
       "      <td>feature_798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>946882.203333</td>\n",
       "      <td>(875049.62, 1018714.78)</td>\n",
       "      <td>1.162969e+06</td>\n",
       "      <td>(1108511.32, 1217425.78)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.648082</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.978760</td>\n",
       "      <td>0.805836</td>\n",
       "      <td>0.713876</td>\n",
       "      <td>0.405585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1023</td>\n",
       "      <td>feature_2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16095.622427</td>\n",
       "      <td>(11673.5, 20517.75)</td>\n",
       "      <td>3.557970e+04</td>\n",
       "      <td>(28260.81, 42898.59)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.536441</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.924560</td>\n",
       "      <td>0.039842</td>\n",
       "      <td>4.157318</td>\n",
       "      <td>0.051355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>860</td>\n",
       "      <td>feature_1694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10635.238960</td>\n",
       "      <td>(9448.76, 11821.71)</td>\n",
       "      <td>1.451864e+04</td>\n",
       "      <td>(13307.95, 15729.32)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.487887</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.954816</td>\n",
       "      <td>0.243567</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.993251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>427</td>\n",
       "      <td>feature_797</td>\n",
       "      <td>Fumarate</td>\n",
       "      <td>776393.200000</td>\n",
       "      <td>(712656.7, 840129.7)</td>\n",
       "      <td>9.626533e+05</td>\n",
       "      <td>(912536.31, 1012770.19)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.459599</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.953663</td>\n",
       "      <td>0.227583</td>\n",
       "      <td>0.525940</td>\n",
       "      <td>0.474558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>653</td>\n",
       "      <td>feature_1258</td>\n",
       "      <td>Bis(2-chloroethyl)ether</td>\n",
       "      <td>68406.560643</td>\n",
       "      <td>(64669.71, 72143.41)</td>\n",
       "      <td>6.357805e+04</td>\n",
       "      <td>(61337.27, 65818.83)</td>\n",
       "      <td>0</td>\n",
       "      <td>2.082001</td>\n",
       "      <td>0.048176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>10.345</td>\n",
       "      <td>6.667</td>\n",
       "      <td>14.286</td>\n",
       "      <td>0.884019</td>\n",
       "      <td>0.007007</td>\n",
       "      <td>5.506002</td>\n",
       "      <td>0.027541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2467</td>\n",
       "      <td>feature_5151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24768.040182</td>\n",
       "      <td>(15890.98, 33645.1)</td>\n",
       "      <td>1.477369e+04</td>\n",
       "      <td>(10492.97, 19054.42)</td>\n",
       "      <td>0</td>\n",
       "      <td>2.088952</td>\n",
       "      <td>0.048495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>17.241</td>\n",
       "      <td>26.667</td>\n",
       "      <td>7.143</td>\n",
       "      <td>0.896624</td>\n",
       "      <td>0.018266</td>\n",
       "      <td>2.542647</td>\n",
       "      <td>0.125075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>1088</td>\n",
       "      <td>feature_2170</td>\n",
       "      <td>2-(4-Chlorophenyl)-3-phenyl-3-(2-pyridinyl)acr...</td>\n",
       "      <td>6439.745827</td>\n",
       "      <td>(5146.49, 7733.0)</td>\n",
       "      <td>8.044895e+03</td>\n",
       "      <td>(7377.99, 8711.8)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.097040</td>\n",
       "      <td>0.049604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>27.586</td>\n",
       "      <td>26.667</td>\n",
       "      <td>28.571</td>\n",
       "      <td>0.964756</td>\n",
       "      <td>0.616512</td>\n",
       "      <td>2.614282</td>\n",
       "      <td>0.122388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>1367</td>\n",
       "      <td>feature_2789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22468.833667</td>\n",
       "      <td>(17385.47, 27552.2)</td>\n",
       "      <td>1.641096e+04</td>\n",
       "      <td>(13933.78, 18888.14)</td>\n",
       "      <td>0</td>\n",
       "      <td>2.052431</td>\n",
       "      <td>0.049938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.912075</td>\n",
       "      <td>0.019296</td>\n",
       "      <td>7.779649</td>\n",
       "      <td>0.009568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>426</td>\n",
       "      <td>feature_796</td>\n",
       "      <td>Chlorochrymorin</td>\n",
       "      <td>31559.939867</td>\n",
       "      <td>(27845.21, 35274.67)</td>\n",
       "      <td>3.764751e+04</td>\n",
       "      <td>(33118.5, 42176.53)</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.056089</td>\n",
       "      <td>0.049942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.448</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.143</td>\n",
       "      <td>0.967347</td>\n",
       "      <td>0.511532</td>\n",
       "      <td>0.246932</td>\n",
       "      <td>0.623420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>332 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Idx          Name                                              Label  \\\n",
       "425    684  feature_1304                                         Coformycin   \n",
       "292    428   feature_798                                                NaN   \n",
       "630   1023  feature_2010                                                NaN   \n",
       "532    860  feature_1694                                                NaN   \n",
       "291    427   feature_797                                           Fumarate   \n",
       "...    ...           ...                                                ...   \n",
       "400    653  feature_1258                            Bis(2-chloroethyl)ether   \n",
       "1140  2467  feature_5151                                                NaN   \n",
       "675   1088  feature_2170  2-(4-Chlorophenyl)-3-phenyl-3-(2-pyridinyl)acr...   \n",
       "826   1367  feature_2789                                                NaN   \n",
       "290    426   feature_796                                    Chlorochrymorin   \n",
       "\n",
       "          Grp0_Mean           Grp0_Mean-95CI     Grp1_Mean  \\\n",
       "425    44536.883867     (38115.23, 50958.54)  7.148859e+04   \n",
       "292   946882.203333  (875049.62, 1018714.78)  1.162969e+06   \n",
       "630    16095.622427      (11673.5, 20517.75)  3.557970e+04   \n",
       "532    10635.238960      (9448.76, 11821.71)  1.451864e+04   \n",
       "291   776393.200000     (712656.7, 840129.7)  9.626533e+05   \n",
       "...             ...                      ...           ...   \n",
       "400    68406.560643     (64669.71, 72143.41)  6.357805e+04   \n",
       "1140   24768.040182      (15890.98, 33645.1)  1.477369e+04   \n",
       "675     6439.745827        (5146.49, 7733.0)  8.044895e+03   \n",
       "826    22468.833667      (17385.47, 27552.2)  1.641096e+04   \n",
       "290    31559.939867     (27845.21, 35274.67)  3.764751e+04   \n",
       "\n",
       "                Grp1_Mean-95CI  Sign  TTestStat  TTestPvalue  bhQvalue  \\\n",
       "425       (62322.62, 80654.55)     1  -4.772834     0.000056       NaN   \n",
       "292   (1108511.32, 1217425.78)     1  -4.648082     0.000078       NaN   \n",
       "630       (28260.81, 42898.59)     1  -4.536441     0.000106       NaN   \n",
       "532       (13307.95, 15729.32)     1  -4.487887     0.000121       NaN   \n",
       "291    (912536.31, 1012770.19)     1  -4.459599     0.000130       NaN   \n",
       "...                        ...   ...        ...          ...       ...   \n",
       "400       (61337.27, 65818.83)     0   2.082001     0.048176       NaN   \n",
       "1140      (10492.97, 19054.42)     0   2.088952     0.048495       NaN   \n",
       "675          (7377.99, 8711.8)     1  -2.097040     0.049604       NaN   \n",
       "826       (13933.78, 18888.14)     0   2.052431     0.049938       NaN   \n",
       "290        (33118.5, 42176.53)     1  -2.056089     0.049942       NaN   \n",
       "\n",
       "      TotalMissing  PercTotalMissing  Grp0_Missing  Grp1_Missing  ShapiroW  \\\n",
       "425              0             0.000         0.000         0.000  0.976994   \n",
       "292              0             0.000         0.000         0.000  0.978760   \n",
       "630              0             0.000         0.000         0.000  0.924560   \n",
       "532              0             0.000         0.000         0.000  0.954816   \n",
       "291              0             0.000         0.000         0.000  0.953663   \n",
       "...            ...               ...           ...           ...       ...   \n",
       "400              3            10.345         6.667        14.286  0.884019   \n",
       "1140             5            17.241        26.667         7.143  0.896624   \n",
       "675              8            27.586        26.667        28.571  0.964756   \n",
       "826              0             0.000         0.000         0.000  0.912075   \n",
       "290              1             3.448         0.000         7.143  0.967347   \n",
       "\n",
       "      ShapiroPvalue   LeveneW  LevenePvalue  \n",
       "425        0.757372  1.242547      0.274805  \n",
       "292        0.805836  0.713876      0.405585  \n",
       "630        0.039842  4.157318      0.051355  \n",
       "532        0.243567  0.000073      0.993251  \n",
       "291        0.227583  0.525940      0.474558  \n",
       "...             ...       ...           ...  \n",
       "400        0.007007  5.506002      0.027541  \n",
       "1140       0.018266  2.542647      0.125075  \n",
       "675        0.616512  2.614282      0.122388  \n",
       "826        0.019296  7.779649      0.009568  \n",
       "290        0.511532  0.246932      0.623420  \n",
       "\n",
       "[332 rows x 19 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ttestsigTable = statsTable[statsTable['TTestPvalue'] < 0.05 ].sort_values(by='TTestPvalue')\n",
    "missing_count = ttestsigTable['Label'].isnull().sum()\n",
    "print('there are {} compounds significantly different between treatments under ttest.'.format(len(ttestsigTable)))\n",
    "print(\"{} of those have putative KEGG annoations\".format(len(ttestsigTable)-missing_count))\n",
    "display(ttestsigTable)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge significant compound statistics with the compound information to be output for future reference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Idx_x</th>\n",
       "      <th>Name</th>\n",
       "      <th>Label_x</th>\n",
       "      <th>Grp0_Mean</th>\n",
       "      <th>Grp0_Mean-95CI</th>\n",
       "      <th>Grp1_Mean</th>\n",
       "      <th>Grp1_Mean-95CI</th>\n",
       "      <th>Sign</th>\n",
       "      <th>TTestStat</th>\n",
       "      <th>TTestPvalue</th>\n",
       "      <th>...</th>\n",
       "      <th>LevenePvalue</th>\n",
       "      <th>Idx_y</th>\n",
       "      <th>mz</th>\n",
       "      <th>rt</th>\n",
       "      <th>Label_y</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>mol_formula</th>\n",
       "      <th>neutral_mass</th>\n",
       "      <th>database_match_info</th>\n",
       "      <th>QC_RSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>684</td>\n",
       "      <td>feature_1304</td>\n",
       "      <td>Coformycin</td>\n",
       "      <td>44536.883867</td>\n",
       "      <td>(38115.23, 50958.54)</td>\n",
       "      <td>7.148859e+04</td>\n",
       "      <td>(62322.62, 80654.55)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.772834</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>...</td>\n",
       "      <td>0.274805</td>\n",
       "      <td>684</td>\n",
       "      <td>283.1031</td>\n",
       "      <td>3.44</td>\n",
       "      <td>Coformycin</td>\n",
       "      <td>Coformycin</td>\n",
       "      <td>C11H16N4O5</td>\n",
       "      <td>284.1121</td>\n",
       "      <td>KEGG C01677</td>\n",
       "      <td>10.436836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>428</td>\n",
       "      <td>feature_798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>946882.203333</td>\n",
       "      <td>(875049.62, 1018714.78)</td>\n",
       "      <td>1.162969e+06</td>\n",
       "      <td>(1108511.32, 1217425.78)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.648082</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.405585</td>\n",
       "      <td>428</td>\n",
       "      <td>134.0160</td>\n",
       "      <td>1.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.610556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1023</td>\n",
       "      <td>feature_2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16095.622427</td>\n",
       "      <td>(11673.5, 20517.75)</td>\n",
       "      <td>3.557970e+04</td>\n",
       "      <td>(28260.81, 42898.59)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.536441</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051355</td>\n",
       "      <td>1023</td>\n",
       "      <td>451.1241</td>\n",
       "      <td>15.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.665130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>860</td>\n",
       "      <td>feature_1694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10635.238960</td>\n",
       "      <td>(9448.76, 11821.71)</td>\n",
       "      <td>1.451864e+04</td>\n",
       "      <td>(13307.95, 15729.32)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.487887</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993251</td>\n",
       "      <td>860</td>\n",
       "      <td>171.0655</td>\n",
       "      <td>12.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.968941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>427</td>\n",
       "      <td>feature_797</td>\n",
       "      <td>Fumarate</td>\n",
       "      <td>776393.200000</td>\n",
       "      <td>(712656.7, 840129.7)</td>\n",
       "      <td>9.626533e+05</td>\n",
       "      <td>(912536.31, 1012770.19)</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.459599</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.474558</td>\n",
       "      <td>427</td>\n",
       "      <td>115.0021</td>\n",
       "      <td>1.44</td>\n",
       "      <td>Fumarate</td>\n",
       "      <td>Fumarate</td>\n",
       "      <td>C4H4O4</td>\n",
       "      <td>116.0110</td>\n",
       "      <td>KEGG C00122</td>\n",
       "      <td>5.031928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idx_x          Name     Label_x      Grp0_Mean           Grp0_Mean-95CI  \\\n",
       "0    684  feature_1304  Coformycin   44536.883867     (38115.23, 50958.54)   \n",
       "1    428   feature_798         NaN  946882.203333  (875049.62, 1018714.78)   \n",
       "2   1023  feature_2010         NaN   16095.622427      (11673.5, 20517.75)   \n",
       "3    860  feature_1694         NaN   10635.238960      (9448.76, 11821.71)   \n",
       "4    427   feature_797    Fumarate  776393.200000     (712656.7, 840129.7)   \n",
       "\n",
       "      Grp1_Mean            Grp1_Mean-95CI  Sign  TTestStat  TTestPvalue  ...  \\\n",
       "0  7.148859e+04      (62322.62, 80654.55)     1  -4.772834     0.000056  ...   \n",
       "1  1.162969e+06  (1108511.32, 1217425.78)     1  -4.648082     0.000078  ...   \n",
       "2  3.557970e+04      (28260.81, 42898.59)     1  -4.536441     0.000106  ...   \n",
       "3  1.451864e+04      (13307.95, 15729.32)     1  -4.487887     0.000121  ...   \n",
       "4  9.626533e+05   (912536.31, 1012770.19)     1  -4.459599     0.000130  ...   \n",
       "\n",
       "   LevenePvalue  Idx_y        mz     rt     Label_y  compound_name  \\\n",
       "0      0.274805    684  283.1031   3.44  Coformycin     Coformycin   \n",
       "1      0.405585    428  134.0160   1.44         NaN            NaN   \n",
       "2      0.051355   1023  451.1241  15.99         NaN            NaN   \n",
       "3      0.993251    860  171.0655  12.06         NaN            NaN   \n",
       "4      0.474558    427  115.0021   1.44    Fumarate       Fumarate   \n",
       "\n",
       "   mol_formula  neutral_mass  database_match_info     QC_RSD  \n",
       "0   C11H16N4O5      284.1121          KEGG C01677  10.436836  \n",
       "1          NaN           NaN                  NaN   2.610556  \n",
       "2          NaN           NaN                  NaN  16.665130  \n",
       "3          NaN           NaN                  NaN  18.968941  \n",
       "4       C4H4O4      116.0110          KEGG C00122   5.031928  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SigPeakInfo = ttestsigTable.merge(peakTable, on ='Name', how='left')\n",
    "SigPeakInfo.to_excel(\"sigpeaks.xlsx\")\n",
    "SigPeakInfo.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset the initial quant dataframe exported by MZmine by significant peaks to isolate quantitative information about peaks with a significant p.value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "SigCompoundList = list(ttestsigTable['Name']) \n",
    "list2 = ['Idx', 'SampleID', 'Label', 'Class', 'Year', 'Company/Management', 'Vineyard', 'Block']\n",
    "SigCompoundList = list2 + SigCompoundList \n",
    "SigDataTable = dataTable[SigCompoundList]\n",
    "SigDataTable.to_excel(\"SigDataTable.xlsx\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The compounds shown to have a significant difference were searched by parent mass and KEGG annotation in the hormonomics database. \n",
    "\n",
    "All KEGG id's of annotated compounds regardless of significance were searched in the KEGG pathway finder via MetaboAnalayst"
   ]
  }
 ],
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