Mito_database coverage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "located-leonard",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import sys\n",
    "import os\n",
    "import mygene\n",
    "mg = mygene.MyGeneInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "changing-album",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mito_annotation(df):    \n",
    "    mitochondrial = []\n",
    "    for i in range(len(df)):\n",
    "        if 'Verified mitochondrial' in df['IMPI_new'][i] or str(df['SubMitoLocalization'][i])!='nan':\n",
    "            mitochondrial.append('Mitochondrial')\n",
    "        elif 'Ancillary' in df['IMPI_new'][i] or 'Associated' in df['IMPI_new'][i]:\n",
    "            mitochondrial.append('Associated')\n",
    "        else:\n",
    "            mitochondrial.append('Not Mitochondrial')\n",
    "            \n",
    "    return mitochondrial\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "electrical-update",
   "metadata": {},
   "outputs": [],
   "source": [
    "##for all ids\n",
    "\n",
    "path_databases = os.path.dirname(os.path.abspath('__file__'))\n",
    "df_MC3 = pd.read_excel(path_databases+'\\\\Mouse_MitoCarta3_0.xls', sheet_name = [0,1,2])\n",
    "df_IMPI = pd.read_csv(path_databases +'\\IMPI_2021_Q4pre_Mus_Musculus.csv')\n",
    "df_IMPI['Mitochondrial'] = ['mito'  if df_IMPI['IMPI Class'][i] == 'Verified mitochondrial' else 'not_mito' for i in range(len(df_IMPI))]\n",
    "df_IMPI = df_IMPI[df_IMPI['Mitochondrial'] == 'mito'].reset_index(drop= True)\n",
    "\n",
    "path = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "protein_table = pd.read_csv(path +'Prep_Protein Groups_cut0_notimuted_mito.csv')\n",
    "IMPI_theo = len(protein_table[pd.notnull(protein_table['IMPI_new'])])\n",
    "protein_table['IMPI_new'] = [protein_table['IMPI_new'][i].split(';')  if pd.notnull(protein_table['IMPI_new'][i]) == True else 'NA' for i in range(len(protein_table))]\n",
    "\n",
    "protein_table['all_id']= protein_table[['Valid values_B_',\n",
    "            'Valid values_BAT_',\n",
    "            'Valid values_H_',\n",
    "            'Valid values_K_',\n",
    "            'Valid values_L_',\n",
    "            'Valid values_S_',\n",
    "            'Valid values_SKM_',]].sum(axis=1)\n",
    "\n",
    "protein_table = protein_table[protein_table['all_id'] !=0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "macro-paper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Protein IDs</th>\n",
       "      <th>Majority protein IDs</th>\n",
       "      <th>Peptide counts (all)</th>\n",
       "      <th>Peptide counts (razor+unique)</th>\n",
       "      <th>Peptide counts (unique)</th>\n",
       "      <th>Protein names</th>\n",
       "      <th>Gene names</th>\n",
       "      <th>Fasta headers</th>\n",
       "      <th>Number of proteins</th>\n",
       "      <th>Peptides</th>\n",
       "      <th>...</th>\n",
       "      <th>Median_Z-score_B_</th>\n",
       "      <th>Median_Z-score_BAT_</th>\n",
       "      <th>Median_Z-score_H_</th>\n",
       "      <th>Median_Z-score_K_</th>\n",
       "      <th>Median_Z-score_L_</th>\n",
       "      <th>Median_Z-score_S_</th>\n",
       "      <th>Median_Z-score_SKM_</th>\n",
       "      <th>valid</th>\n",
       "      <th>Protein_Selection_ID</th>\n",
       "      <th>all_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q8WUR0;J3QPY0;A0A0U1RNT4</td>\n",
       "      <td>Q8WUR0;J3QPY0;A0A0U1RNT4</td>\n",
       "      <td>3;2;2</td>\n",
       "      <td>3;2;2</td>\n",
       "      <td>3;2;2</td>\n",
       "      <td>Protein C19orf12 homolog</td>\n",
       "      <td>1600014C10Rik</td>\n",
       "      <td>sp|Q8WUR0|CS012_MOUSE Protein C19orf12 homolog...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.464476</td>\n",
       "      <td>-0.339811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1600014C10Rik_Q8WUR0;J3QPY0;A0A0U1RNT4</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B8JJ69;B8JJ66;F6X1P1;F6W3U3;B8JJ67</td>\n",
       "      <td>B8JJ69;B8JJ66;F6X1P1</td>\n",
       "      <td>5;4;3;2;1</td>\n",
       "      <td>5;4;3;2;1</td>\n",
       "      <td>5;4;3;2;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2310061I04Rik</td>\n",
       "      <td>tr|B8JJ69|B8JJ69_MOUSE RIKEN cDNA 2310061I04 g...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.117215</td>\n",
       "      <td>0.111434</td>\n",
       "      <td>0.082728</td>\n",
       "      <td>0.126037</td>\n",
       "      <td>-1.765570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.150607</td>\n",
       "      <td>True</td>\n",
       "      <td>2310061I04Rik_B8JJ69;B8JJ66;F6X1P1;F6W3U3;B8JJ67</td>\n",
       "      <td>5.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E9QMK9;Q8BH86;Q8BH86-2;F7CUW7</td>\n",
       "      <td>E9QMK9;Q8BH86;Q8BH86-2</td>\n",
       "      <td>31;31;26;6</td>\n",
       "      <td>31;31;26;6</td>\n",
       "      <td>31;31;26;6</td>\n",
       "      <td>UPF0317 protein C14orf159 homolog, mitochondrial</td>\n",
       "      <td>9030617O03Rik</td>\n",
       "      <td>tr|E9QMK9|E9QMK9_MOUSE D-glutamate cyclase, mi...</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.653074</td>\n",
       "      <td>-0.457492</td>\n",
       "      <td>0.642939</td>\n",
       "      <td>1.748685</td>\n",
       "      <td>0.380967</td>\n",
       "      <td>-0.509695</td>\n",
       "      <td>-0.215366</td>\n",
       "      <td>True</td>\n",
       "      <td>9030617O03Rik_E9QMK9;Q8BH86;Q8BH86-2;F7CUW7</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q9WVM8;Q3UNS8</td>\n",
       "      <td>Q9WVM8;Q3UNS8</td>\n",
       "      <td>34;31</td>\n",
       "      <td>34;31</td>\n",
       "      <td>34;31</td>\n",
       "      <td>Kynurenine/alpha-aminoadipate aminotransferase...</td>\n",
       "      <td>Aadat</td>\n",
       "      <td>sp|Q9WVM8|AADAT_MOUSE Kynurenine/alpha-aminoad...</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.520823</td>\n",
       "      <td>-1.578075</td>\n",
       "      <td>1.213162</td>\n",
       "      <td>0.525379</td>\n",
       "      <td>-0.653298</td>\n",
       "      <td>-1.347777</td>\n",
       "      <td>True</td>\n",
       "      <td>Aadat_Q9WVM8;Q3UNS8</td>\n",
       "      <td>3.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q14CH7</td>\n",
       "      <td>Q14CH7</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Alanine--tRNA ligase, mitochondrial</td>\n",
       "      <td>Aars2</td>\n",
       "      <td>sp|Q14CH7|SYAM_MOUSE Alanine--tRNA ligase, mit...</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067081</td>\n",
       "      <td>1.168704</td>\n",
       "      <td>-0.248838</td>\n",
       "      <td>0.374337</td>\n",
       "      <td>0.372171</td>\n",
       "      <td>0.062087</td>\n",
       "      <td>-0.375545</td>\n",
       "      <td>True</td>\n",
       "      <td>Aars2_Q14CH7</td>\n",
       "      <td>7.000000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>Q9CQF4</td>\n",
       "      <td>Q9CQF4</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Uncharacterized protein C6orf203 homolog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sp|Q9CQF4|MRES1_MOUSE Mitochondrial transcript...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175676</td>\n",
       "      <td>0.542065</td>\n",
       "      <td>0.965922</td>\n",
       "      <td>-0.065728</td>\n",
       "      <td>-1.544730</td>\n",
       "      <td>-1.364856</td>\n",
       "      <td>1.177470</td>\n",
       "      <td>True</td>\n",
       "      <td>nan_Q9CQF4</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>Q9CR13</td>\n",
       "      <td>Q9CR13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>UPF0562 protein C7orf55 homolog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sp|Q9CR13|FMC1_MOUSE Protein FMC1 homolog OS=M...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.040934</td>\n",
       "      <td>1.271138</td>\n",
       "      <td>1.349083</td>\n",
       "      <td>-0.345234</td>\n",
       "      <td>-0.633456</td>\n",
       "      <td>-0.885528</td>\n",
       "      <td>0.519074</td>\n",
       "      <td>True</td>\n",
       "      <td>nan_Q9CR13</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>Q9CWB7</td>\n",
       "      <td>Q9CWB7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Glutaredoxin-like protein C5orf63 homolog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sp|Q9CWB7|YD286_MOUSE Glutaredoxin-like protei...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.227345</td>\n",
       "      <td>0.884579</td>\n",
       "      <td>0.751669</td>\n",
       "      <td>0.659973</td>\n",
       "      <td>-1.782377</td>\n",
       "      <td>-0.337364</td>\n",
       "      <td>-0.290941</td>\n",
       "      <td>True</td>\n",
       "      <td>nan_Q9CWB7</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>Q9D1I2</td>\n",
       "      <td>Q9D1I2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Bcl10-interacting CARD protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sp|Q9D1I2|CAR19_MOUSE Caspase recruitment doma...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.279992</td>\n",
       "      <td>-0.257369</td>\n",
       "      <td>1.448344</td>\n",
       "      <td>-1.002468</td>\n",
       "      <td>-0.858959</td>\n",
       "      <td>0.382958</td>\n",
       "      <td>True</td>\n",
       "      <td>nan_Q9D1I2</td>\n",
       "      <td>5.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>Q9D9H8</td>\n",
       "      <td>Q9D9H8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>UPF0565 protein C2orf69 homolog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sp|Q9D9H8|CB069_MOUSE UPF0565 protein C2orf69 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.496881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.483934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>nan_Q9D9H8</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1221 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Protein IDs      Majority protein IDs  \\\n",
       "0               Q8WUR0;J3QPY0;A0A0U1RNT4  Q8WUR0;J3QPY0;A0A0U1RNT4   \n",
       "1     B8JJ69;B8JJ66;F6X1P1;F6W3U3;B8JJ67      B8JJ69;B8JJ66;F6X1P1   \n",
       "2          E9QMK9;Q8BH86;Q8BH86-2;F7CUW7    E9QMK9;Q8BH86;Q8BH86-2   \n",
       "3                          Q9WVM8;Q3UNS8             Q9WVM8;Q3UNS8   \n",
       "4                                 Q14CH7                    Q14CH7   \n",
       "...                                  ...                       ...   \n",
       "1216                              Q9CQF4                    Q9CQF4   \n",
       "1217                              Q9CR13                    Q9CR13   \n",
       "1218                              Q9CWB7                    Q9CWB7   \n",
       "1219                              Q9D1I2                    Q9D1I2   \n",
       "1220                              Q9D9H8                    Q9D9H8   \n",
       "\n",
       "     Peptide counts (all) Peptide counts (razor+unique)  \\\n",
       "0                   3;2;2                         3;2;2   \n",
       "1               5;4;3;2;1                     5;4;3;2;1   \n",
       "2              31;31;26;6                    31;31;26;6   \n",
       "3                   34;31                         34;31   \n",
       "4                      23                            23   \n",
       "...                   ...                           ...   \n",
       "1216                   12                            12   \n",
       "1217                    3                             3   \n",
       "1218                    3                             3   \n",
       "1219                    9                             9   \n",
       "1220                    4                             4   \n",
       "\n",
       "     Peptide counts (unique)  \\\n",
       "0                      3;2;2   \n",
       "1                  5;4;3;2;1   \n",
       "2                 31;31;26;6   \n",
       "3                      34;31   \n",
       "4                         23   \n",
       "...                      ...   \n",
       "1216                      12   \n",
       "1217                       3   \n",
       "1218                       3   \n",
       "1219                       9   \n",
       "1220                       4   \n",
       "\n",
       "                                          Protein names     Gene names  \\\n",
       "0                              Protein C19orf12 homolog  1600014C10Rik   \n",
       "1                                                   NaN  2310061I04Rik   \n",
       "2      UPF0317 protein C14orf159 homolog, mitochondrial  9030617O03Rik   \n",
       "3     Kynurenine/alpha-aminoadipate aminotransferase...          Aadat   \n",
       "4                   Alanine--tRNA ligase, mitochondrial          Aars2   \n",
       "...                                                 ...            ...   \n",
       "1216           Uncharacterized protein C6orf203 homolog            NaN   \n",
       "1217                    UPF0562 protein C7orf55 homolog            NaN   \n",
       "1218          Glutaredoxin-like protein C5orf63 homolog            NaN   \n",
       "1219                     Bcl10-interacting CARD protein            NaN   \n",
       "1220                    UPF0565 protein C2orf69 homolog            NaN   \n",
       "\n",
       "                                          Fasta headers  Number of proteins  \\\n",
       "0     sp|Q8WUR0|CS012_MOUSE Protein C19orf12 homolog...                   3   \n",
       "1     tr|B8JJ69|B8JJ69_MOUSE RIKEN cDNA 2310061I04 g...                   5   \n",
       "2     tr|E9QMK9|E9QMK9_MOUSE D-glutamate cyclase, mi...                   4   \n",
       "3     sp|Q9WVM8|AADAT_MOUSE Kynurenine/alpha-aminoad...                   2   \n",
       "4     sp|Q14CH7|SYAM_MOUSE Alanine--tRNA ligase, mit...                   1   \n",
       "...                                                 ...                 ...   \n",
       "1216  sp|Q9CQF4|MRES1_MOUSE Mitochondrial transcript...                   1   \n",
       "1217  sp|Q9CR13|FMC1_MOUSE Protein FMC1 homolog OS=M...                   1   \n",
       "1218  sp|Q9CWB7|YD286_MOUSE Glutaredoxin-like protei...                   1   \n",
       "1219  sp|Q9D1I2|CAR19_MOUSE Caspase recruitment doma...                   1   \n",
       "1220  sp|Q9D9H8|CB069_MOUSE UPF0565 protein C2orf69 ...                   1   \n",
       "\n",
       "      Peptides  ...  Median_Z-score_B_  Median_Z-score_BAT_  \\\n",
       "0            3  ...                NaN                  NaN   \n",
       "1            5  ...           1.117215             0.111434   \n",
       "2           31  ...          -1.653074            -0.457492   \n",
       "3           34  ...                NaN            -0.520823   \n",
       "4           23  ...           0.067081             1.168704   \n",
       "...        ...  ...                ...                  ...   \n",
       "1216        12  ...           0.175676             0.542065   \n",
       "1217         3  ...          -1.040934             1.271138   \n",
       "1218         3  ...          -0.227345             0.884579   \n",
       "1219         9  ...                NaN             0.279992   \n",
       "1220         4  ...           0.496881                  NaN   \n",
       "\n",
       "      Median_Z-score_H_  Median_Z-score_K_  Median_Z-score_L_  \\\n",
       "0                   NaN                NaN          -0.464476   \n",
       "1              0.082728           0.126037          -1.765570   \n",
       "2              0.642939           1.748685           0.380967   \n",
       "3             -1.578075           1.213162           0.525379   \n",
       "4             -0.248838           0.374337           0.372171   \n",
       "...                 ...                ...                ...   \n",
       "1216           0.965922          -0.065728          -1.544730   \n",
       "1217           1.349083          -0.345234          -0.633456   \n",
       "1218           0.751669           0.659973          -1.782377   \n",
       "1219          -0.257369           1.448344          -1.002468   \n",
       "1220                NaN                NaN          -1.483934   \n",
       "\n",
       "      Median_Z-score_S_  Median_Z-score_SKM_  valid  \\\n",
       "0             -0.339811                  NaN   True   \n",
       "1                   NaN             0.150607   True   \n",
       "2             -0.509695            -0.215366   True   \n",
       "3             -0.653298            -1.347777   True   \n",
       "4              0.062087            -0.375545   True   \n",
       "...                 ...                  ...    ...   \n",
       "1216          -1.364856             1.177470   True   \n",
       "1217          -0.885528             0.519074   True   \n",
       "1218          -0.337364            -0.290941   True   \n",
       "1219          -0.858959             0.382958   True   \n",
       "1220                NaN                  NaN   True   \n",
       "\n",
       "                                  Protein_Selection_ID    all_id  \n",
       "0               1600014C10Rik_Q8WUR0;J3QPY0;A0A0U1RNT4  1.500000  \n",
       "1     2310061I04Rik_B8JJ69;B8JJ66;F6X1P1;F6W3U3;B8JJ67  5.333333  \n",
       "2          9030617O03Rik_E9QMK9;Q8BH86;Q8BH86-2;F7CUW7  7.000000  \n",
       "3                                  Aadat_Q9WVM8;Q3UNS8  3.833333  \n",
       "4                                         Aars2_Q14CH7  7.000000  \n",
       "...                                                ...       ...  \n",
       "1216                                        nan_Q9CQF4  7.000000  \n",
       "1217                                        nan_Q9CR13  6.666667  \n",
       "1218                                        nan_Q9CWB7  6.333333  \n",
       "1219                                        nan_Q9D1I2  5.833333  \n",
       "1220                                        nan_Q9D9H8  1.333333  \n",
       "\n",
       "[1221 rows x 189 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##for filtered ids\n",
    "\n",
    "path_databases = os.path.dirname(os.path.abspath('__file__'))\n",
    "df_MC3 = pd.read_excel(path_databases+'\\\\Mouse_MitoCarta3_0.xls', sheet_name = [0,1,2])\n",
    "df_IMPI = pd.read_csv(path_databases +'\\IMPI_2021_Q4pre_Mus_Musculus.csv')\n",
    "df_IMPI['Mitochondrial'] = ['mito'  if df_IMPI['IMPI Class'][i] == 'Verified mitochondrial' else 'not_mito' for i in range(len(df_IMPI))]\n",
    "df_IMPI = df_IMPI[df_IMPI['Mitochondrial'] == 'mito'].reset_index(drop= True)\n",
    "\n",
    "path = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "protein_table = pd.read_csv(path +'Prep_Protein Groups_mito.csv')\n",
    "\n",
    "IMPI_theo = len(protein_table[pd.notnull(protein_table['IMPI_new'])])\n",
    "protein_table['IMPI_new'] = [protein_table['IMPI_new'][i].split(';')  if pd.notnull(protein_table['IMPI_new'][i]) == True else 'NA' for i in range(len(protein_table))]\n",
    "\n",
    "protein_table['all_id']= protein_table[['Valid values_B_',\n",
    "            'Valid values_BAT_',\n",
    "            'Valid values_H_',\n",
    "            'Valid values_K_',\n",
    "            'Valid values_L_',\n",
    "            'Valid values_S_',\n",
    "            'Valid values_SKM_',]].sum(axis=1)\n",
    "\n",
    "protein_table[protein_table['all_id'] !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "white-origin",
   "metadata": {},
   "outputs": [],
   "source": [
    "zz= []\n",
    "for i in range(len(df_MC3[1])):\n",
    "    if 'ensembl' not in mg.getgene(int(df_MC3[1]['MouseGeneID'][i])):\n",
    "        zz.append('nan')\n",
    "        #print('1')\n",
    "    elif type(mg.getgene(int(df_MC3[1]['MouseGeneID'][i]))['ensembl']) == dict:\n",
    "        zz.append(mg.getgene(int(df_MC3[1]['MouseGeneID'][i]))['ensembl']['gene'])\n",
    "        #print('2')\n",
    "    elif type(mg.getgene(int(df_MC3[1]['MouseGeneID'][i]))['ensembl']) == list:\n",
    "        aa = [(mg.getgene(int(df_MC3[1]['MouseGeneID'][i]))['ensembl'])[x]['gene'] for x in range(len(mg.getgene(int(df_MC3[1]['MouseGeneID'][i]))['ensembl']))]\n",
    "        zz.append(aa)\n",
    "        #print('3')\n",
    "\n",
    "df_MC3[1]['ESNG'] = zz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "expensive-warning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MitoCarta3.0 coverage: 0.9017543859649123%\n",
      "IMPI \"Verified mitochondrial\" coverage: 0.823574144486692%\n",
      "MitoCarta3.0 +IMPI coverage: 0.8222222222222222%\n"
     ]
    }
   ],
   "source": [
    "MitoCarta_id = len(protein_table[pd.notnull(protein_table['SubMitoLocalization'])])\n",
    "#IMPI_predict_id = len([i for i in range(len(protein_table)) if 'Predicted mitochondrial' in protein_table['IMPI_new'][i]])\n",
    "IMPI_known_id = len([i for i in range(len(protein_table)) if 'Verified mitochondrial' in protein_table['IMPI_new'][i]])\n",
    "#IMPI_id = len([i for i in range(len(protein_table)) if 'Known mitochondrial' in protein_table['IMPI_new'][i]] or 'Predicted mitochondrial' in protein_table['IMPI_new'][i])\n",
    "All_id = len(protein_table)\n",
    "\n",
    "MitoCarta_id_theo = len(df_MC3[1])\n",
    "#IMPI_predict_id_theo = len(df_IMPI[df_IMPI['IMPI Class (2020Q3pre)'] == 'Predicted mitochondrial'])\n",
    "IMPI_known_id_theo = len(df_IMPI[df_IMPI['IMPI Class'] == 'Verified mitochondrial'])\n",
    "#IMPI_theo = len(df_IMPI)\n",
    "\n",
    "dd = (list(df_IMPI['Ensembl Gene ID Mus Musculus'])+list(df_MC3[1]['ESNG']))\n",
    "All_id_theo = len(set([dd[i] for i in range(len(dd)) if type(dd[i]) == str]))+len([dd[i] for i in range(len(dd)) if type(dd[i]) != str])\n",
    "\n",
    "MitoCarta_cov = MitoCarta_id/MitoCarta_id_theo\n",
    "#IMPI_predict_cov = IMPI_predict_id/IMPI_predict_id_theo\n",
    "IMPI_known_cov = IMPI_known_id/IMPI_known_id_theo\n",
    "#IMPI_cov = IMPI_id/IMPI_theo\n",
    "All_id_cov = All_id/All_id_theo\n",
    "\n",
    "print('MitoCarta3.0 coverage: '+str(MitoCarta_cov)+'%\\n'+\n",
    "      'IMPI \"Verified mitochondrial\" coverage: '+str(IMPI_known_cov)+'%\\n'+\n",
    "      #'IMPI \"predicted mitochondrial\" coverage: '+str(IMPI_predict_cov)+'%\\n'+\n",
    "      #'IMPI coverage: '+str(IMPI_cov)+'%\\n'+\n",
    "      'MitoCarta3.0 +IMPI coverage: '+str(All_id_cov)+'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "possible-fishing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1028"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MitoCarta_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "democratic-import",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1083"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMPI_known_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "extensive-spokesman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1221"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "becoming-apparatus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1140"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MitoCarta_id_theo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "straight-sense",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1315"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMPI_known_id_theo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stuffed-function",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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