Revisions PAA.ipynb

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  {
   "cell_type": "markdown",
   "id": "384a039b",
   "metadata": {},
   "source": [
    "# Revisions PAA\n",
    "## Goal: \n",
    "To review to Reviewer's 4 request to demonstrate that the tools, established with data from the Bhatia lab, provide similar results for a given protease when populated with data from the literature."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e58452e",
   "metadata": {},
   "source": [
    "To this end, we will apply our kinetic package to reanalyze data from Holt et al. 2022. In this paper, the authors provide raw data from a fluorogenis screen of 11 proteases against 11 substrates and subsequently present the results in the form of a heatmap of product formation rates (RFU/min). \n",
    "\n",
    "We will use our \"kinetic\" package to automatically analyze these raw kinetic inputs and our \"vis\" package to produce visualizations of te aggregated results, including a heatmap, that should match that previously published and included in Figure 4A. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "314c2c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/mariaalonso/Work/protease_activity_analysis/notebooks'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4916310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mariaalonso/Work/protease_activity_analysis\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67675b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import protease_activity_analysis as paa\n",
    "from utils import get_output_dir\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55013b57",
   "metadata": {},
   "source": [
    "We will first create a directory to store our results for the data from Holt et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39fb91cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "in_path = 'data/alternative data sources/Heatmap Raw Data_Holt_2022.xlsx'\n",
    "out_dir = os.path.join(get_output_dir(), 'Holt2022')\n",
    "in_dir = os.path.join('data', 'Holt2022')\n",
    "\n",
    "#Create a new directory if it does not exist \n",
    "if not os.path.exists(out_dir):\n",
    "    # Create a new directory because it does not exist \n",
    "    os.makedirs(out_dir)\n",
    "\n",
    "    #Create a new directory if it does not exist \n",
    "if not os.path.exists(in_dir):\n",
    "    # Create a new directory because it does not exist \n",
    "    os.makedirs(in_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f609b531",
   "metadata": {},
   "source": [
    "We will next create a dictionary to map tab names to protease names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d80922b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THROMBIN\n"
     ]
    }
   ],
   "source": [
    "prot_map = {'KLK2': 'KLK2', 'CTSS': 'CTSS', 'ADAM': 'ADAMTS1', \n",
    "            'PLAS': 'PLASMIN', 'THROMB':'THROMBIN', 'FACXIA': 'Factor XIa',\n",
    "            'MP7': 'MMP7', 'M11': 'MMP11', 'M8': 'MMP8', 'M1': 'MMP1', \n",
    "            'M13': 'MMP13'}\n",
    "sheet_name= 'THROMB'\n",
    "prot_name = prot_map[sheet_name]\n",
    "print(prot_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e576d75",
   "metadata": {},
   "source": [
    "We will next have to do a bit of reformatting, namely tansposing the data, resetting the index, dropping NaNs and renaming the first column to get raw data in the correct format for input into the kinetic dataset class. We can iterate across all protease tabs to reformat the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c77e762d",
   "metadata": {},
   "outputs": [
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       "4      sub5    5929.0     7926.0    10556.0    12386.0    13831.0    14896.0   \n",
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       "6      sub7    5974.0     4499.0     4505.0     4502.0     4428.0     4418.0   \n",
       "7      sub8  215180.0   340117.0   614158.0   885977.0  1138342.0  1376276.0   \n",
       "8      sub9    5247.0     4741.0     4780.0     4810.0     4751.0     4824.0   \n",
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       "10    sub11    5944.0     5607.0     5476.0     5495.0     5438.0     5513.0   \n",
       "\n",
       "Time       36.0       42.0       48.0       54.0       60.0       66.0  \n",
       "0        9045.0     9337.0     9768.0    10044.0    10295.0    10439.0  \n",
       "1      373930.0   384165.0   396559.0   403664.0   415625.0   419486.0  \n",
       "2        5202.0     5244.0     5230.0     5186.0     5315.0     5264.0  \n",
       "3     3242936.0  3250337.0  3250635.0  3256028.0  3259243.0  3279218.0  \n",
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       "10       5422.0     5355.0     5425.0     5353.0     5375.0     5407.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prot=sheet_name\n",
    "sheet_data = pd.read_excel(in_path,\n",
    "                sheet_name=prot, header=3, index_col=0, engine='openpyxl')\n",
    "sheet_data_t=sheet_data.T #transpose\n",
    "sheet_data_t_i=sheet_data_t.reset_index() #reset index\n",
    "in_data = sheet_data_t_i.iloc[0:11,0:13] #delete nan\n",
    "in_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71c7281c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5f7ba8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KLK2\n",
      "CTSS\n",
      "ADAMTS1\n",
      "PLASMIN\n",
      "THROMBIN\n",
      "Factor XIa\n",
      "MMP7\n",
      "MMP11\n",
      "MMP8\n",
      "MMP1\n",
      "MMP13\n"
     ]
    }
   ],
   "source": [
    "for prot in prot_map.keys():\n",
    "    prot_name = prot_map[prot] # fetch true protease name\n",
    "    print(prot_name)\n",
    "    sheet_data = pd.read_excel(in_path,\n",
    "                sheet_name=prot, header=3, index_col=0, engine='openpyxl') #import\n",
    "    sheet_data_t=sheet_data.T #transpose\n",
    "    sheet_data_t_i=sheet_data_t.reset_index() #reset index\n",
    "    in_data = sheet_data_t_i.iloc[0:11,0:13] #delete nan\n",
    "    in_data = in_data.rename(columns={'index': prot_name+'-Substrate'}) #rename first col\n",
    "    in_data.to_excel(os.path.join(in_dir, prot_name+'.xlsx'), index=False) #save excel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3b4c53",
   "metadata": {},
   "source": [
    "Lets try loading 1 file, e.g. THROMBIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8261a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "in_path = os.path.join(in_dir, 'THROMBIN.xlsx')\n",
    "fc_time = 6.0\n",
    "linear_time = 6.0\n",
    "data = paa.kinetic.KineticDataset(in_path, fc_time, linear_time, out_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587aa0db",
   "metadata": {},
   "source": [
    "If we rank order substrates based on the initial rates returned by Kinetic dataset, we appreciate that substrates 4,10,6 and 8 are best cleaved by thrombin (from best to worst). This is in agreement with the heatmap of Fig 4A. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8fa4e8c0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Initial rate at t=6.0</th>\n",
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       "                    Initial rate at t=6.0\n",
       "THROMBIN-Substrate                       \n",
       "sub1                            95.333333\n",
       "sub10                       106202.000000\n",
       "sub11                          -56.166667\n",
       "sub2                          5291.166667\n",
       "sub3                          -133.000000\n",
       "sub4                        397278.666667\n",
       "sub5                           332.833333\n",
       "sub6                         44252.166667\n",
       "sub7                          -245.833333\n",
       "sub8                         20822.833333\n",
       "sub9                           -84.333333"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.get_initial_rate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3169af04",
   "metadata": {},
   "source": [
    "We can also use the plotting functionality of KineticDataset to plot the raw and fold change data for each substarte and thrombin. These plots are also in agreement with the plots provided in S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0bf525ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'THROMBIN'}, xlabel='Time (min)', ylabel='Raw Intensity'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot raw kinetic data - also saves 'ADAMTS1_Raw Intensity_kinetic.pdf' in output folder\n",
    "data.plot_kinetic(data.get_raw(), title = data.sample_name, ylabel = 'Raw Intensity', close_plot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba1c5a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'THROMBIN'}, xlabel='Time (min)', ylabel='Fold Change'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot kinetic data as a function of fold change over time -  saves 'ADAMTS1_Fold Change_kinetic.pdf' in output folder\n",
    "data.plot_kinetic(data.get_fc(), title = data.sample_name, ylabel = 'Fold Change', close_plot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de5e433",
   "metadata": {},
   "source": [
    "Having validated that KineticDataset works well for a given protease, we will now apply the KineticDataset class across the list of files we have reformatted to plot a heatmap of PFR across all protease-substrates pairs as shown in Fig 4A. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7c686ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing  ADAMTS1.xlsx\n",
      "Analyzing  MMP1.xlsx\n",
      "Analyzing  MMP11.xlsx\n",
      "Analyzing  THROMBIN.xlsx\n",
      "Analyzing  CTSS.xlsx\n",
      "Analyzing  MMP7.xlsx\n",
      "Analyzing  MMP13.xlsx\n",
      "Analyzing  PLASMIN.xlsx\n",
      "Analyzing  MMP8.xlsx\n",
      "Analyzing  Factor XIa.xlsx\n",
      "Analyzing  KLK2.xlsx\n",
      "['ADAMTS1', 'MMP1', 'MMP11', 'THROMBIN', 'CTSS', 'MMP7', 'MMP13', 'PLASMIN', 'MMP8', 'Factor XIa', 'KLK2']\n"
     ]
    }
   ],
   "source": [
    "# Define path for folder where all raw input files for a screen are saved, load and analyze all relevant data, save fc as csv for subsequent data aggregation and visualization\n",
    "in_path = in_dir\n",
    "fc_time = 6.0\n",
    "linear_time = 6.0\n",
    "sample_list = []\n",
    "\n",
    "for filename in os.listdir(in_path):\n",
    "    if not filename.startswith('.'):\n",
    "        print('Analyzing ', filename)\n",
    "        full_file_path = os.path.join(in_path, filename)\n",
    "        data_temp = paa.kinetic.KineticDataset(full_file_path, fc_time, linear_time, out_dir)\n",
    "        data_temp.write_csv(data_temp.get_initial_rate(), 'PFR_6')\n",
    "        data_temp.plot_kinetic(data_temp.get_raw(), title = data_temp.sample_name, ylabel = 'Raw Intensity', close_plot=True)\n",
    "        sample_list.append(data_temp.get_sample_name())\n",
    "    \n",
    "print(sample_list) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa965cbe",
   "metadata": {},
   "source": [
    "We will next aggregate the product formation rates for each substrate across all proteases to make a heatmap of RFUs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33e665d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADAMTS1</th>\n",
       "      <th>MMP1</th>\n",
       "      <th>MMP11</th>\n",
       "      <th>THROMBIN</th>\n",
       "      <th>CTSS</th>\n",
       "      <th>MMP7</th>\n",
       "      <th>MMP13</th>\n",
       "      <th>PLASMIN</th>\n",
       "      <th>MMP8</th>\n",
       "      <th>Factor XIa</th>\n",
       "      <th>KLK2</th>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sub1</th>\n",
       "      <td>18.333333333333332</td>\n",
       "      <td>63.5</td>\n",
       "      <td>14.166666666666666</td>\n",
       "      <td>95.33333333333333</td>\n",
       "      <td>444.6666666666667</td>\n",
       "      <td>4.333333333333333</td>\n",
       "      <td>121.16666666666667</td>\n",
       "      <td>1171.3333333333333</td>\n",
       "      <td>148.5</td>\n",
       "      <td>12.666666666666666</td>\n",
       "      <td>-59.333333333333336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub10</th>\n",
       "      <td>3038.6666666666665</td>\n",
       "      <td>2276.5</td>\n",
       "      <td>3719.6666666666665</td>\n",
       "      <td>106202.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3547.8333333333335</td>\n",
       "      <td>3197.5</td>\n",
       "      <td>241630.0</td>\n",
       "      <td>3490.1666666666665</td>\n",
       "      <td>250975.33333333334</td>\n",
       "      <td>41632.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub11</th>\n",
       "      <td>-114.16666666666667</td>\n",
       "      <td>-7.166666666666667</td>\n",
       "      <td>-115.66666666666667</td>\n",
       "      <td>-56.166666666666664</td>\n",
       "      <td>-46.5</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-106.16666666666667</td>\n",
       "      <td>959.0</td>\n",
       "      <td>-76.83333333333333</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>-52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub2</th>\n",
       "      <td>4755.833333333333</td>\n",
       "      <td>-1480.3333333333333</td>\n",
       "      <td>3385.8333333333335</td>\n",
       "      <td>5291.166666666667</td>\n",
       "      <td>5113.5</td>\n",
       "      <td>5468.166666666667</td>\n",
       "      <td>3314.1666666666665</td>\n",
       "      <td>312673.1666666667</td>\n",
       "      <td>4120.166666666667</td>\n",
       "      <td>224061.0</td>\n",
       "      <td>73777.16666666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub3</th>\n",
       "      <td>-139.83333333333334</td>\n",
       "      <td>-32.833333333333336</td>\n",
       "      <td>-147.83333333333334</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-126.66666666666667</td>\n",
       "      <td>265.5</td>\n",
       "      <td>15.166666666666666</td>\n",
       "      <td>-271.6666666666667</td>\n",
       "      <td>150.83333333333334</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>-149.66666666666666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub4</th>\n",
       "      <td>935.0</td>\n",
       "      <td>41.166666666666664</td>\n",
       "      <td>1446.5</td>\n",
       "      <td>397278.6666666667</td>\n",
       "      <td>829.6666666666666</td>\n",
       "      <td>2007.6666666666667</td>\n",
       "      <td>1823.3333333333333</td>\n",
       "      <td>186347.66666666666</td>\n",
       "      <td>-920.1666666666666</td>\n",
       "      <td>310244.3333333333</td>\n",
       "      <td>2066.6666666666665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub5</th>\n",
       "      <td>-109.33333333333333</td>\n",
       "      <td>-66.66666666666667</td>\n",
       "      <td>-87.0</td>\n",
       "      <td>332.8333333333333</td>\n",
       "      <td>-50.833333333333336</td>\n",
       "      <td>45.333333333333336</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1748.1666666666667</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>925.8333333333334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub6</th>\n",
       "      <td>-1122.8333333333333</td>\n",
       "      <td>354.1666666666667</td>\n",
       "      <td>-1114.5</td>\n",
       "      <td>44252.166666666664</td>\n",
       "      <td>-6.333333333333333</td>\n",
       "      <td>-197.66666666666666</td>\n",
       "      <td>-882.1666666666666</td>\n",
       "      <td>446943.6666666667</td>\n",
       "      <td>-582.0</td>\n",
       "      <td>2874.3333333333335</td>\n",
       "      <td>-1141.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub7</th>\n",
       "      <td>-210.16666666666666</td>\n",
       "      <td>-144.16666666666666</td>\n",
       "      <td>-199.0</td>\n",
       "      <td>-245.83333333333334</td>\n",
       "      <td>-140.0</td>\n",
       "      <td>41.333333333333336</td>\n",
       "      <td>-120.33333333333333</td>\n",
       "      <td>-191.83333333333334</td>\n",
       "      <td>-73.83333333333333</td>\n",
       "      <td>-185.0</td>\n",
       "      <td>-201.33333333333334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub8</th>\n",
       "      <td>-668.3333333333334</td>\n",
       "      <td>370.8333333333333</td>\n",
       "      <td>-200.33333333333334</td>\n",
       "      <td>20822.833333333332</td>\n",
       "      <td>1357.3333333333333</td>\n",
       "      <td>-1370.8333333333333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>973874.1666666666</td>\n",
       "      <td>360.3333333333333</td>\n",
       "      <td>-386.1666666666667</td>\n",
       "      <td>-153.66666666666666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub9</th>\n",
       "      <td>-79.16666666666667</td>\n",
       "      <td>-46.333333333333336</td>\n",
       "      <td>-54.5</td>\n",
       "      <td>-84.33333333333333</td>\n",
       "      <td>-59.166666666666664</td>\n",
       "      <td>-78.83333333333333</td>\n",
       "      <td>-70.83333333333333</td>\n",
       "      <td>2772.1666666666665</td>\n",
       "      <td>-49.333333333333336</td>\n",
       "      <td>-73.33333333333333</td>\n",
       "      <td>12.166666666666666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ADAMTS1                 MMP1                MMP11  \\\n",
       "                                                                       \n",
       "sub1    18.333333333333332                 63.5   14.166666666666666   \n",
       "sub10   3038.6666666666665               2276.5   3719.6666666666665   \n",
       "sub11  -114.16666666666667   -7.166666666666667  -115.66666666666667   \n",
       "sub2     4755.833333333333  -1480.3333333333333   3385.8333333333335   \n",
       "sub3   -139.83333333333334  -32.833333333333336  -147.83333333333334   \n",
       "sub4                 935.0   41.166666666666664               1446.5   \n",
       "sub5   -109.33333333333333   -66.66666666666667                -87.0   \n",
       "sub6   -1122.8333333333333    354.1666666666667              -1114.5   \n",
       "sub7   -210.16666666666666  -144.16666666666666               -199.0   \n",
       "sub8    -668.3333333333334    370.8333333333333  -200.33333333333334   \n",
       "sub9    -79.16666666666667  -46.333333333333336                -54.5   \n",
       "\n",
       "                  THROMBIN                 CTSS                 MMP7  \\\n",
       "                                                                       \n",
       "sub1     95.33333333333333    444.6666666666667    4.333333333333333   \n",
       "sub10             106202.0                 98.0   3547.8333333333335   \n",
       "sub11  -56.166666666666664                -46.5                -88.0   \n",
       "sub2     5291.166666666667               5113.5    5468.166666666667   \n",
       "sub3                -133.0  -126.66666666666667                265.5   \n",
       "sub4     397278.6666666667    829.6666666666666   2007.6666666666667   \n",
       "sub5     332.8333333333333  -50.833333333333336   45.333333333333336   \n",
       "sub6    44252.166666666664   -6.333333333333333  -197.66666666666666   \n",
       "sub7   -245.83333333333334               -140.0   41.333333333333336   \n",
       "sub8    20822.833333333332   1357.3333333333333  -1370.8333333333333   \n",
       "sub9    -84.33333333333333  -59.166666666666664   -78.83333333333333   \n",
       "\n",
       "                     MMP13              PLASMIN                 MMP8  \\\n",
       "                                                                       \n",
       "sub1    121.16666666666667   1171.3333333333333                148.5   \n",
       "sub10               3197.5             241630.0   3490.1666666666665   \n",
       "sub11  -106.16666666666667                959.0   -76.83333333333333   \n",
       "sub2    3314.1666666666665    312673.1666666667    4120.166666666667   \n",
       "sub3    15.166666666666666   -271.6666666666667   150.83333333333334   \n",
       "sub4    1823.3333333333333   186347.66666666666   -920.1666666666666   \n",
       "sub5                  10.5   1748.1666666666667                 -2.5   \n",
       "sub6    -882.1666666666666    446943.6666666667               -582.0   \n",
       "sub7   -120.33333333333333  -191.83333333333334   -73.83333333333333   \n",
       "sub8                   4.0    973874.1666666666    360.3333333333333   \n",
       "sub9    -70.83333333333333   2772.1666666666665  -49.333333333333336   \n",
       "\n",
       "               Factor XIa                 KLK2  \n",
       "                                                \n",
       "sub1   12.666666666666666  -59.333333333333336  \n",
       "sub10  250975.33333333334              41632.5  \n",
       "sub11               -54.0                -52.0  \n",
       "sub2             224061.0    73777.16666666667  \n",
       "sub3               -117.0  -149.66666666666666  \n",
       "sub4    310244.3333333333   2066.6666666666665  \n",
       "sub5                -74.0    925.8333333333334  \n",
       "sub6   2874.3333333333335              -1141.5  \n",
       "sub7               -185.0  -201.33333333333334  \n",
       "sub8   -386.1666666666667  -153.66666666666666  \n",
       "sub9   -73.33333333333333   12.166666666666666  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all paths to output files of interest (e.g. PFR at 6 min), for proteases of interest, here all proteases in the recombinant screen\n",
    "fc_files = []\n",
    "for prot in sample_list:\n",
    "    fc_files.append(os.path.join(out_dir, prot, str(prot)+'_PFR_6.csv'))\n",
    "\n",
    "# Aggregate all PFR values\n",
    "agg_df = paa.vis.aggregate_data(fc_files, out_dir, 'Holt_agg_PFR')\n",
    "\n",
    "# Visualize matrix\n",
    "agg_df.columns = sample_list\n",
    "agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83f9bcbf",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub1</th>\n",
       "      <th>sub10</th>\n",
       "      <th>sub11</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADAMTS1</th>\n",
       "      <td>18.333333</td>\n",
       "      <td>3038.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4755.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>935.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP1</th>\n",
       "      <td>63.500000</td>\n",
       "      <td>2276.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>354.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>370.833333</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>MMP11</th>\n",
       "      <td>14.166667</td>\n",
       "      <td>3719.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3385.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1446.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THROMBIN</th>\n",
       "      <td>95.333333</td>\n",
       "      <td>106202.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5291.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>397278.666667</td>\n",
       "      <td>332.833333</td>\n",
       "      <td>44252.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20822.833333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTSS</th>\n",
       "      <td>444.666667</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5113.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>829.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1357.333333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP7</th>\n",
       "      <td>4.333333</td>\n",
       "      <td>3547.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5468.166667</td>\n",
       "      <td>265.500000</td>\n",
       "      <td>2007.666667</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP13</th>\n",
       "      <td>121.166667</td>\n",
       "      <td>3197.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3314.166667</td>\n",
       "      <td>15.166667</td>\n",
       "      <td>1823.333333</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLASMIN</th>\n",
       "      <td>1171.333333</td>\n",
       "      <td>241630.000000</td>\n",
       "      <td>959.0</td>\n",
       "      <td>312673.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>186347.666667</td>\n",
       "      <td>1748.166667</td>\n",
       "      <td>446943.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>973874.166667</td>\n",
       "      <td>2772.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP8</th>\n",
       "      <td>148.500000</td>\n",
       "      <td>3490.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4120.166667</td>\n",
       "      <td>150.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>360.333333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Factor XIa</th>\n",
       "      <td>12.666667</td>\n",
       "      <td>250975.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224061.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>310244.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2874.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLK2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>41632.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73777.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2066.666667</td>\n",
       "      <td>925.833333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sub1          sub10  sub11           sub2        sub3  \\\n",
       "ADAMTS1       18.333333    3038.666667    1.0    4755.833333    1.000000   \n",
       "MMP1          63.500000    2276.500000    1.0       1.000000    1.000000   \n",
       "MMP11         14.166667    3719.666667    1.0    3385.833333    1.000000   \n",
       "THROMBIN      95.333333  106202.000000    1.0    5291.166667    1.000000   \n",
       "CTSS         444.666667      98.000000    1.0    5113.500000    1.000000   \n",
       "MMP7           4.333333    3547.833333    1.0    5468.166667  265.500000   \n",
       "MMP13        121.166667    3197.500000    1.0    3314.166667   15.166667   \n",
       "PLASMIN     1171.333333  241630.000000  959.0  312673.166667    1.000000   \n",
       "MMP8         148.500000    3490.166667    1.0    4120.166667  150.833333   \n",
       "Factor XIa    12.666667  250975.333333    1.0  224061.000000    1.000000   \n",
       "KLK2           1.000000   41632.500000    1.0   73777.166667    1.000000   \n",
       "\n",
       "                     sub4         sub5           sub6       sub7  \\\n",
       "ADAMTS1        935.000000     1.000000       1.000000   1.000000   \n",
       "MMP1            41.166667     1.000000     354.166667   1.000000   \n",
       "MMP11         1446.500000     1.000000       1.000000   1.000000   \n",
       "THROMBIN    397278.666667   332.833333   44252.166667   1.000000   \n",
       "CTSS           829.666667     1.000000       1.000000   1.000000   \n",
       "MMP7          2007.666667    45.333333       1.000000  41.333333   \n",
       "MMP13         1823.333333    10.500000       1.000000   1.000000   \n",
       "PLASMIN     186347.666667  1748.166667  446943.666667   1.000000   \n",
       "MMP8             1.000000     1.000000       1.000000   1.000000   \n",
       "Factor XIa  310244.333333     1.000000    2874.333333   1.000000   \n",
       "KLK2          2066.666667   925.833333       1.000000   1.000000   \n",
       "\n",
       "                     sub8         sub9  \n",
       "ADAMTS1          1.000000     1.000000  \n",
       "MMP1           370.833333     1.000000  \n",
       "MMP11            1.000000     1.000000  \n",
       "THROMBIN     20822.833333     1.000000  \n",
       "CTSS          1357.333333     1.000000  \n",
       "MMP7             1.000000     1.000000  \n",
       "MMP13            4.000000     1.000000  \n",
       "PLASMIN     973874.166667  2772.166667  \n",
       "MMP8           360.333333     1.000000  \n",
       "Factor XIa       1.000000     1.000000  \n",
       "KLK2             1.000000    12.166667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub1</th>\n",
       "      <th>sub10</th>\n",
       "      <th>sub11</th>\n",
       "      <th>sub2</th>\n",
       "      <th>sub3</th>\n",
       "      <th>sub4</th>\n",
       "      <th>sub5</th>\n",
       "      <th>sub6</th>\n",
       "      <th>sub7</th>\n",
       "      <th>sub8</th>\n",
       "      <th>sub9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADAMTS1</th>\n",
       "      <td>2.908721</td>\n",
       "      <td>8.019174</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.467127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.840547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP1</th>\n",
       "      <td>4.151040</td>\n",
       "      <td>7.730394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.717629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.869768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.915753</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP11</th>\n",
       "      <td>2.650892</td>\n",
       "      <td>8.221389</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.127355</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.276902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THROMBIN</th>\n",
       "      <td>4.557380</td>\n",
       "      <td>11.573098</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.573794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.892393</td>\n",
       "      <td>5.807642</td>\n",
       "      <td>10.697660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.943805</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTSS</th>\n",
       "      <td>6.097325</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.539639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.721024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.213277</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP7</th>\n",
       "      <td>1.466337</td>\n",
       "      <td>8.174092</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.606699</td>\n",
       "      <td>5.581615</td>\n",
       "      <td>7.604728</td>\n",
       "      <td>3.814043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.721669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP13</th>\n",
       "      <td>4.797167</td>\n",
       "      <td>8.070125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.105961</td>\n",
       "      <td>2.719100</td>\n",
       "      <td>7.508422</td>\n",
       "      <td>2.351375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLASMIN</th>\n",
       "      <td>7.065898</td>\n",
       "      <td>12.395163</td>\n",
       "      <td>6.865891</td>\n",
       "      <td>12.652914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.135369</td>\n",
       "      <td>7.466323</td>\n",
       "      <td>13.010188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.789037</td>\n",
       "      <td>7.927384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP8</th>\n",
       "      <td>5.000585</td>\n",
       "      <td>8.157705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.323649</td>\n",
       "      <td>5.016175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.887030</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Factor XIa</th>\n",
       "      <td>2.538974</td>\n",
       "      <td>12.433110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.319674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.645115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.963576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLK2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.636636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.208805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.633692</td>\n",
       "      <td>6.830694</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.498700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sub1      sub10     sub11       sub2      sub3       sub4  \\\n",
       "ADAMTS1     2.908721   8.019174  0.000000   8.467127  0.000000   6.840547   \n",
       "MMP1        4.151040   7.730394  0.000000   0.000000  0.000000   3.717629   \n",
       "MMP11       2.650892   8.221389  0.000000   8.127355  0.000000   7.276902   \n",
       "THROMBIN    4.557380  11.573098  0.000000   8.573794  0.000000  12.892393   \n",
       "CTSS        6.097325   4.584967  0.000000   8.539639  0.000000   6.721024   \n",
       "MMP7        1.466337   8.174092  0.000000   8.606699  5.581615   7.604728   \n",
       "MMP13       4.797167   8.070125  0.000000   8.105961  2.719100   7.508422   \n",
       "PLASMIN     7.065898  12.395163  6.865891  12.652914  0.000000  12.135369   \n",
       "MMP8        5.000585   8.157705  0.000000   8.323649  5.016175   0.000000   \n",
       "Factor XIa  2.538974  12.433110  0.000000  12.319674  0.000000  12.645115   \n",
       "KLK2        0.000000  10.636636  0.000000  11.208805  0.000000   7.633692   \n",
       "\n",
       "                sub5       sub6      sub7       sub8      sub9  \n",
       "ADAMTS1     0.000000   0.000000  0.000000   0.000000  0.000000  \n",
       "MMP1        0.000000   5.869768  0.000000   5.915753  0.000000  \n",
       "MMP11       0.000000   0.000000  0.000000   0.000000  0.000000  \n",
       "THROMBIN    5.807642  10.697660  0.000000   9.943805  0.000000  \n",
       "CTSS        0.000000   0.000000  0.000000   7.213277  0.000000  \n",
       "MMP7        3.814043   0.000000  3.721669   0.000000  0.000000  \n",
       "MMP13       2.351375   0.000000  0.000000   1.386294  0.000000  \n",
       "PLASMIN     7.466323  13.010188  0.000000  13.789037  7.927384  \n",
       "MMP8        0.000000   0.000000  0.000000   5.887030  0.000000  \n",
       "Factor XIa  0.000000   7.963576  0.000000   0.000000  0.000000  \n",
       "KLK2        6.830694   0.000000  0.000000   0.000000  2.498700  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transpose matrix, perform hierarchical clustering and plot heatmap, this is not taking the log(x) because of negative numbers\n",
    "agg_df_t = agg_df.T\n",
    "agg_df_t = agg_df_t.astype(float)\n",
    "\n",
    "# Replace all negative numbers with PFR=1 that is defined fro log(x) and repeat\n",
    "agg_df_t = agg_df_t.clip(lower=1)\n",
    "display(agg_df_t)\n",
    "\n",
    "# log transform matrix\n",
    "agg_df_t_copy = agg_df_t.copy()\n",
    "agg_df_t_copy = agg_df_t_copy.applymap(lambda x: np.log(x))\n",
    "agg_df_t_copy\n",
    "\n",
    "# plot hierarchical map of substrate protease pairs\n",
    "paa.vis.plot_heatmap(agg_df_t_copy, out_dir, center=9,  metric='euclidean', method='ward', scale=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "482ab852",
   "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>sub1</th>\n",
       "      <th>sub2</th>\n",
       "      <th>sub3</th>\n",
       "      <th>sub4</th>\n",
       "      <th>sub5</th>\n",
       "      <th>sub6</th>\n",
       "      <th>sub7</th>\n",
       "      <th>sub8</th>\n",
       "      <th>sub9</th>\n",
       "      <th>sub10</th>\n",
       "      <th>sub11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KLK2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.208805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.633692</td>\n",
       "      <td>6.830694</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.498700</td>\n",
       "      <td>10.636636</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTSS</th>\n",
       "      <td>6.097325</td>\n",
       "      <td>8.539639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.721024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.213277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADAMTS1</th>\n",
       "      <td>2.908721</td>\n",
       "      <td>8.467127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.840547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.019174</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLASMIN</th>\n",
       "      <td>7.065898</td>\n",
       "      <td>12.652914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.135369</td>\n",
       "      <td>7.466323</td>\n",
       "      <td>13.010188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.789037</td>\n",
       "      <td>7.927384</td>\n",
       "      <td>12.395163</td>\n",
       "      <td>6.865891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THROMBIN</th>\n",
       "      <td>4.557380</td>\n",
       "      <td>8.573794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.892393</td>\n",
       "      <td>5.807642</td>\n",
       "      <td>10.697660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.943805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.573098</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Factor XIa</th>\n",
       "      <td>2.538974</td>\n",
       "      <td>12.319674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.645115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.963576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.433110</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP7</th>\n",
       "      <td>1.466337</td>\n",
       "      <td>8.606699</td>\n",
       "      <td>5.581615</td>\n",
       "      <td>7.604728</td>\n",
       "      <td>3.814043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.721669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.174092</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP11</th>\n",
       "      <td>2.650892</td>\n",
       "      <td>8.127355</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.276902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.221389</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP8</th>\n",
       "      <td>5.000585</td>\n",
       "      <td>8.323649</td>\n",
       "      <td>5.016175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.887030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.157705</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP1</th>\n",
       "      <td>4.151040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.717629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.869768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.915753</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.730394</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP13</th>\n",
       "      <td>4.797167</td>\n",
       "      <td>8.105961</td>\n",
       "      <td>2.719100</td>\n",
       "      <td>7.508422</td>\n",
       "      <td>2.351375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.070125</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sub1       sub2      sub3       sub4      sub5       sub6  \\\n",
       "KLK2        0.000000  11.208805  0.000000   7.633692  6.830694   0.000000   \n",
       "CTSS        6.097325   8.539639  0.000000   6.721024  0.000000   0.000000   \n",
       "ADAMTS1     2.908721   8.467127  0.000000   6.840547  0.000000   0.000000   \n",
       "PLASMIN     7.065898  12.652914  0.000000  12.135369  7.466323  13.010188   \n",
       "THROMBIN    4.557380   8.573794  0.000000  12.892393  5.807642  10.697660   \n",
       "Factor XIa  2.538974  12.319674  0.000000  12.645115  0.000000   7.963576   \n",
       "MMP7        1.466337   8.606699  5.581615   7.604728  3.814043   0.000000   \n",
       "MMP11       2.650892   8.127355  0.000000   7.276902  0.000000   0.000000   \n",
       "MMP8        5.000585   8.323649  5.016175   0.000000  0.000000   0.000000   \n",
       "MMP1        4.151040   0.000000  0.000000   3.717629  0.000000   5.869768   \n",
       "MMP13       4.797167   8.105961  2.719100   7.508422  2.351375   0.000000   \n",
       "\n",
       "                sub7       sub8      sub9      sub10     sub11  \n",
       "KLK2        0.000000   0.000000  2.498700  10.636636  0.000000  \n",
       "CTSS        0.000000   7.213277  0.000000   4.584967  0.000000  \n",
       "ADAMTS1     0.000000   0.000000  0.000000   8.019174  0.000000  \n",
       "PLASMIN     0.000000  13.789037  7.927384  12.395163  6.865891  \n",
       "THROMBIN    0.000000   9.943805  0.000000  11.573098  0.000000  \n",
       "Factor XIa  0.000000   0.000000  0.000000  12.433110  0.000000  \n",
       "MMP7        3.721669   0.000000  0.000000   8.174092  0.000000  \n",
       "MMP11       0.000000   0.000000  0.000000   8.221389  0.000000  \n",
       "MMP8        0.000000   5.887030  0.000000   8.157705  0.000000  \n",
       "MMP1        0.000000   5.915753  0.000000   7.730394  0.000000  \n",
       "MMP13       0.000000   1.386294  0.000000   8.070125  0.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now rearrange the rows and columns such that they are the same as in the paper\n",
    "agg_df_t_copy = agg_df_t_copy[['sub1', 'sub2', 'sub3', 'sub4','sub5', 'sub6','sub7', 'sub8','sub9', 'sub10','sub11']]\n",
    "agg_df_t_copy = agg_df_t_copy.reindex(['KLK2', 'CTSS', 'ADAMTS1', 'PLASMIN', 'THROMBIN', 'Factor XIa', 'MMP7', 'MMP11', 'MMP8', 'MMP1', 'MMP13'])\n",
    "\n",
    "# plot reordered hierarchical map of substrate protease pairs\n",
    "paa.vis.plot_heatmap(agg_df_t_copy, out_dir, center=9,  metric='euclidean', method='ward', scale=None, row_cluster=False, col_cluster=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "841a4e99",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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