Make_Figure_1C.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day 0 vs Day 30 subject 1\n",
    "The data used in this notebook is lymphocyte data for subject 1's B cells at 2 time points. We use this data to show the proteome variation between collection times. \n",
    "\n",
    "Here, we note presence/absense changes and quantify fold changes using a T test.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import longitudinalCLL\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from numpy import log10\n",
    "import pandas as pd\n",
    "from matplotlib_venn import venn2\n",
    "from matplotlib_venn import venn3\n",
    "from gprofiler import GProfiler\n",
    "import numpy as np\n",
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access data\n",
    "First, we import our data package. This brings the functions defined there into scope so we can use them in this script. Then we can create a proteomic dataset object and store it as <code>prot</code>. You can select versions and subjects with <code>load_dataset</code>.\n",
    "\n",
    "Calling <code>head</code> shows the first several lines of the dataframe, which provides an idea of the type of data present and the structure of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject1_B_cells_062920_C_10</th>\n",
       "      <th>Subject1_B_cells_062920_C_11</th>\n",
       "      <th>Subject1_B_cells_062920_C_12</th>\n",
       "      <th>Subject1_B_cells_062920_C_13</th>\n",
       "      <th>Subject1_B_cells_062920_C_9</th>\n",
       "      <th>Subject1_B_cells_072920_C_4</th>\n",
       "      <th>Subject1_B_cells_072920_C_5</th>\n",
       "      <th>Subject1_B_cells_072920_C_6</th>\n",
       "      <th>Subject1_B_cells_072920_C_8</th>\n",
       "      <th>Subject1_B_cells_072920_C_9</th>\n",
       "      <th>Subject1_T_cells_062920_D_10</th>\n",
       "      <th>Subject1_T_cells_062920_D_11</th>\n",
       "      <th>Subject1_T_cells_062920_D_12</th>\n",
       "      <th>Subject1_T_cells_062920_D_13</th>\n",
       "      <th>Subject1_T_cells_062920_D_9</th>\n",
       "      <th>Subject1_T_cells_072920_D_2</th>\n",
       "      <th>Subject1_T_cells_072920_D_4</th>\n",
       "      <th>Subject1_T_cells_072920_D_5</th>\n",
       "      <th>Subject1_T_cells_072920_D_6</th>\n",
       "      <th>Subject1_T_cells_072920_D_7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein 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",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A0A0B4J2D5</th>\n",
       "      <td>3203277.5</td>\n",
       "      <td>4697996.0</td>\n",
       "      <td>7346776.5</td>\n",
       "      <td>8472867.0</td>\n",
       "      <td>4280919.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4787781.50</td>\n",
       "      <td>2153860.2</td>\n",
       "      <td>5444238.5</td>\n",
       "      <td>5514300.50</td>\n",
       "      <td>1452451.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219754.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>679607.9</td>\n",
       "      <td>2275364.50</td>\n",
       "      <td>3411472.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1205392.9</td>\n",
       "      <td>1030751.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0AVT1</th>\n",
       "      <td>1917388.2</td>\n",
       "      <td>3033529.2</td>\n",
       "      <td>3773018.8</td>\n",
       "      <td>1865758.6</td>\n",
       "      <td>5191332.5</td>\n",
       "      <td>3332031.5</td>\n",
       "      <td>2464089.00</td>\n",
       "      <td>2290868.2</td>\n",
       "      <td>2486001.2</td>\n",
       "      <td>2596365.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2869977.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1394525.6</td>\n",
       "      <td>2804357.20</td>\n",
       "      <td>1830829.1</td>\n",
       "      <td>1185091.1</td>\n",
       "      <td>1250515.2</td>\n",
       "      <td>1041313.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0FGR8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>756851.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1197022.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A6NHR9</th>\n",
       "      <td>2650513.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3788095.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1845555.5</td>\n",
       "      <td>933499.44</td>\n",
       "      <td>1958162.5</td>\n",
       "      <td>1604476.4</td>\n",
       "      <td>418360.38</td>\n",
       "      <td>907359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>159779.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1074709.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A8K2U0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1149867.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2272656.2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "            Subject1_B_cells_062920_C_10  Subject1_B_cells_062920_C_11  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     3203277.5                     4697996.0   \n",
       "A0AVT1                         1917388.2                     3033529.2   \n",
       "A0FGR8                               0.0                           0.0   \n",
       "A6NHR9                         2650513.5                           0.0   \n",
       "A8K2U0                               0.0                           0.0   \n",
       "\n",
       "            Subject1_B_cells_062920_C_12  Subject1_B_cells_062920_C_13  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     7346776.5                     8472867.0   \n",
       "A0AVT1                         3773018.8                     1865758.6   \n",
       "A0FGR8                               0.0                           0.0   \n",
       "A6NHR9                         3788095.8                           0.0   \n",
       "A8K2U0                               0.0                     1149867.8   \n",
       "\n",
       "            Subject1_B_cells_062920_C_9  Subject1_B_cells_072920_C_4  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    4280919.5                          0.0   \n",
       "A0AVT1                        5191332.5                    3332031.5   \n",
       "A0FGR8                              0.0                          0.0   \n",
       "A6NHR9                              0.0                    1845555.5   \n",
       "A8K2U0                              0.0                          0.0   \n",
       "\n",
       "            Subject1_B_cells_072920_C_5  Subject1_B_cells_072920_C_6  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                   4787781.50                    2153860.2   \n",
       "A0AVT1                       2464089.00                    2290868.2   \n",
       "A0FGR8                             0.00                          0.0   \n",
       "A6NHR9                        933499.44                    1958162.5   \n",
       "A8K2U0                             0.00                          0.0   \n",
       "\n",
       "            Subject1_B_cells_072920_C_8  Subject1_B_cells_072920_C_9  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    5444238.5                   5514300.50   \n",
       "A0AVT1                        2486001.2                   2596365.20   \n",
       "A0FGR8                              0.0                         0.00   \n",
       "A6NHR9                        1604476.4                    418360.38   \n",
       "A8K2U0                        2272656.2                         0.00   \n",
       "\n",
       "            Subject1_T_cells_062920_D_10  Subject1_T_cells_062920_D_11  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     1452451.2                           0.0   \n",
       "A0AVT1                               0.0                     2869977.5   \n",
       "A0FGR8                               0.0                           0.0   \n",
       "A6NHR9                          907359.0                           0.0   \n",
       "A8K2U0                               0.0                           0.0   \n",
       "\n",
       "            Subject1_T_cells_062920_D_12  Subject1_T_cells_062920_D_13  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     219754.92                          0.00   \n",
       "A0AVT1                              0.00                          0.00   \n",
       "A0FGR8                              0.00                          0.00   \n",
       "A6NHR9                              0.00                     159779.72   \n",
       "A8K2U0                              0.00                          0.00   \n",
       "\n",
       "            Subject1_T_cells_062920_D_9  Subject1_T_cells_072920_D_2  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                     679607.9                   2275364.50   \n",
       "A0AVT1                        1394525.6                   2804357.20   \n",
       "A0FGR8                              0.0                    756851.44   \n",
       "A6NHR9                              0.0                         0.00   \n",
       "A8K2U0                              0.0                         0.00   \n",
       "\n",
       "            Subject1_T_cells_072920_D_4  Subject1_T_cells_072920_D_5  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    3411472.8                          0.0   \n",
       "A0AVT1                        1830829.1                    1185091.1   \n",
       "A0FGR8                              0.0                          0.0   \n",
       "A6NHR9                              0.0                          0.0   \n",
       "A8K2U0                              0.0                          0.0   \n",
       "\n",
       "            Subject1_T_cells_072920_D_6  Subject1_T_cells_072920_D_7  \n",
       "Protein ID                                                            \n",
       "A0A0B4J2D5                    1205392.9                    1030751.8  \n",
       "A0AVT1                        1250515.2                    1041313.3  \n",
       "A0FGR8                              0.0                    1197022.1  \n",
       "A6NHR9                        1074709.6                          0.0  \n",
       "A8K2U0                              0.0                          0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prot = longitudinalCLL.get_proteomic()\n",
    "prot.load_dataset(version='July_noMBR_FP', subjects = [1])\n",
    "\n",
    "prot.data_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next steps will require separating the runs by cell type, to compare June and July. As you see above, the pattern for naming the columns is Subject, B or T cell type, date, and the replicate number. Here we define the list of cell types that the functions should look for based on the naming pattern.\n",
    "\n",
    "Note that the <code>prot</code> data is only from subject 1, which could also be specified <code>cell_types=['Subject1_B_cells_062920', 'Subject1_B_cells_072920']</code>\n",
    "This could also be simplified as <code>cell_types=['B_cells_07', 'B_cells_06']</code> as the only requirement is that this string is in all replicates that you want to include and no other samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_types=['B_cells_072920', 'B_cells_062920']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check which proteins are only identified in one cell type. We'll set these aside for now, in the <code>data_in_one_type</code> dataframe. For our analysis, present in more than half, or at least 3 out of 5 replicates, is considered clear although it is interesting to note those proteins identified in all replicates of one type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proteins identified only in:\n",
      "B_cells_072920    78\n",
      "B_cells_062920    12\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "present_only_in = prot.check_presence_absence(min_reps=3, cell_types=cell_types, null_value=0).dropna()\n",
    "\n",
    "print(\"Proteins identified only in:\")\n",
    "print(present_only_in.value_counts())\n",
    "data_in_one_type =prot.data_frame.loc[present_only_in.index]\n",
    "data_in_one_type['Type'] = present_only_in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <code>min_reps</code> is the minimum number of replicates required to list a protein as present in one type. This can be changed, i.e., <code>min_reps=5</code> to determine the proteins identified in all 5 replicates of a single type. Be careful not set it to a number greater than the replicates run or no proteins will be returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "present_5of5_only_in = prot.check_presence_absence(min_reps=5, cell_types=cell_types, null_value=0).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proteins identified in 5/5 replicates only in:\n",
      "B_cells_072920    11\n",
      "B_cells_062920     4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Proteins identified in 5/5 replicates only in:\")\n",
    "print(present_5of5_only_in.value_counts())\n",
    "data_in_5of5_one_type =prot.data_frame.loc[present_5of5_only_in.index]\n",
    "data_in_5of5_one_type['Type'] = present_5of5_only_in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter data \n",
    "Next, we select the proteins that are measured in at least three samples from each group, allowing the calculations to proceed without imputed zero-handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "indecies = prot.check_n_of_each_type(cell_types=cell_types, null_value=0)\n",
    "prot.data_frame = prot.data_frame[indecies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total groups identified:\n",
      "2426\n",
      "Proteins identified in at least 3 of each cell type:\t\n",
      "1122\n"
     ]
    }
   ],
   "source": [
    "print (\"Total groups identified:\")\n",
    "print(prot.data_raw.shape[0])\n",
    "\n",
    "print (\"Proteins identified in at least 3 of each cell type:\\t\")\n",
    "print (prot.data_frame.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before any analysis, we log normalize and median normalize across runs. We need to do this after filtering for consistently expressed proteins so those only identified in some rows do not throw off the normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Subject1_B_cells_062920_C_10</th>\n",
       "      <th>Subject1_B_cells_062920_C_11</th>\n",
       "      <th>Subject1_B_cells_062920_C_12</th>\n",
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       "      <th>Subject1_B_cells_072920_C_5</th>\n",
       "      <th>Subject1_B_cells_072920_C_6</th>\n",
       "      <th>Subject1_B_cells_072920_C_8</th>\n",
       "      <th>Subject1_B_cells_072920_C_9</th>\n",
       "      <th>Subject1_T_cells_062920_D_10</th>\n",
       "      <th>Subject1_T_cells_062920_D_11</th>\n",
       "      <th>Subject1_T_cells_062920_D_12</th>\n",
       "      <th>Subject1_T_cells_062920_D_13</th>\n",
       "      <th>Subject1_T_cells_062920_D_9</th>\n",
       "      <th>Subject1_T_cells_072920_D_2</th>\n",
       "      <th>Subject1_T_cells_072920_D_4</th>\n",
       "      <th>Subject1_T_cells_072920_D_5</th>\n",
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       "      <th>Subject1_T_cells_072920_D_7</th>\n",
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       "    <tr>\n",
       "      <th>Protein ID</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A0A0B4J2D5</th>\n",
       "      <td>-0.907284</td>\n",
       "      <td>-0.614455</td>\n",
       "      <td>0.324358</td>\n",
       "      <td>0.129667</td>\n",
       "      <td>-0.620956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.311569</td>\n",
       "      <td>-1.484273</td>\n",
       "      <td>-0.248072</td>\n",
       "      <td>-0.127279</td>\n",
       "      <td>-2.251177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.932781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.325712</td>\n",
       "      <td>-1.427978</td>\n",
       "      <td>-0.862720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.361136</td>\n",
       "      <td>-2.695371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0AVT1</th>\n",
       "      <td>-1.647690</td>\n",
       "      <td>-1.245503</td>\n",
       "      <td>-0.637034</td>\n",
       "      <td>-2.053420</td>\n",
       "      <td>-0.342772</td>\n",
       "      <td>-0.837361</td>\n",
       "      <td>-1.269872</td>\n",
       "      <td>-1.395303</td>\n",
       "      <td>-1.378975</td>\n",
       "      <td>-1.213963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.206813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.288712</td>\n",
       "      <td>-1.126406</td>\n",
       "      <td>-1.760617</td>\n",
       "      <td>-2.441295</td>\n",
       "      <td>-2.308117</td>\n",
       "      <td>-2.680664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A8MWD9</th>\n",
       "      <td>-1.705659</td>\n",
       "      <td>-0.897494</td>\n",
       "      <td>-1.544551</td>\n",
       "      <td>-0.890077</td>\n",
       "      <td>-1.331158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.411948</td>\n",
       "      <td>-0.585962</td>\n",
       "      <td>-1.004273</td>\n",
       "      <td>-0.964572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.440606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.463485</td>\n",
       "      <td>-0.448648</td>\n",
       "      <td>-1.677343</td>\n",
       "      <td>-1.502907</td>\n",
       "      <td>-0.581798</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O00148</th>\n",
       "      <td>1.756529</td>\n",
       "      <td>1.663173</td>\n",
       "      <td>1.728240</td>\n",
       "      <td>1.342271</td>\n",
       "      <td>1.153263</td>\n",
       "      <td>1.725132</td>\n",
       "      <td>1.529139</td>\n",
       "      <td>1.568251</td>\n",
       "      <td>1.306042</td>\n",
       "      <td>1.397598</td>\n",
       "      <td>1.304878</td>\n",
       "      <td>1.166107</td>\n",
       "      <td>1.406438</td>\n",
       "      <td>1.304433</td>\n",
       "      <td>1.253088</td>\n",
       "      <td>1.272149</td>\n",
       "      <td>1.186986</td>\n",
       "      <td>1.227216</td>\n",
       "      <td>1.426776</td>\n",
       "      <td>1.299393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O00151</th>\n",
       "      <td>0.363874</td>\n",
       "      <td>0.358541</td>\n",
       "      <td>0.339119</td>\n",
       "      <td>0.198522</td>\n",
       "      <td>0.203450</td>\n",
       "      <td>0.979911</td>\n",
       "      <td>1.094552</td>\n",
       "      <td>0.934537</td>\n",
       "      <td>0.569258</td>\n",
       "      <td>0.845804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.448459</td>\n",
       "      <td>-2.321541</td>\n",
       "      <td>-2.206959</td>\n",
       "      <td>-0.557094</td>\n",
       "      <td>-1.260376</td>\n",
       "      <td>-0.836514</td>\n",
       "      <td>-0.851017</td>\n",
       "      <td>-0.579521</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P62891</th>\n",
       "      <td>-1.492198</td>\n",
       "      <td>-1.406654</td>\n",
       "      <td>-1.376146</td>\n",
       "      <td>-1.055795</td>\n",
       "      <td>-1.227822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.456711</td>\n",
       "      <td>-0.283975</td>\n",
       "      <td>-0.606453</td>\n",
       "      <td>-0.430345</td>\n",
       "      <td>-0.791613</td>\n",
       "      <td>-1.003668</td>\n",
       "      <td>-0.173203</td>\n",
       "      <td>-0.546825</td>\n",
       "      <td>-0.716860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.203491</td>\n",
       "      <td>-0.139948</td>\n",
       "      <td>-0.078113</td>\n",
       "      <td>-0.708715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q16563</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.274117</td>\n",
       "      <td>-0.733469</td>\n",
       "      <td>-0.523787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.479278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.663565</td>\n",
       "      <td>-1.861305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q6BDS2</th>\n",
       "      <td>4.617630</td>\n",
       "      <td>2.760630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.859740</td>\n",
       "      <td>2.562322</td>\n",
       "      <td>3.907445</td>\n",
       "      <td>2.983677</td>\n",
       "      <td>1.548954</td>\n",
       "      <td>3.721366</td>\n",
       "      <td>4.738559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.612580</td>\n",
       "      <td>4.037585</td>\n",
       "      <td>1.445404</td>\n",
       "      <td>4.536025</td>\n",
       "      <td>3.901479</td>\n",
       "      <td>1.143441</td>\n",
       "      <td>3.373929</td>\n",
       "      <td>1.327737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q8NDC0</th>\n",
       "      <td>-1.554426</td>\n",
       "      <td>-2.347147</td>\n",
       "      <td>-0.550999</td>\n",
       "      <td>-7.565106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.833633</td>\n",
       "      <td>-1.163423</td>\n",
       "      <td>-0.971098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.057692</td>\n",
       "      <td>-2.166828</td>\n",
       "      <td>-2.776075</td>\n",
       "      <td>-11.163840</td>\n",
       "      <td>-1.814741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.233578</td>\n",
       "      <td>-2.325011</td>\n",
       "      <td>-1.772960</td>\n",
       "      <td>-1.884140</td>\n",
       "      <td>-1.633109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9Y237</th>\n",
       "      <td>-1.553604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.038530</td>\n",
       "      <td>-2.532553</td>\n",
       "      <td>-2.635800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.312178</td>\n",
       "      <td>-2.264584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.311214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.994957</td>\n",
       "      <td>-2.577067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.837646</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1122 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Subject1_B_cells_062920_C_10  Subject1_B_cells_062920_C_11  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     -0.907284                     -0.614455   \n",
       "A0AVT1                         -1.647690                     -1.245503   \n",
       "A8MWD9                         -1.705659                     -0.897494   \n",
       "O00148                          1.756529                      1.663173   \n",
       "O00151                          0.363874                      0.358541   \n",
       "...                                  ...                           ...   \n",
       "P62891                         -1.492198                     -1.406654   \n",
       "Q16563                               NaN                     -1.274117   \n",
       "Q6BDS2                          4.617630                      2.760630   \n",
       "Q8NDC0                         -1.554426                     -2.347147   \n",
       "Q9Y237                         -1.553604                           NaN   \n",
       "\n",
       "            Subject1_B_cells_062920_C_12  Subject1_B_cells_062920_C_13  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                      0.324358                      0.129667   \n",
       "A0AVT1                         -0.637034                     -2.053420   \n",
       "A8MWD9                         -1.544551                     -0.890077   \n",
       "O00148                          1.728240                      1.342271   \n",
       "O00151                          0.339119                      0.198522   \n",
       "...                                  ...                           ...   \n",
       "P62891                         -1.376146                     -1.055795   \n",
       "Q16563                         -0.733469                     -0.523787   \n",
       "Q6BDS2                               NaN                           NaN   \n",
       "Q8NDC0                         -0.550999                     -7.565106   \n",
       "Q9Y237                               NaN                     -2.038530   \n",
       "\n",
       "            Subject1_B_cells_062920_C_9  Subject1_B_cells_072920_C_4  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    -0.620956                          NaN   \n",
       "A0AVT1                        -0.342772                    -0.837361   \n",
       "A8MWD9                        -1.331158                          NaN   \n",
       "O00148                         1.153263                     1.725132   \n",
       "O00151                         0.203450                     0.979911   \n",
       "...                                 ...                          ...   \n",
       "P62891                        -1.227822                          NaN   \n",
       "Q16563                              NaN                          NaN   \n",
       "Q6BDS2                         2.859740                     2.562322   \n",
       "Q8NDC0                              NaN                    -0.833633   \n",
       "Q9Y237                        -2.532553                    -2.635800   \n",
       "\n",
       "            Subject1_B_cells_072920_C_5  Subject1_B_cells_072920_C_6  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    -0.311569                    -1.484273   \n",
       "A0AVT1                        -1.269872                    -1.395303   \n",
       "A8MWD9                        -0.411948                    -0.585962   \n",
       "O00148                         1.529139                     1.568251   \n",
       "O00151                         1.094552                     0.934537   \n",
       "...                                 ...                          ...   \n",
       "P62891                        -0.456711                    -0.283975   \n",
       "Q16563                        -1.479278                          NaN   \n",
       "Q6BDS2                         3.907445                     2.983677   \n",
       "Q8NDC0                        -1.163423                    -0.971098   \n",
       "Q9Y237                              NaN                          NaN   \n",
       "\n",
       "            Subject1_B_cells_072920_C_8  Subject1_B_cells_072920_C_9  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    -0.248072                    -0.127279   \n",
       "A0AVT1                        -1.378975                    -1.213963   \n",
       "A8MWD9                        -1.004273                    -0.964572   \n",
       "O00148                         1.306042                     1.397598   \n",
       "O00151                         0.569258                     0.845804   \n",
       "...                                 ...                          ...   \n",
       "P62891                        -0.606453                    -0.430345   \n",
       "Q16563                        -0.663565                    -1.861305   \n",
       "Q6BDS2                         1.548954                     3.721366   \n",
       "Q8NDC0                              NaN                    -1.057692   \n",
       "Q9Y237                        -2.312178                    -2.264584   \n",
       "\n",
       "            Subject1_T_cells_062920_D_10  Subject1_T_cells_062920_D_11  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     -2.251177                           NaN   \n",
       "A0AVT1                               NaN                     -1.206813   \n",
       "A8MWD9                               NaN                     -2.440606   \n",
       "O00148                          1.304878                      1.166107   \n",
       "O00151                               NaN                           NaN   \n",
       "...                                  ...                           ...   \n",
       "P62891                         -0.791613                     -1.003668   \n",
       "Q16563                               NaN                           NaN   \n",
       "Q6BDS2                          4.738559                           NaN   \n",
       "Q8NDC0                         -2.166828                     -2.776075   \n",
       "Q9Y237                               NaN                     -3.311214   \n",
       "\n",
       "            Subject1_T_cells_062920_D_12  Subject1_T_cells_062920_D_13  \\\n",
       "Protein ID                                                               \n",
       "A0A0B4J2D5                     -4.932781                           NaN   \n",
       "A0AVT1                               NaN                           NaN   \n",
       "A8MWD9                               NaN                           NaN   \n",
       "O00148                          1.406438                      1.304433   \n",
       "O00151                         -3.448459                     -2.321541   \n",
       "...                                  ...                           ...   \n",
       "P62891                         -0.173203                     -0.546825   \n",
       "Q16563                               NaN                           NaN   \n",
       "Q6BDS2                          4.612580                      4.037585   \n",
       "Q8NDC0                        -11.163840                     -1.814741   \n",
       "Q9Y237                               NaN                     -1.994957   \n",
       "\n",
       "            Subject1_T_cells_062920_D_9  Subject1_T_cells_072920_D_2  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    -3.325712                    -1.427978   \n",
       "A0AVT1                        -2.288712                    -1.126406   \n",
       "A8MWD9                        -2.463485                    -0.448648   \n",
       "O00148                         1.253088                     1.272149   \n",
       "O00151                        -2.206959                    -0.557094   \n",
       "...                                 ...                          ...   \n",
       "P62891                        -0.716860                          NaN   \n",
       "Q16563                              NaN                          NaN   \n",
       "Q6BDS2                         1.445404                     4.536025   \n",
       "Q8NDC0                              NaN                    -1.233578   \n",
       "Q9Y237                        -2.577067                          NaN   \n",
       "\n",
       "            Subject1_T_cells_072920_D_4  Subject1_T_cells_072920_D_5  \\\n",
       "Protein ID                                                             \n",
       "A0A0B4J2D5                    -0.862720                          NaN   \n",
       "A0AVT1                        -1.760617                    -2.441295   \n",
       "A8MWD9                        -1.677343                    -1.502907   \n",
       "O00148                         1.186986                     1.227216   \n",
       "O00151                        -1.260376                    -0.836514   \n",
       "...                                 ...                          ...   \n",
       "P62891                        -0.203491                    -0.139948   \n",
       "Q16563                              NaN                          NaN   \n",
       "Q6BDS2                         3.901479                     1.143441   \n",
       "Q8NDC0                        -2.325011                    -1.772960   \n",
       "Q9Y237                              NaN                          NaN   \n",
       "\n",
       "            Subject1_T_cells_072920_D_6  Subject1_T_cells_072920_D_7  \n",
       "Protein ID                                                            \n",
       "A0A0B4J2D5                    -2.361136                    -2.695371  \n",
       "A0AVT1                        -2.308117                    -2.680664  \n",
       "A8MWD9                        -0.581798                          NaN  \n",
       "O00148                         1.426776                     1.299393  \n",
       "O00151                        -0.851017                    -0.579521  \n",
       "...                                 ...                          ...  \n",
       "P62891                        -0.078113                    -0.708715  \n",
       "Q16563                              NaN                          NaN  \n",
       "Q6BDS2                         3.373929                     1.327737  \n",
       "Q8NDC0                        -1.884140                    -1.633109  \n",
       "Q9Y237                        -2.837646                          NaN  \n",
       "\n",
       "[1122 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prot.normalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "Now we calulate p values using a ttest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Call ttest function\n",
    "tstats = prot.ttest_wrapper(cell_types=cell_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate fold change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_comp = 'log2(30)-log2(0)'\n",
    "tstats[fc_comp] = prot.get_fold_changes(cell_types=cell_types)\n",
    "tstats['-log p'] = -log10(tstats['pvalue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will identify the altered proteins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "altered = prot.is_altered(tstats, pvalue=.05, change_factor=False,\n",
    "                          change_col=fc_comp, cell_types=cell_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statistic</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>log2(30)-log2(0)</th>\n",
       "      <th>-log p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O00151</th>\n",
       "      <td>6.156584</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.592112</td>\n",
       "      <td>3.565388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O00483</th>\n",
       "      <td>2.821230</td>\n",
       "      <td>0.022451</td>\n",
       "      <td>0.463044</td>\n",
       "      <td>1.648758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14745</th>\n",
       "      <td>3.443456</td>\n",
       "      <td>0.008779</td>\n",
       "      <td>2.421507</td>\n",
       "      <td>2.056555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14773</th>\n",
       "      <td>5.752113</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.643337</td>\n",
       "      <td>3.368598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14979</th>\n",
       "      <td>-4.488614</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>-0.741820</td>\n",
       "      <td>2.691975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q96C86</th>\n",
       "      <td>2.575954</td>\n",
       "      <td>0.032822</td>\n",
       "      <td>0.393685</td>\n",
       "      <td>1.483832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q99832</th>\n",
       "      <td>3.066962</td>\n",
       "      <td>0.015419</td>\n",
       "      <td>0.353774</td>\n",
       "      <td>1.811949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9UII2</th>\n",
       "      <td>-6.801759</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>-1.155283</td>\n",
       "      <td>3.861457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9Y2S6</th>\n",
       "      <td>-3.389959</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>-0.452812</td>\n",
       "      <td>2.022252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q96JE7</th>\n",
       "      <td>-3.454878</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>-0.578946</td>\n",
       "      <td>2.063858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            statistic    pvalue  log2(30)-log2(0)    -log p\n",
       "Protein ID                                                 \n",
       "O00151       6.156584  0.000272          0.592112  3.565388\n",
       "O00483       2.821230  0.022451          0.463044  1.648758\n",
       "O14745       3.443456  0.008779          2.421507  2.056555\n",
       "O14773       5.752113  0.000428          0.643337  3.368598\n",
       "O14979      -4.488614  0.002032         -0.741820  2.691975\n",
       "...               ...       ...               ...       ...\n",
       "Q96C86       2.575954  0.032822          0.393685  1.483832\n",
       "Q99832       3.066962  0.015419          0.353774  1.811949\n",
       "Q9UII2      -6.801759  0.000138         -1.155283  3.861457\n",
       "Q9Y2S6      -3.389959  0.009501         -0.452812  2.022252\n",
       "Q96JE7      -3.454878  0.008633         -0.578946  2.063858\n",
       "\n",
       "[168 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tstats.loc[altered.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are significantly altered (p<0.05) without restricting by the magnitude of change:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B_cells_072920    118\n",
       "B_cells_062920     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "altered.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are counts for other p and change cutoffs:\n",
      "P<0.05, >150% of the other type\n",
      "B_cells_072920    59\n",
      "B_cells_062920    23\n",
      "dtype: int64\n",
      "\n",
      "P<0.05, >200% of the other type\n",
      "B_cells_072920    20\n",
      "B_cells_062920     5\n",
      "dtype: int64\n",
      "\n",
      "P<0.05, >500% of the other type\n",
      "B_cells_072920    1\n",
      "dtype: int64\n",
      "\n",
      "P<0.01, >150% of the other type\n",
      "B_cells_072920    27\n",
      "B_cells_062920    11\n",
      "dtype: int64\n",
      "\n",
      "P<0.01, >200% of the other type\n",
      "B_cells_072920    10\n",
      "B_cells_062920     4\n",
      "dtype: int64\n",
      "\n",
      "P<0.01, >500% of the other type\n",
      "B_cells_072920    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are counts for other p and change cutoffs:\")\n",
    "print(\"P<0.05, >150% of the other type\")\n",
    "print(prot.is_altered(tstats, pvalue=.05,change_factor=1.5,change_col=fc_comp, cell_types=cell_types).value_counts()) #50% increase from either\n",
    "print(\"\\nP<0.05, >200% of the other type\")\n",
    "print(prot.is_altered(tstats, pvalue=.05,change_factor=2,change_col=fc_comp, cell_types=cell_types).value_counts()) #Double\n",
    "print(\"\\nP<0.05, >500% of the other type\")\n",
    "print(prot.is_altered(tstats, pvalue=.05,change_factor=5,change_col=fc_comp, cell_types=cell_types).value_counts()) #5 times larger\n",
    "\n",
    "print(\"\\nP<0.01, >150% of the other type\")\n",
    "print(prot.is_altered(tstats, pvalue=.01,change_factor=1.5,change_col=fc_comp, cell_types=cell_types).value_counts()) #50% increase from either\n",
    "print(\"\\nP<0.01, >200% of the other type\")\n",
    "print(prot.is_altered(tstats, pvalue=.01,change_factor=2,change_col=fc_comp, cell_types=cell_types).value_counts()) #Double\n",
    "print(\"\\nP<0.01, >500% of the other type\")\n",
    "print(prot.is_altered(tstats, pvalue=.01,change_factor=5,change_col=fc_comp, cell_types=cell_types).value_counts()) #5 times larger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differentially expressed proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Day 0 vs Day 30 Subject 1')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = sns.scatterplot(x=fc_comp,y='-log p',data=tstats)\n",
    "figure2 = sns.scatterplot(x=fc_comp,y='-log p',\n",
    "                          data=tstats.loc[altered.index])\n",
    "plt.rc('axes',labelsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlim(-3.1, 3.1)\n",
    "plt.title(\"Day 0 vs Day 30 Subject 1\", fontsize=20)\n",
    "\n",
    "#plt.savefig(\"data/fig_1c.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the differentially expressed proteins. Recall that this dataset is restricted to those expressed in at least three of each cell type.\n",
    "\n",
    "The next step is to identify those proteins by name. Here we get the protein names from the fasta file, which remain the same regardless of which software the data was quantified using."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "*Note that this function doesn't really have a place in the new set up yet.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_fasta(file=\"data/uniprot-filtered-proteome_3AUP000005640_reviewed_human.fasta\"):\n",
    "    \n",
    "    #file is formated:\n",
    "    #>sp|Q96IY4|CBPB2_HUMAN Carboxypeptidase B2 OS=Homo sapiens OX=9606 GN=CPB2 PE=1 SV=2\n",
    "    #MKLCS...\n",
    "    headings = {}\n",
    "    with open(file) as f:\n",
    "        for line in f:\n",
    "            if line.startswith('>'):#header line\n",
    "                ID = line.split('|')[1]\n",
    "                name=line.split('|')[2].split('=')[0].strip('OS')\n",
    "                headings[ID]=name\n",
    "    headings = pd.Series(list(headings.values()), index=headings.keys())\n",
    "    \n",
    "    return headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_names = load_fasta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_names_by_group={}\n",
    "for ID in prot.data_raw.index:\n",
    "    if ';' in ID:\n",
    "        ids = ID.split(';')\n",
    "        names=''\n",
    "        for i in ids:\n",
    "            if i in protein_names.index:\n",
    "                names=names + protein_names[i] + ';'\n",
    "        names = names[:-1]\n",
    "        protein_names_by_group[ID] = names\n",
    "    else:\n",
    "        if ID in protein_names.index:\n",
    "            protein_names_by_group[ID]=protein_names[ID]\n",
    "protein_names_by_group = pd.Series(list(protein_names_by_group.values()), index=protein_names_by_group.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we add the names to the <code>tstats</code> dataframe, which contains the fold changes for each protein. Then we can list the proteins that are altered. Optionally, use <code> head()</code> to display the first few or <code>to_csv</code> to write out to a file. We now split up the proteins heightened in June from those heightened in July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tstats['Name']=protein_names_by_group\n",
    "tstats['GeneID'] = tstats.Name.apply(lambda n: str(n).split('_HUMAN')[0])\n",
    "\n",
    "altered_proteins = tstats.loc[altered.index].sort_values(fc_comp,\n",
    "                        ascending=False)[['Name','GeneID',fc_comp,'pvalue']]\n",
    "higher_in_group1 = altered_proteins.loc[altered_proteins[fc_comp] >0]\n",
    "higher_in_group2 = altered_proteins.loc[altered_proteins[fc_comp] <0]\n",
    "\n",
    "#To write out to a file, use:\n",
    "#altered_proteins.to_csv('data/filename.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we do the same for those only identified in one cell type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_in_one_type['Name'] = protein_names_by_group\n",
    "data_in_one_type['GeneID']=data_in_one_type.Name.apply(lambda n: str(n).split('_HUMAN')[0])\n",
    "\n",
    "group1_only_geneset = data_in_one_type[(data_in_one_type.Type==cell_types[0])]\n",
    "group2_only_geneset= data_in_one_type[(data_in_one_type.Type==cell_types[1])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins Higher in July:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Name</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>log2(30)-log2(0)</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O14745</th>\n",
       "      <td>NHRF1_HUMAN Na(+)/H(+) exchange regulatory cof...</td>\n",
       "      <td>NHRF1</td>\n",
       "      <td>2.421507</td>\n",
       "      <td>0.008779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P02763</th>\n",
       "      <td>A1AG1_HUMAN Alpha-1-acid glycoprotein 1</td>\n",
       "      <td>A1AG1</td>\n",
       "      <td>2.179159</td>\n",
       "      <td>0.015043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O75306</th>\n",
       "      <td>NDUS2_HUMAN NADH dehydrogenase [ubiquinone] ir...</td>\n",
       "      <td>NDUS2</td>\n",
       "      <td>2.102583</td>\n",
       "      <td>0.024425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P14854</th>\n",
       "      <td>CX6B1_HUMAN Cytochrome c oxidase subunit 6B1</td>\n",
       "      <td>CX6B1</td>\n",
       "      <td>1.482313</td>\n",
       "      <td>0.046868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P69892</th>\n",
       "      <td>HBG2_HUMAN Hemoglobin subunit gamma-2</td>\n",
       "      <td>HBG2</td>\n",
       "      <td>1.351113</td>\n",
       "      <td>0.042037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P07741</th>\n",
       "      <td>APT_HUMAN Adenine phosphoribosyltransferase</td>\n",
       "      <td>APT</td>\n",
       "      <td>0.231686</td>\n",
       "      <td>0.032584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q14847</th>\n",
       "      <td>LASP1_HUMAN LIM and SH3 domain protein 1</td>\n",
       "      <td>LASP1</td>\n",
       "      <td>0.216764</td>\n",
       "      <td>0.034158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P13639</th>\n",
       "      <td>EF2_HUMAN Elongation factor 2</td>\n",
       "      <td>EF2</td>\n",
       "      <td>0.194526</td>\n",
       "      <td>0.040831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P32969</th>\n",
       "      <td>RL9_HUMAN 60S ribosomal protein L9</td>\n",
       "      <td>RL9</td>\n",
       "      <td>0.193292</td>\n",
       "      <td>0.044398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9UJU6</th>\n",
       "      <td>DBNL_HUMAN Drebrin-like protein</td>\n",
       "      <td>DBNL</td>\n",
       "      <td>0.173180</td>\n",
       "      <td>0.043022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Name GeneID  \\\n",
       "Protein ID                                                             \n",
       "O14745      NHRF1_HUMAN Na(+)/H(+) exchange regulatory cof...  NHRF1   \n",
       "P02763               A1AG1_HUMAN Alpha-1-acid glycoprotein 1   A1AG1   \n",
       "O75306      NDUS2_HUMAN NADH dehydrogenase [ubiquinone] ir...  NDUS2   \n",
       "P14854          CX6B1_HUMAN Cytochrome c oxidase subunit 6B1   CX6B1   \n",
       "P69892                 HBG2_HUMAN Hemoglobin subunit gamma-2    HBG2   \n",
       "...                                                       ...    ...   \n",
       "P07741           APT_HUMAN Adenine phosphoribosyltransferase     APT   \n",
       "Q14847              LASP1_HUMAN LIM and SH3 domain protein 1   LASP1   \n",
       "P13639                         EF2_HUMAN Elongation factor 2     EF2   \n",
       "P32969                    RL9_HUMAN 60S ribosomal protein L9     RL9   \n",
       "Q9UJU6                       DBNL_HUMAN Drebrin-like protein    DBNL   \n",
       "\n",
       "            log2(30)-log2(0)    pvalue  \n",
       "Protein ID                              \n",
       "O14745              2.421507  0.008779  \n",
       "P02763              2.179159  0.015043  \n",
       "O75306              2.102583  0.024425  \n",
       "P14854              1.482313  0.046868  \n",
       "P69892              1.351113  0.042037  \n",
       "...                      ...       ...  \n",
       "P07741              0.231686  0.032584  \n",
       "Q14847              0.216764  0.034158  \n",
       "P13639              0.194526  0.040831  \n",
       "P32969              0.193292  0.044398  \n",
       "Q9UJU6              0.173180  0.043022  \n",
       "\n",
       "[118 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_in_group1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sanity check with a couple proteins\n",
    "#prot.data_frame.loc['P02775']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins Only in July:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Name</th>\n",
       "      <th>GeneID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O00161</th>\n",
       "      <td>NP23_HUMAN Synaptosomal-associated protein 23</td>\n",
       "      <td>NP23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O00244</th>\n",
       "      <td>ATOX1_HUMAN Copper transport protein ATOX1</td>\n",
       "      <td>ATOX1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O00442</th>\n",
       "      <td>RTCA_HUMAN RNA 3'-terminal phosphate cyclase</td>\n",
       "      <td>RTCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O43681</th>\n",
       "      <td>ASNA_HUMAN ATPase ASNA1</td>\n",
       "      <td>ASNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O60610</th>\n",
       "      <td>DIAP1_HUMAN Protein diaphanous homolog 1</td>\n",
       "      <td>DIAP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P28068</th>\n",
       "      <td>DMB_HUMAN HLA class II histocompatibility anti...</td>\n",
       "      <td>DMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P35030</th>\n",
       "      <td>TRY3_HUMAN Trypsin-3</td>\n",
       "      <td>TRY3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P37235</th>\n",
       "      <td>HPCL1_HUMAN Hippocalcin-like protein 1</td>\n",
       "      <td>HPCL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P41567</th>\n",
       "      <td>EIF1_HUMAN Eukaryotic translation initiation f...</td>\n",
       "      <td>EIF1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9Y3C4</th>\n",
       "      <td>TPRKB_HUMAN EKC/KEOPS complex subunit TPRKB</td>\n",
       "      <td>TPRKB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Name GeneID\n",
       "Protein ID                                                          \n",
       "O00161         NP23_HUMAN Synaptosomal-associated protein 23    NP23\n",
       "O00244            ATOX1_HUMAN Copper transport protein ATOX1   ATOX1\n",
       "O00442          RTCA_HUMAN RNA 3'-terminal phosphate cyclase    RTCA\n",
       "O43681                               ASNA_HUMAN ATPase ASNA1    ASNA\n",
       "O60610              DIAP1_HUMAN Protein diaphanous homolog 1   DIAP1\n",
       "...                                                       ...    ...\n",
       "P28068      DMB_HUMAN HLA class II histocompatibility anti...    DMB\n",
       "P35030                                  TRY3_HUMAN Trypsin-3    TRY3\n",
       "P37235                HPCL1_HUMAN Hippocalcin-like protein 1   HPCL1\n",
       "P41567      EIF1_HUMAN Eukaryotic translation initiation f...   EIF1\n",
       "Q9Y3C4           TPRKB_HUMAN EKC/KEOPS complex subunit TPRKB   TPRKB\n",
       "\n",
       "[78 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1_only_geneset[['Name', 'GeneID']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins higher or only in July by GeneID\n",
    "<code>group1_set</code> is defined here as both the above lists of names, those higher or only in group 1 cells.\n",
    "Note that this might not be the most interesting set of proteins, ie, when comparing data that was not run together those that appear only in one time are much more likely to be run errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "group1_set = list(higher_in_group1.GeneID)+list(group1_only_geneset.GeneID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(group1_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins Higher in June:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Name</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>log2(30)-log2(0)</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P40925</th>\n",
       "      <td>MDHC_HUMAN Malate dehydrogenase, cytoplasmic</td>\n",
       "      <td>MDHC</td>\n",
       "      <td>-0.697160</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P27695</th>\n",
       "      <td>APEX1_HUMAN DNA-(apurinic or apyrimidinic site...</td>\n",
       "      <td>APEX1</td>\n",
       "      <td>-0.714168</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9UII2</th>\n",
       "      <td>ATIF1_HUMAN ATPase inhibitor, mitochondrial</td>\n",
       "      <td>ATIF1</td>\n",
       "      <td>-1.155283</td>\n",
       "      <td>0.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P09960</th>\n",
       "      <td>LKHA4_HUMAN Leukotriene A-4 hydrolase</td>\n",
       "      <td>LKHA4</td>\n",
       "      <td>-0.486866</td>\n",
       "      <td>0.000316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9UI08</th>\n",
       "      <td>EVL_HUMAN Ena/VASP-like protein</td>\n",
       "      <td>EVL</td>\n",
       "      <td>-0.573145</td>\n",
       "      <td>0.000534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P56279</th>\n",
       "      <td>TCL1A_HUMAN T-cell leukemia/lymphoma protein 1A</td>\n",
       "      <td>TCL1A</td>\n",
       "      <td>-1.367644</td>\n",
       "      <td>0.000978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P22626</th>\n",
       "      <td>ROA2_HUMAN Heterogeneous nuclear ribonucleopro...</td>\n",
       "      <td>ROA2</td>\n",
       "      <td>-0.391689</td>\n",
       "      <td>0.001100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q5SSJ5</th>\n",
       "      <td>HP1B3_HUMAN Heterochromatin protein 1-binding ...</td>\n",
       "      <td>HP1B3</td>\n",
       "      <td>-0.373794</td>\n",
       "      <td>0.001354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P61956</th>\n",
       "      <td>UMO2_HUMAN Small ubiquitin-related modifier 2</td>\n",
       "      <td>UMO2</td>\n",
       "      <td>-0.998122</td>\n",
       "      <td>0.001437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q92522</th>\n",
       "      <td>H1X_HUMAN Histone H1x</td>\n",
       "      <td>H1X</td>\n",
       "      <td>-0.945849</td>\n",
       "      <td>0.001721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O14979</th>\n",
       "      <td>HNRDL_HUMAN Heterogeneous nuclear ribonucleopr...</td>\n",
       "      <td>HNRDL</td>\n",
       "      <td>-0.741820</td>\n",
       "      <td>0.002032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P31150</th>\n",
       "      <td>GDIA_HUMAN Rab GDP dissociation inhibitor alpha</td>\n",
       "      <td>GDIA</td>\n",
       "      <td>-0.426028</td>\n",
       "      <td>0.002105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P02545</th>\n",
       "      <td>LMNA_HUMAN Prelamin-A/C</td>\n",
       "      <td>LMNA</td>\n",
       "      <td>-1.323313</td>\n",
       "      <td>0.003027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P19338</th>\n",
       "      <td>NUCL_HUMAN Nucleolin</td>\n",
       "      <td>NUCL</td>\n",
       "      <td>-0.575978</td>\n",
       "      <td>0.003579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P28062</th>\n",
       "      <td>PSB8_HUMAN Proteasome subunit beta type-8</td>\n",
       "      <td>PSB8</td>\n",
       "      <td>-0.836701</td>\n",
       "      <td>0.003627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P80723</th>\n",
       "      <td>BASP1_HUMAN Brain acid soluble protein 1</td>\n",
       "      <td>BASP1</td>\n",
       "      <td>-0.524138</td>\n",
       "      <td>0.003710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q562R1</th>\n",
       "      <td>ACTBL_HUMAN Beta-actin-like protein 2</td>\n",
       "      <td>ACTBL</td>\n",
       "      <td>-0.481293</td>\n",
       "      <td>0.004549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q03252</th>\n",
       "      <td>LMNB2_HUMAN Lamin-B2</td>\n",
       "      <td>LMNB2</td>\n",
       "      <td>-0.364407</td>\n",
       "      <td>0.005372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P50452</th>\n",
       "      <td>PB8_HUMAN Serpin B8</td>\n",
       "      <td>PB8</td>\n",
       "      <td>-1.031299</td>\n",
       "      <td>0.007051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P31146</th>\n",
       "      <td>COR1A_HUMAN Coronin-1A</td>\n",
       "      <td>COR1A</td>\n",
       "      <td>-0.266249</td>\n",
       "      <td>0.008513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q96JE7</th>\n",
       "      <td>C16B_HUMAN Protein transport protein Sec16B</td>\n",
       "      <td>C16B</td>\n",
       "      <td>-0.578946</td>\n",
       "      <td>0.008633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O95758</th>\n",
       "      <td>PTBP3_HUMAN Polypyrimidine tract-binding prote...</td>\n",
       "      <td>PTBP3</td>\n",
       "      <td>-0.807682</td>\n",
       "      <td>0.009382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9Y2S6</th>\n",
       "      <td>TMA7_HUMAN Translation machinery-associated pr...</td>\n",
       "      <td>TMA7</td>\n",
       "      <td>-0.452812</td>\n",
       "      <td>0.009501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q14103</th>\n",
       "      <td>HNRPD_HUMAN Heterogeneous nuclear ribonucleopr...</td>\n",
       "      <td>HNRPD</td>\n",
       "      <td>-0.577587</td>\n",
       "      <td>0.009776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P27348</th>\n",
       "      <td>1433T_HUMAN 14-3-3 protein theta</td>\n",
       "      <td>1433T</td>\n",
       "      <td>-0.303326</td>\n",
       "      <td>0.010550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O15400</th>\n",
       "      <td>TX7_HUMAN Syntaxin-7</td>\n",
       "      <td>TX7</td>\n",
       "      <td>-0.827576</td>\n",
       "      <td>0.010951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P11279</th>\n",
       "      <td>LAMP1_HUMAN Lysosome-associated membrane glyco...</td>\n",
       "      <td>LAMP1</td>\n",
       "      <td>-0.618533</td>\n",
       "      <td>0.011641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P84243</th>\n",
       "      <td>H33_HUMAN Histone H3.3</td>\n",
       "      <td>H33</td>\n",
       "      <td>-0.591614</td>\n",
       "      <td>0.011863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P01903</th>\n",
       "      <td>DRA_HUMAN HLA class II histocompatibility anti...</td>\n",
       "      <td>DRA</td>\n",
       "      <td>-0.439124</td>\n",
       "      <td>0.011915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P05114</th>\n",
       "      <td>HMGN1_HUMAN Non-histone chromosomal protein HM...</td>\n",
       "      <td>HMGN1</td>\n",
       "      <td>-0.465116</td>\n",
       "      <td>0.017358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P62081</th>\n",
       "      <td>RS7_HUMAN 40S ribosomal protein S7</td>\n",
       "      <td>RS7</td>\n",
       "      <td>-0.332155</td>\n",
       "      <td>0.018246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P06748</th>\n",
       "      <td>NPM_HUMAN Nucleophosmin</td>\n",
       "      <td>NPM</td>\n",
       "      <td>-0.555314</td>\n",
       "      <td>0.018818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P39687</th>\n",
       "      <td>AN32A_HUMAN Acidic leucine-rich nuclear phosph...</td>\n",
       "      <td>AN32A</td>\n",
       "      <td>-0.666261</td>\n",
       "      <td>0.018893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O00571</th>\n",
       "      <td>DDX3X_HUMAN ATP-dependent RNA helicase DDX3X</td>\n",
       "      <td>DDX3X</td>\n",
       "      <td>-0.843072</td>\n",
       "      <td>0.019564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P0DOY2</th>\n",
       "      <td>IGLC2_HUMAN Immunoglobulin lambda constant 2</td>\n",
       "      <td>IGLC2</td>\n",
       "      <td>-0.826326</td>\n",
       "      <td>0.022483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P62328</th>\n",
       "      <td>TYB4_HUMAN Thymosin beta-4</td>\n",
       "      <td>TYB4</td>\n",
       "      <td>-0.329898</td>\n",
       "      <td>0.022886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P30048</th>\n",
       "      <td>PRDX3_HUMAN Thioredoxin-dependent peroxide red...</td>\n",
       "      <td>PRDX3</td>\n",
       "      <td>-0.868514</td>\n",
       "      <td>0.024270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P13796</th>\n",
       "      <td>PLSL_HUMAN Plastin-2</td>\n",
       "      <td>PLSL</td>\n",
       "      <td>-0.511349</td>\n",
       "      <td>0.025458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P05387</th>\n",
       "      <td>RLA2_HUMAN 60S acidic ribosomal protein P2</td>\n",
       "      <td>RLA2</td>\n",
       "      <td>-0.991500</td>\n",
       "      <td>0.026815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P26599</th>\n",
       "      <td>PTBP1_HUMAN Polypyrimidine tract-binding prote...</td>\n",
       "      <td>PTBP1</td>\n",
       "      <td>-0.473579</td>\n",
       "      <td>0.030043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O60493</th>\n",
       "      <td>NX3_HUMAN Sorting nexin-3</td>\n",
       "      <td>NX3</td>\n",
       "      <td>-0.446077</td>\n",
       "      <td>0.032081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P35527</th>\n",
       "      <td>K1C9_HUMAN Keratin, type I cytoskeletal 9</td>\n",
       "      <td>K1C9</td>\n",
       "      <td>-1.699721</td>\n",
       "      <td>0.032449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P23497</th>\n",
       "      <td>P100_HUMAN Nuclear autoantigen Sp-100</td>\n",
       "      <td>P100</td>\n",
       "      <td>-0.569253</td>\n",
       "      <td>0.036142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P25787</th>\n",
       "      <td>PSA2_HUMAN Proteasome subunit alpha type-2</td>\n",
       "      <td>PSA2</td>\n",
       "      <td>-0.977536</td>\n",
       "      <td>0.036548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O75367</th>\n",
       "      <td>H2AY_HUMAN Core histone macro-H2A.1</td>\n",
       "      <td>H2AY</td>\n",
       "      <td>-0.195983</td>\n",
       "      <td>0.037900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P00491</th>\n",
       "      <td>PNPH_HUMAN Purine nucleoside phosphorylase</td>\n",
       "      <td>PNPH</td>\n",
       "      <td>-0.927830</td>\n",
       "      <td>0.042794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q71DI3</th>\n",
       "      <td>H32_HUMAN Histone H3.2</td>\n",
       "      <td>H32</td>\n",
       "      <td>-0.486497</td>\n",
       "      <td>0.045601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P68431</th>\n",
       "      <td>H31_HUMAN Histone H3.1</td>\n",
       "      <td>H31</td>\n",
       "      <td>-0.486497</td>\n",
       "      <td>0.045601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P55263</th>\n",
       "      <td>ADK_HUMAN Adenosine kinase</td>\n",
       "      <td>ADK</td>\n",
       "      <td>-0.578616</td>\n",
       "      <td>0.047041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P29508</th>\n",
       "      <td>PB3_HUMAN Serpin B3</td>\n",
       "      <td>PB3</td>\n",
       "      <td>-0.858792</td>\n",
       "      <td>0.049556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Name GeneID  \\\n",
       "Protein ID                                                             \n",
       "P40925          MDHC_HUMAN Malate dehydrogenase, cytoplasmic    MDHC   \n",
       "P27695      APEX1_HUMAN DNA-(apurinic or apyrimidinic site...  APEX1   \n",
       "Q9UII2           ATIF1_HUMAN ATPase inhibitor, mitochondrial   ATIF1   \n",
       "P09960                 LKHA4_HUMAN Leukotriene A-4 hydrolase   LKHA4   \n",
       "Q9UI08                       EVL_HUMAN Ena/VASP-like protein     EVL   \n",
       "P56279       TCL1A_HUMAN T-cell leukemia/lymphoma protein 1A   TCL1A   \n",
       "P22626      ROA2_HUMAN Heterogeneous nuclear ribonucleopro...   ROA2   \n",
       "Q5SSJ5      HP1B3_HUMAN Heterochromatin protein 1-binding ...  HP1B3   \n",
       "P61956         UMO2_HUMAN Small ubiquitin-related modifier 2    UMO2   \n",
       "Q92522                                 H1X_HUMAN Histone H1x     H1X   \n",
       "O14979      HNRDL_HUMAN Heterogeneous nuclear ribonucleopr...  HNRDL   \n",
       "P31150       GDIA_HUMAN Rab GDP dissociation inhibitor alpha    GDIA   \n",
       "P02545                               LMNA_HUMAN Prelamin-A/C    LMNA   \n",
       "P19338                                  NUCL_HUMAN Nucleolin    NUCL   \n",
       "P28062             PSB8_HUMAN Proteasome subunit beta type-8    PSB8   \n",
       "P80723              BASP1_HUMAN Brain acid soluble protein 1   BASP1   \n",
       "Q562R1                 ACTBL_HUMAN Beta-actin-like protein 2   ACTBL   \n",
       "Q03252                                  LMNB2_HUMAN Lamin-B2   LMNB2   \n",
       "P50452                                   PB8_HUMAN Serpin B8     PB8   \n",
       "P31146                                COR1A_HUMAN Coronin-1A   COR1A   \n",
       "Q96JE7           C16B_HUMAN Protein transport protein Sec16B    C16B   \n",
       "O95758      PTBP3_HUMAN Polypyrimidine tract-binding prote...  PTBP3   \n",
       "Q9Y2S6      TMA7_HUMAN Translation machinery-associated pr...   TMA7   \n",
       "Q14103      HNRPD_HUMAN Heterogeneous nuclear ribonucleopr...  HNRPD   \n",
       "P27348                      1433T_HUMAN 14-3-3 protein theta   1433T   \n",
       "O15400                                  TX7_HUMAN Syntaxin-7     TX7   \n",
       "P11279      LAMP1_HUMAN Lysosome-associated membrane glyco...  LAMP1   \n",
       "P84243                                H33_HUMAN Histone H3.3     H33   \n",
       "P01903      DRA_HUMAN HLA class II histocompatibility anti...    DRA   \n",
       "P05114      HMGN1_HUMAN Non-histone chromosomal protein HM...  HMGN1   \n",
       "P62081                    RS7_HUMAN 40S ribosomal protein S7     RS7   \n",
       "P06748                               NPM_HUMAN Nucleophosmin     NPM   \n",
       "P39687      AN32A_HUMAN Acidic leucine-rich nuclear phosph...  AN32A   \n",
       "O00571          DDX3X_HUMAN ATP-dependent RNA helicase DDX3X   DDX3X   \n",
       "P0DOY2          IGLC2_HUMAN Immunoglobulin lambda constant 2   IGLC2   \n",
       "P62328                            TYB4_HUMAN Thymosin beta-4    TYB4   \n",
       "P30048      PRDX3_HUMAN Thioredoxin-dependent peroxide red...  PRDX3   \n",
       "P13796                                  PLSL_HUMAN Plastin-2    PLSL   \n",
       "P05387            RLA2_HUMAN 60S acidic ribosomal protein P2    RLA2   \n",
       "P26599      PTBP1_HUMAN Polypyrimidine tract-binding prote...  PTBP1   \n",
       "O60493                             NX3_HUMAN Sorting nexin-3     NX3   \n",
       "P35527             K1C9_HUMAN Keratin, type I cytoskeletal 9    K1C9   \n",
       "P23497                 P100_HUMAN Nuclear autoantigen Sp-100    P100   \n",
       "P25787            PSA2_HUMAN Proteasome subunit alpha type-2    PSA2   \n",
       "O75367                   H2AY_HUMAN Core histone macro-H2A.1    H2AY   \n",
       "P00491            PNPH_HUMAN Purine nucleoside phosphorylase    PNPH   \n",
       "Q71DI3                                H32_HUMAN Histone H3.2     H32   \n",
       "P68431                                H31_HUMAN Histone H3.1     H31   \n",
       "P55263                            ADK_HUMAN Adenosine kinase     ADK   \n",
       "P29508                                   PB3_HUMAN Serpin B3     PB3   \n",
       "\n",
       "            log2(30)-log2(0)    pvalue  \n",
       "Protein ID                              \n",
       "P40925             -0.697160  0.000072  \n",
       "P27695             -0.714168  0.000076  \n",
       "Q9UII2             -1.155283  0.000138  \n",
       "P09960             -0.486866  0.000316  \n",
       "Q9UI08             -0.573145  0.000534  \n",
       "P56279             -1.367644  0.000978  \n",
       "P22626             -0.391689  0.001100  \n",
       "Q5SSJ5             -0.373794  0.001354  \n",
       "P61956             -0.998122  0.001437  \n",
       "Q92522             -0.945849  0.001721  \n",
       "O14979             -0.741820  0.002032  \n",
       "P31150             -0.426028  0.002105  \n",
       "P02545             -1.323313  0.003027  \n",
       "P19338             -0.575978  0.003579  \n",
       "P28062             -0.836701  0.003627  \n",
       "P80723             -0.524138  0.003710  \n",
       "Q562R1             -0.481293  0.004549  \n",
       "Q03252             -0.364407  0.005372  \n",
       "P50452             -1.031299  0.007051  \n",
       "P31146             -0.266249  0.008513  \n",
       "Q96JE7             -0.578946  0.008633  \n",
       "O95758             -0.807682  0.009382  \n",
       "Q9Y2S6             -0.452812  0.009501  \n",
       "Q14103             -0.577587  0.009776  \n",
       "P27348             -0.303326  0.010550  \n",
       "O15400             -0.827576  0.010951  \n",
       "P11279             -0.618533  0.011641  \n",
       "P84243             -0.591614  0.011863  \n",
       "P01903             -0.439124  0.011915  \n",
       "P05114             -0.465116  0.017358  \n",
       "P62081             -0.332155  0.018246  \n",
       "P06748             -0.555314  0.018818  \n",
       "P39687             -0.666261  0.018893  \n",
       "O00571             -0.843072  0.019564  \n",
       "P0DOY2             -0.826326  0.022483  \n",
       "P62328             -0.329898  0.022886  \n",
       "P30048             -0.868514  0.024270  \n",
       "P13796             -0.511349  0.025458  \n",
       "P05387             -0.991500  0.026815  \n",
       "P26599             -0.473579  0.030043  \n",
       "O60493             -0.446077  0.032081  \n",
       "P35527             -1.699721  0.032449  \n",
       "P23497             -0.569253  0.036142  \n",
       "P25787             -0.977536  0.036548  \n",
       "O75367             -0.195983  0.037900  \n",
       "P00491             -0.927830  0.042794  \n",
       "Q71DI3             -0.486497  0.045601  \n",
       "P68431             -0.486497  0.045601  \n",
       "P55263             -0.578616  0.047041  \n",
       "P29508             -0.858792  0.049556  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_in_group2.sort_values('pvalue', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins Only in June:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Name</th>\n",
       "      <th>GeneID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O95969</th>\n",
       "      <td>G1D2_HUMAN Secretoglobin family 1D member 2</td>\n",
       "      <td>G1D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P01833</th>\n",
       "      <td>PIGR_HUMAN Polymeric immunoglobulin receptor</td>\n",
       "      <td>PIGR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P02814</th>\n",
       "      <td>MR3B_HUMAN Submaxillary gland androgen-regulat...</td>\n",
       "      <td>MR3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P28799</th>\n",
       "      <td>GRN_HUMAN Progranulin</td>\n",
       "      <td>GRN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P47929</th>\n",
       "      <td>LEG7_HUMAN Galectin-7</td>\n",
       "      <td>LEG7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q6VY07</th>\n",
       "      <td>PACS1_HUMAN Phosphofurin acidic cluster sortin...</td>\n",
       "      <td>PACS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q8TAX7</th>\n",
       "      <td>MUC7_HUMAN Mucin-7</td>\n",
       "      <td>MUC7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9H1E5</th>\n",
       "      <td>TMX4_HUMAN Thioredoxin-related transmembrane p...</td>\n",
       "      <td>TMX4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9UGM3</th>\n",
       "      <td>DMBT1_HUMAN Deleted in malignant brain tumors ...</td>\n",
       "      <td>DMBT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q5VSP4</th>\n",
       "      <td>LC1L1_HUMAN Putative lipocalin 1-like protein 1</td>\n",
       "      <td>LC1L1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P02808</th>\n",
       "      <td>TAT_HUMAN Statherin</td>\n",
       "      <td>TAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q04941</th>\n",
       "      <td>PLP2_HUMAN Proteolipid protein 2</td>\n",
       "      <td>PLP2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Name GeneID\n",
       "Protein ID                                                          \n",
       "O95969           G1D2_HUMAN Secretoglobin family 1D member 2    G1D2\n",
       "P01833          PIGR_HUMAN Polymeric immunoglobulin receptor    PIGR\n",
       "P02814      MR3B_HUMAN Submaxillary gland androgen-regulat...   MR3B\n",
       "P28799                                 GRN_HUMAN Progranulin     GRN\n",
       "P47929                                 LEG7_HUMAN Galectin-7    LEG7\n",
       "Q6VY07      PACS1_HUMAN Phosphofurin acidic cluster sortin...  PACS1\n",
       "Q8TAX7                                    MUC7_HUMAN Mucin-7    MUC7\n",
       "Q9H1E5      TMX4_HUMAN Thioredoxin-related transmembrane p...   TMX4\n",
       "Q9UGM3      DMBT1_HUMAN Deleted in malignant brain tumors ...  DMBT1\n",
       "Q5VSP4       LC1L1_HUMAN Putative lipocalin 1-like protein 1   LC1L1\n",
       "P02808                                   TAT_HUMAN Statherin     TAT\n",
       "Q04941                      PLP2_HUMAN Proteolipid protein 2    PLP2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group2_only_geneset[['Name', 'GeneID']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins higher or only in June by GeneID\n",
    "<code>group2_set</code> is defined here as both the above lists of names, those higher or only in June."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "group2_set = list(higher_in_group2.GeneID)+list(group2_only_geneset.GeneID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(group2_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check to see that June and July differential sets we compiled are distinct lists\n",
    "#venn2(subsets = (set(group1_set), set(group2_set)), set_labels = ('July', 'June'))\n",
    "#plt.savefig('data/diff_overlap.png')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KEGG pathway enrichment\n",
    "Here I am doing a functional pathway enrichment. I use GProfiler to run a KEGG analysis on the differential proteins.\n",
    "<code>group1_set</code> is proteins differentially expressed in July\n",
    "<code>group2_set</code> is proteins differentially expressed in June"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "gp = GProfiler(return_dataframe = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>native</th>\n",
       "      <th>name</th>\n",
       "      <th>p_value</th>\n",
       "      <th>significant</th>\n",
       "      <th>description</th>\n",
       "      <th>term_size</th>\n",
       "      <th>query_size</th>\n",
       "      <th>intersection_size</th>\n",
       "      <th>effective_domain_size</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>query</th>\n",
       "      <th>parents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>KEGG</td>\n",
       "      <td>KEGG:05131</td>\n",
       "      <td>Shigellosis</td>\n",
       "      <td>0.005086</td>\n",
       "      <td>True</td>\n",
       "      <td>Shigellosis</td>\n",
       "      <td>246</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>8000</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.03252</td>\n",
       "      <td>query_1</td>\n",
       "      <td>[KEGG:00000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>KEGG</td>\n",
       "      <td>KEGG:04510</td>\n",
       "      <td>Focal adhesion</td>\n",
       "      <td>0.009669</td>\n",
       "      <td>True</td>\n",
       "      <td>Focal adhesion</td>\n",
       "      <td>200</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>8000</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.03500</td>\n",
       "      <td>query_1</td>\n",
       "      <td>[KEGG:00000]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source      native            name   p_value  significant     description  \\\n",
       "43   KEGG  KEGG:05131     Shigellosis  0.005086         True     Shigellosis   \n",
       "52   KEGG  KEGG:04510  Focal adhesion  0.009669         True  Focal adhesion   \n",
       "\n",
       "    term_size  query_size  intersection_size  effective_domain_size  \\\n",
       "43        246          46                  8                   8000   \n",
       "52        200          46                  7                   8000   \n",
       "\n",
       "    precision   recall    query       parents  \n",
       "43   0.173913  0.03252  query_1  [KEGG:00000]  \n",
       "52   0.152174  0.03500  query_1  [KEGG:00000]  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1 = gp.profile(organism='hsapiens', query=list(group1_set))\n",
    "group1[group1[\"source\"] == \"KEGG\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>native</th>\n",
       "      <th>name</th>\n",
       "      <th>p_value</th>\n",
       "      <th>significant</th>\n",
       "      <th>description</th>\n",
       "      <th>term_size</th>\n",
       "      <th>query_size</th>\n",
       "      <th>intersection_size</th>\n",
       "      <th>effective_domain_size</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>query</th>\n",
       "      <th>parents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [source, native, name, p_value, significant, description, term_size, query_size, intersection_size, effective_domain_size, precision, recall, query, parents]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group2 = gp.profile(organism='hsapiens', query=list(group2_set))\n",
    "group2[group2[\"source\"] == \"KEGG\"]"
   ]
  }
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