03_EMT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply LASSO model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import io\n",
    "import re\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.framework import ops\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Correlation between histone PTMs and metabolomics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_gcp = pd.read_excel(\n",
    "    io=\"D:/Chandrasekaran/Projects/EMT/Data/Proteomics/Garcia/raw/histone-PTM-EMT.xlsx\",\n",
    "    sheet_name=\"LFC\"\n",
    ")\n",
    "\n",
    "app_metabolomics = pd.read_excel(\n",
    "    io=\"C:/Users/Scott/Desktop/application.xlsx\",\n",
    "    sheet_name=\"Log2FC\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Single PTM</th>\n",
       "      <th>H3K4me1</th>\n",
       "      <th>H3K4me2</th>\n",
       "      <th>H3K4ac1</th>\n",
       "      <th>H3K9me1</th>\n",
       "      <th>H3K9me2</th>\n",
       "      <th>H3K9me3</th>\n",
       "      <th>H3K9ac1</th>\n",
       "      <th>H3K14ac1</th>\n",
       "      <th>H3K18ac1</th>\n",
       "      <th>H3K23ac1</th>\n",
       "      <th>...</th>\n",
       "      <th>H3S28ph</th>\n",
       "      <th>H3K36me1</th>\n",
       "      <th>H3K36me2</th>\n",
       "      <th>H3K36me3</th>\n",
       "      <th>H3K56me1</th>\n",
       "      <th>H3K56me2</th>\n",
       "      <th>H3K56me3</th>\n",
       "      <th>H3K79me1</th>\n",
       "      <th>H3K79me2</th>\n",
       "      <th>H3K79ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LFC_24</th>\n",
       "      <td>0.155593</td>\n",
       "      <td>-2.092323</td>\n",
       "      <td>0.287772</td>\n",
       "      <td>-0.357557</td>\n",
       "      <td>0.003452</td>\n",
       "      <td>-0.042854</td>\n",
       "      <td>0.757362</td>\n",
       "      <td>-0.099339</td>\n",
       "      <td>-0.007080</td>\n",
       "      <td>0.279713</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.380691</td>\n",
       "      <td>-0.010528</td>\n",
       "      <td>-0.038472</td>\n",
       "      <td>-0.180321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.239336</td>\n",
       "      <td>-0.540247</td>\n",
       "      <td>-0.062017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LFC_48</th>\n",
       "      <td>-0.262970</td>\n",
       "      <td>-1.383009</td>\n",
       "      <td>-1.057151</td>\n",
       "      <td>-1.472531</td>\n",
       "      <td>-0.038144</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>-0.024306</td>\n",
       "      <td>-0.107917</td>\n",
       "      <td>-0.034959</td>\n",
       "      <td>0.085595</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.365702</td>\n",
       "      <td>-0.128719</td>\n",
       "      <td>0.219449</td>\n",
       "      <td>-1.172673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.212597</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014075</td>\n",
       "      <td>0.497883</td>\n",
       "      <td>-0.237196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Single PTM   H3K4me1   H3K4me2   H3K4ac1   H3K9me1   H3K9me2   H3K9me3  \\\n",
       "LFC_24      0.155593 -2.092323  0.287772 -0.357557  0.003452 -0.042854   \n",
       "LFC_48     -0.262970 -1.383009 -1.057151 -1.472531 -0.038144  0.130709   \n",
       "\n",
       "Single PTM   H3K9ac1  H3K14ac1  H3K18ac1  H3K23ac1  ...   H3S28ph  H3K36me1  \\\n",
       "LFC_24      0.757362 -0.099339 -0.007080  0.279713  ... -0.380691 -0.010528   \n",
       "LFC_48     -0.024306 -0.107917 -0.034959  0.085595  ... -0.365702 -0.128719   \n",
       "\n",
       "Single PTM  H3K36me2  H3K36me3  H3K56me1  H3K56me2  H3K56me3  H3K79me1  \\\n",
       "LFC_24     -0.038472 -0.180321       0.0  0.017738       0.0 -0.239336   \n",
       "LFC_48      0.219449 -1.172673       0.0 -0.212597       0.0  0.014075   \n",
       "\n",
       "Single PTM  H3K79me2   H3K79ac  \n",
       "LFC_24     -0.540247 -0.062017  \n",
       "LFC_48      0.497883 -0.237196  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_gcp = app_gcp[['Single PTM', 'LFC_24', 'LFC_48']]\n",
    "app_gcp = app_gcp.set_index('Single PTM')\n",
    "app_gcp = app_gcp.T\n",
    "app_gcp.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Compound name</th>\n",
       "      <th>1-Methylnicotinamide</th>\n",
       "      <th>10-Hydroxydecanoate</th>\n",
       "      <th>2-Deoxyribose 1-phosphate</th>\n",
       "      <th>2-Deoxyribose 5-phosphate</th>\n",
       "      <th>2-Hydroxybutyrate</th>\n",
       "      <th>2-Hydroxyglutarate</th>\n",
       "      <th>2-Hydroxyisobutyrate</th>\n",
       "      <th>2-Oxoglutarate</th>\n",
       "      <th>2-Oxoisopentanoate</th>\n",
       "      <th>2,3-DPG</th>\n",
       "      <th>...</th>\n",
       "      <th>UDP</th>\n",
       "      <th>UDP-glucose</th>\n",
       "      <th>UDP-glucuronate</th>\n",
       "      <th>UDP-N-acetylglucosamine</th>\n",
       "      <th>UMP</th>\n",
       "      <th>Urate</th>\n",
       "      <th>Urea</th>\n",
       "      <th>UTP</th>\n",
       "      <th>Val</th>\n",
       "      <th>XMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24 hours</th>\n",
       "      <td>0.648350</td>\n",
       "      <td>1.183867</td>\n",
       "      <td>-0.452678</td>\n",
       "      <td>0.040272</td>\n",
       "      <td>1.515536</td>\n",
       "      <td>0.518912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.224656</td>\n",
       "      <td>0.018983</td>\n",
       "      <td>0.722190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214262</td>\n",
       "      <td>0.064724</td>\n",
       "      <td>-0.136610</td>\n",
       "      <td>0.285272</td>\n",
       "      <td>0.054890</td>\n",
       "      <td>1.043209</td>\n",
       "      <td>0.572612</td>\n",
       "      <td>-0.177705</td>\n",
       "      <td>-0.046262</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48 hours</th>\n",
       "      <td>0.793587</td>\n",
       "      <td>1.193061</td>\n",
       "      <td>0.455793</td>\n",
       "      <td>-1.156795</td>\n",
       "      <td>-0.660990</td>\n",
       "      <td>0.422370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.063019</td>\n",
       "      <td>-0.791557</td>\n",
       "      <td>0.480208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.443411</td>\n",
       "      <td>-0.073162</td>\n",
       "      <td>-0.357369</td>\n",
       "      <td>0.017943</td>\n",
       "      <td>-0.435983</td>\n",
       "      <td>1.192923</td>\n",
       "      <td>0.148768</td>\n",
       "      <td>0.165137</td>\n",
       "      <td>-0.189555</td>\n",
       "      <td>-0.732316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Compound name  1-Methylnicotinamide  10-Hydroxydecanoate  \\\n",
       "24 hours                   0.648350             1.183867   \n",
       "48 hours                   0.793587             1.193061   \n",
       "\n",
       "Compound name  2-Deoxyribose 1-phosphate  2-Deoxyribose 5-phosphate  \\\n",
       "24 hours                       -0.452678                   0.040272   \n",
       "48 hours                        0.455793                  -1.156795   \n",
       "\n",
       "Compound name  2-Hydroxybutyrate  2-Hydroxyglutarate  2-Hydroxyisobutyrate  \\\n",
       "24 hours                1.515536            0.518912                   0.0   \n",
       "48 hours               -0.660990            0.422370                   0.0   \n",
       "\n",
       "Compound name  2-Oxoglutarate  2-Oxoisopentanoate   2,3-DPG  ...       UDP  \\\n",
       "24 hours             0.224656            0.018983  0.722190  ...  0.214262   \n",
       "48 hours            -0.063019           -0.791557  0.480208  ... -0.443411   \n",
       "\n",
       "Compound name  UDP-glucose  UDP-glucuronate  UDP-N-acetylglucosamine  \\\n",
       "24 hours          0.064724        -0.136610                 0.285272   \n",
       "48 hours         -0.073162        -0.357369                 0.017943   \n",
       "\n",
       "Compound name       UMP     Urate      Urea       UTP       Val       XMP  \n",
       "24 hours       0.054890  1.043209  0.572612 -0.177705 -0.046262  0.000000  \n",
       "48 hours      -0.435983  1.192923  0.148768  0.165137 -0.189555 -0.732316  \n",
       "\n",
       "[2 rows x 193 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_metabolomics = app_metabolomics[['Compound name', '24 hours', '48 hours']]\n",
    "app_metabolomics = app_metabolomics.set_index('Compound name')\n",
    "app_metabolomics = app_metabolomics.T\n",
    "app_metabolomics.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "def calculate_PCC(df1: pd.DataFrame, df2: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  INPUTS:\n",
    "    * df1: Pandas dataframe containing only numerical values.\n",
    "    * df2: Pandas dataframe containing only numerical values.\n",
    "  OUTPUT:\n",
    "    * pcc: Numpy array containing pearson correlation coefficients between df1 \n",
    "           and df2 columns.\n",
    "    * pval: Numpy array containing p-values corresponding to PCCs.\n",
    "  \"\"\"\n",
    "  # Initialize variables\n",
    "  pcc = np.zeros((df1.shape[1], df2.shape[1]))\n",
    "  pval = np.zeros((df1.shape[1], df2.shape[1]))\n",
    "  \n",
    "  # Compute PCCs and P-values\n",
    "  i = 0\n",
    "  for col1 in df1.columns:\n",
    "    j = 0\n",
    "    for col2 in df2.columns:\n",
    "      [r, p] = pearsonr(df1[col1], df2[col2])\n",
    "      pcc[i, j] = r\n",
    "      pval[i, j] = p\n",
    "      j += 1\n",
    "    i +=1\n",
    "  return pcc, pval\n",
    "\n",
    "def two_df_correlation(df1: pd.DataFrame, df2: pd.DataFrame):\n",
    "  pcc, pval = calculate_PCC(df1, df2)\n",
    "  new_pcc_df = pd.DataFrame(pcc, \n",
    "                   index=df1.columns, \n",
    "                   columns=df2.columns)\n",
    "  new_pval_df = pd.DataFrame(pval, \n",
    "                   index=df1.columns, \n",
    "                   columns=df2.columns)\n",
    "  \n",
    "  return new_pcc_df, new_pval_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then calculate the correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcc, pval = two_df_correlation(app_gcp, app_metabolomics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have two data points, they are assuming values of either 1 or -1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Map metabolomics data between CCLE and application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a function that we'll be able to use to map the metabolomics data to common identifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "metaboanalyst_url = \"http://api.xialab.ca/mapcompounds\"\n",
    "def get_query(metabolite_name: dict, metaboanalyst_url: str):\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    headers = {\n",
    "        'Content-Type': \"application/json\",\n",
    "        'cache-control': \"no-cache\",\n",
    "    }\n",
    "    response = requests.request(\"POST\", \n",
    "                                metaboanalyst_url, \n",
    "                                data=metabolite_name, \n",
    "                                headers=headers)\n",
    "    return response\n",
    "    \n",
    "def generate_metabolite_map(df:pd.DataFrame, axis=0, url=\"http://api.xialab.ca/mapcompounds\") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Map the metabolite dataframe using the metaboanalyst API.\n",
    "    \"\"\"\n",
    "    if axis == 0:\n",
    "        met_list = list(df.index)\n",
    "    else:\n",
    "        met_list = list(df.columns)\n",
    "    met_list = [re.sub(r\"/`\", \"/'\", met) for met in met_list]\n",
    "    mets = ';'.join(met_list)\n",
    "    payload = '{\"queryList\": \"' + mets + '\",\"inputType\": \"name\"}'\n",
    "    payload = payload.encode('utf-8')\n",
    "\n",
    "    map_obj = get_query(payload, url)\n",
    "    map = pd.read_json(io.StringIO(map_obj.content.decode('utf-8')))\n",
    "    return map\n",
    "\n",
    "def map_metabolites(df:pd.DataFrame, axis=0, url=\"http://api.xialab.ca/mapcompounds\") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    map = generate_metabolite_map(df, axis=0, url=\"http://api.xialab.ca/mapcompounds\")\n",
    "    return pd.merge(map, df, \n",
    "                    how='inner',\n",
    "                    left_on=\"Query\", right_index=True), map\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the metabolomics into a suitable format."
   ]
  },
  {
   "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>Query</th>\n",
       "      <th>Match</th>\n",
       "      <th>HMDB</th>\n",
       "      <th>PubChem</th>\n",
       "      <th>ChEBI</th>\n",
       "      <th>KEGG</th>\n",
       "      <th>METLIN</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylnicotinamide</td>\n",
       "      <td>1-Methylnicotinamide</td>\n",
       "      <td>HMDB0000699</td>\n",
       "      <td>457</td>\n",
       "      <td>16797</td>\n",
       "      <td>C02918</td>\n",
       "      <td>5667</td>\n",
       "      <td>C[N+]1=CC=CC(=C1)C(N)=O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10-Hydroxydecanoate</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2-Deoxyribose 1-phosphate</td>\n",
       "      <td>Deoxyribose 1-phosphate</td>\n",
       "      <td>HMDB0001351</td>\n",
       "      <td>439287</td>\n",
       "      <td>28542</td>\n",
       "      <td>C00672</td>\n",
       "      <td>6182</td>\n",
       "      <td>OC[C@H]1OC(C[C@@H]1O)OP(O)(O)=O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Query                    Match         HMDB PubChem  \\\n",
       "0       1-Methylnicotinamide     1-Methylnicotinamide  HMDB0000699     457   \n",
       "1        10-Hydroxydecanoate                       NA           NA      NA   \n",
       "2  2-Deoxyribose 1-phosphate  Deoxyribose 1-phosphate  HMDB0001351  439287   \n",
       "\n",
       "   ChEBI    KEGG METLIN                           SMILES  Comment  \n",
       "0  16797  C02918   5667          C[N+]1=CC=CC(=C1)C(N)=O        1  \n",
       "1     NA      NA     NA                               NA        0  \n",
       "2  28542  C00672   6182  OC[C@H]1OC(C[C@@H]1O)OP(O)(O)=O        1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map = generate_metabolite_map(app_metabolomics, axis=1)\n",
    "map.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ChEBI</th>\n",
       "      <th>16797</th>\n",
       "      <th>NA</th>\n",
       "      <th>28542</th>\n",
       "      <th>16132</th>\n",
       "      <th>1148</th>\n",
       "      <th>17084</th>\n",
       "      <th>50129</th>\n",
       "      <th>30915</th>\n",
       "      <th>NA</th>\n",
       "      <th>17720</th>\n",
       "      <th>...</th>\n",
       "      <th>17659</th>\n",
       "      <th>46229</th>\n",
       "      <th>17200</th>\n",
       "      <th>16264</th>\n",
       "      <th>16695</th>\n",
       "      <th>17775</th>\n",
       "      <th>16199</th>\n",
       "      <th>15713</th>\n",
       "      <th>16414</th>\n",
       "      <th>15652</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24 hours</th>\n",
       "      <td>0.648350</td>\n",
       "      <td>1.183867</td>\n",
       "      <td>-0.452678</td>\n",
       "      <td>0.040272</td>\n",
       "      <td>1.515536</td>\n",
       "      <td>0.518912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.224656</td>\n",
       "      <td>0.018983</td>\n",
       "      <td>0.722190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214262</td>\n",
       "      <td>0.064724</td>\n",
       "      <td>-0.136610</td>\n",
       "      <td>0.285272</td>\n",
       "      <td>0.054890</td>\n",
       "      <td>1.043209</td>\n",
       "      <td>0.572612</td>\n",
       "      <td>-0.177705</td>\n",
       "      <td>-0.046262</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48 hours</th>\n",
       "      <td>0.793587</td>\n",
       "      <td>1.193061</td>\n",
       "      <td>0.455793</td>\n",
       "      <td>-1.156795</td>\n",
       "      <td>-0.660990</td>\n",
       "      <td>0.422370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.063019</td>\n",
       "      <td>-0.791557</td>\n",
       "      <td>0.480208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.443411</td>\n",
       "      <td>-0.073162</td>\n",
       "      <td>-0.357369</td>\n",
       "      <td>0.017943</td>\n",
       "      <td>-0.435983</td>\n",
       "      <td>1.192923</td>\n",
       "      <td>0.148768</td>\n",
       "      <td>0.165137</td>\n",
       "      <td>-0.189555</td>\n",
       "      <td>-0.732316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ChEBI        16797        NA     28542     16132      1148     17084  50129  \\\n",
       "24 hours  0.648350  1.183867 -0.452678  0.040272  1.515536  0.518912    0.0   \n",
       "48 hours  0.793587  1.193061  0.455793 -1.156795 -0.660990  0.422370    0.0   \n",
       "\n",
       "ChEBI        30915        NA     17720  ...     17659     46229     17200  \\\n",
       "24 hours  0.224656  0.018983  0.722190  ...  0.214262  0.064724 -0.136610   \n",
       "48 hours -0.063019 -0.791557  0.480208  ... -0.443411 -0.073162 -0.357369   \n",
       "\n",
       "ChEBI        16264     16695     17775     16199     15713     16414     15652  \n",
       "24 hours  0.285272  0.054890  1.043209  0.572612 -0.177705 -0.046262  0.000000  \n",
       "48 hours  0.017943 -0.435983  1.192923  0.148768  0.165137 -0.189555 -0.732316  \n",
       "\n",
       "[2 rows x 193 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_metabolomics.columns = map.ChEBI\n",
    "app_metabolomics.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, let's repeat for the CCLE dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2-aminoadipate</th>\n",
       "      <th>3-phosphoglycerate</th>\n",
       "      <th>alpha-glycerophosphate</th>\n",
       "      <th>4-pyridoxate</th>\n",
       "      <th>aconitate</th>\n",
       "      <th>adenine</th>\n",
       "      <th>adipate</th>\n",
       "      <th>alpha-ketoglutarate</th>\n",
       "      <th>AMP</th>\n",
       "      <th>citrate</th>\n",
       "      <th>...</th>\n",
       "      <th>heptanoylcarnitine</th>\n",
       "      <th>lauroylcarnitine</th>\n",
       "      <th>myristoylcarnitine</th>\n",
       "      <th>palmitoylcarnitine</th>\n",
       "      <th>stearoylcarnitine</th>\n",
       "      <th>oleylcarnitine</th>\n",
       "      <th>arachidonyl_carnitine</th>\n",
       "      <th>sarcosine</th>\n",
       "      <th>beta-alanine</th>\n",
       "      <th>anserine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepMap_ID</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",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACH-000004</th>\n",
       "      <td>-0.113363</td>\n",
       "      <td>0.174728</td>\n",
       "      <td>0.661087</td>\n",
       "      <td>-0.370089</td>\n",
       "      <td>-0.045246</td>\n",
       "      <td>0.434350</td>\n",
       "      <td>0.174760</td>\n",
       "      <td>-0.043661</td>\n",
       "      <td>1.249415</td>\n",
       "      <td>-0.213278</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487840</td>\n",
       "      <td>0.222715</td>\n",
       "      <td>0.947898</td>\n",
       "      <td>1.162742</td>\n",
       "      <td>0.784755</td>\n",
       "      <td>0.727978</td>\n",
       "      <td>0.297024</td>\n",
       "      <td>-0.350366</td>\n",
       "      <td>-0.215865</td>\n",
       "      <td>-0.154495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000007</th>\n",
       "      <td>-0.246379</td>\n",
       "      <td>-0.277360</td>\n",
       "      <td>-0.482734</td>\n",
       "      <td>-0.143775</td>\n",
       "      <td>0.042996</td>\n",
       "      <td>-0.544958</td>\n",
       "      <td>0.215370</td>\n",
       "      <td>0.003411</td>\n",
       "      <td>-0.258807</td>\n",
       "      <td>0.060592</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129539</td>\n",
       "      <td>-0.516559</td>\n",
       "      <td>-0.514631</td>\n",
       "      <td>-0.252187</td>\n",
       "      <td>-0.198568</td>\n",
       "      <td>-0.371144</td>\n",
       "      <td>0.280362</td>\n",
       "      <td>0.039629</td>\n",
       "      <td>0.191910</td>\n",
       "      <td>0.178265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000008</th>\n",
       "      <td>-0.301735</td>\n",
       "      <td>0.424280</td>\n",
       "      <td>0.549118</td>\n",
       "      <td>-0.056079</td>\n",
       "      <td>0.612689</td>\n",
       "      <td>-0.069253</td>\n",
       "      <td>-0.286402</td>\n",
       "      <td>0.373105</td>\n",
       "      <td>0.268231</td>\n",
       "      <td>0.521923</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056436</td>\n",
       "      <td>0.233915</td>\n",
       "      <td>-0.086494</td>\n",
       "      <td>-0.137966</td>\n",
       "      <td>-0.400825</td>\n",
       "      <td>-0.259514</td>\n",
       "      <td>-0.452228</td>\n",
       "      <td>-0.038606</td>\n",
       "      <td>0.253871</td>\n",
       "      <td>-0.314390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            2-aminoadipate  3-phosphoglycerate  alpha-glycerophosphate  \\\n",
       "DepMap_ID                                                                \n",
       "ACH-000004       -0.113363            0.174728                0.661087   \n",
       "ACH-000007       -0.246379           -0.277360               -0.482734   \n",
       "ACH-000008       -0.301735            0.424280                0.549118   \n",
       "\n",
       "            4-pyridoxate  aconitate   adenine   adipate  alpha-ketoglutarate  \\\n",
       "DepMap_ID                                                                      \n",
       "ACH-000004     -0.370089  -0.045246  0.434350  0.174760            -0.043661   \n",
       "ACH-000007     -0.143775   0.042996 -0.544958  0.215370             0.003411   \n",
       "ACH-000008     -0.056079   0.612689 -0.069253 -0.286402             0.373105   \n",
       "\n",
       "                 AMP   citrate  ...  heptanoylcarnitine  lauroylcarnitine  \\\n",
       "DepMap_ID                       ...                                         \n",
       "ACH-000004  1.249415 -0.213278  ...           -0.487840          0.222715   \n",
       "ACH-000007 -0.258807  0.060592  ...           -0.129539         -0.516559   \n",
       "ACH-000008  0.268231  0.521923  ...           -0.056436          0.233915   \n",
       "\n",
       "            myristoylcarnitine  palmitoylcarnitine  stearoylcarnitine  \\\n",
       "DepMap_ID                                                               \n",
       "ACH-000004            0.947898            1.162742           0.784755   \n",
       "ACH-000007           -0.514631           -0.252187          -0.198568   \n",
       "ACH-000008           -0.086494           -0.137966          -0.400825   \n",
       "\n",
       "            oleylcarnitine  arachidonyl_carnitine  sarcosine  beta-alanine  \\\n",
       "DepMap_ID                                                                    \n",
       "ACH-000004        0.727978               0.297024  -0.350366     -0.215865   \n",
       "ACH-000007       -0.371144               0.280362   0.039629      0.191910   \n",
       "ACH-000008       -0.259514              -0.452228  -0.038606      0.253871   \n",
       "\n",
       "            anserine  \n",
       "DepMap_ID             \n",
       "ACH-000004 -0.154495  \n",
       "ACH-000007  0.178265  \n",
       "ACH-000008 -0.314390  \n",
       "\n",
       "[3 rows x 136 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basepath = \"D:/Chandrasekaran/Projects/Epigenetics-Metabolism/Data/processed/model_inputs.xlsx\"\n",
    "met_df = pd.read_excel(basepath, 'MET_nonsignaling', index_col=0)\n",
    "met_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccle_map = generate_metabolite_map(met_df, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Query</th>\n",
       "      <th>Match</th>\n",
       "      <th>HMDB</th>\n",
       "      <th>PubChem</th>\n",
       "      <th>ChEBI</th>\n",
       "      <th>KEGG</th>\n",
       "      <th>METLIN</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2-aminoadipate</td>\n",
       "      <td>Aminoadipic acid</td>\n",
       "      <td>HMDB0000510</td>\n",
       "      <td>469</td>\n",
       "      <td>37024</td>\n",
       "      <td>C00956</td>\n",
       "      <td>5496</td>\n",
       "      <td>NC(CCCC(=O)O)C(=O)O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3-phosphoglycerate</td>\n",
       "      <td>3-Phosphoglyceric acid</td>\n",
       "      <td>HMDB0000807</td>\n",
       "      <td>724</td>\n",
       "      <td>17050</td>\n",
       "      <td>C00597</td>\n",
       "      <td>150</td>\n",
       "      <td>OC(COP(O)(O)=O)C(O)=O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alpha-glycerophosphate</td>\n",
       "      <td>Glycerol 3-phosphate</td>\n",
       "      <td>HMDB0000126</td>\n",
       "      <td>439162</td>\n",
       "      <td>15978</td>\n",
       "      <td>C00093</td>\n",
       "      <td>5161</td>\n",
       "      <td>C([C@H](COP(=O)(O)O)O)O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4-pyridoxate</td>\n",
       "      <td>4-Pyridoxic acid</td>\n",
       "      <td>HMDB0000017</td>\n",
       "      <td>6723</td>\n",
       "      <td>17405</td>\n",
       "      <td>C00847</td>\n",
       "      <td>239</td>\n",
       "      <td>CC1=NC=C(CO)C(C(O)=O)=C1O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aconitate</td>\n",
       "      <td>cis-Aconitic acid</td>\n",
       "      <td>HMDB0000072</td>\n",
       "      <td>643757</td>\n",
       "      <td>32805</td>\n",
       "      <td>C00417</td>\n",
       "      <td>5130</td>\n",
       "      <td>OC(=O)C\\C(=C\\C(O)=O)C(O)=O</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>oleylcarnitine</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>arachidonyl_carnitine</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>sarcosine</td>\n",
       "      <td>Sarcosine</td>\n",
       "      <td>HMDB0000271</td>\n",
       "      <td>1088</td>\n",
       "      <td>15611</td>\n",
       "      <td>C00213</td>\n",
       "      <td>51</td>\n",
       "      <td>CNCC(O)=O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>beta-alanine</td>\n",
       "      <td>beta-Alanine</td>\n",
       "      <td>HMDB0000056</td>\n",
       "      <td>239</td>\n",
       "      <td>16958</td>\n",
       "      <td>C00099</td>\n",
       "      <td>5119</td>\n",
       "      <td>NCCC(O)=O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>anserine</td>\n",
       "      <td>Anserine</td>\n",
       "      <td>HMDB0000194</td>\n",
       "      <td>112072</td>\n",
       "      <td>18323</td>\n",
       "      <td>C01262</td>\n",
       "      <td>5209</td>\n",
       "      <td>CN1C=NC=C1C[C@H](NC(=O)CCN)C(O)=O</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Query                   Match         HMDB PubChem  \\\n",
       "0            2-aminoadipate        Aminoadipic acid  HMDB0000510     469   \n",
       "1        3-phosphoglycerate  3-Phosphoglyceric acid  HMDB0000807     724   \n",
       "2    alpha-glycerophosphate    Glycerol 3-phosphate  HMDB0000126  439162   \n",
       "3              4-pyridoxate        4-Pyridoxic acid  HMDB0000017    6723   \n",
       "4                 aconitate       cis-Aconitic acid  HMDB0000072  643757   \n",
       "..                      ...                     ...          ...     ...   \n",
       "131          oleylcarnitine                      NA           NA      NA   \n",
       "132   arachidonyl_carnitine                      NA           NA      NA   \n",
       "133               sarcosine               Sarcosine  HMDB0000271    1088   \n",
       "134            beta-alanine            beta-Alanine  HMDB0000056     239   \n",
       "135                anserine                Anserine  HMDB0000194  112072   \n",
       "\n",
       "     ChEBI    KEGG METLIN                             SMILES  Comment  \n",
       "0    37024  C00956   5496                NC(CCCC(=O)O)C(=O)O        1  \n",
       "1    17050  C00597    150              OC(COP(O)(O)=O)C(O)=O        1  \n",
       "2    15978  C00093   5161            C([C@H](COP(=O)(O)O)O)O        1  \n",
       "3    17405  C00847    239          CC1=NC=C(CO)C(C(O)=O)=C1O        1  \n",
       "4    32805  C00417   5130         OC(=O)C\\C(=C\\C(O)=O)C(O)=O        1  \n",
       "..     ...     ...    ...                                ...      ...  \n",
       "131     NA      NA     NA                                 NA        0  \n",
       "132     NA      NA     NA                                 NA        0  \n",
       "133  15611  C00213     51                          CNCC(O)=O        1  \n",
       "134  16958  C00099   5119                          NCCC(O)=O        1  \n",
       "135  18323  C01262   5209  CN1C=NC=C1C[C@H](NC(=O)CCN)C(O)=O        1  \n",
       "\n",
       "[136 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccle_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ChEBI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24 hours</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48 hours</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [24 hours, 48 hours]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccle_met = 'NAD'\n",
    "np.sum(map.Match == ccle_met)\n",
    "#app_metabolomics[map.loc[map.Match == ccle_met, :]['Query']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['alpha-Aminoadipate'], dtype='object', name='ChEBI')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4388/3670971734.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m             \u001b[0mremapped_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mccle_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mccle_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMatch\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mccle_met\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Query'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapp_metabolomics\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'NADP+'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m             \u001b[0mremapped_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mccle_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mccle_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMatch\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mccle_met\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Query'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapp_metabolomics\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMatch\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mccle_met\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Query'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[0mremapped_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mccle_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mccle_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMatch\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mccle_met\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Query'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Scott\\Envs\\train_lasso\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3509\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3510\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3511\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3512\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3513\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Scott\\Envs\\train_lasso\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   5780\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5782\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5783\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5784\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Scott\\Envs\\train_lasso\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   5840\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5841\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5842\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5844\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['alpha-Aminoadipate'], dtype='object', name='ChEBI')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "remapped_df = pd.DataFrame(columns=ccle_map.Query)\n",
    "for idx, ccle_met in enumerate(ccle_map.Match):\n",
    "    if ccle_met in map.Match.to_list():\n",
    "        if ccle_met == 'NA':\n",
    "            remapped_df[ccle_map.loc[ccle_map.Match == ccle_met, 'Query'][idx]] = 0\n",
    "        elif ccle_met == 'NADH':\n",
    "            remapped_df[ccle_map.loc[ccle_map.Match == ccle_met, 'Query'][idx]] = app_metabolomics['NAD+']\n",
    "        elif ccle_met == 'NADPH':\n",
    "            remapped_df[ccle_map.loc[ccle_map.Match == ccle_met, 'Query'][idx]] = app_metabolomics['NADP+']\n",
    "        else:\n",
    "            remapped_df[ccle_map.loc[ccle_map.Match == ccle_met, 'Query'][idx]] = app_metabolomics[map.loc[map.Match == ccle_met, :]['Query']]\n",
    "    else:\n",
    "        remapped_df[ccle_map.loc[ccle_map.Match == ccle_met, 'Query'][idx]] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remapped_df.to_csv(\"C:/Users/Scott/Desktop/mapped_app_metabolomics.csv\", index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Predict using the LASSO model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>2-aminoadipate</th>\n",
       "      <th>3-phosphoglycerate</th>\n",
       "      <th>alpha-glycerophosphate</th>\n",
       "      <th>4-pyridoxate</th>\n",
       "      <th>aconitate</th>\n",
       "      <th>adenine</th>\n",
       "      <th>adipate</th>\n",
       "      <th>alpha-ketoglutarate</th>\n",
       "      <th>AMP</th>\n",
       "      <th>citrate</th>\n",
       "      <th>...</th>\n",
       "      <th>heptanoylcarnitine</th>\n",
       "      <th>lauroylcarnitine</th>\n",
       "      <th>myristoylcarnitine</th>\n",
       "      <th>palmitoylcarnitine</th>\n",
       "      <th>stearoylcarnitine</th>\n",
       "      <th>oleylcarnitine</th>\n",
       "      <th>arachidonyl_carnitine</th>\n",
       "      <th>sarcosine</th>\n",
       "      <th>beta-alanine</th>\n",
       "      <th>anserine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24 hours</th>\n",
       "      <td>0.509673</td>\n",
       "      <td>0.618688</td>\n",
       "      <td>0.239351</td>\n",
       "      <td>0</td>\n",
       "      <td>0.216156</td>\n",
       "      <td>-0.022994</td>\n",
       "      <td>0.129087</td>\n",
       "      <td>0.224656</td>\n",
       "      <td>0.420370</td>\n",
       "      <td>0.113862</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.750412</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48 hours</th>\n",
       "      <td>0.134410</td>\n",
       "      <td>1.429310</td>\n",
       "      <td>0.119972</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111125</td>\n",
       "      <td>-0.235745</td>\n",
       "      <td>-0.096311</td>\n",
       "      <td>-0.063019</td>\n",
       "      <td>0.056378</td>\n",
       "      <td>-0.082637</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.347671</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          2-aminoadipate  3-phosphoglycerate  alpha-glycerophosphate  \\\n",
       "24 hours        0.509673            0.618688                0.239351   \n",
       "48 hours        0.134410            1.429310                0.119972   \n",
       "\n",
       "          4-pyridoxate  aconitate   adenine   adipate  alpha-ketoglutarate  \\\n",
       "24 hours             0   0.216156 -0.022994  0.129087             0.224656   \n",
       "48 hours             0   0.111125 -0.235745 -0.096311            -0.063019   \n",
       "\n",
       "               AMP   citrate  ...  heptanoylcarnitine  lauroylcarnitine  \\\n",
       "24 hours  0.420370  0.113862  ...                   0                 0   \n",
       "48 hours  0.056378 -0.082637  ...                   0                 0   \n",
       "\n",
       "          myristoylcarnitine  palmitoylcarnitine  stearoylcarnitine  \\\n",
       "24 hours                   0                   0                  0   \n",
       "48 hours                   0                   0                  0   \n",
       "\n",
       "          oleylcarnitine  arachidonyl_carnitine  sarcosine  beta-alanine  \\\n",
       "24 hours               0                      0  -0.750412             0   \n",
       "48 hours               0                      0  -0.347671             0   \n",
       "\n",
       "          anserine  \n",
       "24 hours         0  \n",
       "48 hours         0  \n",
       "\n",
       "[2 rows x 136 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_met = pd.read_csv(\"C:/Users/Scott/Desktop/mapped_app_metabolomics.csv\", index_col=0)\n",
    "application_met"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>H3K4me1</th>\n",
       "      <th>H3K4me2</th>\n",
       "      <th>H3K4ac1</th>\n",
       "      <th>H3K9me1</th>\n",
       "      <th>H3K9me2</th>\n",
       "      <th>H3K9me3</th>\n",
       "      <th>H3K9ac1</th>\n",
       "      <th>H3K14ac1</th>\n",
       "      <th>H3K18ac1</th>\n",
       "      <th>H3K23ac1</th>\n",
       "      <th>H3K36me1</th>\n",
       "      <th>H3K36me2</th>\n",
       "      <th>H3K36me3</th>\n",
       "      <th>H3K27me1</th>\n",
       "      <th>H3K27me2</th>\n",
       "      <th>H3K27me3</th>\n",
       "      <th>H3K27ac1</th>\n",
       "      <th>H3K56me1</th>\n",
       "      <th>H3K79me1</th>\n",
       "      <th>H3K79me2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LFC_24</th>\n",
       "      <td>0.155593</td>\n",
       "      <td>-2.092323</td>\n",
       "      <td>0.287772</td>\n",
       "      <td>-0.357557</td>\n",
       "      <td>0.003452</td>\n",
       "      <td>-0.042854</td>\n",
       "      <td>0.757362</td>\n",
       "      <td>-0.099339</td>\n",
       "      <td>-0.007080</td>\n",
       "      <td>0.279713</td>\n",
       "      <td>-0.010528</td>\n",
       "      <td>-0.038472</td>\n",
       "      <td>-0.180321</td>\n",
       "      <td>-0.854200</td>\n",
       "      <td>-0.035135</td>\n",
       "      <td>0.198701</td>\n",
       "      <td>0.434725</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.239336</td>\n",
       "      <td>-0.540247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LFC_48</th>\n",
       "      <td>-0.262970</td>\n",
       "      <td>-1.383009</td>\n",
       "      <td>-1.057151</td>\n",
       "      <td>-1.472531</td>\n",
       "      <td>-0.038144</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>-0.024306</td>\n",
       "      <td>-0.107917</td>\n",
       "      <td>-0.034959</td>\n",
       "      <td>0.085595</td>\n",
       "      <td>-0.128719</td>\n",
       "      <td>0.219449</td>\n",
       "      <td>-1.172673</td>\n",
       "      <td>-1.696381</td>\n",
       "      <td>-0.012389</td>\n",
       "      <td>0.254031</td>\n",
       "      <td>-1.533607</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014075</td>\n",
       "      <td>0.497883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         H3K4me1   H3K4me2   H3K4ac1   H3K9me1   H3K9me2   H3K9me3   H3K9ac1  \\\n",
       "LFC_24  0.155593 -2.092323  0.287772 -0.357557  0.003452 -0.042854  0.757362   \n",
       "LFC_48 -0.262970 -1.383009 -1.057151 -1.472531 -0.038144  0.130709 -0.024306   \n",
       "\n",
       "        H3K14ac1  H3K18ac1  H3K23ac1  H3K36me1  H3K36me2  H3K36me3  H3K27me1  \\\n",
       "LFC_24 -0.099339 -0.007080  0.279713 -0.010528 -0.038472 -0.180321 -0.854200   \n",
       "LFC_48 -0.107917 -0.034959  0.085595 -0.128719  0.219449 -1.172673 -1.696381   \n",
       "\n",
       "        H3K27me2  H3K27me3  H3K27ac1  H3K56me1  H3K79me1  H3K79me2  \n",
       "LFC_24 -0.035135  0.198701  0.434725         0 -0.239336 -0.540247  \n",
       "LFC_48 -0.012389  0.254031 -1.533607         0  0.014075  0.497883  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_gcp = pd.read_excel(\n",
    "    io=\"D:/Chandrasekaran/Projects/EMT/Data/Proteomics/Garcia/raw/histone-PTM-EMT.xlsx\",\n",
    "    sheet_name=\"Application\",\n",
    "    index_col=0\n",
    ")\n",
    "app_gcp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "from sklearn.model_selection import train_test_split\n",
    "import keras\n",
    "\n",
    "def evaluate_models(input, output, model_loadpath:str=None, training_set=False):\n",
    "    \"\"\"Train LASSO models for a single- or multi-target problem.\"\"\"\n",
    "    predictions = list()\n",
    "    all_mse = list()\n",
    "\n",
    "    for target in output.columns:\n",
    "        y = output[target].astype(float).values.astype('float32')\n",
    "        X = input.values.astype('float32')\n",
    "        if training_set:\n",
    "            _, X, _, y = train_test_split(X, y, train_size=0.8)\n",
    "            \n",
    "        model = keras.models.load_model(model_loadpath+'/'+target)\n",
    "\n",
    "        pred = model.predict(X)\n",
    "        predictions.append(pred)\n",
    "        all_mse.append(np.square(np.subtract(y, pred)).mean())\n",
    "\n",
    "    return predictions, all_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000023FC542FF70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000023FC42B9DC0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    }
   ],
   "source": [
    "predictions, all_mse = evaluate_models(\n",
    "                                        input=application_met, \n",
    "                                        output=app_gcp,\n",
    "                                        model_loadpath=\"D:/Chandrasekaran/Projects/Epigenetics-Metabolism/Data/models/LASSO/met2gcp\",\n",
    "                                        training_set=False\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.07653227,\n",
       " 3.5918355,\n",
       " 0.5604329,\n",
       " 1.0352639,\n",
       " 0.04356305,\n",
       " 0.35271132,\n",
       " 0.21092743,\n",
       " 0.07384495,\n",
       " 0.020950686,\n",
       " 0.12794665,\n",
       " 0.2819704,\n",
       " 0.15051848,\n",
       " 0.46029568,\n",
       " 2.6781735,\n",
       " 0.047262356,\n",
       " 0.09770525,\n",
       " 1.1896667,\n",
       " 0.6483732,\n",
       " 0.08614795,\n",
       " 0.37214616]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "basepath = \"D:/Chandrasekaran/Projects/Epigenetics-Metabolism/Data/processed/model_inputs.xlsx\"\n",
    "gcp_df = pd.read_excel(basepath, 'GCP_all', index_col=0)\n",
    "met_df = pd.read_excel(basepath, 'MET_nonsignaling', index_col=0)\n",
    "\n",
    "ytest_pred, ytest_mse = evaluate_models(\n",
    "                 input=met_df, \n",
    "                 output=gcp_df,\n",
    "                 model_loadpath=\"D:/Chandrasekaran/Projects/Epigenetics-Metabolism/Data/models/LASSO/met2gcp\",\n",
    "                 training_set=True\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.32844037,\n",
       " 0.3144507,\n",
       " 1.6203355,\n",
       " 0.15081422,\n",
       " 0.19407704,\n",
       " 0.40171656,\n",
       " 0.5742547,\n",
       " 0.31545675,\n",
       " 0.561554,\n",
       " 0.26717842,\n",
       " 0.7234865,\n",
       " 0.9362439,\n",
       " 0.42974675,\n",
       " 0.43425503,\n",
       " 0.46779132,\n",
       " 0.57765424,\n",
       " 1.0642766,\n",
       " 0.31674278,\n",
       " 0.20258175,\n",
       " 0.56791437]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>PTM</th>\n",
       "      <th>Application</th>\n",
       "      <th>CCLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H3K4me1</td>\n",
       "      <td>0.076532</td>\n",
       "      <td>0.328440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H3K4me2</td>\n",
       "      <td>3.591835</td>\n",
       "      <td>0.314451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H3K4ac1</td>\n",
       "      <td>0.560433</td>\n",
       "      <td>1.620335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H3K9me1</td>\n",
       "      <td>1.035264</td>\n",
       "      <td>0.150814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H3K9me2</td>\n",
       "      <td>0.043563</td>\n",
       "      <td>0.194077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H3K9me3</td>\n",
       "      <td>0.352711</td>\n",
       "      <td>0.401717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H3K9ac1</td>\n",
       "      <td>0.210927</td>\n",
       "      <td>0.574255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H3K14ac1</td>\n",
       "      <td>0.073845</td>\n",
       "      <td>0.315457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>H3K18ac1</td>\n",
       "      <td>0.020951</td>\n",
       "      <td>0.561554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>H3K23ac1</td>\n",
       "      <td>0.127947</td>\n",
       "      <td>0.267178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>H3K36me1</td>\n",
       "      <td>0.281970</td>\n",
       "      <td>0.723486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>H3K36me2</td>\n",
       "      <td>0.150518</td>\n",
       "      <td>0.936244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>H3K36me3</td>\n",
       "      <td>0.460296</td>\n",
       "      <td>0.429747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>H3K27me1</td>\n",
       "      <td>2.678174</td>\n",
       "      <td>0.434255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>H3K27me2</td>\n",
       "      <td>0.047262</td>\n",
       "      <td>0.467791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>H3K27me3</td>\n",
       "      <td>0.097705</td>\n",
       "      <td>0.577654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>H3K27ac1</td>\n",
       "      <td>1.189667</td>\n",
       "      <td>1.064277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>H3K56me1</td>\n",
       "      <td>0.648373</td>\n",
       "      <td>0.316743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>H3K79me1</td>\n",
       "      <td>0.086148</td>\n",
       "      <td>0.202582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>H3K79me2</td>\n",
       "      <td>0.372146</td>\n",
       "      <td>0.567914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PTM  Application      CCLE\n",
       "0    H3K4me1     0.076532  0.328440\n",
       "1    H3K4me2     3.591835  0.314451\n",
       "2    H3K4ac1     0.560433  1.620335\n",
       "3    H3K9me1     1.035264  0.150814\n",
       "4    H3K9me2     0.043563  0.194077\n",
       "5    H3K9me3     0.352711  0.401717\n",
       "6    H3K9ac1     0.210927  0.574255\n",
       "7   H3K14ac1     0.073845  0.315457\n",
       "8   H3K18ac1     0.020951  0.561554\n",
       "9   H3K23ac1     0.127947  0.267178\n",
       "10  H3K36me1     0.281970  0.723486\n",
       "11  H3K36me2     0.150518  0.936244\n",
       "12  H3K36me3     0.460296  0.429747\n",
       "13  H3K27me1     2.678174  0.434255\n",
       "14  H3K27me2     0.047262  0.467791\n",
       "15  H3K27me3     0.097705  0.577654\n",
       "16  H3K27ac1     1.189667  1.064277\n",
       "17  H3K56me1     0.648373  0.316743\n",
       "18  H3K79me1     0.086148  0.202582\n",
       "19  H3K79me2     0.372146  0.567914"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_df = pd.DataFrame()\n",
    "mse_df['PTM'] = gcp_df.columns\n",
    "mse_df['Application'] = all_mse\n",
    "mse_df['CCLE'] = ytest_mse\n",
    "mse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "_, axs = plt.subplots(figsize=(7.5, 10), ncols=2, sharey=True, sharex=True)\n",
    "sns.set(font_scale=1.5)\n",
    "sns.barplot(data=mse_df, y='PTM', x='Application', ax=axs[0])\n",
    "sns.barplot(data=mse_df, y='PTM', x='CCLE', ax=axs[1])\n",
    "axs[1].set_ylabel('')\n",
    "axs[0].set_ylabel('')\n",
    "axs[0].set_xlabel('Validation set')\n",
    "axs[1].set_xlabel('Test set (CCLE)')\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.6 ('train_lasso')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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